Electrocardiogram signal quality assessment using an artificially reconstructed target lead
Electrocardiogram signal quality assessment using an artificially reconstructed target lead
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DOI：
10.1080/10255842.2013.875163
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发表时间：
2015-07-27
影响因子：
1.6
通讯作者：
Homaeinezhad, M. R.
中科院分区：
文献类型：
--
作者：
Naseri, H.;Homaeinezhad, M. R.
在实际应用中，即使是基于研究数据库的最精确的心电图（ECG）分析算法，如果在分析之前没有精确地应用质量测量技术，也可能完全失效。本研究的重点是描述和发展一种可靠的心电信号质量评估技术。该算法包括三个主要阶段：预处理、能量凹度指数（ECI）分析和基于相关性的检测子程序。预处理步骤包括去除基线漫游和高频干扰。根据心电信号的能量和凹凸度，基于ECI的质量测量分为两个阶段。基于相关的质量测量步骤主要是利用经过适当训练的神经网络估计出的导联之间的相关性来建立的。ECI检测高能噪声的灵敏度（Se）为77.04%，阳性预测值（PPV）为90.53%。基于相关性的技术在检测高能噪声和识别任何其他类型的干扰（Se=92.36%， PPV=94.77%）方面取得了最好的分数（Se=100%， PPV=98.92%）。尽管ECI分析对高能扰动有效，但在扰动能量不太大的情况下，其性能很差。然而，基于相关性的方法能够发现各种干扰。为了正式评估提议的算法，2012年2月27日向2011年心脏病学计算挑战赛发送了一份参赛作品；最终得分（准确率）为93.60%。
In real applications, even the most accurate electrocardiogram (ECG) analysis algorithm, based on research databases, might breakdown completely if a quality measurement technique is not applied precisely before the analysis. The major concentration of this study is to describe and develop a reliable ECG signal quality assessment technique. The proposed algorithm includes three major stages: preprocessing, energy-concavity index (ECI) analysis and a correlation-based examination subroutine. The preprocessing step includes the removal of baseline wanders and high-frequency disturbances. The quality measurement based on ECI includes two separate stages according to the energy and concavity of the ECG signal. The correlation-based quality measurement step is mainly established by using the correlation between ECG leads estimated by applying a suitably trained neural network. The operating characteristics of the proposed ECI are sensitivity (Se) of 77.04% with a positive predictive value (PPV) of 90.53% for detecting high-energy noise. The correlation-based technique achieved the best scores (Se=100%; PPV=98.92%) for detecting high-energy noise and for recognising any other kind of disturbances (Se=92.36%; PPV=94.77%). Although ECI analysis acts effectively against high-energy disturbances, very poor performance is obtained in cases where the energy of the disturbances is not considerable. However, the correlation-based method is able to find all kinds of disturbances. For officially evaluating the proposed algorithm, an entry was sent to the Computing-in-Cardiology Challenge 2011 on 27 February 2012; a final score (accuracy) of 93.60% was achieved.