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Adaptive Neuro-Fuzzy Inference Systems for Extracting Fetal Electrocardiogram

Authors: Khaled Assaleh;

Adaptive Neuro-Fuzzy Inference Systems for Extracting Fetal Electrocardiogram

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.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
12
Top 10%
Top 10%
Average
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