Accurate detection of cardiac pathological events is an important part of electrocardiogram (ECG) evaluation and subsequent correct treatment of the patient. The paper introduces the results of a complex study, where various aspects of automatic classification of various heartbeat types have been addressed. Particularly, non-ischemic, ischemic (of two different grades) and subsequent ventricular premature beats were classified in this combination for the first time. ECGs recorded in rabbit isolated hearts under non-ischemic and ischemic conditions were used for analysis. Various morphological and spectral features (both commonly used and newly proposed) as well as classification models were tested on the same data set. It was found that: a) morphological features are generally more suitable than spectral ones; b) successful results (accuracy up to 98.3% and 96.2% for morphological and spectral features, respectively) can be achieved using features calculated without time-consuming delineation of QRS-T segment; c) use of reduced number of features (3 to 14 features) for model training allows achieving similar or even better performance as compared to the whole feature sets (10 to 29 features); d) k-nearest neighbours and support vector machine seem to be the most appropriate models (accuracy up to 98.6% and 93.5%, respectively).

Department of Biomedical Engineering Faculty of Electrical Engineering and Communication Brno University of Technology Technická 12 Brno 616 00 Czech Republic

Department of Physiology Faculty of Medicine Masaryk University Kamenice 5 Brno 625 00 Czech Republic

Institute of Scientific Instruments The Czech Academy of Sciences Královopolská 147 Brno 612 64 Czech Republic

Zobrazit více v PubMed

Mendis, S., Puska, P. & Norrving, B. Global atlas on cardiovascular disease prevention and control. (World Health Organization in collaboration with the World Heart Federation and the World Stroke Organization, c2011).

Fisch C. Centennial of the string galvanometer and the electrocardiogram. Journal of the American College of Cardiology. 2000;36:1737–1745. doi: 10.1016/S0735-1097(00)00976-1. PubMed DOI

Correa R, Arini PD, Valentinuzzi ME, Laciar E. Novel set of vectorcardiographic parameters for the identification of ischemic patients. Medical Engineering. 2013;35:16–22. doi: 10.1016/j.medengphy.2012.03.005. PubMed DOI

Firoozabadi R, Gregg RE, Babaeizadeh S, Laciar E. Identification of exercise-induced ischemia using QRS slopes. Journal of Electrocardiology. 2016;49:55–59. doi: 10.1016/j.jelectrocard.2015.09.001. PubMed DOI

Murthy HSN, Meenakshi DM, Babaeizadeh S, Laciar E. ANN, SVM and KNN Classifiers for Prognosis of Cardiac Ischemia- A Comparison. Bonfring International Journal of Research in Communication Engineering. 2015;5:07–11. doi: 10.9756/BIJRCE.8030. DOI

Tseng Y-L, Lin K-S, Jaw F-S, Laciar E. Comparison of Support-Vector Machine and Sparse Representation Using a Modified Rule-Based Method for Automated Myocardial Ischemia Detection. Computational and Mathematical Methods in Medicine. 2016;2016:1–8. doi: 10.1155/2016/9460375. PubMed DOI PMC

Alajlan N, Bazi Y, Melgani F, Malek S, Bencherif MA. Detection of premature ventricular contraction arrhythmias in electrocardiogram signals with kernel methods. Signal, Image and Video Processing. 2014;8:931–942. doi: 10.1007/s11760-012-0339-8. DOI

Arif, M., Akram, M. U., Afsar, F. A., Malek, S. & Bencherif, M. A. Arrhythmia Beat Classification Using Pruned Fuzzy K-Nearest Neighbor Classifier. 2009 International Conference of Soft Computing and Pattern Recognition8, 37–42 (2009).

Balli T, Palaniappan R, Afsar FA, Malek S, Bencherif MA. Classification of biological signals using linear and nonlinear features. Physiological Measurement. 2010;31:903–920. doi: 10.1088/0967-3334/31/7/003. PubMed DOI

Doquire G, de Lannoy G, François D, Verleysen M, Bencherif MA. Feature Selection for Interpatient Supervised Heart Beat Classification. Physiological Measurement. 2010;31:903–920. doi: 10.1088/0967-3334/31/7/003. PubMed DOI PMC

Karimifard S, Ahmadian A, François D, Verleysen M, Bencherif MA. A robust method for diagnosis of morphological arrhythmias based on Hermitian model of higher-order statistics. BioMedical Engineering OnLine. 2011;10:22. doi: 10.1186/1475-925X-10-22. PubMed DOI PMC

Kutlu Y, Kuntalp D, François D, Verleysen M, Bencherif MA. Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients. Computer Methods and Programs in Biomedicine. 2012;105:257–267. doi: 10.1016/j.cmpb.2011.10.002. PubMed DOI

Sayadi O, Shamsollahi MB, Clifford GD, Verleysen M, Bencherif MA. Robust Detection of Premature Ventricular Contractions Using a Wave-Based Bayesian Framework. IEEE Transactions on Biomedical Engineering. 2010;57:353–362. doi: 10.1109/TBME.2009.2031243. PubMed DOI PMC

Shen C-P, et al. Detection of cardiac arrhythmia in electrocardiograms using adaptive feature extraction and modified support vector machines. Expert Systems with Applications. 2012;39:7845–7852. doi: 10.1016/j.eswa.2012.01.093. DOI

Ronzhina, M. et al. Classification of ventricular premature and ischemic beats in animal electrograms. 2015 Computing in Cardiology Conference (CinC)39, 1137-1140 (2015).

