Decoding the correlation between heart activation and walking path by information-based analysis

Abstract

BACKGROND:

One of the important areas of heart research is to analyze heart rate variability during (HRV) walking.

OBJECTIVE:

In this research, we investigated the correction between heart activation and the variations of walking paths.

METHOD:

We employed Shannon entropy to analyze how the information content of walking paths affects the information content of HRV. Eight healthy students walked on three designed walking paths with different information contents while we recorded their ECG signals. We computed and analyzed the Shannon entropy of the R-R interval time series (as an indicator of HRV) versus the Shannon entropy of different walking paths and accordingly evaluated their relation.

RESULTS:

According to the obtained results, walking on the path that contains more information leads to less information in the R-R time series.

CONCLUSION:

The analysis method employed in this research can be extended to analyze the relation between other physiological signals (such as brain or muscle reactions) and the walking path.

Keywords

Heart Rate Variability (HRV)walking path Shannon entropy information content R-R interval time series

1. Introduction

One of the important areas of heart research is to analyze heart rate variability (HRV) during walking. For this purpose, many researchers analyzed HRV during walking. In fact, they employed different techniques and accordingly analyzed HRV in different conditions. Previous studies evaluated the influence of graded forward and backward walking [1], self-selected walking speed [2], speed and duration of walking [3], dog-walking [4], age and sex of subjects [5], walking with cane, tripod, and walking frame [6], supervised walking [7] and regular walking during a golf game [8] on HRV. In addition, some studies reviewed the effects of frequency, intensity, duration and volume of walking interventions on cardiovascular disease (CVD risk factors [9].

However, besides all reported studies on the analysis of heart reaction during walking, no work has been conducted yet that investigated the relation between heart activity and path of movement from an information point of view. Our problem statement is to find the same feature between walking path and HRV, and since both can be quantified using the entropy concept, we benefit from Shannon entropy for our investigation. Shannon entropy indicates the information content where its greater values stand for greater information in the system [10]. Therefore, in this research we analyze the Shannon entropy of HRV signal versus walking paths to evaluate how the information content of paths can affect the information content of heart reaction.

Based on the literature, besides reported studies that employed different techniques such as fractal theory [11, 12, 13, 14] to analyze different biological and physiological signals [15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61], several works have been reported that benefited from different types of entropy for their investigations. Previous works employed entropy for analysis of Electroencephalogram (EEG) signals [62, 63, 64, 65, 66, 67, 68, 69, 70, 71], Magnetoencephalogram (MEG) signals [72, 73], eye movements [74, 75, 76, 77], Galvanic Skin Response (GSR) signals [78, 79], voice signals [80], and Electromyogram (EMG) signals [81, 82, 83, 84, 85].

Similarly, some researchers analyzed the entropy of the R-R time series (as HRV) to study its variations in different conditions. Previous works employed sample entropy [86, 87], approximate entropy [88, 89], Shannon entropy [90, 91], multiscale entropy [92, 93], Permutation entropy [94, 95], and fuzzy entropy [96, 97] for analysis of HRV in different conditions.

Therefore, in this research, for the first time, we employ Shannon entropy to study the correlation between heart activation and the variations of walking paths. First, we outline the method of analysis. Then, we explain our data collection procedure and how we analyzed the recorded data. The analysis results that also contain statistical analysis will be provided thereafter. In the last section of this paper we provide our discussion containing some future works.

2. Method

In this research, we aim to investigate the relation between the information content of HRV and the information content of the path of movement. In other words, we want to analyze how the information content of the walking path affects the information content of the HRV signal. For our analysis, we considered the R-R interval time series as HRV. We employ Shannon entropy and analyze the variations of the information content of the R-R time series versus the variations of the information content of the path of movement.

Shannon entropy is the indicator of the information embedded in a system. In fact, a system can be such as a signal or image that, in this research considered as R-R time series. A greater value of Shannon entropy stands for greater information embedded in the system.

In general, Shannon entropy is defined as [85]:

$\displaystyle H=-\Sigma_{i}p_{i}\text{log}p_{i}$ (1)

In Eq. (1), $p_{i}$ indicates the probability of the R-R time series having the value of $a_{i}$ , where $i$ ranges over all values of the time series.

Table 1

The Shannon entropy of different walking paths

No.	Shannon entropy
Second path	2,538,200
Third path	2,572,400
Fourth path	2,266,500

Figure 1.

The designed walking paths with arbitrary units (subjects walked from left to the right).

