Optimizing Fetal ECG Denoising with Modified Pan-Tompkins Algorithm

The electrocardiogram (ECG) is vital for cardiac monitoring. However, capturing a clear fetal ECG (FECG) is often difficult due to interference from the mother's signals and surrounding noise. This study proposes an optimized denoising approach integrating adaptive filtering and wavelet transform with a modified Pan-Tompkins algorithm to enhance the FECG signal clarity. The modifications to the Pan-Tompkins algorithm include optimized band-pass filter values, refined double differentiation, and improved squaring operations, which collectively enhance fetal QRS complex detection and reduce noise-induced distortions. These improvements lead to better identification of key waveform elements, including the P wave, QRS complex, and T wave, ultimately improving the accuracy of fetal heart rate monitoring and aiding in early detection of fetal distress.