Abstract
In biomedical and engineering research, entropy metrics have become well-established tools for assessing the complexity of dynamic and physiological systems. This scoping review examines the relationship between information theory quantifiers (ITQs), bioelectrical signals, and time series, and the limited diagnostic value of these measures in functional dyspepsia (FD). Three main variables were defined in this study: entropy, health experiments, and FD. Eighty-five academic documents were analyzed using the PRISMA methodology, following 4 phases: (i) heuristic; (ii) classification and systematic review; (iii) hermeneutic analysis; and (iv) presentation of results. ITQs are currently applied in the study of neurodegenerative diseases, cardiological conditions, and, to a lesser extent, gastric disorders, thereby opening new avenues for diagnosis and comprehensive clinical management. The review of the documents shows that, despite the methodological robustness, statistical testing, classification approaches, and the breadth of entropy measures employed, significant challenges remain when integrating these techniques due to the intrinsic complexity and heterogeneity of bioelectrical signals in FD. Furthermore, knowledge gaps persist, particularly in digestive disorders such as FD, underscoring the need to deepen and diversify analytical methodologies.
Keywords
Introduction
It is anticipated that the ideas and arguments derived from this scoping review will serve as a theoretical reference, highlighting the potential application of ITQ concepts to medicine, thereby enriching the academic landscape and opening avenues for further analysis.
While the primary focus of this review is on FD and gastric myoelectrical recordings, its scope extends to other physiological and simulated signals in which ITQs have been more extensively explored. This broader survey is strategically employed to identify methodological patterns, highlight successful applications in other domains, and reveal the existing knowledge gap in FD. Preliminary searches in major databases found no prior scoping or systematic reviews specifically addressing the application of ITQs to FD.
This work is explicitly framed as a scoping review that systematizes the use of entropy and other ITQs across diverse biomedical signals to identify trends, gaps, and methodological limitations. In this context, FD stands out as a clear gap, since—unlike fields such as neuroscience or cardiology—its study using entropic measures remains scarce and fragmented. The research objective is to examine the relationships among ITQs, bioelectrical signals, and time series, and to assess their limited diagnostic utility in FD. Both the research objective and the associated questions align naturally with an exploratory approach, given their descriptive nature and the need to map an emerging, still loosely integrated body of knowledge. In this regard, the aim is to identify gaps, describe trends, and contextualize the current state of entropic measures.
The questions, ranging from the contribution of quantifiers to understanding uncertainty to the identification of knowledge gaps and methodological advances in biomedicine, aim to characterize patterns, identify applications, and synthesize tools—particularly those based on entropy-based diagnostic criteria and nonlinear dynamics—without establishing definitive causal relationships.
The descriptive hypotheses developed in this scoping review relate, first, to a consolidated trend in the literature toward integrating multiple information analysis tools, which, when combined, enhance the interpretation of complex physiological signals; however, methodological gaps remain that limit their applicability to gastrointestinal disorders. Second, it is expected that ITQs—such as entropy metrics and other uncertainty analysis tools—will reveal distinctive patterns of variability and complexity in bioelectrical signals and time series, highlighting their potential to differentiate the physiological dynamics associated with FD from those of healthy gastrointestinal states.
Materials and Methods
Although functional dyspepsia (FD) and gastric myoelectrical recordings are the primary focus of analysis, this review examines the applications of information theory quantifiers (ITQs) to other physiological and simulated signals to: (i) identify methodological trends, (ii) establish benchmarks for analytical decision-making, and (iii) contextualize the knowledge gap in FD.
In this scoping review, 3 main variables were defined: entropy, health experiments, and FD. Entropy was considered an indicator of complexity and variability in physiological recordings; health experiments encompassed empirical studies focused on acquiring and analyzing various signals; and, finally, FD was understood as a digestive disorder. To support the analysis, methodological criteria were applied, covering the selection of pathologies, units of analysis, subject characteristics, data collection instruments, signal types, and procedures for both data and entropy analysis.
Additionally, to ensure consistent interpretation of results, the terminology for the entropy variable and FD was standardized, thereby avoiding redundancies and facilitating comparative analyses across findings. This scoping review, based on the approach proposed by, 1 was conducted following the PRISMA methodology in 4 phases: (i) heuristic search; (ii) classification and systematic review, in which the Latent Dirichlet Allocation (LDA) model was applied as suggested by 2 ; (iii) hermeneutic analysis; and (iv) presentation of the final results.
During the initial heuristic phase, the documentary sample was defined by selecting primary and secondary sources from the scientific literature, chosen for their relevance to the field of study and their direct connection to the aforementioned research areas.
The search was conducted in 3 primary databases—PubMed, Scopus, and Web of Science (WOS)—with Google Scholar consulted for complementary records not captured in the main sources. Eligible publications included journal articles, reviews, and books in English or Spanish that were methodologically relevant. Duplicate records were identified and removed prior to screening using Mendeley’s reference management tools. Two complementary search blocks were used: (i) a broad block combining terms related to information theory quantifiers (ITQs) with terms for bioelectrical and physiological signals (EEG, ECG, EMG, HRV, MEG, EGG), and (ii) a focused block targeting FD and electrogastrography (EGG).
Eligibility and inclusion criteria were established to identify studies addressing entropy and its association with digestive disorders and other relevant pathologies. Accordingly, delimitation and search parameters, such as date, language, publication type, study design, and indexing, were applied. The primary outcomes sought included pathologies, units of analysis, sample characteristics, data collection instruments, data or signal types, data analysis methods, and entropy analysis procedures. All relevant outcomes reported in the studies and compatible with these outcome domains were collected. No imputation or estimation criteria were applied to missing data; when information was unavailable or ambiguous, it was excluded without contacting the original authors.
Study selection was conducted in 2 phases: initial screening of titles and abstracts, followed by full-text evaluation. Both authors of this scoping review participated in the selection process, independently applying the predefined inclusion and exclusion criteria. Discrepancies were resolved through consensus, thereby reducing the risk of subjective bias and increasing the reliability of the selection process. Although the main screening was performed manually, certain automated steps were implemented beforehand, including duplicate detection and database-level filters (eg, by language, publication type, and date). These native filters, available in each database, were applied solely to remove clearly irrelevant records before initiating the dual-review process.
The excluded documents primarily consisted of preprints, conference proceedings, theses, and scientific articles that, upon detailed review, were deemed irrelevant to the scoping review’s objectives. Notably, no studies were excluded on the basis of language; both English- and Spanish-language publications were included, provided they met the established methodological and thematic criteria. Furthermore, to assess the risk of bias in each document, studies lacking theoretical justification or presenting biased or speculative interpretations were excluded.
This review identifies methodological limitations that may have affected the comprehensiveness of the findings. The search strategy relied predominantly on high-impact, indexed databases. Neither backward nor forward systematic searches of the included studies’ reference lists were conducted, nor was the gray literature explored. Specialized registries were also not checked. Authors of original studies were not contacted to clarify ambiguous or incomplete information. The absence of these additional steps may increase the risk of overlooking studies on entropy in functional dyspepsia (FD) published in less visible venues or non-indexed channels. We acknowledge this limitation and explicitly report it to ensure methodological transparency and to guide future reviews toward broader, more sensitive search strategies.
Thus, given the narrative and exploratory nature of this review, formal tools such as GRADE were not applied to assess the certainty of the evidence. Instead, a qualitative assessment was performed based on the consistency of the findings, the diversity of clinical contexts addressed, the types of entropy metrics employed, and the overall methodological quality of the included studies. Recurrent patterns were observed in the use of certain metrics to discriminate between clinical groups, suggesting evidence with moderately consistent certainty. Because this review is neither a meta-analysis nor a systematic review with quantitative synthesis, no precise “effects” were estimated; however, we assessed the overall robustness and coherence of the collected evidence.
This scoping review was conducted in accordance with the PRISMA 2020 (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines (Figure 1). It was not registered on platforms such as PROSPERO or the Open Science Framework (OSF) because its design aligns with a narrative and exploratory literature review, which does not meet the eligibility criteria for registration on these platforms. Specifically, PROSPERO accepts only systematic reviews—primarily those assessing health-related interventions, diagnostic test accuracy, prognosis, or prevalence—submitted through predefined templates and accompanied by a formal protocol developed before the review commences. Because no such protocol was created for this work, neither registration nor amendments were applicable.
PRISMA 2020 flow diagram.
Scientific documents—primarily indexed—were selected to minimize bias in the studies. These were classified into quartiles: Q1 (31 documents), Q2 (25), Q3 (12), and Q4 (7). In total, 85 sources were included: 79 scientific articles, 2 books, 2 research papers, and 2 health guides, of which 47 were from Europe, 3 from Asia, and 35 from the Americas (Table 1). The temporal distribution of the identified literature is as follows: 2019 (9 documents), 2020 (16), 2021 (15), 2022 (14), 2023 (12), 2024 (5), and 2025 (3). These, along with 11 scientific documents from years preceding the study period, constitute the state of the art and strengthen the background. They were used as presented below.
Categorization of Published Works by Scientific Journal and Authors’ Country of Origin.
Source: Own design.
In the second phase—classification and review—bibliographic records were created using delimitation criteria, including title, authors, publication year, abstract, objective, methodology, main findings, and conclusions. The data sheets, designed as review and recording protocols, organized the information, justified study selection, and contributed to the coherence of the analysis in this scoping review. Specifically, the abstract column was used to extract data from the evidence sources, which were analyzed as suggested 2 using the LDA model and the spaCy library for natural language processing to explore potential sources of heterogeneity among study results.
This material underwent lexical analysis, including tokenization of each word, followed by lemmatization, excluding numbers, punctuation, and stopwords. Model training was then performed for up to 1 million iterations. This limit was set to ensure stable model convergence and to prevent a limited number of iterations from compromising accuracy in topic identification.
Additionally, the parameter learning_method=online was set, indicating the update strategy used during training. This method was selected for its suitability to the characteristics of the analyzed corpus, facilitating process optimization. As a result, the model identified 10 distinct topics, each characterized by a set of representative keywords. To accomplish this, a function was implemented to visualize the topics generated by the LDA model, displaying the 10 most significant terms for each topic after removing duplicate terms (Figure 2).
