Affiliations: [a] Department of Electronics and Instrumentation Engineering, St. Joseph's College of Engineering, Anna University, OMR, Chennai, India | [b] Department of Computer Science and Engineering, St. Joseph's College of Engineering, Anna University, OMR, Chennai, India
Correspondence:
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Corresponding author: Palani Thanaraj, Department of Electronics and Instrumentation Engineering, St. Joseph's College of Engineering, Anna University, OMR, Chennai 600 119, India. Tel.: +91 9884735283; Fax: +91 4424500861; E-mail:[email protected]
Abstract: BACKGROUND: The fetal electrocardiogram (FECG) signals are
essential to monitor the health condition of the baby. Fetal heart rate
(FHR) is commonly used for diagnosing certain abnormalities in the formation
of the heart. Usually, non-invasive abdominal electrocardiogram (AbECG)
signals are obtained by placing surface electrodes in the abdomen region of
the pregnant woman. AbECG signals are often not suitable for the direct
analysis of fetal heart activity. Moreover, the strength and magnitude of
the FECG signals are low compared to the maternal electrocardiogram (MECG)
signals. The MECG signals are often superimposed with the FECG signals that
make the monitoring of FECG signals a difficult
task. OBJECTIVE: Primary goal of the paper is to separate
the fetal electrocardiogram (FECG) signals from the unwanted maternal
electrocardiogram (MECG) signals. METHOD: A multivariate
signal processing procedure is proposed here that combines the Multivariate
Empirical Mode Decomposition (MEMD) and Independent Component Analysis (ICA). RESULTS: The proposed method is evaluated with
clinical abdominal signals taken from three pregnant women (N= 3) recorded
during the 38-41 weeks of the gestation period. The number of fetal R-wave
detected (NEFQRS), the number of unwanted maternal peaks (NMQRS),
the number of undetected fetal R-wave (NUFQRS) and the FHR detection
accuracy quantifies the performance of our method. Clinical investigation
with three test subjects shows an overall detection accuracy of 92.8%. CONCLUSION: Comparative analysis with benchmark
signal processing method such as ICA suggests the noteworthy performance of
our method.
