SVM Based ECG Beat Classification Method for Unsupervised ECG Diagnosis Systems
International journal of modern trends in science and technology
This paper presents a support vector machine (SVM) based electrocardiogram (ECG) beat classification method for clear identification of nature of illness under unsupervised ECG diagnosis environments. The proposed method takes reference of data on the linear separable line. The linear line is termed as decision line where the positive information and negative information will be separated by the hyper plane and required data will be acquired. So that SVM gives best classification output in
... tion output in terms of accuracy. Based on the previous signal quality assessment (SQA) method the accuracy and robustness are evaluated using different normal and abnormal ECG signals taken from the standard MIT-BIH arrhythmia database. The SQA method undergoes three stages: the ECG signal quality assessment, ECG signal reconstruction and R-peak detection, ECG beat classification. In ECG signal by preserving QRS waveform we can decrease background noises.