Isolation of Fetal ECG Signals from Abdominal ECG Using Wavelet Analysis

被引:30
作者
Alshebly, Y. S. [1 ]
Nafea, M. [1 ]
机构
[1] Univ Nottingham Malaysia, Dept Elect & Elect Engn, Fac Sci & Engn, Semenyih 43500, Selangor, Malaysia
关键词
Fetal electrocardiogram; Wavelet analysis; FECG extraction; QRS complex; Fetal heart rate; EXTRACTION; FRAMEWORK; REMOVAL;
D O I
10.1016/j.irbm.2019.12.002
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Objective: Monitoring the heartbeat of the fetus during pregnancy is a vital part in determining their health. Current fetal heart monitoring techniques lack the accuracy in fetal heart rate monitoring and features acquisition, resulting in diagnostic medical issues. The demand for a reliable method of non-invasive fetal heart monitoring is of high importance. Method: Electrocardiogram (ECG) is a method of monitoring the electrical activity produced by the heart. The extraction of the fetal ECG (FECG) from the abdominal ECG (AECG) is challenging since both ECGs of the mother and the baby share similar frequency components, adding to the fact that the signals are corrupted by white noise. This paper presents a method of FECG extraction by eliminating all other signals using AECG. The algorithm is based on attenuating the maternal ECG (MECG) by filtering and wavelet analysis to find the locations of the FECG, and thus isolating them based on their locations. Two signals of AECG collected at different locations on the abdomens are used. The ECG data used contains MECG of a power of five to ten times that of the FECG. Results: The FECG signals were successfully isolated from the AECG using the proposed method through which the QRS complex of the heartbeat was conserved, and heart rate was calculated. The fetal heart rate was 135 bpm and the instantaneous heart rate was 131.58 bpm. The heart rate of the mother was at 90 bpm with an instantaneous heart rate of 81.9 bpm. Conclusion: The proposed method is promising for FECG extraction since it relies on filtering and wavelet analysis of two abdominal signals for the algorithm. The method implemented is easily adjusted based on the power levels of signals, giving it great ease of adaptation to changing signals in different biosignals applications. (C) 2019 AGBM. Published by Elsevier Masson SAS. All rights reserved.
引用
收藏
页码:252 / 260
页数:9
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