This paper highlights a new method for the detection of ischaemic episodes using statistical features derived from ST segment deviations in electrocardiogram (ECG) signal. Firstly, ECG records are pre-processed for the removal of artifacts followed by the delineation process. Then region of interest (ROI) is defined for ST segment and isoelectric reference to compute the ST segment deviation. The mean thresholds for ST segment deviations are used to differentiate the ischaemic beats from normal beats in two stages. The window characterization algorithm is developed for filtration of spurious beats in ischaemic episodes. The ischaemic episode detection is made through the coefficient of variation (COV), kurtosis and form factor. A bell-shaped normal distribution graph is generated for normal and ischaemic ST segments. The results show average sensitivity (Se) 97.71% and positive predictivity (+P) 96.89% for 90 records of the annotated European ST-T database (EDB) after validation. These results are significantly better than those of the available methods reported in the literature. The simplicity and automatic discarding of irrelevant beats makes this method feasible for use in clinical systems.
AfsarFAArifMYangJ (2008) Detection of ST segment deviation episodes in ECG using KLT with an ensemble neural classifier. Physiological Measurement29(7): 747–760.
2.
AndreaoRVDorizziBBoudyJ. (2004) ST segment analysis using hidden Markov model beat segmentation: application to ischemia detection. In: IEEE Conference on Computers in Cardiology, pp. 381–384.
3.
BanerjeeSGuptaRMitraM (2012) Delineation of ECG characteristic features using multi-resolution wavelet analysis method. Measurement45(3): 474–487.
4.
BaxtWG (1991) Use of an artificial neural network for the diagnosis of myocardial infarction. Annals of Internal Medicine115(11): 843–848.
5.
BeenaMMPrabavathySPriestlyBS. (2014) Robust rule based de-noising scheme using wavelets. Journal of Medical Imaging and Health Informatics4(4): 457–468.
6.
BianchiAMMainardiLTCeruttiS (2000) Time–frequency analysis of biomedical signals. Transactions of the Institute of Measurement and Control22(3): 215–230.
7.
BrownfieldJHerbertM (2008) EKG criteria for fibrinolysis: what’s up with the J point?Western Journal of Emergency Medicine9(1): 40–42.
8.
CorreaRAriniPDCorreaLS. (2014) Novel technique for ST-T interval characterization in patients with acute myocardial ischemia. Computers in Biology and Medicine50: 49–55.
9.
DashPK (2002) Electrocardiogram monitoring. Indian Journal of Anaesthesia46(4): 251–260.
10.
DaubechiesI (1988) Ortho-normal bases of compactly supported wavelets. Communication in Pure Applied Mathematics41(7): 909–966.
11.
DonSChungDMinD (2013) Analysis of electrocardiogram signals of arrhythmia and ischemia using fractal and statistical features. Journal of Mechanics in Medicine and Biology13: 1350008-8.
12.
ExarchosTPTsipourasMGExarchosCP. (2007) A methodology for the automated creation of fuzzy expert systems for ischemic and arrhythmic beat classification based on a set of rules obtained by a decision tree. Artificial Intelligence in Medicine40(3): 187–200.
13.
GacekAPedryczW (2012) ECG Signal Processing, Classification and Interpretation. London: Springer-Verlag.
14.
GarciaJSörnmoLOlmosS. (2000) Automatic detection of ST-T complex changes on the ECG using filtered RMS difference series: application to ambulatory ischemia monitoring. IEEE Transactions on Biomedical Engineering47(9): 1195–1201.
15.
GhaffariaMRHAkraminiaaMAtarodaM. (2009) A robust wavelet-based multi-lead electrocardiogram delineation algorithm. Medical Engineering & Physics31(10): 1219–1227.
16.
GoldbergerA (1981) Electrocardiogram in ischemic heart disease. The Western Journal of Medicine135(2): 104–109.
17.
