Evidence map›Paper›PMID 40887188›Full record

ArticleSheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi2025

[A signal sensing system for monitoring the movement of human respiratory muscle based on the thin-film varistor].

Yueyang Yuan, Zhongping Zhang, Lixin Xie, Haoxuan Huang, Wei Liu

Abstract readEnglish Abstract
In one paragraph

Article in Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Yueyang YuanInnovation Base of Intelligent Diagnostic and Therapeutic in Respiration, Hunan City University, Yiyang, Hunan 413099, P. R. China.
Zhongping ZhangInnovation Base of Intelligent Diagnostic and Therapeutic in Respiration, Hunan City University, Yiyang, Hunan 413099, P. R. China.
Lixin XieDepartment of Respiratory and Critical Care Medicine, Chinese PLA General Hospital, Beijing 100853, P. R. China.
Haoxuan HuangDepartment of Research and Development, Hunan Micomme Medical Development Co., Ltd, Changsha 410000, P. R. China.
Wei LiuDepartment of Research and Development, Hunan Micomme Medical Development Co., Ltd, Changsha 410000, P. R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In order to accurately capture the respiratory muscle movement and extract the synchronization signals corresponding to the breathing phases, a comprehensive signal sensing system for sensing the movement of the respiratory muscle was developed with applying the thin-film varistor FSR402 IMS-C07A in this paper. The system integrated a sensor, a signal processing circuit, and an application program to collect, amplify and denoise electronic signals. Based on the respiratory muscle movement sensor and a STM32F107 development board, an experimental platform was designed to conduct experiments. The respiratory muscle movement data and respiratory airflow data were collected from 3 healthy adults for comparative analysis. In this paper, the results demonstrated that the method for determining respiratory phase based on the sensing the respiratory muscle movement exhibited strong real-time performance. Compared to traditional airflow-based respiratory phase detection, the proposed method showed a lead times ranging from 33 to 210 ms [(88.3 ± 47.9) ms] for expiration switched into inspiration and 17 to 222 ms [(92.9 ± 63.8) ms] for inspiration switched into expiration, respectively. When this system is applied to trigger the output of the ventilator, it will effectively improve the patient-ventilator synchrony and facilitate the ventilation treatment for patients with respiratory diseases.

Indexed as

Respiratory MusclesSignal Processing, Computer-AssistedAdultHumansMonitoring, PhysiologicMovementRespirationPatient-ventilator synchronyRespiratory muscle movementThin-film varistor

Identifiers

PMID40887188
PMCPMC12409494

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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.