标题: 适用于可移动心电图之心房震颤检测法:使用支持向量机之学习法则
Detection of Atrial Fibrillation for Ambulatory ECG System: A Learning Method based on Support Vector Machine
作者: 叶昱贤
伍绍勋
Yeh, Yu-Hsien
Wu, Sau-Hsuan
生医工程研究所
关键字: 心房震颤;支持向量机;atrial fibrillation;support vector machine;ambulatory ECG
公开日期: 2017
摘要: 近20年来,心房震颤已经在西方国家成为最重要的健康问题之一,在2014年,心房震颤影响了欧洲及北美大约2%~3%的人口。为了达成长期照护的目标,我们团队设计了一个无线多导极心电图系统附有即时侦测心律以及警示系统。基于两个原因,我们选择心房震颤当做我们检测的目标病征: (1) 心房震颤在已开发国家以及年长族群中是非常普遍的疾病,因为全球人口老化的关系,在未来心房震颤将会成为一个重要议题。 (2) 心房震颤虽然没有立即致死性,但是其与某些重症的疾病息息相关,例如:缺血性中风。病患的ECG心电图讯号可以用来判别病患是否有心房震颤,P波消失以及不规律的RR区间是可以用来判别心房震颤的两个ECG波形。在此篇论文中,我们撰写MATLAB程式语言并且使用LIBSVM来模拟心房震颤演算法,LIBSVM是一个免费的开发工具,用于实作支持向量机(SVM)。演算法总共拥有19个特征,包含时域特征、频域特征以及非线性特征。训练资料以及测试资料皆来自MIT-BIH里面的心房震颤资料库。我们也实作了另一个由C. Huang等人所研究的心房震颤侦测演算法为了比较我们演算法的性能。两个演算法有相同的输入。实验结果显示我们演算法的精确率达97.57%,优于C. Huang等人的90.06%。在未来,这个演算法会实作到云端平台上,以达到可以即时侦测心电讯号的用途。
In the last 20 years, atrial fibrillation has become one of the most important health problems in western countries. In 2014, atrial fibrillation affects about 2% to 3% of the population in Europe and North America. In order to monitor heart wave signal for a long time, our team designed a wireless multi-lead ECG system for long-term heart monitoring including real-time detecting and alarming system. We choose Atrial Fibrillation (AF) as the target arrhythmia of our detecting algorithm due to two reasons: (1) AF is a prevalent arrhythmia in the developed country and the group of elders. Owing to the global population aging, it will be a significant issue in the future. (2) AF is not related to imminent mortality but it is related to some highly morbid conditions like embolic stroke. The patient’s ECG signals can be used to determine whether the patient has AF or not, the absence of P wave and irregular RR interval are two ECG traces can be used to determine AF. In this thesis, we used MATLAB to simulate an AF detecting algorithm implemented by LIBSVM which is a free tool can implement support vector machine (SVM). The algorithm has totally 19 features, including time domain, frequency domain, and nonlinear features. The training data and the testing data are both from the MIT-BIH atrial fibrillation database. We also implemented another AF detection algorithm made by C. Huang et al for the purpose of comparing with our algorithm’s performance. Two algorithms were used the same input. The experimental result manifested that the accuracy of our algorithm is 97.57%, it is better than C. Huang et al’s 90.06%. In the future, the algorithm will be implemented on the cloud platform to realize the real-time heart wave signal detecting.
URI: http://etd.lib.nctu.edu.tw/cdrfb3/record/nctu/#GT070356737
http://hdl.handle.net/11536/140437
显示于类别:Thesis