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An automated ECG signal quality assessment method for unsupervised diagnostic systems

机译：无监督诊断系统自动化ECG信号质量评估方法

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摘要

In this paper, the authors present an automated method for quality assessment of electrocardiogram (ECG) signal. Our proposed method not only detects and classifies the ECG noises but also localizes the ECG noises which can play a crucial role in extracting reliable clinical features for ECG analysis systems. The proposed method is based on three stages: Wavelet decomposition of ECG signal into sub-bands; simultaneous ECG signal and noise reconstruction; extraction of temporal features such as maximum absolute amplitude, zerocrossings, kurtosis and autocorrelation function for detection, localization and classification of ECG noises including flat line (FL), time-varying noise or pause (TVN), baseline wander (BW), abrupt change (AB), power line interference (PLI), muscle artifacts (MA) and additive white Gaussian noise (AWGN). The proposed method is tested and validated against manually annotated ECG signals corrupted with aforementioned noises taken from MIT-BIH arrhythmia database, Physionet challenge database, and real-time recorded ECG signals. Comparative detection and classification results depict the superior performance of the proposed method over state of art methods. Detection results show that our method can achieve an average sensitivity (Se), average specificity (Sp) and accuracy (A) of 99.61%, 98.51%, 99.49% respectively. Also, the method achieves a Se of 98.18%, and Sp of 94.97% for real-time recorded ECG signals. The method has an average timing error of 0.14 s in localizing the noise segments. Further, classification results demonstrate that the proposed method achieves an average sensitivity (Se), average positive predictivity (PP) and classification accuracy (Ac) of 98.53%, 98.89%, 97.50% respectively. (C) 2017 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.
机译：本文提出了一种自动化的心电图（ECG）信号质量评估的自动化方法。我们所提出的方法不仅检测和分类ECG噪声，还可以定位ECG噪声，这可能在提取ECG分析系统中提取可靠的临床特征方面发挥至关重要的作用。所提出的方法基于三个阶段：ECG信号的小波分解为子带;同时ECG信号和噪声重建;提取时间特征，如最大绝对幅度，Zerocross，kurtosis和自相关函数的检测，定位和分类，包括扁平线（FL），时变噪声或暂停（TVN），基线漫游（BW），突然变化（AB），电力线干扰（PLI），肌肉伪影（MA）和添加性白色高斯噪声（AWGN）。通过从MIT-BIH心律失常数据库，物理体挑战数据库和实时记录的ECG信号造成的手动注释的ECG信号测试并验证了所提出的方法并验证。比较检测和分类结果描绘了所提出的方法的优越性化方法。检测结果表明，我们的方法可以达到平均灵敏度（SE），平均特异性（SP）和精度（a）分别为99.61％，98.51％，99.49％。此外，该方法实现了98.18％的SE，SE为94.97％，用于实时记录的ECG信号。该方法在定位噪声段时具有0.14秒的平均定时误差。此外，分类结果表明，所提出的方法实现了平均灵敏度（SE），平均阳性预测性（PP）和分类准确度（AC）分别为98.53％，98.89％，97.50％。 （c）2017年纳雷斯州纳雷斯省生物庭院研究所和波兰科学院生物医学工程。 elsevier b.v出版。保留所有权利。

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