ECG Pattern Classification Based on Generic Feature Extraction
نویسنده
چکیده
In this paper, we propose a mew ECG pattern classification model based on a generic feature extraction method. The proposed classifier is applied for indicating supraventricual arrhythmia in order to verify the performance of the proposed approach. A generic approach based on a histogram of 1 derivative of signals is applied for feature extraction. Principal component analysis (PCA) is considered for both reducing dimension of features and extracting more plausible features from the extracted features. A simple k-means algorithm works for ECG signal classification in feature space for discriminating abnormal ECG beats caused by supraventricular arrhythmia from normal ECG ones. Key-Words: ECG signal processing, Generic feature extraction, PCA, K-means algorithm, Supraventricular arrhythmia
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