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ToolsPhillips, John S., Newman, Jacob, Garioch, Jennifer and Nunney, Ian (2020) Electrode pad suitability for repeated application to the head and neck. Health Management Technology, 10 (5). 1325–1329. ISSN 2190-7196
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Abstract
Electrode pads are an integral component of ambulatory monitoring, providing an interface to conduct and record bioelectrical signals. During a previous clinical investigation involving our device for monitoring patients with dizziness, 53% of individuals experienced temporary redness and inflammation after wearing standard ECG electrode pads on the face for up to 30 days. We postulated that this was caused by a combination of the material composition of the electrode pads and through skin stripping due to their daily replacement. To test this hypothesis, we undertook a further study to determine which combination of electrode pad and replacement regime was most tolerable to the skin. Participants wore three different electrode pads on both sides of their face for 30 days. Electrode pads on the left were replaced daily, and those on the right on alternate days. Participants were instructed to inspect their skin after removal and to not reapply a new electrode pad if they noticed any unexpected changes to the condition of their skin. Electrode pad performance was measured by the duration of wear. The two best performing electrode pads contained a wet gel rather than a solid, adhesive gel, but the advantage of replacing electrode pads on alternate days was negligible. The chloride concentration of the gels was found to be likely factor in determining tolerability. The results suggest that optimal tolerability for ambulatory monitoring on the head and neck is provided by electrode pads containing a wet gel and a low chloride concentration.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | ambulatory monitoring,electrode pads,physiological monitoring,biotechnology,bioengineering,applied microbiology and biotechnology,biomedical engineering ,/dk/atira/pure/subjectarea/asjc/1300/1305 |
| Faculty \ School: | Faculty of Medicine and Health Sciences > Norwich Medical School Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Medicine and Health Sciences > Research Centres > Population Health Faculty of Science > Research Groups > Data Science and AI |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 24 Jun 2020 00:02 |
| Last Modified: | 10 Dec 2024 01:34 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/75748 |
| DOI: | 10.1007/s12553-020-00444-3 |
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