Hsu Po-Ya, Cheng Chung-Kuan
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:292-295. doi: 10.1109/EMBC44109.2020.9176679.
Arrhythmia is a serious cardiovascular disease, and early diagnosis of arrhythmia is critical. In this study, we present a waveform-based signal processing (WBSP) method to produce state-of-the-art performance in arrhythmia classification. When performing WBSP, we first filtered ECG signals, searched local minima, and removed baseline wandering. Subsequently, we fit the processed ECG signals with Gaussians and extracted the parameters. Afterwards, we exploited the products of WBSP to accomplish arrhythmia classification with our proposed machine learning-based and deep learning-based classifiers. We utilized MIT-BIH Arrhythmia Database to validate WBSP. Our best classifier achieved 98.8% accuracy. Moreover, it reached 96.3% sensitivity in class V and 98.6% sensitivity in class Q, which both share one of the best among the related works. In addition, our machine learning-based classifier accomplished identifying four waveform components essential for automated arrhythmia classification: the similarity of QRS complex to a Gaussian curve, the sharpness of the QRS complex, the duration of and the area enclosed by P-wave.Clinical relevance- Early diagnosis and automated classification of arrhythmia is clinically essential.
心律失常是一种严重的心血管疾病，早期诊断心律失常至关重要。在本研究中，我们提出了一种基于波形的信号处理（WBSP）方法，以在心律失常分类中产生最先进的性能。在执行WBSP时，我们首先对心电图信号进行滤波，搜索局部最小值，并去除基线漂移。随后，我们用高斯函数拟合处理后的心电图信号并提取参数。之后，我们利用WBSP的产物，通过我们提出的基于机器学习和深度学习的分类器来完成心律失常分类。我们使用麻省理工学院 - 贝斯以色列女执事医疗中心心律失常数据库来验证WBSP。我们最好的分类器准确率达到了98.8%。此外，它在V类中灵敏度达到96.3%，在Q类中灵敏度达到98.6%，这两者在相关工作中均处于最佳水平之一。此外，我们基于机器学习的分类器完成了识别自动心律失常分类所需的四个波形成分：QRS波群与高斯曲线的相似度、QRS波群的尖锐度、P波的持续时间和所包围的面积。临床相关性 - 心律失常的早期诊断和自动分类在临床上至关重要。
