Key result
An R-wave detector using continuous wavelet transform modulus maxima achieved 99.53% sensitivity and 99.73% positive predictive value in clinical signals, and 99.70% and 99.68% in MIT/BIH data.
Why the study?
Does continuous wavelet transform modulus maxima (CWTMM) analysis accurately detect R-waves in ECG signals?
Does continuous wavelet transform modulus maxima (CWTMM) analysis accurately detect R-waves in ECG signals?
Answers are grounded in this paper and cite their sources.
Continuous wavelet transform modulus maxima analysis provides highly sensitive and accurate automatic R-wave detection in ECG signals.
The problem of automatic beat recognition in the ECG is tackled using continuous wavelet transform modulus maxima (CWTMM). Features within a variety of ECG signals can be shown to correspond to various morphologies in the CWTMM domain. This domain has an easy interpretation and offers a useful tool for the automatic characterization of the different components observed in the ECG in health and disease. As an application of this enhanced time-frequency analysis technique for ECG signals, an R-wave detector is developed and tested using patient signals recorded in the Coronary Care Unit of the Royal Infirmary of Edinburgh (attaining a sensitivity of 99.53% and a positive predictive value of 99.73%) and with the MIT/BIH database (attaining a sensitivity of 99.70% and a positive predictive value of 99.68%).
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Legarreta et al. (2005) studied ECG signals in health and disease. Continuous wavelet transform modulus maxima (CWTMM) R-wave detector was evaluated on Sensitivity and positive predictive value for R-wave detection. An R-wave detector using continuous wavelet transform modulus maxima achieved 99.53% sensitivity and 99.73% positive predictive value in clinical signals, and 99.70% and 99.68% in MIT/BIH data.
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