Automated Diagnosis of Tachycardia Beats
Due to tachycardia, heart generates lethal arrhythmia beats namely atrial flutter (AFL), atrial fibrillation (A-Fib), and ventricular fibrillation (V-Fib). These irregular patterns are very effectively and noninvasively reflected using standard electrocardiogram (ECG). In this study, an automated diagnosis support system (DSS) is developed for accurate discrimination and classification of complete classes of tachycardia beats (atrial as well as ventricular) using higher-order spectra (HOS). In this multiclass diagnosis problem, dimensionality of HOS third-order cumulants is reduced using independent component analysis (ICA) and fed for standard hypothesis test ANOVA (p < 0.05). Finally, statistical significant components are subjected for ensemble classification using random forest (RAF) and rotation forest (ROF) classifiers and to realize best performance tenfold classification is performed. Further, the consistency of classifiers is assessed using Cohen’s kappa matric. Proposed DSS achieved overall classification accuracy of 99.54% using ROF. Our reported results are highest than published in the earlier works.
KeywordsMulticlass diagnosis Ensemble classifiers MIT-BIH atrial fibrillation database
Authors are grateful to management of NMAM Institute of Technology, Udupi and REVA University, Bengaluru, for providing the research facilities.
- 1.Goldberger, A.L.: Clinical Electrocardiography: A Simplified Approach. Mosby, St. Louis, MO (2012)Google Scholar
- 2.World Health Organization: Cardiovascular Disease: Global Atlas on Cardiovascular Disease Prevention and Control. WHO, Geneva (2012)Google Scholar
- 4.Christov, I., Bortolan, G., Daskalov, I.: Sequential analysis for automatic detection of atrial fibrillation and flutter. In: Computers in Cardiology 2001. IEEE, Piscataway pp. 293–296 (2001)Google Scholar
- 5.Tsipouras, M.G., et al.: Classification of atrial tachyarrhythmias in electrocardiograms using time frequency analysis. In: Computers in Cardiology, 2004, pp. 245–248. IEEE (2004)Google Scholar
- 6.Martis, R.J., et al.: Application of higher order spectra for accurate delineation of atrial arrhythmia. In: Proceedings on Annual International Conference on IEEE Engineering in Medicine and Biology Society, pp. 57–60 (2013)Google Scholar
- 11.Desai, U., Nayak, C.G., Seshikala, G.: An application of EMD technique in detection of tachycardia beats. In: 2016 International Conference on Communication and Signal Processing (ICCSP), pp. 1420–1424. IEEE (2016)Google Scholar
- 13.Desai, U., et al.: Machine intelligent diagnosis of ECG for arrhythmia classification using DWT, ICA and SVM techniques. In: 2015 Annual IEEE India Conference (INDICON), pp. 1–4 (2015)Google Scholar
- 15.Desai, U., Nayak, C.G., Seshikala, G.: An efficient technique for automated diagnosis of cardiac rhythms using electrocardiogram. In: IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology. IEEE (2016)Google Scholar
- 16.Nayak, C.G., et al.: Identification of arrhythmia classes using machine-learning techniques. Int. J. Biol. Biomed. 1, 48–53 (2016)Google Scholar