FETAL HEART RATE DETECTION IN MULTICHANNEL ECG SIGNALS RECORDED FROM MOTHER’S ABDOMEN
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The analysis of an electrocardiogram (ECG) is a crucial method for detecting various heart diseases. One of the approaches to detecting heart anomalies in fetuses is by extracting the fetal heart rate (FHR) from fetal scalp leads during labor. However, obtaining a reliable fetal ECG signal through electrodes placed on the mother’s abdomen is challenging due to various factors such as low signal- to-noise ratio, muscle contractions, and fetal movement during pregnancy. To address this issue, the Pan Tompkins algorithm has been extensively studied for the extraction of fetal ECG QRS complex. This algorithm is particularly effective in filtering ECG signals from noise and detecting QRS waves efficiently. The detection of fetal R peaks becomes easier with this algorithm, as it isolates R peaks from the QRS complex found in the ECG readings. The method computes the slope, amplitude, and amplitude of QRS complexes when assessing the attenuated ECG signal. The Pan Tompkins algorithm involves three phases of peak detection, namely Learning Phase 1, Learning Phase 2, and Detection. In Learning Phase 1, the signal and noise thresholds are configured, and in Learning Phase 2, the RR intervals and RR limit values are configured. The detection phase operates by adjusting the thresholds and identifying the QRS complexes. With this algorithm, both mother’s and fetus’s R-peaks can be detected, and the signal-to- noise ratio and detection sensitivity are improved due to the dual threshold being used.
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