Monitoring and Analysis of Multiplicative Characteristic Variations for Adhesive Electrode by Using Self-Electrocardiogram Signals

Jingyi Song, Chun-Shu Wei, Tzyy Ping Jung, Yikai Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

This study proposes a flexible method to track and quantify the ac-coupled gain variations in electrode to tissue interface (ETI) of adhesive/reusable electrodes. Particularly, this study focuses on the effects of multiplicative motion artifacts (MMA) on ETIs and proposes a continuously monitoring technique to assess ETI multiplicative variations. The proposed method only requires one additional channel of self electrocardiogram (self-ECG). Both gel-covered and foam-based adhesive electrodes were tested. Empirical results show that the ETI multiplicative variations can be characterized by the proposed convenient method. Meanwhile, we successfully demonstrate different characteristics for two types of electrodes under five motion postures including suppress motion, fist/stretch palm, twist, bent inward and arm lift, Furthermore, The long-Term degradation experiment illustrates that the average characteristics variation of ETI is less than 6% within 3-day of use.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages761-764
Number of pages4
ISBN (Electronic)9781479986965
DOIs
StatePublished - 12 Jan 2016
EventIEEE International Conference on Systems, Man, and Cybernetics, SMC 2015 - Kowloon Tong, Hong Kong
Duration: 9 Oct 201512 Oct 2015

Publication series

NameProceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015

Conference

ConferenceIEEE International Conference on Systems, Man, and Cybernetics, SMC 2015
CountryHong Kong
CityKowloon Tong
Period9/10/1512/10/15

Keywords

  • ac-coupled gain
  • ETI
  • interface variation
  • long-Term observation
  • motion artifact

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