Gomes Pedro R, Soares Filomena O, Correia J H, Lima C S
Faculty of Engineering of University Lusiada, Largo Tinoco de Sousa, V. N. Famalicao Portugal.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:4727-30. doi: 10.1109/IEMBS.2009.5334192.
This paper reports a comparative study of feature extraction methods regarding cardiac arrhythmia classification, using state of the art Hidden Markov Models. The types of beat being selected are normal (N), premature ventricular contraction (V) which is often precursor of ventricular arrhythmia, two of the most common class of supra-ventricular arrhythmia (S), named atrial fibrillation (AF), atrial flutter (AFL), and normal rhythm (N). The considered feature extraction methods are the standard linear segmentation and wavelet based feature extraction. The followed approach regarding wavelets was to observe simultaneously the signal at different scales, which means with different level of focus. Experimental results are obtained in real data from MIT-BIH Arrhythmia Database and show that wavelet transform outperforms the conventional standard linear segmentation.
本文报告了一项关于心律失常分类的特征提取方法的比较研究，使用了最先进的隐马尔可夫模型。所选择的心跳类型包括正常（N）、室性早搏（V，通常是室性心律失常的先兆）、两种最常见的室上性心律失常（S），即心房颤动（AF）、心房扑动（AFL）以及正常心律（N）。所考虑的特征提取方法是标准线性分割和基于小波的特征提取。关于小波的后续方法是同时在不同尺度上观察信号，这意味着具有不同的聚焦程度。实验结果来自麻省理工学院 - 贝斯以色列女执事医疗中心心律失常数据库的真实数据，结果表明小波变换优于传统的标准线性分割。
