Title :
Adaptive Neuro-Fuzzy Inference Systems for Extracting Fetal Electrocardiogram
Author_Institution :
Dept. of Electr. Eng., Sharjah American Univ.
Abstract :
In this paper, we present an efficient technique for extracting the fetal electrocardiogram (FECG) from a composite ECG recording. Our technique uses an adaptive neuro-fuzzy inference system (ANFIS) that operates on two ECG signals recorded at the thoracic and abdominal areas of the mother´s skin. The thoracic ECG is assumed to be purely maternal. However, the abdominal ECG will contain both a maternal component as well as a fetal one. This maternal component is considered to be a nonlinearly transformed version of the thoracic ECG. Once this nonlinear transformation is determined, the thoracic ECG signal can be aligned with the maternal component in the abdominal ECG signal. We use ANFIS to perform this nonlinear alignment. The FECG signal is then extracted by simply subtracting the aligned version of the thoracic ECG signal from the abdominal ECG signal
Keywords :
electrocardiography; fuzzy neural nets; fuzzy reasoning; medical signal processing; neural nets; obstetrics; patient diagnosis; abdominal ECG; adaptive neuro-fuzzy inference systems; composite ECG recording; fetal electrocardiogram extraction; nonlinear transformation; thoracic ECG; Abdomen; Adaptive signal processing; Adaptive systems; Data mining; Electrocardiography; Filtering; Independent component analysis; Interference; Noise reduction; Source separation; Fetal electrocardiogram; adaptive neuron-fuzzy systems; noninvasive extraction; nonlinear transformation;
Conference_Titel :
Signal Processing and Information Technology, 2006 IEEE International Symposium on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-9753-3
Electronic_ISBN :
0-7803-9754-1
DOI :
10.1109/ISSPIT.2006.270782