Article: A novel classification approach to detect the presence of foetal cardiac anomaly from foetal electrocardiogram Journal: International Journal of Biomedical Engineering and Technology (IJBET) 2020 Vol.33 No.1 pp.28 - 53 Abstract: Foetal cardiac anomaly interpretation from foetal electrocardiogram (FECG) is a challenging effort. Foetal cardiac activity can be assessed by scrutinising FECG because clinically crucial features are hidden in the amplitudes and waveform time duration of FECG, and foetal heart rate (FHR). These features are vital in foetal cardiac anomaly interpretation. Hence, here an attempt is made to detect the presence of foetal cardiac anomaly using support vector machine (SVM) classifier with polynomial kernel based on the patterns extricated from FHR, frequency domain of FECG signals, foetal cardiac time intervals and FECG morphology. Performance evaluation is done on real FECG signals with different combination of features set and the obtained results are compared. SVM showed good performance with 92% of classification accuracy when all the features are fed to the classifiers. Results evince that the proposed approach has immense prospective and guarantee in early foetal cardiac anomaly detection from FECG. Inderscience Publishers - linking academia, business and industry through research

Title: A novel classification approach to detect the presence of foetal cardiac anomaly from foetal electrocardiogram

Authors: M. Anisha; S.S. Kumar; M. Benisha

Addresses: Department of Biomedical Engineering, Kalasaligam Academy of Research and Education, Deemed to be University, Krishnankoil, 626126, Tamil Nadu, India ' Department of Electronics and Instrumentation Engineering, Noorul Islam Centre for Higher Education, Kumaracoil 629180, Tamil Nadu, India ' Department of Electronics and Communication Engineering, Jeppiaar Institute of Technology, Chennai, India

Abstract: Foetal cardiac anomaly interpretation from foetal electrocardiogram (FECG) is a challenging effort. Foetal cardiac activity can be assessed by scrutinising FECG because clinically crucial features are hidden in the amplitudes and waveform time duration of FECG, and foetal heart rate (FHR). These features are vital in foetal cardiac anomaly interpretation. Hence, here an attempt is made to detect the presence of foetal cardiac anomaly using support vector machine (SVM) classifier with polynomial kernel based on the patterns extricated from FHR, frequency domain of FECG signals, foetal cardiac time intervals and FECG morphology. Performance evaluation is done on real FECG signals with different combination of features set and the obtained results are compared. SVM showed good performance with 92% of classification accuracy when all the features are fed to the classifiers. Results evince that the proposed approach has immense prospective and guarantee in early foetal cardiac anomaly detection from FECG.

Keywords: foetal electrocardiogram; FECG; foetal heart rate; FHR; support vector machine; SVM; foetal cardiac anomaly; foetal cardiac activity.

DOI: 10.1504/IJBET.2020.107649

International Journal of Biomedical Engineering and Technology, 2020 Vol.33 No.1, pp.28 - 53

Received: 31 May 2017
Accepted: 04 Sep 2017
Published online: 04 Jun 2020 *

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Title: A novel classification approach to detect the presence of foetal cardiac anomaly from foetal electrocardiogram

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