Maheshwari S, et al. An automated algorithm for online detection of fragmented QRS and identification of its various morphologies. Journal of The Royal Society Interface. 2013;10:20130761–20130761. doi: 10.1098/rsif.2013.0761. PubMed DOI PMC

Maheshwari S, et al. Accurate and reliable 3-lead to 12-lead ECG reconstruction methodology for remote health monitoring applications. IRBM. 2014;35:341–350. doi: 10.1016/j.irbm.2014.07.004. DOI

Ronzhina M, et al. Spectral and higher-order statistics analysis of ECG: application to study of ischemia in rabbit isolated hearts. 2012 Computing in Cardiology Conference (CinC) 2012;36:645–648.

The CSE Working Party. Recommendations for measurement standards in quantitative electrocardiography. Eur Heart J. 10, 815–825 (1985). PubMed

Goldberger AL, et al. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation. 2000;101:e215–e220. doi: 10.1161/01.CIR.101.23.e215. PubMed DOI

Smíšek R, et al. CSE database: extended annotations and new recommendations for ECG software testing. Medical and Biological Engineering and Computing. 2016;54(1-10):109. PubMed

Kolářová J, et al. Isolated rabbit hearts – databases of EGs and MAP signals. 2013 Computing in Cardiology Conference (CinC) 2013;37:551–554.

Wagner GS, et al. AHA/ACCF/HRS Recommendations for the Standardization and Interpretation of the Electrocardiogram. Journal of the American College of Cardiology. 2009;53:1003–1011. doi: 10.1016/j.jacc.2008.12.016. PubMed DOI

El-Dahshan E-SA, et al. Genetic algorithm and wavelet hybrid scheme for ECG signal denoising. Telecommunication Systems. 2011;46:209–215. doi: 10.1007/s11235-010-9286-2. DOI

Celler BG, de Chazal P. Low computational cost classifiers for ECG diagnosis using neural networks. Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 1998;20:1337–1340.

Singh BN, Tiwari AK. Optimal selection of wavelet basis function applied to ECG signal denoising. Digital Signal Processing. 2006;16:275–287. doi: 10.1016/j.dsp.2005.12.003. DOI

Amit G, Granot Y, Abboud S. Quantifying QRS changes during myocardial ischemia: Insights from high frequency electrocardiography. Journal of Electrocardiology. 2014;47:505–511. doi: 10.1016/j.jelectrocard.2014.03.006. PubMed DOI

Plesinger F, Jurco J, Halamek J, Jurak P. SignalPlant: an open signal processing software platform. Physiological Measurement. 2016;37:N38–N48. doi: 10.1088/0967-3334/37/7/N38. PubMed DOI

Duda, R. O., Hart, P. E. & Stork, D. G. Pattern classification. (Wiley, c2001).

Guyon I, Elisseeff A. An introduction to variable and feature selection. Journal of Machine Learning Research. 2003;3:1157–1182.

Bishop, C. M., Hart, P. E. & Stork, D. G. Pattern recognition and machine learning. (Springer, c2006).

Theodoridis, S., Koutroumbas, K. & Stork, D. G. Pattern recognition. (Academic Press, c2009).

Sheskin, D., Koutroumbas, K. & Stork, D. G. Handbook of parametric and nonparametric statistical procedures. (Chapman, c2000).

Labatut, V., Cherifi, H. Accuracy measures for the comparison of classifiers. Proceedings of The 5th International Conference on Information Technology (2011).

Christov I, et al. Comparative study of morphological and time-frequency ECG descriptors for heartbeat classification. Medical Engineering. 2006;28:876–887. doi: 10.1016/j.medengphy.2005.12.010. PubMed DOI

Hejč J, et al. A Wavelet-Based ECG Delineation Method: Adaptation to an Experimental Electrograms with Manifested Global Ischemia. Cardiovascular Engineering and Technology. 2015;6:364–375. doi: 10.1007/s13239-015-0224-z. PubMed DOI

Sharir T, et al. Use of Electrocardiographic Depolarization Abnormalities for Detection of Stress-Induced Ischemia as Defined by Myocardial Perfusion Imaging: Adaptation to an Experimental Electrograms with Manifested Global Ischemia. The American Journal of Cardiology. 2012;109:642–650. doi: 10.1016/j.amjcard.2011.10.022. PubMed DOI

Llamedo M, et al. Isolated rabbit hearts – databases of EGs and MAP signals. Morpholofic features of the ECG for detection of stress-induced ischemia. Computing in Cardiology Conference (CinC) 2013;40:591–594.

Golden Standard or Obsolete Method? Review of ECG Applications in Clinical and Experimental Context

Reliable P wave detection in pathological ECG signals

Advanced P Wave Detection in Ecg Signals During Pathology: Evaluation in Different Arrhythmia Contexts