Characteristic wave detection in ECG using complex-valued Continuous Wavelet Transforms
2
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
In this study the complex-valued continuous wavelet transform (CWT) has been applied in detection of Electrocardiograms (ECG) as response to various signal classification methods such as Fourier transforms and other tools of time frequency analysis. Experiments have shown that CWT may serve as a detector of non-stationary signal changes as ECG. The tested signal is corrupted by short time events. We applied CWT to detect short-time event and the result image representation of the signal has showed us that one can easily find the discontinuity at the time scale representation. Analysis of ECG signal using complex-valued continuous wavelet transform is the first step to detect possible changes and alternans. In the second step, modulus and phase must be thoroughly examined. Thus, short time events in the ECG signal, and other important characteristic points such as frequency overlapping, wave onsets/offsets extrema and discontinuities even inflection points are found to be detectable. We have proved that the complex-valued CWT can be used as a powerful detector in ECG signal analysis.
Abstract
In this study the complex-valued continuous wavelet transform (CWT) has been applied in detection of Electrocardiograms (ECG) as response to various signal classification methods such as Fourier transforms and other tools of time frequency analysis. Experiments have shown that CWT may serve as a detector of non-stationary signal changes as ECG. The tested signal is corrupted by short time events. We applied CWT to detect short-time event and the result image representation of the signal has showed us that one can easily find the discontinuity at the time scale representation. Analysis of ECG signal using complex-valued continuous wavelet transform is the first step to detect possible changes and alternans. In the second step, modulus and phase must be thoroughly examined. Thus, short time events in the ECG signal, and other important characteristic points such as frequency overlapping, wave onsets/offsets extrema and discontinuities even inflection points are found to be detectable. We have proved that the complex-valued CWT can be used as a powerful detector in ECG signal analysis.
참고문헌 (10)
- Lilly LS, "Pathophysiology of Heart Disease", pp.57~90 (2003), 3rd Ed. Philade- lphia, Lippincott Williams & Wilkins
- L. Senhadji, "Comparing Wavelet Transforms for Recognizing Cardiac Patterns", IEEE Eng Med Biol, 14(2), pp.167~173 (1995)
- Sahambi J.S, "Using wavelet transforms for ECG characterization: An on-line digital signal processing system", IEEE Eng. Med. Biol, 1, pp.7783~ (1997)
- ddison P.S., "Evaluating arrhythmias in ECG signals using wavelet transforms", IEEE Eng. Med. Biol, 19, pp.104~109 (2000)
- S G Mallat, "A Theory for Multiresolution Signal Decomposition: The Wavelet Representation", IEEE Trans. Pattern Reco. and Machine Int, 11(7), pp.674~693 (1989)
- I.W.Selesnick, "The double-density dual-tree DWT", pp.13041314~ (2004), IEEE Trans. Signal Proc. 52
- K. Dubowik, "ECG Features Extraction - QRS Detection and Shape Recognition", Journal of Applied Computer Science, 9(1), pp.7~21 (2002)
- S. Mallat, "Singularity detection and processing with wavelets", IEEE Trans. Inf. Theory, 38(2), pp.617643~ (1992)
- S. Mallat, "A theory for multiresolution signal decomposition: the wavelet representation", IEEE Trans. Patt. Anal. Machine Intell, 11(7), pp.674693~ (1989)
- G.B. Moody, "The MIT-BIH arrhythmia database on CD-ROM and software for use with it", pp.185~188 (1990), in Computers in Cardiology, IEEE Computer Society Press