Combination of ECG parameters with support vector machines for the detection of life-threatening arrhythmias

Early detection of ventricular fibrillation (VF) and fast 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 constructed by considering each parameter individually. This study aimed to analyze the performance of combining previously defined ECG parameters for the detection of life-threatening arrhythmias using support vector machines (SVM). A total of 11 parameters have been computed, namely, TCI, STE, MEA, CM, VFleak, M, A2, FM, MAV, PSR and HILB. We studied two different binary detection scenarios: shockable (FV plus TV) vs nonshockable arrhythmias, and VF vs nonVF rhythms. We used the MITDB, the CUDB, and the VFDB to evaluate our algorithms. Sensitivity and specificity analysis show that the combination of parameters with SVM outperforms individual detection algorithms.