NDLI: Cardiac arrhythmias predictive detection methods with wavelet-SVD analysis and support vector machines

Content Provider	IEEE Xplore Digital Library
Author	Namarvar, H.H. Shahidi, A.V.
Copyright Year	2004
Description	Author affiliation: Dept. of Biomed. Eng., Southern California Univ., Los Angeles, CA, USA (Namarvar, H.H.)
Abstract	We expand the idea to develop new bio-signal processing tools that could predict possibility of future risk of abnormalities in ECG signals. The goal is to detect an inherent defect hidden in an ECG signal using wavelet analysis and support vector machines. We apply singular value decomposition analysis of spectral energy distribution in time-frequency plane to extract features, which is essentially independent of the actual duration of each event. We then classify the life threatening cardiac arrhythmias using support vector machines. We also investigate robustness of the developed system under presence of continuous Gaussian white noise. We obtained 92% sensitivity and 75% specificity for clean data and 81% sensitivity and 62% specificity for noisy data on our database. The proposed method could assist the health care professionals by earlier prediction of a disease and hence could facilitate in patient management, i.e., to provide a proper treatment for prevention or reduction of the future risk.
Starting Page	365
Ending Page	368
File Size	810463
Page Count	4
File Format	PDF
ISBN	0780384393
DOI	10.1109/IEMBS.2004.1403168
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-09-01
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wavelet analysis Support vector machines Electrocardiography Support vector machine classification Signal processing Signal analysis Singular value decomposition Spectral analysis Time frequency analysis Data mining and Wavelet-SVD Cardiac Arrhythmias SVM
Content Type	Text
Resource Type	Article

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