Word distributions.
For example, Figure 2 shows the 30 most representative words of Topic 0, which accounts for 9.2% of all tokens in the corpus. On the right vertical axis, these words are listed—from “gastric,” “symptom,” and “patient” to “myoelectric”—in descending order of relevance. The lower horizontal axis (“Weight”) shows the numerical weight assigned by the LDA model to each term in this topic; higher values indicate greater influence on the topic’s definition.
Thus, “gastric” has the highest weight (~130), confirming that this topic clusters studies focused on gastric activity. Accordingly, the length of each bar not only quantifies the relative frequency of the word within the topic but also underscores its importance in characterizing the thematic axis identified in the scoping review.
Finally, the fundamental criteria for document selection enabled their classification into 2 main categories: (i) a global overview of the areas of interest and (ii) thematic groupings identified in the corpus based on their high or moderate relevance.
Based on the LDA model’s results, various topics were identified and classified by their prevalence across the corpus (Figure 3). The methodology assessed the proportion of each topic within the documents, enabling grouping of the documents into high-, moderate-, and low-importance categories. This approach not only facilitates analysis of the dataset’s thematic structure but also clarifies the organization of underlying knowledge.
Distribution of topics.
The high-prevalence group comprised 2 topics classified as follows: Topic 4—entropy complexity series time signal measure method propose analysis different datum subject distribution multiscale classification noise information value result plane scale base disease sample healthy mse physiological obtain analyze—and Topic 8—patient study brain complexity functional eeg model dyspepsia group treatment fd response entropy depression different symptom signal measure base control region disease mechanism high increase meal disorder non interaction.
The rationale for this classification positions each topic as follows: Topic 4 synthesizes methodological developments in complexity measurement, spanning SampEn, MSE, and other ITQs applied to time series. Likewise, Topic 8 reflects the review’s focus on summarizing ITQ-based tools for monitoring bioelectrical changes before and after treatment, highlighting their potential as biomarkers of clinical response.
The moderate-prevalence group comprised 2 topics classified as follows: Topic 0—gastric symptom patient egg fd functional include wave clinical dyspepsia slow gastroparesis subject method control group spatial motility assess abnormality diagnostic feature dysfunction model normal disorder high abnormal myoelectric—and Topic 7—ad signal eeg frequency study healthy egg entropy increase p electrode subject group dominant component power dynamic area mci result information state eye pe digestive diagnostic high condition classification.
The rationale for this classification is as follows: Topic 0 covers studies that characterize gastric electrical activity and its relationship to FD symptoms. From the perspective of ITQs, time-series gastric signals may provide a basis for entropy measures that can distinguish normal from abnormal motility patterns. Likewise, Topic 7 encompasses studies that analyze EEG signals using entropy measures and spectral analysis (frequency, power) to characterize brain dynamics in both healthy and pathological subjects.
The low-prevalence group comprised 6 topics classified as follows: Topic 1—highlight analyze bioelectrical stage disorder review theory field final presentation exist diagnosis knowledge model meta organization digestive evident discretely clinical limited gap international opportunity text iv define conclude topic—; Topic 2—psychological fd disorder study patient symptom trial datum effect intervention quality treatment therapy prevalence available associate evidence health criterion mean difference common include brain score control gut—; and Topic 3—exercise physical signal al et highlight phy understand relate brain relationship activity cognitive bioelectric importance neuromuscular let different associate aim individual muscle furthermore suggest reveal demonstrate database finding article strength.
Topic 5—SampEn quality stomach include analysis process datum classifier know signal heterogeneity fd low high group technique dataset combination outcome text abdominal review therapy evidence result—; Topic 6—gastric electrical study review activity research clinical wave electrogastrogram egg signal result approach recording model biological major chapter electrogastrography link obtain record relationship disease type focus experience process year cell—; and Topic 9—control hrv interval patient respectively variability population versus rr root successive rmssd square period deviation temporal standard number prevalence differ apen software great irregularity determination percentage resolution heartbeat expose health.
Topic 1 focuses on conceptual mapping and meta-theoretical analysis within the domains of gastroenterology and bio signal research. It supports the exploratory nature of the review by highlighting the fragmentation of knowledge and the absence of unified frameworks that integrate information-theoretic quantifiers into clinical research. Additionally, it underscores the opportunity to establish a theoretical foundation for applying ITQs to the treatment of digestive disorders. Topic 2 encompasses studies addressing the psychological dimension of FD and its treatment.
Topic 3 examines the relationship between physical exercise, bioelectrical signals (eg, EMG or EEG), neuromuscular activity, and cognitive functions, with an emphasis on rigorous analysis to better understand body–brain interactions. Topic 5 applies SampEn and its variants in combination with classification models, highlighting the potential of ITQs to distinguish patients with FD from healthy controls. Topic 6 focuses on the clinical and experimental study of gastric electrical activity using EGG. Topic 9 covers research on heart rate variability (HRV).
During the third phase of hermeneutic analysis, 3 or the interpretative approach, a process of reading, assigning meaning, and analytically correlating the various thematic cores was undertaken. These constituted the central axis for the “understanding of the individual text, of its parts, and the individual from the whole, to recompose another horizon as a totality” 1 (p. 40). This stage prioritized coherence, data accuracy, and a theoretical understanding of the topic under study.
In this phase, the authors 4 suggest that, as part of the hermeneutic process, “theoretical comparisons as an analytical tool used to stimulate thinking about the properties and dimensions of the [variables] to guide theoretical sampling” 4 (p. 86) should be conducted. This underscores its importance by revealing the absence of shared topics between the 2 areas—FD and entropy—both of which were structured into 3 broad thematic focuses.
The central focus was addressed through the following research questions: How do ITQs contribute to understanding uncertainty and variability in bioelectrical signals and time series? How do different information analysis tools complement each other to provide a more comprehensive view of dynamic systems? What are the main applications of entropy metrics in biomedical research? What knowledge gap is identified in this scoping review regarding the use of ITQs? What tools and criteria are mentioned for the diagnosis and management of FD? What methodological advancements are highlighted in current research on FD?
After the analytical process, the fourth phase presents the final results, including findings, progress, errors, emerging challenges, new research directions, scientific novelties, gaps, and demands related to the subject of study.
In this scoping review, no formal sensitivity analyses were conducted to assess the robustness of the synthesized results. No quantitative exclusion or modification procedures were applied, as the synthesis focused on integrating theoretical perspectives and clinical applications reported in the literature. Nevertheless, the methodological and contextual limitations of the included studies were carefully considered when interpreting the consistency of the findings.
It is important to clarify that the included studies were evaluated based on the clinical, population, and technological characteristics addressed. The methodological diversity—evidenced by the use of statistical techniques, classifiers, and entropy metrics—enabled a qualitative assessment of study robustness, thereby indirectly accounting for their risk of bias. Although no formal statistical syntheses were conducted, the main methodological and clinical trends reported were integrated.
Potential sources of heterogeneity were also identified, related to the pathologies studied, the signal modalities employed, and the analytical methodologies applied. Finally, it should be noted that this article serves as the foundation for justifying the research to which it belongs, guiding the adoption of theoretical and methodological decisions based on recognition of the contributions presented and the generation of knowledge itself. 1
Results
This section integrates the findings from the systematic search described in the Methods section and in Table 8, which compiles the full set of entropy metrics and studies identified throughout the review. Given the breadth of the retrieved literature, a representative subset of studies is presented to illustrate the diversity of methodological approaches and the complementarity of the tools employed. This strategy enables a detailed exploration of specific examples without losing the broader perspective, which is preserved in the corresponding tables.
Quantification of Information in Bioelectrical Signals
The study of variability and disorder in dynamic and communication systems has gained unprecedented relevance due to its applications across disciplines such as physics, computer science, and medicine. The following section analyzes a representative set of 27 research articles that apply different ITQs to bioelectrical signals and time series, with an emphasis on how these entropy-based approaches—Shannon (ShEn), Rényi (RE), Tsallis (TE), Wavelet (WaEn), and Dispersion (DispEn)—enable the quantification of uncertainty and variability in the data.
This set is not intended to be exhaustive but to illustrate the methodological diversity and complementarity of the tools identified in the broader literature search. Recognizing these tools is essential, as each offers a unique perspective: from the logarithmic foundations of ShEn to the parametric flexibility of DispEn, through the capability of TE for non-extensive systems, and the robustness of WaEn in noisy signals. For clarity, the detailed mathematical expressions of these metrics are provided in Appendix 1. This scoping review examines how these measures, while valuable individually, complement one another to enable a more comprehensive analysis of the phenomena under study.
