Title: Multi-scale similarity entropy as a complexity descriptor to discriminate healthy to distress foetus
Authors: Jean-Marc Girault; Souad Oudjemia; Iulian Voicu
Addresses: University Francois Rabelais of Tours, UMR INSERM U930, PRES Centre-Val de Loire University, Tours, France ' University of Mouloud Mammeri, Tizzi Ouzoun, Algeria ' University Francois Rabelais of Tours, UMR INSERM U930, PRES Centre-Val de Loire University, Tours, France
Abstract: This paper deals with the discrimination between suffering foetuses and normal foetuses by means of a multi-scale similarity entropy. Sample entropy and similarity entropy are evaluated in multi-scale analysis on foetal heart rate signals. Without multi-scale analysis, our results show that only the similarity entropy differentiate suffering foetuses to normal foetuses. Furthermore, with the multi-scale analysis, our results show that both the sample entropy and the similarity entropy can discriminate the distressed foetuses to normal foetuses. In all cases, the similarity entropy outperforms the sample entropy that is encouraging for another biomedical applications.
Keywords: similarity entropy; complexity; time series; multi-scale analysis; foetal distress; healthy foetus; distressed foetus; foetal heart rate; suffering foetuses; normal foetuses.
International Journal of Systems, Control and Communications, 2013 Vol.5 No.3/4, pp.276 - 284
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 09 Dec 2013 *