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首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >Automated ECG diagnostic P-wave analysis using wavelets.
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Automated ECG diagnostic P-wave analysis using wavelets.

机译：使用小波的自动ECG诊断P波分析。

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摘要

P-wave characteristics in the human ECG are an important source of information in the diagnosis of atrial conduction pathology. However, diagnosis by visual inspection is a difficult task since the P-wave is relatively small and noise masking is often present. This paper introduces novel wavelet characteristics derived from the continuous wavelet transform (CWT) which are shown to be potentially effective discriminators in an automated diagnostic process. Characteristics of the 12-lead ECG P-wave were derived using CWT and statistical methods. A normal control group and an abnormal (atrial conduction pathology) group were compared. The wavelet characteristics captured frequency, magnitude and variance components of the P-wave. The best individual characteristics (i.e. ones that significantly discriminated the groups) were entered into a linear discriminant analysis (LDA) for four different models: two-lead ECG, three-lead ECG, a derived three-lead ECG and a factor analysis solution consisting of wavelet characteristic loadings on the factors. A comparison was also made between wavelet characteristics derived form individual P-waves verses wavelet characteristics derived from a signal-averaged P-wave for each participant. These wavelet models were also compared to standard cardiological measures of duration, terminal force and duration divided by the PR segment. Results for the individual P-wave approach generally outperformed the standard cardiological measures and the signal-averaged P-wave approach. The best wavelet model on the basis of both classification performance and simplicity was the two-lead model that uses leads II and V1. It was concluded that the wavelet approach of automating classification is worth pursuing with larger samples to validate and extend the present study.
机译：人心电图中的P波特征是心房传导病理诊断的重要信息来源。然而，由于P波相对较小并且经常存在噪声掩蔽，因此通过目视检查进行诊断是一项艰巨的任务。本文介绍了源自连续小波变换（CWT）的新颖小波特征，这些特征在自动诊断过程中被证明是潜在有效的鉴别器。使用CWT和统计方法得出12导联心电图P波的特征。比较正常对照组和异常（心房传导病理）组。小波特征捕获了P波的频率，幅度和方差分量。将针对四个不同模型的最佳个体特征（即能明显区分各组的特征）输入到线性判别分析（LDA）中：两导联心电图，三导联心电图，派生三导联心电图和因子分析解决方案，包括小波特征载荷对影响因素的影响。还比较了每个参与者从单个P波得到的小波特性与从信号平均P波得到的小波特性之间的比较。还将这些小波模型与持续时间，末端力和持续时间的标准心脏测量值相除，再除以PR段。单个P波方法的结果通常优于标准的心脏测量和信号平均P波方法。基于分类性能和简单性的最佳小波模型是使用引线II和V1的两引线模型。得出的结论是，自动分类的小波方法值得大样本来验证和扩展本研究。

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