Evidence map›Paper›PMID 36433449›Full record

ArticleSensors (Basel, Switzerland)2022

Design and Validation of a Multimodal Wearable Device for Simultaneous Collection of Electrocardiogram, Electromyogram, and Electrodermal Activity.

Riley McNaboe, Luke Beardslee, Youngsun Kong, Brittany N Smith, I-Ping Chen, Hugo F Posada-Quintero, Ki H Chon

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Riley McNaboeBiomedical Engineering Department, University of Connecticut, Storrs, CT 06269, USA.
Luke BeardsleeInstitute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA.
Youngsun KongBiomedical Engineering Department, University of Connecticut, Storrs, CT 06269, USA.ORCID 0000-0001-5409-3888
Brittany N SmithBiomedical Engineering Department, University of Connecticut, Storrs, CT 06269, USA.ORCID 0000-0003-2147-8735
I-Ping ChenDepartment of Oral Health and Diagnostic Sciences, School of Dental Medicine, University of Connecticut Health, Farmington, CT 06030, USA.
Hugo F Posada-QuinteroBiomedical Engineering Department, University of Connecticut, Storrs, CT 06269, USA.ORCID 0000-0003-4514-4772
Ki H ChonBiomedical Engineering Department, University of Connecticut, Storrs, CT 06269, USA.ORCID 0000-0002-4422-4837

Funding

Quantitative Assessment of Dental Pain using a smartphone-attachable electrodermal activity sensorR21DE029563 · NIDCR · UNIVERSITY OF CONNECTICUT SCH OF MED/DNT · PI CHEN, I-PING, CHON, KI H · 2020 to 2021
$463k
NIH HHS NIH R21 DE029563
6 · The paper itself

Abstract

Bio-signals are being increasingly used for the assessment of pathophysiological conditions including pain, stress, fatigue, and anxiety. For some approaches, a single signal is not sufficient to provide a comprehensive diagnosis; however, there is a growing consensus that multimodal approaches allow higher sensitivity and specificity. For instance, in visceral pain subjects, the autonomic activation can be inferred using electrodermal activity (EDA) and heart rate variability derived from the electrocardiogram (ECG), but including the muscle activation detected from the surface electromyogram (sEMG) can better differentiate the disease that causes the pain. There is no wearable device commercially capable of collecting these three signals simultaneously. This paper presents the validation of a novel multimodal low profile wearable data acquisition device for the simultaneous collection of EDA, ECG, and sEMG signals. The device was validated by comparing its performance to laboratory-scale reference devices. N = 20 healthy subjects were recruited to participate in a four-stage study that exposed them to an array of cognitive, orthostatic, and muscular stimuli, ensuring the device is sensitive to a range of stressors. Time and frequency domain analyses for all three signals showed significant similarities between our device and the reference devices. Correlation of sEMG metrics ranged from 0.81 to 0.95 and EDA/ECG metrics showed few instances of significant difference in trends between our device and the references. With only minor observed differences, we demonstrated the ability of our device to collect EDA, sEMG, and ECG signals. This device will enable future practical and impactful advances in the field of chronic pain and stress measurement and can confidently be implemented in related studies.

Indexed as

Galvanic Skin ResponseWearable Electronic DevicesElectrocardiographyElectromyographyHumansPainelectrocardiogramelectrodermal activityelectromyogramfrequency domaintime domainwearable devices

Identifiers

PMID36433449
PMCPMC9695854

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.