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Comparison of Dry and Wet Electrodes for Detecting Gastrointestinal Activity Patterns from Body Surface Electrical Recordings

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Gastrointestinal motility patterns can be mapped via electrical signals measured non-invasively on the body surface. However, short-term (≈ 2–4 h) meal response studies as well as long-term monitoring (≥ 24 h) may be hindered by skin irritation inherent with traditional Ag/AgCl pre-gelled (“wet”) electrodes. The aim of this work was to investigate the practical utility of using dry electrodes for GI body-surface electrical measurements. To directly compare dry vs. wet electrodes, we simultaneously recorded electrical signals from both types arranged in a 9-electrode array during an ≈ 2.5 h colonic meal-response study. Wavelet-based analyses were used to identify the signature post-meal colonic cyclic motor patterns. Blinded comparison of signal quality was carried out by four expert manual reviewers in order to assess the practical utility of each electrode type for identifying GI activity patterns. Dry electrodes recorded high-quality GI signals with signal-to-noise ratio of 10.0 ± 3.5 dB, comparable to that of wet electrodes (9.9 ± 3.6 dB). Although users rated dry electrodes as slightly more difficult to self-apply, they caused no skin irritation and were thus better tolerated overall. Dry electrodes are a more comfortable alternative to conventional wet electrode systems, and may offer a potentially viable option for long-term GI monitoring studies.

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J. C. Erickson was supported by a Lenfest Fellowship, Washington and Lee University. E. Stepanyan and E. Hassid were supported by the Washington and Lee University Summer Research Scholar funds. We thank the study participants who took part in the meal study and the expert manual reviewers for offering their candid assessment and interpretation of signal quality and study outcomes.

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Correspondence to Jonathan C. Erickson.

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Erickson, J.C., Stepanyan, E. & Hassid, E. Comparison of Dry and Wet Electrodes for Detecting Gastrointestinal Activity Patterns from Body Surface Electrical Recordings. Ann Biomed Eng 51, 1310–1321 (2023).

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