A supervised wavelet transform algorithm for R spike detection in noisy ECGs

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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%. © 2008 Springer-Verlag.

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De Lannoy, G., De Decker, A., & Verleysen, M. (2008). A supervised wavelet transform algorithm for R spike detection in noisy ECGs. In Communications in Computer and Information Science (Vol. 25 CCIS, pp. 256–264). https://doi.org/10.1007/978-3-540-92219-3_19

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