Ventricular Arrhythmia Classification Based on High-Order Statistical Features of ECG Signals
One class SVM classification model based on high-order statistical features of ECG signals is proposed. This utilizes distinct features of variance, skewness and kurtosis between normal signals and ventricular arrhythmia ECG signals. The model based on a few simple features motivates immediate treatment for sudden cardiac event and wearable technology in practice. The classification algorithm shows significantly improved performance of 98.9% accuracy in correct classification in the experiment using the MIT-BIH Malignant Ventricular Arrhythmia Database (VFDB). It is expected to be used in real-time electrocardiogram monitoring system in conjunction with ECG measurement part and application part.
KeywordsClassification Support vector machine Ventricular arrhythmia Kurtosis Skewness Variance
Research supported by the National Research Foundation of Korea grant funded by the Ministry of Education (NRF-2014R1A1A2057732).
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