GramatikovBBrinkerJSunYC. (2000) Wavelet analysis and time–frequency distributions of the body surface ECG before and after angioplasty. Computer Methods and Program Biomedicine62(2): 87–98.
18.
GrapsA (1995) An introduction to wavelets. IEEE Transaction on Computing Science & Engineering2(2): 50–61.
19.
HegdeVNDeekshitRSatyanarayanaPS (2013) Identification of electrocardiogram morphologies using continuous wavelet transform, Journal of Medical Imaging and Health Informatics3(3): 417–423.
20.
JagerFMarkRGMoodyGB. (1992) Analysis of transient ST segment changes during ambulatory monitoring using the Karhunen–Loève transform. In: Proceedings of the IEEE Conference on Computers in Cardiology, pp. 691–694.
21.
JagerFMoodyGBMarkRG (1998) Detection of transient ST segment episodes during ambulatory ECG monitoring, Computers and Biomedical Research31(5): 305–322.
22.
KindiASGRezaT (2011) Real-time detection of myocardial infarction by evaluation of ST-segment in digital ECG. Journal of Medical Imaging and Health Informatics1(1): 225–230.
23.
KumarASinghM (2015) Optimal selection of wavelet function and decomposition level for removal of ECG signal artifacts. Journal of Medical Imaging and Health Informatics5(1): 138–146.
24.
KumarASinghM (2016a) Ischemia Detection using isoelectric energy function. computers in biology & medicine. Computers in Biology and Medicine68: 76–88.
25.
KumarASinghM (2016b) Statistical analysis of ST segments for ischemia detection in ECG signals. Journal of Medical Imaging and Health Informatics6(2): 431–440.
26.
KumarASinghM (2016c) Robust multi-resolution wavelet analysis and window search based approach for electrocardiogram features delineation. Journal of Medical Imaging and Health Informatics6(1): 146–156.
27.
KumarBASonamSK (2016) Statistical analysis of QRS-complex to evaluate the QR versus RS interval alteration during ischemia. Journal of Medical Imaging and Health Informatics6(1): 210–214.
28.
KusumotoFM (2004) Cardiovascular Patho-physiology. Raleigh, NC: Hayes Barton Press.
29.
LemireDPharandCRajaonahJC. (2000) Wavelet time entropy, T wave morphology and myocardial ischemia. IEEE Transaction on Biomedical Engineering47(7): 967–970.
30.
LiCZhengCTaiC (1995) Detection of ECG characteristic points using wavelet transforms. IEEE Transaction on Biomedical Engineering42(1): 21–28.
31.
LiHWangX (2013) Detection of electrocardiogram characteristic points using lifting wavelet transform and Hilbert transform. Transactions of the Institute of Measurement and Control35(5): 574–582.
32.
LiHFengXCaoL. (2015) Heartbeat classification using different classifiers with non-linear feature extraction. Transactions of the Institute of Measurement and Control, doi: 10.1177/0142331215620697.
33.
LiHFengXCaoL. (2016b) A new ECG signal classification based on WPD and ApEn feature extraction. Circuits, Systems, and Signal Processing35(1): 339–352.
34.
LiHLiangHMiaoC. (2016a) Novel ECG signal classification based on KICA nonlinear feature extraction. Circuits, Systems, and Signal Processing35(4): 1187–1197.
35.
LiHWangXChenL. (2014) Denoising and R-peak detection of electrocardiogram signal based on EMD and improved approximate envelope. Circuits, Systems, and Signal Processing33(4): 1261–1276.
36.
LinHYLiangSYHoYL. (2014) Discrete-wavelet-transform-based noise removal and feature extraction for ECG signals. IRBM35(6): 351–361.
MaglaverasNStamkopoulosTPappasC. (1998) An adaptive back propagation neural network for real-time ischemia episodes detection: development and performance analysis using the European ST-T database. IEEE Transactions on Biomedical Engineering45(7): 805–813.
39.
MallatS (1999) A Wavelet Tour of Signal Processing. Burlington, MA: Academic Press.
