A novel framework for the removal of pacing artifacts from bio-electrical recordings. (March 2023)

• Record Type: Journal Article
• Authors: Nagahawatte, Nipuni D.; Paskaranandavadivel, Niranchan; Bear, Laura R.; Avci, Recep; Cheng, Leo K.
• Is Part Of: Computers in biology and medicine. Volume 155(2023)
• Publication Date: 2023-03
• Subjects: Artifacts -- Stimulation -- Pacing -- Hampel-filter -- Signal processing -- Filtering; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2023.106673
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Background: Electroceuticals provide clinical solutions for a range of disorders including Parkinson's disease, cardiac arrythmias and are emerging as a potential treatment option for gastrointestinal disorders. However, pre-clinical investigations are challenged by the large stimulation artifacts registered in bio-electrical recordings. Method: A generalized framework capable of isolating and suppressing stimulation artifacts with minimal intervention was developed. Stimulation artifacts with different pulse-parameters in synthetic and experimental cardiac and gastrointestinal signals were detected using a Hampel filter and reconstructed using 3 methods: i) autoregression, ii) weighted mean, and iii) linear interpolation. Results: Synthetic stimulation artifacts with amplitudes of 2 mV and 4 mV and pulse-widths of 50 ms, 100 ms, and 200 ms were successfully isolated and the artifact window size remained uninfluenced by the pulse-amplitude, but was influenced by pulse-width (e.g., the autoregression method resulted in an identical Root Mean Square Error (RMSE) of 1.64 mV for artifacts with 200 ms pulse-width and both 2 mV and 4 mV amplitudes). The performance of autoregression (RMSE = 1.45 ± 0.16 mV) and linear interpolation (RMSE = 1.22 ± 0.14 mV) methods were comparable and better than weighted mean (RMSE = 5.54 ± 0.56 mV) for synthetic data. However, for experimental recordings, artifact removal by autoregression was superior to both linear interpolation and weighted mean approaches in gastric, small intestinal and cardiac recordings. Conclusions: A novel signal processing framework enabled efficient analysis of bio-electrical recordings with stimulation artifacts. This will allow the bio-electrical events induced by stimulation protocols to be efficiently and systematically evaluated, resulting in improved stimulation therapies. Highlights: Pacing and stimulation artifacts challenge the analysis of bioelectrical measurements. A generalized artifact removal framework was developed for pre-clinical research. Pacing artifacts were detected using a semi-automated Hampel filter. The detected artifacts were suppressed using 3 reconstruction algorithms. The framework was validated on gastric, small intestinal and cardiac pacing signals.