Automatic feature extraction of ECG signal based on adaptive window dependent differential histogram approach and validation with CSE database Online publication date: Mon, 30-Apr-2018
by Basudev Halder; Sucharita Mitra; Madhuchhanda Mitra
International Journal of Computational Systems Engineering (IJCSYSE), Vol. 4, No. 2/3, 2018
Abstract: A very simple and novel idea based on adaptive window dependent differential histogram approach has been proposed for automatic detection and identification of ECG waves with its characteristic features. To facilitate the estimation of the waves, the normalised signal has been divided into a few small windows by an adaptive window selection technique. By counting the number of changes between successive samples as frequency, the differential histogram has been plotted. Some of the zones having an area more than a pre-defined threshold are depicted as QRS zones. The local maxima of these zones are referred as the R-peaks. T and P peaks are also detected. Baseline point and clinically significant time plane features have been computed and validated with reference values of the CSE database. The proposed technique achieved better performance in comparison with CSE groups. Its accuracy is achieved in sensitivity (99.86%), positive productivity (99.76%) and detection accuracy (99.8%).
Online publication date: Mon, 30-Apr-2018
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