40.
ManochaAKSinghM (2011) An overview of ischemia detection techniques. International Journal of Science and Engineering Research2(11): 1–6.
41.
MartisRJAcharyaURAdeliH (2014) Current methods in electrocardiogram characterization. Computers in Biology & Medicine48: 133–149.
42.
MartisRJAcharyaURMinLC (2013) ECG beat classification using PCA, LDA, ICA and discrete wavelet transform. Biomedical Signal Processing and Control8(5): 437–448.
43.
MuruganSRadhakrishnanS (2010) Rule based classification of ischemic ECG beats using ant-miner. International Journal of Engineering Science and Technology2(8): 3929–3935.
44.
PapadimitriouSMavroudiSViadutuL. (2001) Ischemia detection with a self organizing map supplemented by supervised learning. IEEE Transaction on Neural Networks12(3): 503–515.
45.
PapaloukasCFotiadisDILiavasAP. (2001) A knowledge-based technique for automated detection of ischemic episodes in long duration electrocardiograms. Medical Biology Engineering Computer39(1): 105–112.
46.
PapaloukasCFotiadisDILikasA. (2002) An ischemia detection method based on artificial neural networks. Artificial Intelligence in Medicine24(2): 167–178.
47.
PapaloukasCGoletsisTFotiadisDI. (2004) Automated ischemic beat classification using genetic algorithm and multi-criteria decision analysis. IEEE Transaction on Biomedical Engineering51(10): 1717–1725.
48.
ParkJPedryczWJeonM (2012) Ischemia episode detection in ECG using kernel density estimation, support vector machine and feature selection. Biomedical Engineering On-line, 11: 1–22, doi: 10.1186/1475-925X-11-30.
49.
RanjithPBabyPCJosephP (2003) ECG analysis using wavelet transform: application to myocardial ischemia detection. ITBM-RBM24(1): 44–47.
50.
RochaTS.CarvalhoPPHenriquesJ. (2010) A lead dependent ischemic episodes detection strategy using Hermite functions. Biomedical Signal Processing and Control5(4): 271–281.
51.
SilipoRMarchesiC (1998) Artificial neural networks for automatic ECG analysis. IEEE Transaction Signal Processing46(5): 1417–1425.
52.
SilipoRTaddeiAMarchesiC (1994) Continuous monitoring and detection of ST-T changes in ischemic patients. In: Proceedings of the IEEE Conference on Computers in Cardiology, pp. 225–228.
53.
SinghM (2010) Introduction to Biomedical Instrumentation. New Delhi: Prentice Hall of India.
54.
StamkopoulosTDiamantarasKMaglaverasN. (1998) ECG analysis using nonlinear PCA neural networks for ischemia detection. IEEE Transactions on Signal Processing46(11): 3058–3067.
55.
StamkopoulosTStrintzisMPappasC. (1992) One-lead ischemia detection using a new backpropagation algorithm and the European ST-T database. In: Proceedings of the IEEE Conference on Computers in Cardiology, pp. 663–666.
56.
StrangGNguyenT (1996) Wavelets and Filter Banks. Wellesley, MA: Wellesley-Cambridge Press.
57.
TaddeiACostantinoGSilipoR. (1995) A system for the detection of ischemic episodes in ambulatory ECG. In: Proceedings of the IEEE Conference on Computers in Cardiology, pp. 705–708.
58.
TaddeiADistanteGEmdinM. (1992) The European ST-T database: standard for evaluating systems for the analysis of ST-T changes in ambulatory electrocardiography. European Heart Journal13(9): 1164–1172.
59.
TsipourasMGCostasVFotiadisDI (2007) A framework for fuzzy expert system creation – application to cardiovascular disease. IEEE Transactions on Biomedical Engineering54: 2089–2105.
60.
VilaJPresedoJDelgadoM. (1997) SUTIL: intelligent ischemia monitoring system. International Journal of Medical Informatics47(3): 193–214.
