Automatic SWT Based QRS Detection Using Weighted Subbands and Shannon Energy Peak Amplification for ECG Signal Analysis Devices

Jomole Varghese V, M. Manikandan, R. B. Pachori
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引用次数: 2

Abstract

In this paper, we present a straightforward automatic QRS complex detection method for electrocardiogram (ECG) signal analysis applications. The proposed method consists of stationary wavelet transform (SWT) for suppressing low- and high-frequency noises and extracting QRS complexes, amplitude thresholding to suppress the effect residual noise components, Shannon energy based peak amplitude normalization, negative zero-crossing for detecting peaks candidate smoothed QRS complex waveform and peak correction for determining true R peaks in the ECG signal. On the standard MIT-BIH database, our method had an accuracy of 99.50%, sensitivity of 99.69%, and a positive predictivity of 99.81 %. The proposed method outperforms other existing methods which included sets of amplitude-and duration-dependent thresholds to include or reject missed R peaks and noise peaks, respectively that may not work in practise for the case of QRS complex with irregular rates and long-pause between two consecutive QRS complexes.
基于加权子带和Shannon能量峰值放大的自动SWT QRS检测在心电信号分析设备中的应用
在本文中,我们提出了一种简单的自动QRS复合体检测方法,用于心电图信号分析。该方法包括平稳小波变换(SWT),用于抑制低频和高频噪声并提取QRS复波;幅度阈值化,用于抑制残余噪声分量的影响;基于Shannon能量的峰值幅度归一化;负过零,用于检测候选的平滑QRS复波;在标准的MIT-BIH数据库上,我们的方法准确率为99.50%,灵敏度为99.69%,阳性预测率为99.81%。所提出的方法优于其他现有的方法,这些方法包括一系列与幅度和持续时间相关的阈值,分别包括或拒绝缺失的R峰和噪声峰,这些方法在实践中可能不适用于具有不规则速率和两个连续QRS复合体之间长暂停的QRS复合体。
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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