It should be mentioned that in our main experiment that investigated the influence of the complexity of walking paths on the complexity of heart rate, five walking paths were designed based on their fractal exponents. However, here we only consider three paths for our investigation. This selection is because only three paths have significant variations in their Shannon entropy and choosing these paths enables us to examine the effect of variations of the walking paths on variations of entropy of heart rate. These walking paths are shown in Fig. 1. It should be noted that the indicated path numbers are based on their original number in our main experiment. Each path includes 120 points that are randomly distributed on it. Table 1 lists the Shannon entropy of different walking paths. The values of Shannon entropy were calculated using a code that we wrote in MATLAB (MathWorks, USA). As can be seen in this table, the third and fourth path respectively has the greatest and smallest Shannon entropy. In other words, we can say that the third and fourth path respectively has the greatest and lowest information content. In fact, by choosing these paths we can investigate the relationship between the information content of heart rate and the information content of walking paths.

Therefore, using Shannon entropy analysis, we would like to analyze how the variations of the information content of different walking paths affect the information content of the R-R time series, or in other words, how their information contents are correlated.

2.1 Data collection and analysis

All procedures of recruiting subjects and conducting the experiment were approved by Monash University Human Research Ethics Committee (MUHREC) with approval number 19719. We conducted the study under the approved guidelines.

We have conducted the experiment on eight healthy students from Monash University Malaysia. The informed consent form was collected from subjects after we explained the experiment to them and they agreed to participate. It should be noted that we also asked several questions from subjects to ensure their health conditions. We started the experiment on subjects who did not have any record of heart disorders and did not use alcohol or caffeine within 48 hours before the experiment.

Figure 2.

Venue of the experiment and walking paths.

Figure 2 shows the experiment’s venue and walking path. As can be seen in the figure, the experiment was conducted in a closed hall to reduce the effect of unwanted stimuli on the recorded data. We instructed subjects to focus on their breathing while walking without doing any other job.

To record ECG signals, we used a Shimmer ECG device. This device gave us the ability to record ECG signals from subjects while they walked. The sampling frequency of 256 Hz was chosen for data collection.

We started collecting data by recording ECG signals from subjects during rest for one minute. As was mentioned before, subjects walked on different paths with different complexities. However, in this research we considered the second, third and fourth paths as their Shannon entropy changed significantly. Since subjects walked at different speeds, the duration of walking was different in the case of different subjects. We gave one-minute rest to subjects between walking on different paths. In fact, this rest period brought the subject’s heart activity to the normal level and prepared it for the next walking period The data collection was repeated in the second session to consider the repeatability of recorded data.

Since we considered the R-R interval time series as HRV, we wrote a set of codes in MATLAB that firstly found R peaks of ECG signals and accordingly generated the R-R interval time series. The extracted R peaks from MATLAB code were visually verified for better accuracy.

We calculated the Shannon entropy of the R-R interval time series in case of rest and different walking paths It should be noted that since each subject walked at different speeds, therefore we had different lengths of data for different subjects in case of walking on each path However, we processed the same length of data (58.19 seconds) for all subjects in case of rest. It should be noted that this duration was less than a minute due to inconsistency in the sampling frequency of the recording device which sometimes caused the recording of shorter data.

We used the Post-hoc Tukey test to compare the Shannon entropy of the R-R interval time series between different conditions. The effect of variations of walking paths on variations of Shannon entropy of R-R interval time series was evaluated using effect size analysis. The significance level of 95% was chosen in the case of all statistical analyses.

Figure 3.

The Shannon entropy of the R-R interval time series in case of rest and different walking paths.

3. Results

In this section we outline the result of the analysis. Figures 3 and 4 respectively show the variations of the Shannon entropy of the R-R interval time series and the Shannon entropy of different walking paths.

Based on the obtained results, the R-R time series has the smallest Shannon entropy during rest. Since Shannon entropy indicates the information content, therefore, it can be said that the R-R time series experiences the lowest information content during rest. By looking at the trend of variations of the Shannon entropy in the case of different stimuli, we can see that the R-R time series has the greatest Shannon entropy in the case of walking on the fourth path, which is followed by the second and third walking paths. In other words, the information content of the R-R time series decreases by moving from the fourth to second and third walking paths

By comparing the obtained results in Fig. 3 with the Shannon entropy of different walking paths shown in Fig. 4, it can be said that by moving on a path with greater information content, the information content of the R-R time series decreases. Therefore, based on this result, the information content of HRV is reversely related to the information content of walking paths.