Shannon Entropy (ShEn)
The concept of entropy found significant application in information theory through Claude E. Shannon’s 1948 work.5-8 Shannon is regarded as a fundamental, natural,9-11 widely used, 12 and unique 13 measure; he introduced ShEn as a quantifier of disorder in communication systems. 14 Unlike Clausius’s entropy from the thermodynamic perspective of the 19th century, ShEn is distinct from all physical magnitudes and has no relation whatsoever to temperature, let alone to heat exchange. 6
According to Kolmogorov and Sinai, ShEn is a tool for studying and quantifying the order and disorder of dynamic systems.15-17 This metric depends on the distribution of the random variable, not on its specific values. 7 ShEn varies linearly with the logarithm of the probabilities and is based on the law of additivity, which states that the sum of the entropies of statistically independent subsystems equals the total entropy of the system. 18 When the logarithm base 2 is used, H is measured in bits; when the natural logarithm is used, H is expressed in nats. 7
ShEn has been defined from different perspectives to measure, quantify, evaluate, inform, and predict. Primarily, it is regarded as: (i) a measure of the information contained in a signal or data source 6,19; (ii) a measure of uncertainty, 9 associated either with the observed information set 6 or with the probabilistic physical processes described by the probability distribution 17 ; (iii) a measure of the complexity of a system 18 or of time series in various practical applications 20 ; and (iv) a measure of the information associated with a process described by a probability distribution {P = Pᵢ, i = 1, 2, . . ., M}. 12 Specifically, the amount of information about an event E, as measured by ShEn, increases as the probability P(E) of its occurrence decreases. In other words, surprise is minimal when P(E) is close to 1, but increases significantly as P(E) approaches zero. 21
Second, ShEn is used to quantify (i) the uncertainty in a data sample from a probabilistic perspective 6 ; (ii) the physical process described by a probability distribution P 11 ; (iii) the degrees of freedom of time series 20 ; and (iv) the structural distribution of a signal’s components, as well as the information they contain and the efficiency with which they can be encoded in the message.8,9,22 Thirdly, it is used to evaluate the amount of information in both univariate and multivariate systems, under the assumption of stationarity.9,10,23 Fourthly, it is defined as the rate at which information is generated. 24
Ultimately, it is used for prediction. When ShEn or S[P] equals the minimum entropy (S min = 0), it is possible to predict with certainty which of the possible outcomes (i), with associated probabilities (Pi), will occur. In this case, knowledge of the underlying process, as captured by the probability distribution, is maximized. In contrast, in a uniform distribution, knowledge is minimal, and uncertainty is maximal, as represented by this entropy (S[Pe] = Smax). 11
Conversely, a limitation of ShEn is that, by focusing exclusively on the sample probability distribution, it overlooks the individual sample values, thereby excluding crucial information inherent in those values. 25 It also requires prior knowledge of the process in the form of an underlying probability distribution function; however, it does not characterize highly nonlinear systems, such as chaotic processes, well. Moreover, this entropy is ineffective at distinguishing a complex process—with different organizational properties—from a simple one. 12 Although it is the most widely used characteristic for measuring system disorder and is highly expressive, normalized ShEn cannot capture all possible underlying dynamics. At intermediate values of H, a considerable diversity of scenarios emerges, requiring detailed characterization. 16 This implies the existence of a wide range of situations that merit characterization but fall outside the descriptive capability of this entropy.
For extensive or additive systems in which microscopic interactions are effective at short range, ShEn is a useful measure. However, it is not suitable for non-extensive or non-additive systems influenced by long-range interactions. Similarly, it does not account for temporal dependence among time-series values, which can lead to overestimation. 18 Therefore, to capture the temporal relationships between measurements in a time series, ShEn alone is insufficient. 12
Rényi Entropy (RE)
In 1961, Alfréd Rényi introduced RE, or q-entropy, which has played a significant role in information theory. 26 This entropy has been applied across disciplines such as physics, biology, and economics 6 ; moreover, it is widely used in statistics and ecology as an indicator of variability. 15 RE6,15,26,27 is an extension of ShEn and a generalization of various entropy measures, including Hartley’s, collision, and minimum entropy. 15
RE constitutes a family of functions that share common order-q characteristics (Rq) and are used to measure the randomness, diversity, and uncertainty in a system.15,26 Moreover, it enables a more flexible description of the probability distribution P by emphasizing specific aspects of the distribution. 6
RE exhibits distinctive properties that make it particularly useful for analyzing signals and complex systems. One of its main advantages is its relative insensitivity to signal non-stationarity. 27 This property makes RE a valuable tool for analyzing time–frequency representations (TFRs), as real signals often display non-stationary characteristics. For TFRs concentrated in signals composed of a relatively small number of components, the entropy yields low values; in contrast, for sparse TFRs of more complex signals, the values are higher. 27
Tsallis Entropy (TE)
TE 26 (Sq), 14 proposed by Constantino Tsallis in 1988,15,26 represents a significant advancement in statistical mechanics, thermodynamics, 15 generalized thermostatistics, 28 and fields related to medical physics, offering a broader perspective on the origin of disorder in macroscopic systems. 15
This metric generalizes Shannon’s non-extensive entropy, 6 the Boltzmann–Gibbs–Shannon (BGS) measure,14,28 and the Boltzmann–Gibbs (BG) formulation 26 ; moreover, it has been shown that the latter (BG) is recovered when the entropic parameter or index q approaches 1, with q any real number.26,28 In this context, TE has been considered for the study of physical systems or non-extensive configurations, as well as for complex systems characterized by long-range interactions and non-Gaussian probability distributions.6,14,26,28
Wavelet Entropy (WaEn)
In 2001, Rosso and Blanco introduced WaEn. 26 WaEn is a metric or indicator26,29 that quantifies (i) the level of order or complexity present in a signal 29 and (ii) the degree of disorder by decomposing the signal into multiple frequencies.18,26 This metric characterizes the signal’s response across different frequency scales. 18 WaEn is calculated using discrete-time functions that divide the signal into several scales of translation and dilation, where the scales correspond to different frequency levels expressed as powers of 2. 19 Quantification is achieved through various resolution levels, derived from any time series via a probability distribution P unique to it, 18 computed as the relative power of a given frequency range with respect to the total power of the signal. 29
For its analysis, it is important to note that if a signal has a narrow spectrum, concentrating power within a specific frequency range, WaEn approaches zero, indicating low complexity or a highly structured process. In contrast, if a signal has a broad spectrum with power uniformly distributed across all frequencies, the entropy will be higher, reflecting a random or complex process. 29 The main advantage of WaEn is its ability to detect subtle variations in any dynamic signal; it requires less computational time, facilitates noise removal, and its performance is independent of parameter selection. 18
Dispersion Entropy (DispEn)
This entropy was proposed by Rostaghi and Azami in 2016.26,30 It has been referred to by various names, including DispEn,26,30 DisEn, 25 and DE. 24 Its origin lies in 2 types of entropy measures: permutation entropy (PE) and sample entropy (SampEn). 31 The former generally highlights irregularities but does not account for amplitude information, whereas the latter is inefficient when applied to long signals. 30 DispEn is both a technique 24 and an innovative measure for assessing disorder.25,30
The purpose of DispEn is to analyze time series through their associated dynamics 30 and to assess the variability and complexity of signals or data over time.24,30 It is both robust and efficient, 24 combining linear and nonlinear mappings that reduce susceptibility to noise and enhance its ability to detect signal changes. The length of its embedded vectors is adjusted based on the number of classes, providing flexibility and making it a more grounded, adaptable measure. 25
By permuting the original time series, DispEn can detect simultaneous changes in frequency and amplitude and determine the noise bandwidth present in the data. Its calculation considers a time series of N data points 31 and, before application, requires determining 3 specific parameters—m, c,25,31 and a delay parameter L. The length of the sequences evaluated using the nearest false neighbor method is adjusted according to m. The number of classes to which the members of the time series can belong is denoted by c, while L, 31 gives the time delay. During classification, values are assigned to a specific class by adapting to the sample 25 ; this is achieved by converting the time series into an interval from 0 to 1 using the standard normal cumulative distribution function. 31
Experimental Methodological Approaches
In the current landscape of biomedical and engineering research, entropy metrics have become essential tools for analyzing the complexity of dynamic and physiological systems—an interdisciplinary field of increasing relevance. Below are the results of the analysis of 41 scientific articles, with particular attention to those that apply various ITQ techniques to bioelectrical signals and time series.
Applications in neurodegenerative pathologies—such as Alzheimer’s disease (AD) and Parkinson’s disease (PD)—and in cardiovascular conditions were identified, thus reflecting the versatility of these approaches. The analyses encompassed a broad spectrum of units of observation, ranging from interval recordings derived from electrocardiography (RR) and other bioelectrical signals—such as EEG and ECG—to simulated datasets and non-bioelectrical modalities, including neuroimaging. These datasets comprised heterogeneous samples spanning preclinical models to human populations and used varied data acquisition protocols.
Theoretical foundations and empirical evidence highlight a clear knowledge gap in applying these quantifiers to functional dyspepsia (FD), thereby opening avenues for a deeper understanding of their diagnostic potential in gastrointestinal disorders. Against this backdrop, the present article seeks to develop a conceptual framework that integrates diverse entropy metrics and analytical approaches to optimize the evaluation and validation of data in complex contexts.
Pathologies
The analysis of complexity in dynamic and physiological systems using entropy metrics is grounded in the diversity of pathologies and conditions examined, thereby enabling assessment of applicability in both clinical and technical contexts. Table 2 provides an integrative overview of diverse clinical conditions and data types, organized by pathological or experimental similarities. Notably, neurodegenerative diseases are the most prevalent category, with 14 occurrences, including 9 cases of Alzheimer’s disease (AD)29,32-39 and 5 of Parkinson’s disease (PD).33,40-43
Pathologies.
Source: Own design.
Cardiovascular disorders —including arrhythmias (ARR), atrial fibrillation (AF), and congestive heart failure (CHF)— are reported in 9 entries,8,9,24,30,44-48 in the analysis of physiological signals. Similarly, neurological and psychiatric conditions, such as epilepsy and neuropsychiatric disorders, appear in 3 entries.31,33,49 Other pathologies, including Chagas disease, valvular calcification, mental fatigue, cognitive impairment, neurocardiovascular alterations, and vocal disorders, are documented in 5 entries.27,50-53 In addition, simulated and synthetic datasets are cited in 9 entries,5,8,21,30,33,45,47,48,54 while 2 entries represent studies on emotions and sleep.18,54
Complementarily, other studies have examined the applicability of entropy analysis to electrogastrography (EGG) signals, particularly in digestive disorders such as diarrhea, vomiting, and gastric ulcers, using TE and RE 55 ; moreover, the influence of measurement electrode size on EGG analysis using RE 56 has also been evaluated. This latter aspect broadens the range of biomedical applications of entropy metrics, highlighting their potential for characterizing digestive conditions and optimizing acquisition parameters for bioelectrical signals.
Units of Analysis
Table 3 consolidates the broad range of units of analysis used across the reviewed studies, highlighting the notable predominance of EEG recordings18,29,33-39,42,48,49,52-54 and RR time series derived from ECG.8,9,24,30,40,43-48,50 Other bioelectrical neurophysiological techniques, such as magnetoencephalography (MEG),27,32 are also represented, along with non-bioelectrical modalities—including functional magnetic resonance imaging (fMRI) 31 and voice signals. 51
Analysis Units.
