Key result
The proposed algorithm, utilizing an additive-multiplicative model and adaptive filtering, effectively isolates fetal ECG signals from maternal and environmental noise with an improved signal-to-noise ratio.
Why the study?
Traditional fetal monitoring methods often lack the resolution and continuity needed for timely intervention, particularly in resource-limited settings.
An advanced mathematical model and algorithm using ICA and adaptive filtering can effectively separate fetal ECG signals from maternal and environmental noise for continuous non-invasive monitoring.
Algorithm may aid FECG extraction research; leaves open clinical validation before perinatal practice.
Fetal electrocardiogram (FECG) signal extraction is a critical component of modern perinatal care, enabling continuous, non-invasive monitoring of fetal health. This approach is essential for the early detection of complications such as fetal hypoxia, arrhythmias, and other potentially life-threatening conditions. Traditional methods of fetal monitoring, including Doppler and intermittent auscultation, often do not provide the resolution and continuity required for timely intervention, especially in resource-limited settings where access to advanced technology is limited. To address these challenges, this study presents an innovative algorithm to extract FECS signals with improved accuracy and reliability. The algorithm uses a structured sequence of processing steps, including noise filtering, R-peak detection, and advanced filtering techniques to isolate fetal ECS from maternal signals and environmental noise. High-pass and low-pass filters and normalization ensure signal clarity and consistency in various conditions. Adaptive filtering dynamically adjusts to fluctuations in noise levels, increasing stability while preserving critical waveform characteristics such as the P-wave, QRS complex, and T-wave. These improvements are key to accurately assessing fetal heart rate and variability, enabling healthcare providers to detect early signs of fetal distress. Quantitative analysis demonstrates significant improvements in signal-to-noise ratio (SNR), supporting reliable and accurate diagnosis. The continuous, real-time monitoring capabilities align with the World Health Organisation's goal of reducing perinatal mortality to less than 12 per 1,000 births by 2030. In addition, its scalability and cost-effectiveness make it a promising solution for addressing disparities in antenatal care, especially in low- and middle-income countries. This study highlights the transformative potential of fetal echocardiography to improve maternal and fetal health globally, increase diagnostic accuracy, and promote health equity through innovative, affordable technology
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Franchevska et al. (2024) studied Fetal monitoring. Fetal ECG extraction algorithm (additive-multiplicative model, adaptive filtering, ICA) vs. Traditional linear filters was evaluated on Signal-to-noise ratio (SNR) and signal clarity. The proposed algorithm, utilizing an additive-multiplicative model and adaptive filtering, effectively isolates fetal ECG signals from maternal and environmental noise with an improved signal-to-noise ratio.
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