Comput Math Methods Med - A novel automatic detection system for ECG arrhythmias using maximum margin clustering with immune evolutionary algorithm.

Tópicos

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Resumo

This paper presents a novel maximum margin clustering method with immune evolution (IEMMC) for automatic diagnosis of electrocardiogram (ECG) arrhythmias. This diagnostic system consists of signal processing, feature extraction, and the IEMMC algorithm for clustering of ECG arrhythmias. First, raw ECG signal is processed by an adaptive ECG filter based on wavelet transforms, and waveform of the ECG signal is detected; then, features are extracted from ECG signal to cluster different types of arrhythmias by the IEMMC algorithm. Three types of performance evaluation indicators are used to assess the effect of the IEMMC method for ECG arrhythmias, such as sensitivity, specificity, and accuracy. Compared with K-means and iterSVR algorithms, the IEMMC algorithm reflects better performance not only in clustering result but also in terms of global search ability and convergence ability, which proves its effectiveness for the detection of ECG arrhythmias.

Resumo Limpo

paper present novel maximum margin cluster method immun evolut iemmc automat diagnosi electrocardiogram ecg arrhythmia diagnost system consist signal process featur extract iemmc algorithm cluster ecg arrhythmia first raw ecg signal process adapt ecg filter base wavelet transform waveform ecg signal detect featur extract ecg signal cluster differ type arrhythmia iemmc algorithm three type perform evalu indic use assess effect iemmc method ecg arrhythmia sensit specif accuraci compar kmean itersvr algorithm iemmc algorithm reflect better perform cluster result also term global search abil converg abil prove effect detect ecg arrhythmia

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