Cited 0 time in webofscience Cited 1 time in scopus

Classification of cardiac arrhythmias using biorthogonal wavelet preprocessing and SVM

Title
Classification of cardiac arrhythmias using biorthogonal wavelet preprocessing and SVM
Authors
Abibullaev, BerdakhKang, Won-SeokLee, Seung HyunAn, Jinung
DGIST Authors
Abibullaev, Berdakh; Kang, Won-SeokLee, Seung HyunAn, Jinung
Issue Date
2010
Citation
6th International Conference on Networked Computing, INC2010, 332-336
Type
Conference
Article Type
Conference Paper
ISBN
9788988678206
Abstract
In the current study we present a technique for the detection and classification of cardiac arrhythmias using biorthogonal wavelet functions and support vector machines (SVM). First, the wavelet transforms is applied to decompose the ECG signal into wavelet scales. Further, a soft thresholding technique is used to denoise and detect important cardiac events in the signal. Subsequently, we applied SVM classifier to discriminate the detected events into normal or pathological ones in the signal. Numeric computations demonstrate that the efficient wavelet pre-processing provides an accurate estimation of important physiological features of ECG and moreover it improves the SVM classification performance.
URI
http://hdl.handle.net/20.500.11750/3967
Publisher
Institute of Electrical and Electronics Engineers Inc.
Related Researcher
Files:
There are no files associated with this item.
Collection:
Division of IoT∙Robotics Convergence Research2. Conference Papers
Convergence Research Center for Future Automotive Technology2. Conference Papers


qrcode mendeley

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE