Title: A novel ECG segmentation for compression using Fourier series approximation in e-health devices
Authors: Abed Al Raoof K. Bsoul
Addresses: Department of Computer Science, Collage of Information Technology and Computer Sciences, Yarmouk University, Irbid, 21163, Jordan
Abstract: Since ECG signals capture the conduction of the heart, physicians monitor their patients' using special equipments. Because the monitoring lasts for long-time periods, the device should have a reasonable lifetime. Therefore, the recorded signal is manipulated in compressed format, while preserving the diagnostic information of the signal. In this paper, a novel segmentation of electrocardiogram signals is proposed for compression by Fourier series. A set of significant turning points is computed to strip the signal into sharp-peaks segments that limit the use of Fourier approximation. Two datasets are used for testing the algorithm. The percentage root-mean difference (PRD) measure and the weighted diagnostic distortion (WDD) are used to report the results. The method has superb performance at all bit-rates, and good quality score (QS). With some constraints on device's architecture, the algorithm can be implemented and achieves a high compression ratio while preserving the diagnostic features of the signal.
Keywords: biomedical signal processing; ECG compression; electrocardiograms; Fourier approximation; e-health devices; electronic healthcare; ECG segmentation; medical images; ECG signals; percentage root-mean difference; PRD; weighted diagnostic distortion; WDD; heart monitoring; diagnostic information; healthcare technology.
International Journal of Electronic Healthcare, 2015 Vol.8 No.2/3/4, pp.163 - 184
Available online: 15 Mar 2016 *Full-text access for editors Access for subscribers Purchase this article Comment on this article