Detection of Life-Threatening Arrhythmias Using Feature Selection and Support Vector Machines

Abstract—Early detection of ventricular fibrillation (VF) and rapid ventricular tachycardia (VT) is crucial for the success of the defibrillation therapy. A wide variety of detection algorithms have been proposed based on temporal, spectral, or complexity parameters extracted from the ECG. However, these algorithms are mostly constructed by considering each parameter individu-ally. In this study, we present a novel life-threatening arrhythmias detection algorithm that combines a number of previously pro-posed ECG parameters by using support vector machines clas-sifiers. A total of 13 parameters were computed accounting for temporal (morphological), spectral, and complexity features of the ECG signal. A filter-type feature selection (FS) procedure was pro-posed to analyze the relevance of the computed parameters and how they affect the detection performance. The proposed method-ology was evaluated in two different binary detection scenarios: shockable (FV plus VT) versus nonshockable arrhythmias, and VF versus nonVF rhythms, using the information contained in the medical imaging technology database, the Creighton University ventricular tachycardia database, and the ventricular arrhythmia database. sensitivity (SE) and specificity (SP) analysis on the out of sample test data showed values of SE = 95%, SP = 99%, and SE = 92%, SP = 97 % in the case of shockable and VF scenarios, respectively. Our algorithm was benchmarked against individual detection schemes, significantly improving their performance. Our results demonstrate that the combination of ECG parameters us-ing statistical learning algorithms improves the efficiency for the detection of life-threatening arrhythmias. Index Terms—Feature selection (FS), support vector machines (SVM), ventricular fibrillation (VF) detection. I