A Supervised Wavelet Transform Algorithm for R Spike Detection in Noisy ECGs
(2009) Biomedical Engineering Systems and Technologies — ISBN: [978-3-540-92218-6], p. 256-264, published
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- Abstract
- The wavelet transform is a widely used pre-filtering step for subsequent R spike detection by thresholding of the coefficients. The time-frequency decomposition is indeed a powerful tool to analyze non-stationary signals. Still, current methods use consecutive wavelet scales in an a priori restricted range and may therefore lack adaptativity. This paper introduces a supervised learning algorithm which learns the optimal scales for each dataset using the annotations provided by physicians on a small training set. For each record, this method allows a specific set of non consecutive scales to be selected, based on the record’s characteristics. The selected scales are then used for the decomposition of the original long-term ECG signal recording and a hard thresholding rule is applied on the derivative of the wavelet coefficients to label the R spikes. This algorithm has been tested on the MIT-BIH arrhythmia database and obtains an average sensitivity rate of 99.7% and average positive predictivity rate of 99.7%.
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Citations
de Lannoy, G., De Decker, A., & Verleysen, M. (2009). A Supervised Wavelet Transform Algorithm for R Spike Detection in Noisy ECGs. In Ana Fred (ed.), Biomedical Engineering Systems and Technologies (p. p. 256-264). Springer. https://hdl.handle.net/2078.5/254009