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Approach to extract twin fECG for different cardiac conditions during prenatal

Publication Type : Conference Paper

Publisher : IFMBE Proceedings, Springer Verlag,

Source : IFMBE Proceedings, Springer Verlag, Volume 61, p.104-108 (2017)

Url :

ISBN : 9789811042195

Keywords : Bins, biomedical engineering, Cardiac monitoring, Data storage equipment, Differentiators, Electrocardiography, Fetal ECG, Fetal heart rate, Health condition, Health risks, Heart, Medical conditions, Standard deviation, Statistics, Zero-crossing detectors

Campus : Coimbatore

School : School of Engineering

Center : Computational Engineering and Networking

Department : Mechanical Engineering

Year : 2017

Abstract : During multiple fetus pregnancy, degree of risk for distinguishing the information of mother and fetus health condition is high. A proper distinguishable ECG of each fetus and mother gives information about the health conditions of individuals. In case of multiple fetal conditions, the heartbeat of the fetuses will be almost at the same rate. This algorithm has been aimed to separate mECG and the fECGs of the individual fetus. First, the signal for different medical conditions like Fibrillation, Apnea, Ventricular Ectopy, Singleton and Normal has been considered. The synthetic abdECG signal for the above mentioned cases has been formulated by preprocessing and considered as the input signal. RPeak of mECG in the abdECG signal has been located using First Order Gaussian Differentiator and Zero Crossing Detector. QRS complex has been considered around the identified R-Peak of abdECG. Identified QRS has been removed from the abdECG signal to obtain fECG with residual noise. The QRS complexes of fECG are detected the same way as mECG QRS were detected, and is represented as binary signals. The separation of the fetal ECG is done based on the individual presence of the fetus in the signals using Inter-beat averaging and Inter-beat standard deviation of the binary signal. The algorithm has been tested for above mentioned cardiac conditions during prenatal. The algorithm has been able to achieve 99% accuracy for particular cardiac condition with overall system accuracy of 80.4%. The standard cardiac signals of different cases have been sourced from Physionet database to construct the abdECG. © Springer Nature Singapore Pte Ltd. 2017

Cite this Research Publication : R. J. Gini, Dr. K. I. Ramachandran, and Ceerthibala, U. K., “Approach to extract twin fECG for different cardiac conditions during prenatal”, in IFMBE Proceedings, 2017, vol. 61, pp. 104-108.

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