International Journal of Computer Applications |
Foundation of Computer Science (FCS), NY, USA |
Volume 41 - Number 4 |
Year of Publication: 2012 |
Authors: A. Dliou, R. Latif, M. Laaboubi, F. M. R. Maoulainine |
10.5120/5530-7574 |
A. Dliou, R. Latif, M. Laaboubi, F. M. R. Maoulainine . Arrhythmia ECG Signal Analysis using Non Parametric Time-Frequency Techniques. International Journal of Computer Applications. 41, 4 ( March 2012), 25-30. DOI=10.5120/5530-7574
Non-parametric time-frequency analysis for multicomponent nonstationary signals is an important issue in signal processing, especially in the biomedical field. Due to the nonstationary, multicomponent nature of the biomedical ECG signal, the use of time-frequency analysis can be very useful to identify the exact multicomponent structure of this biological signal. In this paper, three time-frequency techniques are applied for analyzing a supraventricular ECG signal. These three time-frequency techniques are the Choi–Williams distribution, the Bessel distribution and the Born-Jordan distribution. Their performance over supraventricular ECG signal as well as over a monocomponent frequency modulation signal in additive Gaussian noise was compared. The results showed that the Choi–Williams technique gives a good performance as compared to other time-frequency techniques.