生物医学工程学杂志

生物医学工程学杂志

基于 RR 间期和多特征值的房颤自动检测分类

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房颤(AF)是一种常见的心率失常疾病,基于心电图(ECG)的房颤检测对临床诊断具有十分重要的意义。由于 ECG 信号的非线性和复杂性,人工检测 ECG 信号的过程需要耗费大量时间且极易出现错误。为了克服上述问题,本文提出基于 RR 间期的特征提取方法,以稳健变异系数(RCV)描述 RR 间期的离散程度,以偏态参数(SKP)描述 RR 间期的分布形状,以 Lempel-Ziv 复杂度(LZC)描述 RR 间期的复杂度。最后将 RCV、SKP、LZC 特征值组成特征向量输入支持向量机(SVM)分类器模型,实现房颤的自动分类检测。为验证本文方法的有效性和实用性,以 MIT-BIH 房颤数据库数据进行验证,最终分类结果显示,灵敏度为 95.81%、特异度为 96.48%、准确率可达到 96.09%,同时在 MIT-BIH 窦性心律数据库中实现了 95.16% 的特异度。实验结果表明,本文所提方法是一种有效的房颤分类方法。

Atrial fibrillation (AF) is a common arrhythmia disease. Detection of atrial fibrillation based on electrocardiogram (ECG) is of great significance for clinical diagnosis. Due to the non-linearity and complexity of ECG signals, the procedure to manually diagnose the ECG signals takes a lot of time and is prone to errors. In order to overcome the above problems, a feature extraction method based on RR interval is proposed in this paper. The discrete degree of RR interval is described with the robust coefficient of variation (RCV), the distribution shape of RR interval is described with the skewness parameter (SKP), and the complexity of RR interval is described with the Lempel-Ziv complexity (LZC). Finally, the feature vectors of RCV, SKP, and LZC are input into the support vector machine (SVM) classifier model to achieve automatic classification and detection of atrial fibrillation. To verify the validity and practicability of the proposed method, the MIT-BIH atrial fibrillation database was used to verify the data. The final classification results show that the sensitivity is 95.81%, the specificity is 96.48%, the accuracy is 96.09%, and the specificity of 95.16% is achieved in the MIT-BIH normal sinus rhythm database. The experimental results show that the proposed method is an effective classification method for atrial fibrillation.

关键词: 房颤; 稳健变异系数; 偏态; Lempel-Ziv 复杂度; 支持向量机

Key words: atrial fibrillation; robust coefficient of variation; skewness; Lempel-Ziv complexity; support vector machine

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