Cardiac arrhythmia classification using multi-modal signal analysis

In this paper, as a contribution to the Physionet/Computing in Cardiology 2015 Challenge, we present individual algorithms to accurately classify five different life threatening arrhythmias with the goal of suppressing false alarm generation in intensive care units. Information obtained by analysing electrocardiogram, photoplethysmogram and arterial blood pressure signals was utilized to develop the classification models. Prior to classification, the signals were subject to a signal pre-processing stage for quality analysis. Classification was performed using a combination of support vector machine based machine learning approach and logical analysis techniques. The predicted result for a certain arrhythmia classification model was verified by logical analysis to aid in reduction of false alarms. Separate feature vectors were formed for predicting the presence or absence of each arrhythmia, using both spectral and time-domain information. The training and test data were obtained from the Physionet/CinC Challenge 2015 database. Classification algorithms were written for two different categories of data, namely real-time and retrospective, whose data lengths were 10 s and an additional 30 s, respectively. For the real-time test dataset, sensitivity of 94% and specificity of 82% were obtained. Similarly, for the retrospective test dataset, sensitivity of 94% and specificity of 86% were obtained.