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Time-frequency domain features of ECG signals: their application in P wave detection using the cross Wigner-Ville distribution
It is argued that the Wigner-Ville time-frequency distribution (WVD) of ECG (electrocardiography) signals reveals additional information that is not apparent in the time domain. The energy distribution and the presence of cross terms in the WVD images are heuristically interpreted on the basis of underlying electrophysiology of the human heart. Using these interpretations, time-frequency domain detection of P waves is examined, and the WVD cross terms are shown to be useful. >
- University of Western Australia Australia
- University of Queensland Australia
- Qatar University Qatar
- Qatar University Qatar
- University of Queensland Australia
ECG, Wigner-Ville time-frequency distribution, electrocardiography, Heart Rate Variability, cross-terms, electrophysiology, time-frequency analysis, time-frequency domain detection, cross Wigner-Ville distribution, quadratic time-frequency distributions, energy distribution, P waves, human heart
ECG, Wigner-Ville time-frequency distribution, electrocardiography, Heart Rate Variability, cross-terms, electrophysiology, time-frequency analysis, time-frequency domain detection, cross Wigner-Ville distribution, quadratic time-frequency distributions, energy distribution, P waves, human heart
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).17 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