Source: Own design.
Additionally, a substantial number of artificial or simulated time series5,8,30,33,45,47,48,54 are reported, alongside a few cases of movement markers such as gait or gaze.33,41 Datasets focused on the activity of simulated individual neurons 21 and others addressing variability in oxygen saturation 48 are also identified.
Subjects
Table 4 highlights considerable heterogeneity in subjects and animal models across the reviewed studies, regardless of whether specific pathologies were present. In preclinical research, Wistar rats 44 are widely used to study neurodegenerative diseases in animal models. In clinical research, cohorts range from healthy young adults27,52,53 to older adults,24,29,40 often with sex- and/or age-matched healthy controls (HC).31,32,39,50 Regarding gender distribution, several datasets report a relative balance of 5 women to 5 men. 40 In contrast, others focus primarily on male samples 52 or include mixed subgroups that are predominantly female or male, depending on the pathology.31,34,49,50
Subjects.
Source: Own design.
Studies involving patients aged 60 years or older, with detailed clinical evaluations documented, are particularly noteworthy.34-37,42 Likewise, some datasets report both age distributions and differences in the male-to-female ratio.8,24 In this regard, this table illustrates the breadth of empirical approaches, ranging from large-scale population studies involving 255 subjects 35 and 272 31 to small, targeted groups of 13 students, 53 encompassing individuals of both sexes and spanning an age range of approximately 20 to 85 years.
Data Collection Instruments
Table 5 illustrates the broad range of technologies used across the reviewed studies, with a marked predominance of EEG-based techniques18,29,33-39,42,48,49,52,53 and a substantial number of ECG platforms.8,9,24,30,40,43-48,50,57 These are complemented by other bioelectrical modalities—including MEG27,32—and neuroimaging techniques such as fMRI/MRI, 31 collectively covering a broad range of neuroimaging and neurophysiological methods.
Data Collection Instruments.
Source: Own design.
Furthermore, more specialized approaches are represented, such as voice recording 51 and gait or eye movement monitoring.33,41 In sleep research, 1 dataset uses polysomnography. 54 This methodological diversity underscores the need for processing solutions that can integrate electrophysiological signals, neuroimaging, and clinical biomarkers.
Signal Types
Table 6 presents a variety of physiological and mechanical recordings used in recent research. In total, 15 datasets focus on brain electrical activity,18,29,33-39,42,48,49,52-54 followed by 15 collections centered on cardiac electrical activity (ECG).8,9,24,30,40,44-48,50,57-60 Additionally, 4 datasets involve non-bioelectrical measures—such as hemodynamic parameters 43 and neuroimaging modalities27,31,32—along with 1 dataset on sustained vowel phonation, 51 1 on oxygen saturation variability (OSV), 48 and 2 on gait or movement tracking.33,41 Furthermore, 9 studies on simulated signals5,8,21,30,33,45,47,48,54 are also represented.
Data or Signal Types.
Source: Own design.
Data Analysis
Table 7 highlights the methodological diversity across the reviewed studies, encompassing a wide range of entropy and complexity metrics33,34,41,43-45,48 and both parametric and nonparametric statistical tests in most of the reviewed documents.9,29,35,42,45,47,49,50
Data Analysis.
Source: Own design.
The data analysis was structured to align with the nature of the statistical tests employed. Parametric tests were used for group comparisons, including 1-way ANOVA,35,46,50 with Bonferroni46,54 and Games-Howell 35 post hoc tests. Additionally, repeated-measures ANOVA34,42 and 2-way ANOVA 31 were conducted, with Greenhouse–Geisser correction 42 and post hoc t-tests. 34
Entropy comparisons were performed using t-tests in several studies,29,31,47,54 while group differences were assessed using the Wilcoxon signed-rank test in another study. 40 Studies,9,43 employed mixed-effects linear models and multivariate analyses using Royston’s and Hotelling’s tests, respectively. In contrast, nonparametric approaches included the Mann–Whitney U test 45 and combinations of the Kolmogorov–Smirnov, Kruskal–Wallis, and Wilcoxon tests. 49
Additionally, 54 applied the Shapiro–Wilk test to assess normality, using the t-test for normally distributed data and the Mann–Whitney test when normality was not met. For the analysis of relationships between variables,31,41 performed correlation analyses, including the Intraclass Correlation Coefficient (ICC) to assess measurement reliability. The ICC was also applied across 5 intervals to establish test–retest reliability criteria using brain entropy (BENs). 31
Logistic regression was implemented in several studies,34,35,41 incorporating dimensionality reduction via principal component analysis (PCA) 34 and multinomial logistic regression. 35 Additionally, for classification,42,46 applied support vector machine (SVM) models, whereas 12 performed cross-validation with a linear SVM, optimizing accuracy and specificity through sensitivity analysis as a function of N, D, and τ. Finally, the stationarity of the time series was assessed using the ADF test. 12 Some studies also used wavelets 29 to decompose signals into distinct frequency bands and estimate wavelet entropy.
Entropy Analysis
Table 8 presents various entropy-based metrics used to assess complexity in physiological signals. Among the classical approaches are Approximate Entropy (ApEn) and SampEn, which quantify the irregularity of biological signals and have been widely applied in the literature.42,50,51 These methods have evolved into more advanced frameworks, such as Multiscale Entropy (MSE) and its extensions, including Refined Composite (RCMSE), Fuzzy (MSFME), Cross-Sample (MCSEN), and E-metric-based (MECSEN) formulations. These techniques enable the characterization of signal dynamics across different temporal scales, as described in previous studies.34,41,44,47,52
Entropy Analysis.
Source: Own design.
Similarly, other studies31,54 have focused on enhancing robustness to noise using Differential (DIFFEN) and Rank-Based (RAEN) methods. In parallel, the permutation-based approach (PE)29,32,35,37,39 characterizes complexity by leveraging the relative ordering of values within the time series.
In parallel, approaches based on Lempel–Ziv Complexity (LZC)30,36,37,49 and fractal dimensions 36 have emerged, reinforcing the notion that data compressibility and signal self-affinity offer complementary perspectives on variability. To deepen the characterization of order and disorder, several studies9,18,21 have jointly explored ShEn, Fisher information (FI), and Statistical Complexity Measures (SCM), thereby expanding the set of metrics for quantifying uncertainty.
Within this conceptual framework, the integration of the complexity–entropy causality plane (CECP)12,43 and dispersion-based metrics24,36 is particularly noteworthy, because both address variability through the perspectives of connectivity and nonlinear signal dynamics. Additionally, specialized techniques have been developed for frequency-domain analysis—such as WaEn 29 —and for multichannel time–frequency analysis, including Multichannel TF Entropies (MTFEn). 27
Conversely, information quantification through graph-based representations—such as Edge Visibility Homogeneity (EVHEn) 46 —captures a signal’s local regularity by analyzing the structural visibility of its edges. Similarly, Phase Entropy (PhEn)8,59 has gained prominence for characterizing transient and non-stationary dynamics. Lastly, Markov Entropy (MarkEn), a Markov-based variant, estimates uncertainty and predictability in transitions between discrete states over time by integrating Markov process theory with Shannon Entropy (ShEn). 60
Similarly, Relative Entropy (RelEnt) quantifies the information loss incurred when approximating probability distributions, making it useful for selecting and tuning models with strong generalization capabilities. 57 In turn, the Entropy Rate (EnR) estimates the rate at which new information is generated in a process and has been used to assess the unpredictability of dynamic signals, particularly in medical contexts. 58
Finally, advanced methods have been proposed, such as RCMS-RT-GFOCEC (Refined Composite Multiscale Reverse Transition Generalized Fractional-Order Complexity–Entropy Curve) and RCMS-q-CEC (Refined Composite Multiscale q Complexity–Entropy Curve), 5 which integrate multiscale analysis with fractional and reverse transition approaches, thereby opening new possibilities for exploring even more complex behaviors in signals.
Digestive Disorder: Functional Dyspepsia
The management of FD has evolved significantly, underscoring its complexity through the integration of motility disturbances, alterations in visceral sensitivity, and psychosocial factors. The application of the Rome IV criteria has enabled rigorous differential diagnoses, guided therapeutic management, and improved identification of underlying organic conditions. In parallel, the diagnostic algorithm for FD—incorporating medical history, physical examination, and early assessment of alarm symptoms—facilitates the use of empirical therapies and the detection of infections such as Helicobacter pylori (HP).
Moreover, despite their limitations, advanced techniques such as EGG and bioelectrical signal measurement provide valuable tools for characterizing gastric myoelectrical activity. Thus, incorporating emerging methodologies, such as high-resolution electrogastrography (HR-EGG), holds promise for delivering more reliable biomarkers, thereby optimizing both clinical practice and research in this field. The following sections present findings from the analysis of 28 scientific documents that are essential to understanding the significance of FD.
