Identifying Hypertrophic Cardiomyopathy Patients by Classifying Individual Heartbeats from 12-lead ECG Signals.

Abstract

Test based on electrocardiograms (ECG) that record the heart electrical activity can help in early detection of patients with hypertrophic cardiomyopathy (HCM) where the heart muscle is partially thickened and blood flow is (potentially fatally) obstructed. This paper presents a cardiovascular-patient classifier we developed to identify HCM patients using standard 10-seconds, 12-lead ECG signals. Patients are classified as having HCM if the majority of the heartbeats are recognized as HCM. Thus, the classifier's underlying task is to recognize individual heartbeats segmented from 12-lead ECG signals as HCM beats, where heartbeats from non-HCM cardiovascular patients are used as controls. We extracted 504 morphological and temporal features - both commonly used and newly-developed ones - from ECG signals for heartbeat classification. To assess classification performance, we trained and tested a random forest classifier and a support vector machine classifier using 5-fold cross validation. The patient-classification precision and F-measure of both classifiers are close to 0.85. Recall (sensitivity) and specificity are approximately 0.90. We also conducted feature selection experiments by gradually removing the least informative features; the results show that a relatively small subset of 304 highly informative features can achieve performance measures comparable to that achieved by using the complete set of features.