Classification of AF and Other Arrhythmias from a Short Segment of ECG Using Dynamic Time Warping

Atrial Fibrillation(AF) is a major public health risk but its identification is challenging because it may be episodic and non-symptomatic. Automatically identifying episodes of AF from a short segment of ECG would, thus, be beneficial. As a response to the Physionet/Computing in Cardiology Challenge 2017 we have implemented a three-stage classifier which can classify segments of ECG into Noisy, Normal, AF or Other Rhythm. We employ a state-of-the-art SQI for identifying noisy segments and then learn two different Support Vector Machine (SVM) classifiers using features extracted from the ECG. The features used are derived using a template matching approach via Dynamic Time Warping and using the statistical characteristics of the R-R intervals. Our average F1 score on the validation set was 0.66.© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. M. Tziakouri, C. Pitris and C. Orphanidou, "Classification of AF and other arrhythmias from a short segment of ECG using dynamic time warping," 2017 Computing in Cardiology (CinC), Rennes, 2017, pp. 1-4. doi: 10.22489/CinC.2017.348-29