Machine Learning Techniques for Automated Detection of Cardiac Arrhythmias




Journal Title

Journal ISSN

Volume Title



Cardiac Arrhythmias are cardiac abnormalities that arise as a consequence of irregularities in the electrical conduction system of the heart. In this dissertation, a comprehensive set of machine learning techniques, complemented by logical analysis, are presented for accurate detection of fifteen different cardiac arrhythmias - both ventricular and supraventricular. This includes, along with normal sinus rhythm, (1) ventricular fibrillation (VF), (2) ventricular tachycardia (VT), (3) premature ventricular complexes (PVC), (4-6) ventricular bigeminy/trigeminy/quadrigeminy, (7) ventricular couplets, (8) atrial fibrillation, (9) supraventricular ectopic beats (SVEB), (10-12) supraventricular bigeminy/trigeminy/quadrigeminy, (13) supraventricular couplets, (14) supraventricular tachycardia and (15) bradycardia. In this dissertation, information from single-lead electrocardiogram (ECG) signals is utilized to create a rich set of arrhythmia-specific features to aid in the development of highly accurate arrhythmia detection models. ECG is a waveform representation of the heart’s electrical activity and cardiac arrhythmias often manifest as morphological variations on the ECG. Prior to performing any arrhythmia analysis, the incoming ECG signal is preprocessed to remove low frequency and high frequency artifacts using Stationary Wavelet Transforms and Denoising Convolutional Autoencoders. This is complemented by signal quality assessment using Convolutional Neural Networks where ECG segments corrupted by high grade motion artifacts are identified and suppressed from further arrhythmia analysis. Following this, detection of Ventricular Fibrillation and Sustained Ventricular Tachycardia is implemented using a Random Forests classifier. Next, beat detection using a combination of Convolutional Autoencoders and adaptive thresholding is carried out to accurately detect R-peak locations which is key to performing robust arrhythmia analysis. Subsequently, algorithms for detection of PVC-beat-based ventricular arrhythmias are implemented using Semisupervised Autoencoders combined with Random Forests and logical analysis. This is followed by atrial fibrillation detection using Markov models in conjunction with Random Forests. Finally, logical sequence analysis techniques are applied to detect additional SVEBbased supraventricular arrhythmias. The algorithms presented in this dissertation achieve a sensitivity of 98.85%, positive predictive value (PPV) of 95.77% and F-Score of 96.82% in detecting ventricular fibrillation/sustained ventricular tachycardia episodes on records from MIT-BIH Malignant Ventricular Ectopy Database and American Heart Association Database. In terms of Rpeak detection, 99.63% sensitivity, 99.88% PPV and 99.75% F-Score is achieved on the MIT-BIH Arrhythmia Database (MITDB) records. Following this, the PVC detection algorithm achieves sensitivity, PPV and F-Score values of 93.17%, 94.41% and 93.78% on the MITDB records. Similarly, the SVEB detection algorithm achieves sensitivity, PPV and F-Score values of 92.11%, 83.77% and 87.74% on the MITDB records. In the context of atrial fibrillation detection, a sensitivity of 96.88%, PPV of 98.87% and F-Score of 97.86% is obtained on the MIT-BIH Atrial Fibrillation records. The working of afore-mentioned algorithms is demonstrated by deploying them in a cloud platform, AutoECG - a web service that facilitates online arrhythmia detection by analyzing ECGs uploaded by authorized users. AutoECG is device-agnostic and can process ECG data of varying duration (30s to 24 hours). Following ECG analysis, the AutoECG software generates an arrhythmia summary report for further review by qualified medical practitioners. This affirms the translational nature of the research presented in this dissertation.



Arrhythmia, Electrocardiography, Machine learning, Ensemble learning (Machine learning) deep learning