Myocardial Infarction Classification by Morphological Feature Extraction from Big 12-Lead ECG Data

淡江大學機構典藏 > 工學院 > 資訊工程學系暨研究所 > 會議論文 > Item 987654321/99573

jsp.display-item.identifier=請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/99573

题名:	Myocardial Infarction Classification by Morphological Feature Extraction from Big 12-Lead ECG Data
作者:	Weng, Julia Tzu-Ya;Lin, Jyun-Jie;Chen, Yi-Cheng;Chang, Pei-Chann
贡献者:	淡江大學資訊工程學系
关键词:	12-lead ECG;Myocardial infarction;Principal component;Polynomial approximation analysis;Support vector machine
日期:	2014-05-13
上传时间:	2014-11-30 13:06:20 (UTC+8)
出版者:	Springner
摘要:	Rapid and accurate diagnosis of patients with acute myocardial infarction is vital. The ST segment in Electrocardiography (ECG) represents the change of electric potential during the period from the end of ventricular depolarization to the beginning of repolarization and plays an important role in the detection of myocardial infarction. However, ECG monitoring generates big volumes of data and the underlying complexity must be extracted by a combination of methods. This study combines the advantages of polynomial approximation and principal component analysis. The proposed approach is stable for the 12-lead ECG data collected from the PTB database and achieves an accuracy of 98.07%.
關聯:	Lecture Notes in Artificial Intelligence 8643, pp.689-699
显示于类别:	[資訊工程學系暨研究所] 會議論文

文件中的档案:

档案	描述	大小	格式	浏览次数
2014_Myocardial Infarction Classification by Morphological Feature Extraction from Big 12-Lead ECG Data.pdf		590Kb	Adobe PDF	393	检视/开启

在機構典藏中所有的数据项都受到原著作权保护.

TAIR相关文章