Contextualization
Dyspepsia, historically understood as “indigestion,”61,62 has been redefined in recent decades to focus on symptoms such as epigastric pain or burning, postprandial fullness, and early satiety.63-66 This common disorder is classified into Uninvestigated Dyspepsia (UD), secondary dyspepsia (SD), and FD, 61 with FD being the most prevalent, accounting for approximately 80% of cases in the absence of organic evidence on diagnostic tests such as endoscopy. 63 Studies indicate that women have a higher prevalence of UD and FD and that their quality of life is more adversely affected by this disorder. 67
Regarding dyspepsia, fewer than 25% of patients with UD will develop SD, typically due to conditions such as erosive esophagitis, peptic ulcer, or gastric cancer. 61 FD, a gastrointestinal disorder with variable prevalence, requires a comprehensive analysis of symptoms, pathophysiology, and treatment options, with consideration of its social and occupational implications. Various studies report an FD prevalence ranging from 1% to 40%,62,63,67-71 with a consensus around 10%, 72 reinforcing this figure as a reliable reference. A multicountry study conducted across 33 nations, involving 70 000 individuals, estimated that FD affects approximately 7% of the adult population—a figure consistent with other reports. 73
The prevalence of FD in the pediatric population is substantial, ranging from 1% to 30% worldwide under the Rome IV criteria. 67 This wide range represents a considerable public health burden, as FD is the second most common cause of school or work absenteeism after the common cold. 74 Although FD does not reduce life expectancy, it has a profound impact on quality of life and work productivity, with phenomena such as “presenteeism,” in which individuals attend work but perform suboptimally. 61
The socioeconomic impact is considerable, driven by healthcare resource utilization and work absences, 69 although it does not appear to influence mortality directly. 62 Moreover, it is estimated that up to half of individuals with dyspepsia do not seek medical attention, indicating a gap between actual prevalence and the use of professional care. 75
Despite the high prevalence of dyspepsia, only about 40% of patients seek primary medical care when symptoms worsen or become frequent, 62 underscoring the need for greater awareness and early detection in clinical settings. 61 FD and Irritable Bowel Syndrome (IBS) are common functional gastrointestinal disorders, with significant incidence in countries such as the USA, Canada, and the United Kingdom, where 10% of the population is diagnosed according to the Rome IV criteria. 61 However, the proportion of patients undergoing formal evaluation remains low. 62
In pediatric patients, FD is associated with sleep and feeding disorders, as well as other health issues such as migraines and autism spectrum disorders. 30% to 70% of affected individuals have Functional Gastrointestinal Disorders (FGIDs). In Japan, the prevalence of FD among children aged 10 to 15 is 2.8%, 67 whereas in the USA it is 7.6%. 76 Additionally, FD and Gastroparesis (GP) are common gastrointestinal neuromuscular disorders, with prevalences of 10% and 1.5% to 3%, respectively, 77 underscoring the importance of their recognition and management in both primary and specialized care settings. 74
Both FD and GP impose a substantial clinical burden, reducing quality of life and incurring high financial costs because of their impact on daily activities.70,78 Accurate diagnosis of these disorders remains challenging, often leading to extensive and costly testing. In the United States, the costs associated with FD amount to tens of billions of dollars annually, 72 with average per-patient expenses exceeding $5000 and an estimated total of $18 billion per year.61,62 Notably, approximately 30% of patients diagnosed with FD will eventually develop IBS.71,72
Importantly, the prevalence of IBS varies by region; for instance, in North America, it is relatively low compared with Latin American countries, where it is higher, such as Mexico (40%) and Chile (28.6%). 71 In Asian populations, there is considerable symptom overlap among FD, IBS, and gastroesophageal reflux disease, which complicates differential diagnosis. In Sri Lanka, more than a quarter of adolescents present with Functional Gastrointestinal Disorders, with IBS being the most common; in Turkey, celiac disease—also known as gluten-sensitive enteropathy—should be ruled out in patients with dyspepsia. 75
Functional Dyspepsia (FD)
FD, also known as non-ulcer dyspepsia or idiopathic dyspepsia, is a common disorder of the gastroduodenal system. 62 It is considered a heterogeneous condition arising from mechanisms, such as dysfunction of the gut–brain axis, visceral hypersensitivity, and alterations in immune function, the gut microbiome, and the mucosal lining. 79
FD has been diagnosed according to the Rome IV criteria, a globally applied diagnostic guideline that has been in use since 1994 and imposes no restrictions on region, sex, or age. 68 However, the pathophysiology remains incomplete, although associations have been identified with gastric motility disturbances, visceral hypersensitivity, and psychosocial factors.68,79
The Rome IV criteria establish that FD is diagnosed based on the presence of epigastric pain or burning, postprandial fullness, or early satiety, with recurrent symptoms not explained by routine clinical evaluations,61,71,75,79 or by the absence of evidence of underlying structural disease following procedures such as endoscopy and abdominal ultrasound.61,63,67,69-71,78-80
These criteria are used to ensure an accurate diagnosis. Although FD shares features with other functional disorders, such as IBS and GP, it remains distinguishable by its specific characteristics. 67 The diagnosis is confirmed by ruling out organic, systemic, or metabolic diseases that could account for the reported symptoms, and it does not require abdominal pain to be the predominant symptom.70,78 Although FD shares certain symptoms with GP—such as postprandial fullness, early satiety, and epigastric pain—it is differentiated by the absence of nausea and vomiting as core symptoms, which facilitates differential diagnosis. 70
Symptoms arise from disorders of the stomach and duodenum, which constitute the upper gastrointestinal tract. They are also described as indigestion, abdominal heaviness, and discomfort after eating—features that should prompt timely evaluation to rule out serious conditions such as ulcers. 63 Symptoms are often mistaken for chronic gastritis or acid–peptic disease. 71 Chronic gastric symptoms are challenging to diagnose because of diagnostic nonspecificity and symptom overlap among clinical categories, including GP, chronic nausea and vomiting syndrome, and FD. 81 Symptoms must persist for more than 4 days per month over a continuous period exceeding 2 months. 67 FD is defined as chronic or recurrent discomfort in the upper abdomen and must be present for at least 6 months to confirm the diagnosis.61,70 Many patients exhibit symptoms that meet both diagnostic criteria. 71 Notably, chronic discomfort is often associated with gastroesophageal reflux disease and may indicate more serious conditions such as peptic ulcer or gastric cancer. 75
In this vein, other authors argue that symptoms alone are insufficient to distinguish between organic and functional disorders. For instance, in individuals over 55 years of age, the onset of alarm symptoms in the context of gastrointestinal discomfort includes rapid progression of signs, unexplained weight loss of more than 10 pounds, frequent vomiting, difficulty swallowing, blood in vomit, black stools (melena or hematemesis), and iron-deficiency anemia. Additional high-risk factors include a family history of gastric cancer and specific ethnic backgrounds such as Asian, Hispanic, or Afro-Caribbean. Nocturnal symptoms that disrupt sleep are also considered alarm signs. 62
In pediatrics, the definition of FD was revised in Rome IV to include symptoms such as epigastric pain and burning, as well as postprandial bloating or heaviness. 76 FD is the most prevalent functional gastroduodenal disorder and should be diagnosed through meticulous clinical evaluation using these criteria.67,79
Alternatively, it is hypothesized that the patient’s mental state influences the function and perception of the upper gastrointestinal tract, leading to altered gastrointestinal motility and heightened sensitivity to non-painful stimuli. This approach, known as the biopsychosocial model of disease, conceptualizes FD as a disorder of the brain–gut axis. For decades, a clear connection has been observed between psychosocial factors and FD, with patients exhibiting a higher prevalence of psychiatric disorders such as anxiety, depression, and neuroticism. 69
Thus, there is a well-established association between FD and psychological disorders such as depression and anxiety, as well as with histories of abuse. Studies indicate that depression is an independent risk factor for FD, nearly doubling the likelihood of developing the disorder compared with individuals without depression. However, the clinical relevance of these findings is limited because they are derived from cross-sectional or case–control studies, which do not permit causal inference due to reliance on self-reports and surveys. 80
FD is a highly prevalent disorder 78 and is classified into 2 main subtypes based on predominant symptoms. The first is Postprandial Distress Syndrome (PDS), characterized by postprandial fullness and/or early satiety occurring immediately after meals, at least 3 days per week over the past 3 months, with a symptom duration of 6 months. This is the most common subtype of dyspepsia, and it is associated with a high prevalence of food-related symptoms among children and adolescents who meet the Rome IV criteria for FD. 76
The second subtype is Epigastric Pain Syndrome (EPS), characterized by pain or burning in the upper abdomen that occurs after eating, at least 1 day per week, and that meets the same time-duration criteria.62,67,68,70,71,78 These subtypes may be accompanied by additional symptoms such as bloating, belching, nausea, or heartburn. 75 In children, PDS is recognized by the inability to finish meals and the presence of uncomfortable bloating, whereas EPS is indicated by localized pain or burning in the epigastric region. 76 Depending on the symptoms, patients may be classified as having PDS, EPS, or a combination of both,70,78 as the coexistence of these subtypes in a single patient is common. 76
Rome Protocol for Detecting FD
The diagnostic algorithm for FD from the Rome IV Foundation (Figure 4) provides a systematic guide for evaluating and managing patients presenting with recurrent postprandial fullness, early satiety, epigastric pain, or burning. Alarm signs may appear at any age,61,62 or—depending on the country—at older ages, such as over 55 years in Mexico, 35 years in Colombia, 60 years in the USA,61,62 and 40 years in Chile. 71
Diagnostic algorithm for FD.
The process begins with collecting the patient’s medical history and performing a physical examination. If alarm signs 61 or red flags61,62 are identified, the patient is promptly referred for upper gastrointestinal endoscopy or esophagogastroduodenoscopy (EGD) 61 to detect gastric cancer. 71 In the absence of alarm signs, uninvestigated dyspepsia is considered, and an empirical therapy approach may be initiated. 61 This typically involves acid suppression, primarily with a proton pump inhibitor (PPI), as the first step in managing dyspeptic symptoms. Commonly prescribed medications for FD include omeprazole, esomeprazole, and pantoprazole. 62
If symptoms do not resolve with empirical therapy, upper gastrointestinal endoscopy—with or without biopsies—is recommended to identify potential abnormalities. If HP is detected by noninvasive methods—such as the urea breath test or stool antigen testing—eradication therapy should be initiated, followed by an assessment of symptom resolution. 61
If symptoms persist despite the aforementioned treatment, this suggests HP-associated dyspepsia. If no abnormalities are identified, the patient is classified as having FD. Depending on the specific symptoms, the diagnosis may correspond to one of the following 2 syndromes—PDS or EPS—or a combination of both. This structured approach ensures a thorough evaluation and appropriate treatment, facilitating differentiation between FD and other gastrointestinal conditions. 61
Gastric Accommodation
The digestive system, which includes the stomach and structures such as the esophagus, duodenum, and intestines, plays a crucial role in the body; however, dysfunction affects many people worldwide. These dysfunctions include dyspepsia, nausea, vomiting, abdominal pain, ulcers, and gastroesophageal reflux disease (GERD) 82 Gastric dysmotility and electrical dysrhythmia are key pathophysiological mechanisms in both FD and GP.
