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首页> 外文期刊>Sensors Journal, IEEE >A New Automated Signal Quality-Aware ECG Beat Classification Method for Unsupervised ECG Diagnosis Environments
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A New Automated Signal Quality-Aware ECG Beat Classification Method for Unsupervised ECG Diagnosis Environments

机译：无监督心电图诊断环境的一种新的自动信号质量感知心电图心跳分类方法

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摘要

In this paper, we propose a new automated quality-aware electrocardiogram (ECG) beat classification method for effective diagnosis of ECG arrhythmias under unsupervised healthcare environments. The proposed method consists of three major stages: 1) the ECG signal quality assessment (“acceptable” or “unacceptable”) based on our previous modified complete ensemble empirical mode decomposition and temporal features; 2) the ECG signal reconstruction and R-peak detection; and 3) the ECG beat classification including the ECG beat extraction, beat alignment, and normalized cross-correlation-based beat classification. The accuracy and robustness of the proposed method are evaluated using different normal and abnormal ECG signals taken from the standard MIT-BIH arrhythmia database. Evaluation results show that the proposed quality-aware ECG beat classification method can significantly achieve false alarm reduction ranging from 24% to 93% under noisy ECG recordings. The R-peak detector achieves the average Se = 99.67% and positive predictivity (Pp) = 93.10% and the average sensitivity (Se) = 99.65% and Pp = 98.88% without and with denoising approaches, respectively. Results further showed that the proposed ECG beat extraction approach can improve the classification accuracy by preserving the QRS complex portion and suppressing the background noises under acceptable level of noises. The quality-aware ECG beat classification methods achieve higher kappa values for the classification accuracies which can be consistent as compared with the heartbeat classification methods without the ECG quality assessment process.
机译：在本文中，我们提出了一种新的自动质量意识心电图（ECG）搏动分类方法，用于在无人看管的医疗环境下有效诊断ECG心律不齐。所提出的方法包括三个主要阶段：1）ECG信号质量评估（“ n <斜体xmlns：mml = ” http：//www.w3.org/1998/Math/MathML ” xmlns：xlink = “ http：//www.w3.org/1999/xlink “>可接受的 n”或“ n 不可接受的 n”），这是基于我们之前修改的完整整体经验模式分解和时间特征得出的； 2）心电信号重建和R峰值检测； 3）ECG搏动分类，包括ECG搏动提取，搏动对齐和基于归一化互相关的搏动分类。使用从标准MIT-BIH心律失常数据库中获取的不同正常和异常ECG信号，评估了该方法的准确性和鲁棒性。评估结果表明，在嘈杂的ECG记录下，提出的质量意识ECG搏动分类方法可以显着降低误报率，范围从24％至93％。 R-peak检测器在不使用降噪方法的情况下，平均Se = 99.67 ％，正预测性（Pp）= 93.10 ％，平均灵敏度（Se）= 99.65 ％，Pp = 98.88 ％，分别。结果进一步表明，提出的心电信号节拍提取方法可以通过保留QRS复杂部分并在可接受的噪声水平下抑制背景噪声来提高分类精度。具有质量意识的ECG搏动分类方法可实现更高的分类精度kappa值，这与没有ECG质量评估过程的心跳分类方法相比是一致的。

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