Support vector machine for arrhythmia discrimination with TCI feature selection

Ventricular fibrillation (VF) is a malignant arrhythmia, which belongs to complex and nonlinear signals. To realize the detection of ventricular fibrillation, a new algorithm, which was based on the improved threshold crossing interval (TCI) algorithm and Support vector machine (SVM), was proposed in this paper. The SWM has great advantages in processing classification and pattern recognition. In this paper, 4-s-sliding-window technology and the improved TCI algorithm are applied to extract features of electrocardiogram (ECG). The improved TCI algorithm was implemented as follows: firstly, the average threshold crossing interval value of the middle 2s was calculated by using absolute threshold at each 4-s-sliding-window; secondly, the feature parameter (TCI value) was input to a pre-designed binary classification support vector machine; lastly, the classification was accomplished. For evaluating the reliability of the new algorithm, both MIT-BIH arrhythmia database and CU ventricular tachyarrhythmia database were used to test. By comparing the sensitivity, specificity, positive predictability and accuracy with other well known methods, the conclusion was made that this method is superior to other methods. This new algorithm is easier to implement and has greater advantages in real-time execution. These advantages make it more suitable in real time ECG monitoring and defibrillator.