Affiliations: [a] College of Engineering and Technology, American University of the Middle East, Kuwait | [b] Biomedical Systems and Medical Informatics Engineering, Yarmouk University, Jordan | [c] Electrical and Electronic Engineering, Avrasya University, Trabzon, Turkey | [d] Electronics Engineering Department, Yarmouk University, Jordan
Correspondence:
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Corresponding author: Bahaa Al-Sheikh, College of Engineering and Technology, American University of the Middle East, P.O. Box 220 Dasman 15453, Kuwait. Tel.: +965 2225 1400 Ext: 1856/+965 9977 2174; E-mail: [email protected].
Abstract: BACKGROUND: Fetal heart activity adds significant information about the status of the fetus health. Early diagnosis of issues in the heart before delivery allows early intervention and significantly improves the treatment. OBJECTIVE: This paper presents a new adaptive filtering algorithm for fetal electrocardiogram (FECG) extraction from the maternal abdominal signal, known in literature as abdominal electrocardiogram (AECG) signal. Fetal QRS complex waves will be identified and extracted accurately for fetal health care and monitoring purposes. METHODS: We use discrete wavelet transform recursive inverse (DWT-RI) adaptive filtering algorithm for this objective. Thoracic maternal electrocardiogram (MECG) is used as a reference in the proposed algorithm and FECG components are extracted from AECG signal after suppressing the MECG projections. The proposed algorithm is compared to other typical adaptive filtering algorithms, least mean squares (LMS), recursive least squares (RLS), and recursive inverse (RI). RESULTS: Fetal QRS waveforms successful identification and extraction from AECG signal is evaluated objectively and visually and compared to other algorithms. We validated the proposed algorithm using both synthetic data and real clinical data. CONCLUSIONS: The proposed algorithm is capable of extracting fetal QRS waveforms successfully from AECG and outperforms other adaptive filtering algorithms in terms of accuracy and positive predictivity.
