文章基本信息

标题：Deep Learning-based Detection of Periodic Abnormal Waves in ECG Data
本地全文：下载
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：2018
卷号：2233&2234
页码：35-39
出版社：Newswood and International Association of Engineers
摘要：Automatic detection of abnormal electrocardiogram (ECG) waves is a key issue in the field of medical engineering. Many sever heart diseases show periodic abnormal waves in ECG. This provide informative suggestions for identifying the staging or abnormal site of heart disease. However, so far, few studies have tackled automatic detection of periodic abnormal ECG wave. In this paper, we propose a new method for detecting periodic abnormal waves in ECG. This method is based on the deep neural network model that learns wave’s shape and their temporal relevance by combing AutoEncoder and Long Short-Term Memory (LSTM). In the experiments, using ECG data of a myocardial infarction patient, it is shown that our method can identify adequately interval of abnormal wave, which the existing method was not able to detect.
关键词：deep learning; electrocardiogram; abnormal