Dissimilarity factor based classification of inferior myocardial infarction ECG

Rajarshi Gupta, P. Kundu
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引用次数: 5

Abstract

Electrocardiography (ECG) is popular non-invasive technique for preliminary level investigation on cardiovascular assessment. Computerized analysis of ECG can significantly contribute towards assisted diagnosis and in early detection of many cardiac diseases. Conventional automated ECG classifiers employing soft computing tools may suffer from the inaccuracies that may result in different clinical feature extraction stages. In this paper, we propose the use of a statistical index, namely, dissimilarity factor (D) for classification of normal and Inferior Myocardial Infarction (IMI) data, without the need of any direct clinical feature extraction. Time aligned ECG beats were obtained through filtering, wavelet decomposition processes, followed by PCA based beat enhancement to generate multivariate time series data. The T wave and QRS segments of IMI datasets from Lead II, III and aVF were extracted and compared with corresponding segments of healthy patients using Physionet ptbdb data. With 35 IMI datasets, the average composite dissimilarity factor Dc between normal data was found to be 0.39, and the same between normal and abnormal data were found to be 0.65. This paper shows the promise of descriptive statistical tools as an alternative for medical signal analysis.
基于不同因素的下壁心肌梗死心电图分型
心电图(Electrocardiography, ECG)是一种常用的无创心血管检查技术。心电图的计算机化分析对辅助诊断和早期发现许多心脏疾病有重要的贡献。采用软计算工具的传统自动ECG分类器可能存在不准确性,这可能导致不同的临床特征提取阶段。在本文中,我们建议使用统计指标,即不相似因子(D)来分类正常和亚下期心肌梗死(IMI)数据,而不需要任何直接的临床特征提取。通过滤波、小波分解得到时间对齐的心电拍,然后基于主成分分析的心跳增强得到多变量时间序列数据。提取导联II期、III期和aVF期IMI数据集的T波和QRS片段,并使用Physionet ptbdb数据与健康患者的相应片段进行比较。在35个IMI数据集中,正常数据之间的平均复合不相似系数Dc为0.39,正常数据与异常数据之间的平均复合不相似系数Dc为0.65。本文展示了描述性统计工具作为医疗信号分析的替代方案的前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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