Wavelet‐Based Denoising Optimization for Endoscopic Gastric Slow‐Wave Recordings

ABSTRACT New, minimally invasive, endoscopic methods for recording gastric bioelectrical slow waves from the mucosal surface are emerging to address the current limitations of invasive recordings. Filtering techniques for these new methods have relied on protocols developed for invasive recordings. Updated signal processing techniques, such as discrete wavelet transformation (DWT), optimised for endoscopic recording conditions, promise more effective noise removal for these signals. Synthetic signals were constructed using averaged slow‐wave data and noise segmented from existing endoscopic gastric bioelectrical recordings from 12 patients. DWT was performed on the synthetic signals using 989 different parameter combinations to remove noise. Savitzky‐Golay (SG) filtering was also performed on the synthetic signals to provide a comparative baseline for classical filter performance. Combined SG filtering and DWT was then investigated using the top‐performing DWT parameters. Filter performance was evaluated using six established metrics, along with the inspection of the power spectral density (PSD) calculated on sample signals. Statistical significance was analysed using a paired two‐tailed Student's t ‐test or Wilcoxon signed‐rank test. For signals with moderate signal‐to‐noise ratio (SNR), DWT‐based methods outperformed traditional SG filtering in all metrics considered: signal‐distortion ratio (0.84 ± 0.45 vs. 1.34 ± 0.99), root‐mean‐square error (280 ± 150 µV vs. 450 ± 330 µV), percentage root‐mean‐square difference (78 ± 42% vs. 113 ± 83%), noise‐correction ratio (0.94±0.17 vs. 0.50±0.26), SNR improvement (5.9±3.0 dB vs. 2.1±2.7 dB) and filter performance metric (0.96 ± 0.42 vs. 1.8 ± 1.2). All p ‐values were