NDLI: Detection of myocardial ischemia episode using morphological features

Content Provider	IEEE Xplore Digital Library
Author	Cheng-Hsiang Fan Yu Hsu Sung-Nien Yu Jou-Wei Lin
Copyright Year	2013
Description	Author affiliation: Cardiovascular Center, Nat. Taiwan Univ. Hosp. Yun-Lin Branch, Dou-Liou, Taiwan (Jou-Wei Lin) \|\| Dept. of Electr. Eng. with High-tech Innovations, Nat. Chung Cheng Univ., Chiayi, Taiwan (Cheng-Hsiang Fan; Yu Hsu; Sung-Nien Yu)
Abstract	In this study, we propose to use morphological features that are easy to identify to differentiate myocardial ischemic beats from normal beats. In general, myocardial ischemia causes alterations in electrocardiographic (ECG) signal such as deviation in the ST segment. When the ST segment level deviates from a certain voltage, the beat would be diagnosing as myocardial ischemia. To emphasize on ST variations, the QRS complex of the ECG signal was first subtracted and replaced with a straight line. Five-level discrete wavelet transform (DWT) followed to decompose the waveform into subband components and the A5 subband, which is most sensitive to the changes in the ST segment, was reconstructed for the calculation of 12 morphological features. The support vector machine (SVM) and the 10-fold cross-validation method were employed to evaluate the performance of the method. The results show high values of 95.20%, 93.29%, and, 93.63% in sensitivity, specificity, and accuracy, respectively, that were demonstrated to outperform the other methods in the literature.
Starting Page	7334
Ending Page	7337
File Size	763932
Page Count	4
File Format	PDF
ISBN	9781457702167
ISSN	1557170X
DOI	10.1109/EMBC.2013.6611252
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electrocardiography Myocardium Discrete wavelet transforms Sensitivity Support vector machines Databases Feature extraction
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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