Key result
A new method for QRS wave location combining local entropic criterion and wavelet transform was introduced and evaluated on the manually annotated QT database.
A novel computational method combining entropic criteria and wavelet transforms was developed for QRS complex detection in ECG signals.
Advances QRS detection methods in ECG; leaves open prospective clinical validation before adoption.
In this paper, we introduce a new method for QRS wave's locations using the local entropic criterion applied to the signal split on different successive scales, based on the G. Gonon and M. Djafari approach, which offers a local estimator for segmenting of a signal based on entropic criteria, and on the work of Mallat and Hwang for singularity detection using local maxima of coefficients wavelet resulting from the decomposition of a signal. The R wave corresponds to two modulus maximum lines with opposite signs (min-max). We evaluated the algorithm on manually annotated databases QT.
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Rekik et al. (2016) studied this question. QRS detection combining entropic criterion and wavelet transform was evaluated on QRS wave location. A new method for QRS wave location combining local entropic criterion and wavelet transform was introduced and evaluated on the manually annotated QT database.
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