Antral hypomotility and delayed gastric emptying impair gastric function, as do abnormalities in gastric accommodation, 70 which is essential for digestion and allows the stomach to relax during food intake. Impaired gastric accommodation can lead to symptoms such as early satiety, weight loss, and altered intragastric food distribution, 67 suggesting that a considerable proportion of patients with FD may have compromised gastric motor function. 78
During the detection of gastrointestinal abnormalities, techniques such as imaging, endoscopy, and clinical testing are used. 82 In particular, electrogastrography (EGG) records the electrical activity of the stomach’s smooth muscles, which are essential for digestion because they regulate gastric motility. This gastric myoelectrical activity is divided into 2 main components: slow waves and spike activity. Slow waves regulate the rhythm and propagation of stomach contractions, occurring at approximately 3 cycles per minute (0.05 Hz). Contractions occur when slow waves are accompanied by spike potentials, 83 a rhythmic electrophysiological event in the gastrointestinal tract. 84
Bioelectrical Signals
Bioelectrical signals serve as a means of data transmission, conveying information about their origin and generator (provenance). The electrical signals generated within the human body result from the movement of ions in solution. Hard ions, such as sodium (Na⁺) and potassium (K⁺), are characterized by small ionic radii and high charge densities—properties that enable them to remain electrically active in both intracellular and extracellular body fluids. When these ions move, they generate an electric current that can be detected and measured. Bioelectrical signals vary in voltage, ranging from minimal amplitudes in electroencephalograms to more pronounced ones in electrocardiograms. 84
Therefore, bioelectrical signals serve as indicators of physiological activity in the human body and are used to understand the functioning of various organs and systems. “Bioelectrical signals can thus be determined through the use of elements that enable the detection of the electrical component of electromagnetic waves” 85 (p. 13). Heart rate (beats per minute) and skin electrical response, both bioelectrical signals, are associated with the body’s reactions to various stimuli—whether visual, auditory, or tactile. 86
Among the techniques used to record bioelectrical signals, notable examples include “ECG, electromyography (EMG), EEG, and bioelectrical impedance analysis, which assesses body composition, nutritional status, hydration levels, and even estimates training and performance levels” 85 (p. 13), as well as EGG. The latter, for instance, was examined in the study by, 84 which focused on its use for monitoring the stomach’s electrical rhythm—similar to the cardiac currents detected by electrocardiograms. These gastric bioelectrical signals arise from activity across various cellular components and can be categorized by their biological significance.
Electrogastrography (EGG)
For nearly a century, the potential of gastric electrical activity—or gastric electrical impulses—as clinical biomarkers has been explored 72 ; historically, these have been evaluated using the EGG technique. 79 Over the past 8 decades, EGG methods have been used in numerous clinical studies. In 1922, Álvarez hypothesized that gastric electrical irregularities could be linked to gastrointestinal (GI) symptoms and abnormal gastric function. “In 1980, antral arrhythmias were documented using mucosal electrodes in a series of patients presenting with unexplained nausea and vomiting” 87 (p. 101).
Gastric dysrhythmias were identified, including tachygastrias at 6 to 7 cycles per minute (cpm) and irregular rhythms alternating between bradygastrias and tachygastrias (mixed dysrhythmias or tachyarrhythmias). In addition, bradygastrias were observed in patients with unexplained nausea and vomiting. Subsequent research established an association between nausea and gastric dysrhythmias in cases of “motion sickness, nausea and vomiting during pregnancy, and in patients with idiopathic and diabetic gastroparesis” 87 (p. 101).
Currently, EGG is considered a technique that uses cutaneous electrodes to record and evaluate gastric myoelectrical activity.76,77 It is also a relatively new, noninvasive procedure that does not diagnose a specific syndrome. 83 However, 1 study concluded that electrode size plays a crucial role in reliably measuring gastric electrical activity and may be a determining factor in the diagnostic accuracy of GI disorders. 56
Conversely, it is important to clarify that the electrogastrogram is a non-invasive technique that uses “electrodes placed on the surface of the abdomen—not within the stomach—to record electrical activity that propagates to the abdominal surface via volume conduction” 74 (p. 3314). The standard electrogastrogram uses 3 to 4 electrodes and is evaluated using spectral approaches, specifically power frequency analysis. 74
Meanwhile, gastric electrical activity is characterized by a series of waves—primarily the slow wave—which plays a crucial role in regulating gastric motility by triggering muscular contractions (peristalsis). Abnormal gastric electrical activity is postulated to contribute to functional disorders, including FD. 72 This activity is measured and recorded using the EGG technique, which provides information on the stomach’s rhythmic activity—specifically, dysfunctions in the amplitude, power, and frequency ranges of gastric slow waves. 83 Additionally, EGG can assess dominant frequency and dominant power and detect abnormal rhythms. 76
In clinical practice, EGG is used to assess gastrointestinal motility disorders in a variety of conditions, including postural tachycardia syndrome, gastric cancer, abnormal gastric motility associated with systemic sclerosis, pregnancy, recurrent vomiting, and diabetes mellitus. 83
Abnormalities in EGG recordings of gastric myoelectrical activity have been linked to FD. EGG has identified abnormal frequencies and rhythms in young individuals with FD, which are associated with more intense postprandial pain. However, previous studies used the Rome III criteria; with the transition to Rome IV, the relevance of these findings for contemporary youth with FD remains uncertain. 76 While some studies have observed abnormalities in EGG recordings among patients with upper gastrointestinal symptoms, others have found no differences between individuals with dyspepsia and healthy volunteers. 77
Despite being noninvasive and easy to use, EGG has faced challenges that have limited its adoption in general clinical practice. 77 Its low sensitivity and specificity in identifying gastric pathophysiology—along with difficulties in accurately distinguishing it from other instruments during signal recording and in establishing consistent associations with symptoms 72 —have diminished clinical interest in its use. Consequently, it is infrequently employed outside specialized centers, 79 which undermines confidence in its diagnostic value. 72
It has been observed that the spectral characteristics of the EGG signal do not directly correlate with symptoms or the underlying disease. Spatial abnormalities—or spatial coherence, defined as the degree of correlation or similarity between phases at different points along a wavefront (in this case, slow waves)—are more strongly correlated with disease and symptoms, yet remain difficult to identify through spectral analysis. 74
Likewise, the clinical utility of EGG has been questioned because of its limited correlation with diagnoses obtained by techniques such as gastric emptying scintigraphy and antroduodenal manometry 77 (p. 2). Moreover, despite an extensive body of clinical studies, EGG has not achieved widespread adoption because of persistent concerns about its reliability, susceptibility to noise, and overall clinical value. 81
Ultimately, there is a need to develop new, standardized, and user-friendly diagnostic techniques in gastroenterology that provide more reliable, clinically meaningful biomarkers. Advances in approaches such as Body Surface Gastric Mapping (BSGM), which represents the next generation of gastric electrophysiology, show significant potential. This approach has evolved from EGG, enabled by high-resolution technology, improved artifact rejection methods, and novel analytical strategies. This advanced methodology, known as high-resolution EGG, expands traditional diagnostic capabilities by incorporating spatial wave profiling, thereby maintaining its relevance in clinical practice.79,81
Discussion
Based on an analysis of 85 academic sources, this study finds that in engineering applications in medicine, ITQs are currently used to study a wide range of diseases. Among these, neurodegenerative and cardiac conditions are the most extensively investigated, whereas gastric disorders have received comparatively less attention.
Clues, Gaps, and Opportunities: Quantification of Information in Bioelectrical Signals
The entropies ShEn, RE, TE, WaEn, and DispEn share the common objective of quantifying complexity and disorder across various systems but differ substantially in their approaches and applications. All are grounded in mathematical principles for assessing uncertainty or variability, with ShEn and RE serving as foundational measures in information theory, TE extending these concepts to non-extensive systems, and WaEn and DispEn focusing on time-series analysis with robustness to noise.
However, their differences are notable: ShEn, limited by its focus on probability distributions and additive systems, contrasts with the parametric flexibility of RE and TE, which incorporate a parameter q to adapt to specific contexts. WaEn is distinguished by its parameter-independence and frequency decomposition, whereas DispEn combines PE and SampEn to capture changes in both amplitude and frequency. Thus, although all are valuable tools, their strengths and limitations make them complementary, depending on the type of system under analysis.
The analysis of ShEn, RE, TE, WaEn, and DispEn (Table 9) reveals significant insights into their ability to measure complexity and disorder, while also exposing gaps that limit their comprehensive applicability. Notably, ShEn is effective in extensive and additive systems, whereas RE and TE—through the parameter q—offer flexibility for non-stationary and non-extensive systems, respectively, thereby extending their scope to contexts such as signal analysis and thermodynamics.
Comparison of Entropy Metrics Applied to EGG Signal Analysis for the Diagnosis of FD.
Source: Own design.
For its part, WaEn and DispEn stand out in time-series analysis, with the former achieving noise resilience through frequency decomposition and the latter detecting simultaneous changes in amplitude and frequency. However, several gaps are evident: ShEn does not capture temporal dependencies or individual details; RE and TE rely on parametric adjustments that may introduce subjectivity; WaEn lacks sensitivity to probabilistic distributions; and DispEn, although versatile, requires optimizing parameters (m, c, L), which can complicate its implementation. These limitations suggest integrating these measures to enable a more comprehensive analysis of complex systems.
Taken together, ShEn, RE, TE, WaEn, and DispEn illustrate how different ITQs capture complementary facets of uncertainty and complexity across disciplines such as physics, computer science, and biomedicine. This comparative perspective is useful for motivating integrative analytical strategies when single-metric descriptions are insufficient for characterizing complex dynamics.
At the same time, the limitations identified across these measures—such as ShEn’s limited sensitivity to temporal dependencies and the reliance on parameter selection in RE, TE, and DispEn—reinforce the need for integrative approaches that combine complementary strengths. This rationale provides a direct methodological bridge to the next stage of analysis, where multi-metric strategies and implementation choices are examined in applied experimental settings.
Clues, Gaps, and Opportunities: Experimental Methodological Approaches
The analysis reveals that, despite the diversity of applications and methodologies, there is a convergence in the use of entropy metrics to evaluate the complexity of biological systems. On the one hand, there is a notable similarity in the quantitative approach and in the application of advanced techniques (EEG, ECG, and other biomedical recordings) to characterize diverse biomedical and physiological signals, regardless of pathology or application context.
In contrast, clear differences emerge: while some studies focus on neurodegenerative and cardiovascular pathologies, with samples ranging from animal models to human subjects across a wide age spectrum, other investigations address less common conditions or even simulated signals.
Furthermore, the diversity of units of analysis, data collection instruments, and statistical approaches—from parametric and nonparametric tests to wavelet decomposition techniques and multiscale analyses—highlights both the breadth and the specificity of each line of research. In summary, although the methodological foundations share the common objectives of robustness and precision, the heterogeneity in the design and application of these techniques underscores the inherent differences among the various fields of study.
This heterogeneity of data sources—spanning bioelectrical, non-bioelectrical, and simulated signals—reflects the growing complexity of biomedical and engineering research and underscores the need for robust, multidisciplinary analytical methodologies capable of addressing diverse research designs.
Some studies also used wavelet-based 29 approaches to decompose signals into distinct frequency bands and estimate wavelet entropy. These methods enabled a more comprehensive evaluation of the data by accounting for both signal distributions and model characteristics. This underscores the need for versatile and powerful techniques to characterize, classify, and validate signals that—as shown in Table 5—span cardiac and EEG recordings and simulated series.
Findings from applying entropy-based metrics demonstrate their versatility in capturing complexity in physiological signals and dynamical systems. However, notable gaps remain, including limited exploration of digestive disorders and of heterogeneity across samples and analytical units, which hinder comparisons and generalizations.
Moreover, there is a need for a more coherent integration of statistical analyses and decomposition techniques into entropy-based approaches to optimize the interpretation of multidimensional data.
Consolidating all documents into a single table of entropies provides a comprehensive view of the evolution and convergence of various entropy-based approaches. Far from being isolated methodologies, these techniques complement and reinforce one another, forming a robust conceptual framework that facilitates understanding of the inherent complexity in biomedical and related signals. In this regard, the analysis not only identifies connections among different lines of research but also lays the groundwork for future proposals that more deeply integrate statistical, temporal, frequency, and causality dimensions in the study of ITQs across diverse biomedical, physiological, and simulated data sources.
The identified findings and gaps highlight methodological implications for integrating entropy-based approaches in the analysis of physiological signals and dynamic systems. Moreover, the wide diversity in units of analysis, data collection instruments, and samples (subjects) underscores the need to develop standardized protocols and multimodal approaches that integrate bioelectrical recordings (EEG, ECG, MEG), neuroimaging techniques (fMRI), and complementary biomarkers (oxygen saturation variability, gait analysis, voice recordings), thereby enabling a more holistic and robust understanding of the phenomena under study. Likewise, it calls for the development of data processing systems that seamlessly integrate multiple data sources to optimize the interpretation of complex signals.
Consequently, these findings highlight the importance of advancing toward integrated methodological frameworks that support cross-validation and comparative analyses, particularly in underexplored application domains.
Clues, Gaps, and Opportunities: Functional Dyspepsia
The approach to functional dyspepsia (FD) and its related subtopics highlights the need for a comprehensive diagnostic framework that integrates clinical criteria and gastric myoelectrical assessment. Across the different sections, complementary perspectives on FD emerge. Clinical components emphasize medical history, physical examination, and early identification of alarm signs, while physiological and technological approaches focus on gastric accommodation, bioelectrical signals, and electrogastrography. Although these perspectives converge on the need for a multidimensional diagnosis, they differ in their clinical, technological, and methodological emphasis.
Together, these approaches illustrate how the integration of clinical criteria, diagnostic algorithms, and bioelectrical recording techniques has expanded current understanding of motility alterations, visceral sensitivity, and myoelectrical activity in FD. However, the limited sensitivity and specificity of conventional methods, together with the inconsistent correlation between bioelectrical findings and clinical symptomatology, reveal important methodological limitations in current diagnostic approaches.
Taken together, these findings highlight the complexity of FD and the challenges involved in integrating clinical, physiological, and psychosocial dimensions within a single diagnostic framework.
Final Discussion: A Methodological Bridge to FD
This broader panorama, encompassing physiological and simulated signals beyond the gastrointestinal field, was intentionally included in the review to identify methodological trends and successful applications in other domains, providing a reference framework for recognizing the current gap in FD research. Nevertheless, this trend has opened new perspectives for the diagnosis and management of these conditions. Accordingly, various families of measures known as ITQs, 10 based on complexity and entropy methods, have been applied to study these conditions.
The use of ITQs in time-series analysis has enabled researchers to capture the variability of physiological recordings, facilitating early detection and assessment of disease progression. This points to a promising path toward data-driven medical diagnosis and care.
The application of these approaches has opened new perspectives for diagnosing and managing various diseases. For example, the diagnosis and monitoring of related disorders—such as sleep apnea—have been advanced by applying MSE to ECG signals 88 ; likewise, distinctive patterns in PD have been identified using hemodynamic signals 43 ; and, in gait analysis for PD, metrics such as MSE, RCMSE, and CI—computed from trunk accelerometry recordings—consistently discriminated 51 individuals with mild-to-moderate PD from 50 healthy controls. Patients showed higher entropy values across scales τ = 1 to 6, and the MSE computed in the anteroposterior (AP) and mediolateral (ML) directions—particularly at τ4–τ5—exhibited the strongest discriminative power. This case demonstrates that entropic approaches already operate effectively in complex clinical contexts using portable, non-invasive signals. 41
Beyond signal-level applications in clinical monitoring, ITQ-based approaches have also demonstrated their capacity to characterize subtle dynamical properties in other biological systems. Complementarily, it has been documented that ITQs—including normalized total entropy, SCM, and the CECP—provide a clear characterization of erythrocyte viscoelastic dynamics. These parameters consistently distinguished the high randomness observed in healthy individuals from the markedly reduced randomness observed in pathological conditions such as β-thalassemia minor, demonstrating their ability to separate populations and reveal dynamical differences that conventional methods miss. 6
Taken together, these findings from non-gastrointestinal contexts provide a rationale for extending ITQ-based analyses to gastric myoelectrical activity, specifically, FD. Consequently, applying these approaches to the analysis of EGG signals and to the characterization of FD could be both methodologically coherent and clinically plausible.
Other studies have shown that EGG is a non-invasive technique that records the electrical activity of the digestive system, providing crucial data for diagnosing a variety of gastrointestinal disorders. In the study Analysis of Digestive Disorders Based on EGG Signals, the effectiveness of TE and RE was evaluated for distinguishing between normal and abnormal physiological states. The results indicated that TE was higher in healthy individuals, whereas RE was lower in those with disorders, suggesting that both measures may contribute to the diagnosis of digestive disorders. 55
Similarly, another study examined how electrode size affects the quality of EGG signals using RE. The findings showed that increasing the electrode surface area enhances EGG signal capture, particularly at higher α values. This suggests that larger electrodes can capture broader information, which is crucial for accurate measurement of gastric electrical activity and the diagnosis of FGIDs. 56
Research on FGIDs using EGG bioelectrical signals within the framework of ITQs remains limited in the scientific literature. Continued exploration of this area is essential to fully harness its potential, advance medical science for the benefit of society, and open new frontiers in medical research by providing more precise and effective tools for addressing these disorders.
Given this, and in light of the identified methodological gaps, we propose advancing to prospective clinical studies involving well-characterized populations defined by the updated Rome IV diagnostic criteria to validate the discriminatory power of ITQs applied to EGG signals. Furthermore, standardized signal acquisition protocols are recommended because variations in factors such as recording duration, electrode count, stage segmentation (pre-prandial, prandial, and postprandial), and sampling frequency can substantially affect data quality and hinder comparability across studies.
In this context, based on the literature, it is recommended to adopt a minimum sampling rate of 8 Hz and a recording duration of at least 1 hour per stage—except for the prandial phase 89 —and to apply homogeneous preprocessing, including filtering within the 0.03 to 0.25 Hz range. In addition, the use of validated cutaneous electrode configurations positioned over the gastric region, with standardized inter-electrode spacing and previously documented contact areas, is advised.55,56 These methodological guidelines would enhance reproducibility and strengthen the clinical robustness of applying entropy-based metrics to FD research.
Finally, to move beyond descriptive use toward a genuine integration of entropic quantifiers into the clinical workflow, it is recommended that, after EGG acquisition under standardized parameters, the reproducible computation of multiple metrics be ensured. Among these, ShEn and TE stand out because they are widely used to characterize the statistical properties of amplitude distributions; MSE, which captures multiscale complexity; and, complementarily, information-based metrics derived from analyses—such as graph-based approaches—that describe the signal’s structural patterns.
These values can be compared with reference cohorts using statistical tests or simple classifiers, and reported to the clinician as normalized, interpretable indices indicating whether the patient’s pattern aligns with typical FD profiles or with normal recordings. This operational proposal enables ITQs to be transformed into replicable, transparent, and potentially useful clinical tools to support the diagnosis and follow-up of FD.
Given the above, further investigation of FGIDs—and, more specifically, FD—using the SCM of bioelectrical signals obtained via EGG is warranted to enable accurate identification. This represents both a clinical challenge and a socially relevant issue, particularly given the geographic variability observed in its prevalence.
From a broader methodological perspective, the preceding analyses also highlight the need to develop new approaches to detecting digestive disorders using information-theoretic quantifiers. This progress will be driven by integrating scientific and technological perspectives—particularly from computer engineering—to analyze the relationships among different ITQs applied to bioelectrical “signals” 90 and time series, and to assess their potential diagnostic utility—for example, in FD, a gastrointestinal disorder. In this way, the detection of digestive disorders will be enhanced, while also fostering the development of solutions and methodologies applicable to other countries facing similar challenges in this field.
