文章基本信息

标题：Discrimination of ECG Abnormality based on a Normal ECG Wave Model Implementing a Denoising Model
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作者：Kaiji Sugimoto ; Saerom Lee ; Yoshifumi Okada 等
期刊名称：Lecture Notes in Engineering and Computer Science
印刷版ISSN：2078-0958
电子版ISSN：2078-0966
出版年度：2019
卷号：2239
页码：184-187
出版社：Newswood and International Association of Engineers
摘要：Automatic detection of abnormal electrocardiogram (ECG) is a key issue in the field of medical engineering because it is essential for diagnosis of heart disease. Typically ECG data contains noises due to body movement and muscle contractions, and hence it makes difficult to detect original abnormal signal. To address this problem, we propose a new method for discriminating abnormality from noisy ECG data. This method discriminates ECG abnormality based on a normal ECG wave model implementing a denoising model. In the experiment, the proposed method is applied to ECG data of healthy subjects, myocardial infarction (MI) patients and arrhythmia patients. As a result, it is shown that implementation of the denoising model is effective for improving discrimination accuracy of ECG abnormality.
关键词：ECG; abnormality detection; denoising