Suddent cardiac death predictor based on spatial QRS-T angle feature and support vector machine case study for cardiac disease detection in Indonesia

This paper presents a case study of sudden cardiac death predictor based on spatial QRS-T angle (spQRSTa) feature and support vector machine (SVM). The comparison between common ECG features and spQRSTa feature is presented in the paper. Eighteen volunteers were involved in the ECG experiement. The ECG data consist of 8 healthy controls and 8 patients with cardiac disease. Four ECG features such as QRS duration, QT interval, QT correction and spQRSTa feature were extracted from raw ECG signal. The two pair combination of 4 features were presented. The results show that the pair combination where the spQRSTa feature included can distinguish the healthy controls and patients with cardiac disease. From the plot results, the pair between QRS feature and spQRSTa feature was selected for SVM classification. The result show that SVM can classify the two classes with 100% accuracy.