Support Vector Machine for Heart Beats Classification Based on Robust Filtering

The Electrocardiogram (ECG) signal is by far the most intensive tool used to inspect the condition of the Heart and to detect early arrhythmia abnormalities, which is a life-saving process. The classification process highly depends on the quality of the ECG signal. Through this paper, we present a comparative study of two preprocessing techniques, namely high-pass derivative and robust neural net-work preprocessing filters. Our work involves de-veloping a Super Vector Machine (SVM) detector and assessing its performance by two preprocessing methods. We evaluated the detector's performance by using the MIT-BIH database under the AAMI EC57 standard and using Synthetic Minority Over-sampling Technique (SMOTE). The robust-based classifier shows higher performance with an overall accuracy of 99,51 % for intra-patient detection and 82,23% for inter-patient classification compared to the derivative-based one. that has an overall accuracy of 99,34% for intra-patient and 73,51 % for inter-patient detection.