title: Extraction of fetal electrocardiogram using adaptive neuro-fuzzy inference systems.
abstract: In this paper, we investigate the use of adaptive neuro-fuzzy inference systems (ANFIS) for fetal electrocardiogram (FECG) extraction from two ECG signals recorded at the thoracic and abdominal areas of the mother's skin. The thoracic ECG is assumed to be almost completely maternal (MECG) while the abdominal ECG is considered to be composite as it contains both the mother's and the fetus' ECG signals. The maternal component in the abdominal ECG signal is a nonlinearly transformed version of the MECG. We use an ANFIS network to identify this nonlinear relationship, and to align the MECG signal with the maternal component in the abdominal ECG signal. Thus, we extract the FECG component by subtracting the aligned version of the MECG signal from the abdominal ECG signal. We validate our technique on both real and synthetic ECG signals. Our results demonstrate the effectiveness of the proposed technique in extracting the FECG component from abdominal signals of very low maternal to fetal signal-to-noise ratios. The results also show that the technique is capable of extracting the FECG even when it is totally embedded within the maternal QRS complex.
authors.affiliation: Department of Electrical Engineering, American University of Sharjah, P. O. Box 26666, UAE. kassaleh@aus.edu
authors.forename: Khaled
authors.initials: K
authors.lastname: Assaleh
mesh_terms: D000005:Abdomen / Q000502:physiology; D000465:Algorithms; D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D005260:Female; D005323:Fetal Monitoring / Q000379:methods*; D017143:Fuzzy Logic; D006801:Humans; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D011247:Pregnancy; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity