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.
| Original language | English |
|---|---|
| Title of host publication | INC2010 - The International Conference on Networked Computing, Proceeding |
| Pages | 332-336 |
| Number of pages | 5 |
| Publication status | Published - 2010 |
| Externally published | Yes |
| Event | 6th International Conference on Networked Computing, INC2010 - Gyeongju, Korea, Republic of Duration: May 11 2010 → May 13 2010 |
Other
| Other | 6th International Conference on Networked Computing, INC2010 |
|---|---|
| Country | Korea, Republic of |
| City | Gyeongju |
| Period | 5/11/10 → 5/13/10 |
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ASJC Scopus subject areas
- Computational Theory and Mathematics
- Computer Networks and Communications
- Software
Cite this
Classification of cardiac arrhythmias using biorthogonal wavelet preprocessing and SVM. / Abibullaev, Berdakh; Kang, Won Seok; Lee, Seung Hyun; An, Jinung.
INC2010 - The International Conference on Networked Computing, Proceeding. 2010. p. 332-336 5484804.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Classification of cardiac arrhythmias using biorthogonal wavelet preprocessing and SVM
AU - Abibullaev, Berdakh
AU - Kang, Won Seok
AU - Lee, Seung Hyun
AU - An, Jinung
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=77954809357&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77954809357&partnerID=8YFLogxK
M3 - Conference contribution
SN - 9788988678206
SP - 332
EP - 336
BT - INC2010 - The International Conference on Networked Computing, Proceeding
ER -