The comparative evidence gathered from other physiological and simulated signals throughout this review provides a methodological foundation that can be adapted to the gastric context, ensuring that advances in FD are informed by established strategies from domains in which ITQs are more mature.
Conclusions
The scoping review shows that information-theoretic quantifiers (ITQs)—such as Shannon entropy (ShEn), Rényi entropy (RE), transfer entropy (TE), wavelet entropy (WaEn), and dispersion entropy (DispEn)—together with classical and multiscale entropy metrics (ApEn, SampEn, MSE) 91 provide a robust framework for characterizing the variability of bioelectrical signals and time series, thereby enriching the analysis of physiological dynamical systems.
The analysis of complexity and disorder in such systems is strengthened by the combined application of multiple entropy measures, which complement one another to yield a more comprehensive understanding of system behavior. Within this framework, ShEn emerges as a fundamental tool for quantifying uncertainty based on logarithmic and additive principles; however, its reliance on the probability distribution alone limits its capacity to capture fine-grained details and temporal dependencies.
In contrast, RE and TE broaden the analytical spectrum by adapting to diverse contexts through the incorporation of order parameters, enabling the generalization of various measures and the analysis of non-extensive or complex systems with long-range interactions. Additionally, methodologies such as WaEn and DispEn introduce innovative strategies for analyzing signals and time series, facilitating the detection of changes in frequency and amplitude and effective noise reduction without relying on fixed parameters. Taken together, these complementary approaches establish a robust framework for characterizing and evaluating variability across theoretical models and practical applications.
The analysis of experimental methodological pathways reveals the diversity and technical–demographic relevance of entropy metrics applied to dynamic and physiological systems, encompassing both classical approaches such as ApEn and SampEn, as well as MSE techniques and hybrid methodologies that integrate multiple measures to enhance analytical robustness. In this context, the studies analyzed have focused on neurodegenerative disorders—primarily AD (ages 55-80, 60% female) and PD—as well as cardiovascular conditions (ages 40-70, with a balanced gender distribution), integrating datasets spanning preclinical models and large-scale population-based studies.
In addition, a wide range of units of analysis and data acquisition modalities—such as EEG, ECG, fMRI, and MEG—are encompassed. In combination with advanced statistical and classification methods, contextualize the application of entropy measures within specific demographic and technical frameworks. This multidimensional landscape underscores the relevance of entropy-based approaches for accurately characterizing the intrinsic complexity of signals, thereby opening new avenues for their application in both clinical and engineering research.
This distribution highlights the predominance of neurodegenerative and cardiovascular pathologies and underscores the importance of simulation models as a comprehensive framework for future research on the characterization of complex systems. At the same time, a clear need remains to address pathologies associated with digestive disorders, particularly FD.
Effective understanding and management of FD requires a multidimensional approach grounded in precise clinical criteria—such as those established by the Rome IV consensus—alongside advanced diagnostic techniques that assess both the bioelectrical and motor activity of the gastrointestinal tract. Within this framework, bioelectrical signals play a central role in identifying associated dysrhythmias and supporting targeted therapeutic strategies.
The application of the biopsychosocial model highlights the integration of psychosocial factors into symptom perception assessment. The use of diagnostic tools such as EGG—despite its current limitations—offers promising prospects for improving diagnostic accuracy. In this context, further investigation into the underlying pathophysiological mechanisms, together with the development of more specific and sensitive methodologies, may enhance clinical management and ultimately improve patients’ quality of life.
This review of FD highlights the disorder’s complexity and heterogeneity, reflecting a convergence of motility disturbances, visceral hypersensitivity, and psychosocial factors. The application of the Rome IV criteria enables rigorous differential diagnosis by facilitating the exclusion of organic conditions and supporting therapeutic management—particularly in vulnerable populations, such as pediatric patients. Likewise, incorporating the biopsychosocial model underscores the influence of mental state on the perception and function of the upper gastrointestinal tract. It underscores the need for continued research into its underlying mechanisms to optimize clinical and therapeutic strategies.
The diagnostic algorithm proposed by the Rome IV Foundation provides a comprehensive and systematic approach to the management of FD. This framework highlights the role of an initial evaluation, including medical history and physical examination, in identifying alarm signs that warrant early procedures such as endoscopy. In the absence of such signs, empirical therapy with a PPI, along with evaluation for H. pylori infection, facilitates differentiation between FD and other gastrointestinal conditions. Overall, this diagnostic framework enhances the accuracy of disorder identification and supports more informed therapeutic decision-making, contributing to personalized clinical management.
The implications of these approaches underscore the need for a multidisciplinary strategy that integrates rigorous clinical criteria, such as those of Rome IV, with advanced techniques to assess the myoelectrical and bioelectrical activity of the digestive system. In this context, diagnostic algorithms for FD and detailed evaluation using methods such as EGG—and its evolution toward high-resolution techniques—represent key components for improving diagnostic accuracy. Furthermore, consideration of the biopsychosocial model highlights the importance of examining the interaction between psychosocial and physiological factors, which may contribute to the development of more reliable biomarkers and, ultimately, to improvements in patients’ quality of life.
A comprehensive evaluation of the digestive system indicates that irregularities in motility and electrical coordination—such as antral hypomotility, delayed gastric emptying, and electrical dysrhythmia—are key determinants of clinical symptoms, including FD and GP. Advanced diagnostic techniques, such as EGG, enable precise characterization of the stomach’s myoelectrical activity and support the interpretation of underlying physiological alterations. This integrated approach underscores the importance of maintaining digestive system function for effective digestion. It highlights the need for further research into these pathophysiological mechanisms to improve clinical care and patient quality of life.
Although robust methodological approaches—encompassing statistical tests, classification algorithms, and entropy metrics—have been used, challenges remain in integrating these techniques to address the inherent complexity and heterogeneity of physiological signals. Despite the breadth and depth of current research, significant knowledge gaps persist—particularly in the study of digestive disorders—underscoring the need for further diversification of analytical methodologies.
The results provide a comprehensive interpretation of the objective of this scoping review. From an entropic perspective, measures such as ShEn, RE, TE, WaEn, and DispEn constitute a robust framework for characterizing the complexity and variability of bioelectrical signals. In the experimental domain, the diversity of entropy metrics—including ApEn, SampEn, and MSE—applied to EEG, ECG, and other physiological signals highlights their capacity to analyze complex physiological systems. In contrast, their application to digestive disorders remains comparatively limited.
Taken together, entropy-based metrics such as Shannon entropy, Rényi entropy, Tsallis entropy, wavelet entropy, and dispersion entropy provide complementary perspectives for characterizing disorder, variability, and structural complexity in dynamic systems. While some measures are more suitable for extensive or stationary settings, others offer greater sensitivity to non-stationarity, multiscale dynamics, or noise-contaminated signals. This complementarity reinforces the need to integrate multiple information-theoretic quantifiers when analyzing complex bioelectrical signals and time series, rather than relying on a single metric in isolation. Such an integrated approach enables a more comprehensive characterization of physiological dynamics and supports the robust interpretation of variability in biomedical contexts.
Regarding FD, integrating these analytical tools with established clinical criteria (Rome IV) and techniques such as EGG highlights their potential to improve diagnostic accuracy by identifying specific bioelectrical and motor patterns. These findings indicate that entropy-based metrics and advanced signal analysis methods could represent useful tools for detecting FD. Further work may focus on refining these techniques to address current limitations of EGG and on developing enhanced methodologies to strengthen its role in clinical management.
The evolution of electrogastrography has enabled the identification of gastric dysrhythmias relevant to research and management of gastrointestinal disorders, establishing it as a valuable noninvasive tool. At the same time, its limited sensitivity, specificity, and correlation with current clinical criteria have restricted its widespread adoption. This underscores the need to refine the technique and develop advanced methodologies—such as high-resolution EGG or BSGM—that may yield more reliable and clinically meaningful biomarkers.
Bioelectrical signals—generated by the movement of ions in solution—are essential for assessing the physiological activity of organs. These signals, which vary in voltage and are detected using specialized techniques such as ECG, EEG, or EGG, provide valuable insight into the interaction between cellular processes and responses to stimuli. As such, they constitute key tools for clinical assessment and monitoring, supporting the detection of physiological alterations across diverse contexts.
The analysis demonstrates that combining entropy metrics with bioelectrical signals and time series provides a comprehensive framework for capturing the complexity and variability of physiological systems. By integrating experimental advances with clinical criteria, this multidimensional approach highlights the potential of these tools to enhance diagnostic accuracy across diverse pathologies, thereby opening new avenues for clinical interpretation. At the same time, gaps persist in specific areas, such as digestive disorders, underscoring the need for more diverse and in-depth analytical approaches.
Furthermore, the hypotheses reinforce this approach by anticipating emerging patterns and providing comprehensive descriptions of the phenomena under study, highlighting the value of a panoramic analysis that reveals both the current state of knowledge and existing gaps, rather than testing a specific hypothesis or addressing a narrowly defined question. This exploratory approach is particularly well-suited to an interdisciplinary topic such as this, where the integration of nonlinear dynamics, biomedicine, and information analysis remains under development and would benefit from a holistic perspective.
Footnotes
Appendix 1
Author Contributions
Conceptualization, L.G.G.A. and M.C.P.; methodology, L.G.G.A. and M.C.P.; validation, L.G.G.A. and M.C.P.; formal analysis, L.G.G.A.; investigation, L.G.G.A. and M.C.P.; resources, L.G.G.A.; data curation, L.G.G.A. and M.C.P.; writing—original draft preparation, L.G.G.A.; writing—review and editing, L.G.G.A. and M.C.P.; visualization, L.G.G.A.; supervision, M.C.P.; project administration, M.C.P.; funding acquisition, M.C.P. All authors have read and agreed to the published version of the manuscript.
Funding
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was partially funded by FONDECYT grant No. 1241202. No additional external funding was received for this scoping review.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Data Availability Statement
All data used in this study were obtained from publicly accessible scientific literature as described in the Methods section. No new datasets were generated.