Evidence map›Paper›PMID 40125566›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025

Accurate and Noninvasive Dysphagia Assessment via a Soft High-Density sEMG Electrode Array Conformal to the Submental and Infrahyoid Muscles.

Weijie Hong, Lin Mao, Kai Lin, Chongyuan Huang, Yanyan Su, Shun Zhang, Chengjun Wang, Daming Wang, Jizhou Song, Zuobing Chen

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

10 authors.

Weijie HongDepartment of Rehabilitation Medicine, The First Affiliated Hospital School of Medicine, Zhejiang University, Hangzhou, 310003, China.
Lin MaoDepartment of Rehabilitation Medicine, The First Affiliated Hospital School of Medicine, Zhejiang University, Hangzhou, 310003, China.
Kai LinKey Laboratory of Soft Machines and Smart Devices of Zhejiang Province, State Key Laboratory of Brain-Machine Intelligence, Department of Engineering Mechanics, Zhejiang University, Hangzhou, 310027, China.
Chongyuan HuangKey Laboratory of Soft Machines and Smart Devices of Zhejiang Province, State Key Laboratory of Brain-Machine Intelligence, Department of Engineering Mechanics, Zhejiang University, Hangzhou, 310027, China.
Yanyan SuKey Laboratory of Soft Machines and Smart Devices of Zhejiang Province, State Key Laboratory of Brain-Machine Intelligence, Department of Engineering Mechanics, Zhejiang University, Hangzhou, 310027, China.
Shun ZhangDepartment of Rehabilitation Medicine, The First Affiliated Hospital School of Medicine, Zhejiang University, Hangzhou, 310003, China.
Chengjun WangDepartment of Rehabilitation Medicine, The First Affiliated Hospital School of Medicine, Zhejiang University, Hangzhou, 310003, China.
Daming WangDepartment of Rehabilitation Medicine, The First Affiliated Hospital School of Medicine, Zhejiang University, Hangzhou, 310003, China.
Jizhou SongDepartment of Rehabilitation Medicine, The First Affiliated Hospital School of Medicine, Zhejiang University, Hangzhou, 310003, China.ORCID https://orcid.org/0000-0003-2821-9429
Zuobing ChenDepartment of Rehabilitation Medicine, The First Affiliated Hospital School of Medicine, Zhejiang University, Hangzhou, 310003, China.

Funding

National Natural Science Foundation of China 12225209National Natural Science Foundation of China 12302223National Natural Science Foundation of China 12321002National Natural Science Foundation of China 12402202Zhejiang Key Research and Development Program of China 2021C03050
6 · The paper itself

Abstract

Accurate, noninvasive dysphagia assessment is important for rehabilitation therapy but current clinical diagnostic methods are either invasive or subjective. Surface electromyography (sEMG) that monitors muscle activity during swallowing, offers a promising alternative. However, existing sEMG electrode arrays for dysphagia assessment remain challenging in combining the advantages of a large coverage area and strong compliance to the entire swallowing muscles. Here, we report a stretchable, breathable, large-area high-density sEMG (HD-sEMG) electrode array, which enables intimate contact to complex surface of the submental and infrahyoid muscles to detect high-fidelity HD-sEMG signals during swallowing. The electrode array features a 64-channel soft on-skin sensing array for comprehensive data capture, and a stiff connector for simple and reliable connection to an external acquisition setup. Systemically experimental studies revealed the easy operability of the soft HD-sEMG electrode array for effortless integration with the skin, as well as the excellent mechanical and electrical characteristics even subject to substantial skin deformations. By comparing HD-sEMG signals collected from 38 participants, three objective indicators for quantitative dysphagia evaluation were discussed. Finally, a machine learning model was developed to accurately and automatically classify the severity of dysphagia, and the factors affecting the recognition accuracy of the model were discussed in depth.

Indexed as

DeglutitionDeglutition DisordersElectromyographyNeck MusclesAdultElectrodesFemaleHumansMaleMiddle Ageddysphagia assessmentmachine learningstretchable high‐density sEMGswallowing

Identifiers

PMID40125566
PMCPMC12224961

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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.