Title: Automatic feature extraction of ECG signal based on adaptive window dependent differential histogram approach and validation with CSE database
Authors: Basudev Halder; Sucharita Mitra; Madhuchhanda Mitra
Addresses: Department of Information Technology, Neotia Institute of Technology, Management and Science, Jhinga, D.H. Road, 24-parganas(s) – 743368, West Bengal, India ' Department of Electronics, Netaji Nagar Day College (affiliated to University of Calcutta), 170/436, NSC Bose Road, Regent Estate, Kolkata 700 092, India ' Department of Applied Physics, Faculty of Technology, University of Calcutta, 92, APC Road, Kolkata-700009, India
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%).
Keywords: adaptive window; differential histogram; CSE database; baseline; sensitivity; ECG signal; QRS zones; R-peaks; distinctive point's; sample values.
International Journal of Computational Systems Engineering, 2018 Vol.4 No.2/3, pp.146 - 155
Received: 24 Oct 2016
Accepted: 17 Apr 2017
Published online: 30 Apr 2018 *