The comparisons of the Shannon entropy of HRV between different pairs of conditions are listed in Table 2. Based on this result, the variations of the Shannon entropy of HRV between rest and different walking paths were significant. However, we cannot see any significant difference in the case of other comparisons. In fact, this result is very dependent on the information content of walking paths, and walking on a path with greater information content may result in significant variations in the Shannon entropy of HRV.

Table 2
Pairwise comparisons of the Shannon entropy of R-R interval time series in case of different paths

Pairwise comparison	$p$ -value
Rest vs. 2nd path	0.0000
Rest vs. 3rd path	0.0000
Rest vs. 4th path	0.0000
2nd path vs. 3rd path	0.9028
2nd path vs. 4th path	0.9510
3rd path vs. 4th path	0.6194

Figure 4.

The Shannon entropy of different walking paths.

Table 3 shows the results of effect size analysis between different pairs of conditions. As can be seen in this table, the fourth path with the smallest information content had the greatest effect on the information content of the R-R time series.

Table 3

Effect sizes for the pairwise comparison of Shannon entropy of R-R interval time series in case of different paths

Pairwise comparison	Effect size (r)
Rest vs. 2nd path	0.85
Rest vs. 3rd path	0.84
Rest vs. 4th path	0.87
2nd path vs. 3rd path	0.11
2nd path vs. 4th path	0.09
3rd path vs. 4th path	0.20

In general, we can conclude that the variations of the information content of HRV are inversely related to the variations of the information content of walking paths; as subjects walk on a path with greater information content, HRV will have lower information.

4. Discussion

In this paper we analyzed the influence of walking on different paths on HRV. For this purpose, we benefited from the entropy concept, and by employing Shannon entropy, we studied the variations in the information content of the R-R time series in case of walking on different paths with different information contents. The analysis results showed that walking on a path with greater information leads to lower information in the R-R time series. In other words, the information content of the R-R time series is related to the information content of the walking path. In fact, the analysis done in this research is one step forward compared to the studies [1, 2, 3, 4, 5, 6, 7, 8, 9] that only analyzed heart rate variations during walking without relating its characteristics to the characteristic of walking paths.

In this research we evaluated the variations in HRV during walking. In further investigation, we can also analyze how the characteristics of different walking paths affect the variations of other physiological signals of humans during walking. For example, since walking affects human respiration [98], we can investigate how the variations in the information content of different walking paths affect the information content of respiration signals. We can also do a similar analysis in case of the brain’s reaction while subjects walk on different paths. It is worthy of mentioning that since the brain controls all parts of the human body [99], there should be a relation between variations of the information content of brain signals (EEG signals) and other physiological signals during walking. This analysis could help us understand how the activities of different organs of the human body are related to brain activity.

We can also extend our analysis to investigate the relation between walking paths and heart reaction in the case of patients with different types of heart diseases [100]. In this way, we can investigate how disorders affect the relation between HRV and walking path in the case of these patients. In fact, this investigation can potentially help us to regulate heart reactions by adjusting the walking path. In order words we can design a walking path in which patients can walk with fewer problems for their hearts.

Modeling of heart reactions during walking on different walking paths is another important future work that can be considered by scientists. For this purpose, different mathematical models (e.g., fractional diffusion equations [101]) can be employed to predict the R-R interval time series. This prediction will help us to know about heart reactions before subjects walk through the path.

5. Conclusion

Overall the investigations carried out in this work are beneficial in understanding heart reaction, which can lead to controlling it, especially in the case of patients with different heart disorders.

Footnotes

Acknowledgments

This work was supported by the SPEV project, Faculty of Informatics and Management, University of Hradec Kralove, Czech Republic (ID: 2102–2022), “Smart Solutions in Ubiquitous Computing Environments”. The authors are also grateful for the support of student Michal Dobrovolny in consultations regarding application aspects.

Conflict of interest

None to report.

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Decoding the correlation between heart activation and walking path by information-based analysis

Abstract

BACKGROND:

OBJECTIVE:

METHOD:

RESULTS:

CONCLUSION:

Keywords

1. Introduction

2. Method

Table 2 Pairwise comparisons of the Shannon entropy of R-R interval time series in case of different paths

5. Conclusion

Footnotes

Acknowledgments

Conflict of interest

References

Table 2
Pairwise comparisons of the Shannon entropy of R-R interval time series in case of different paths