A comparative study of supervised learning techniques for ECG T-wave anomalies detection in a WBS context

International audience; Today, most of Wireless Body Sensors (WBS) for remote monitoring of cardiovascular disease, rarely include automatic analysis and detection of ECG abnormalities, or are limited to cardiac arrhythmia's. The detection of more complex cardiac anomalies such as Ischemia or myocardial infarction, requires an advanced analysis of ECG wave Known as P, Q, R, S, and T, especially the T-wave, which is often associated with serious cardiac anomalies. The goal of this paper is to study the classification of T-wave abnormalities with consideration to a context of wireless monitoring system. The study approach is based on experimentation and comparison of classification performance and response time of 7 supervised learning models. We performed our experiments on a real ECG data from the EDB medical database from Physionet. Our results show that the decision trees models offer better results with, on average, an Accuracy of 92.54 %, a Sensitivity of 96.06%, a Specificity of 55.41% and an Error Rate 7.41%.