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This research presents an abnormal beat detection scheme from lead II Electrocardiogram (ECG) signals along with some improvements on feature extraction. A set of 16 features representing positions, durations, amplitudes and shapes of P, Q, R, S and T waves is proposed in this work for heart beat classification. These features carry important medical information for normal and abnormal beat detection...
A signal quality assessment method for mobile ECG based on fuzzy support vector machines (FSVM) and multi-feature was proposed to help users to determine whether the ECG recordings collected using mobile phone are acceptable or not. The proposed method mainly included two modules: feature extraction and an intelligent classification approach, i.e. a FSVM classifier. First, 27 features derived from...
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