Frequency-domain features for ECG beat discrimination using grey relational analysis-based classifier
Frequency-domain features for ECG beat discrimination using grey relational analysis-based classifier
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DOI：
10.1016/j.camwa.2007.04.035
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发表时间：
2008-02-01
影响因子：
2.9
通讯作者：
Lin, Chia-Hung
中科院分区：
文献类型：
--
作者：
Lin, Chia-Hung
提出了一种基于灰色关联分析（GRA）的心电心跳识别方法。典型的心电信号由p波、QRS复合体和t波组成。我们将每一个QRS复合体转化为心电信号的傅里叶频谱，频谱随节律源和传导路径的变化而变化。在频域0 ~ 20hz范围内观察到功率谱的变化。为了量化各种心电搏动之间的频率成分，采用GRA对心律失常进行分类。根据美国医疗器械进步协会（AAMI）推荐的标准，推荐的心跳类别包括正常心跳、室上异位心跳、束支异位心跳、心室异位心跳、融合心跳和未知心跳。该方法在麻省理工学院-贝斯以色列医院心律失常数据库中进行了测试。与其他人工智能（At）方法比较，结果表明了该方法的有效性，并显示出较高的心电信号检测精度。(c) 2007 Elsevier Ltd.版权所有。
This paper proposes a method for electrocardiogram (ECG) heartbeat discrimination using novel grey relational analysis (GRA). A typical ECG signal consists of the P-wave, QRS complexes and T-wave. We convert each QRS complexes to a Fourier spectrum from ECG signals, the spectrum varies with the rhythm origin and conduction path. The variations of power spectrum are observed in the range of 0-20 Hz in the frequency domain. To quantify the frequency components among the various ECG beats, GRA is performed to classify the cardiac arrhythmias. According to the AAMI (Association for the Advancement of Medical Instrumentation) recommended standard, heartbeat classes are recommended including the normal beat, supraventricular ectopic beat, bundle branch ectopic beat, ventricular ectopic beat, fusion beat and unknown beat. The method was tested on MIT-BIH (Massachusetts Institute of Technology-Beth Israel Hospital) arrhythmia database. Compared with other artificial intelligence (At) methods, the results demonstrate the efficiency of the proposed noninvasive method, and also show high accuracy for detecting ECG signals. (c) 2007 Elsevier Ltd. All rights reserved.