지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
Sarcopenia Diagnostic Technique Based on Artificial Intelligence Using Bio-signal of Neuromuscular System: A Proof-of-Concept Study
Sarcopenia Diagnostic Technique Based on Artificial Intelligence Using Bio-signal of Neuromuscular System: A Proof-of-Concept Study
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한뇌신경재활학회 뇌신경재활 Brain & NeuroRehabilitation Vol.17 No.2 KCI등재후보
- 발행연도
- 2024.7
- 수록면
- 1 - 11 (11page)
- DOI
- 10.12786/bn.2024.17.e12
이용수
Sarcopenia Diagnostic Technique Based on Artificial Intelligence Using Bio-signal of Neuromuscular System: A Proof-of-Concept Study
초록· 키워드
오류제보하기
In this paper, we propose an artificial intelligence (AI)-based sarcopenia diagnostic technique for stroke patients utilizing bio-signals from the neuromuscular system. Handgrip, skeletal muscle mass index, and gait speed are prerequisite components for sarcopenia diagnoses.
However, measurement of these parameters is often challenging for most hemiplegic stroke patients. For these reasons, there is an imperative need to develop a sarcopenia diagnostic technique that requires minimal volitional participation but nevertheless still assesses the muscle changes related to sarcopenia. The proposed AI diagnostic technique collects motor unit responses from stroke patients in a resting state via stimulated muscle contraction signals (SMCSs) recorded from surface electromyography while applying electrical stimulation to the muscle. For this study, we extracted features from SMCS collected from stroke patients and trained our AI model for sarcopenia diagnosis. We validated the performance of the trained AI models for each gender against other diagnostic parameters.
The accuracy of the AI sarcopenia model was 96%, and 95% for male and females, respectively. Through these results, we were able to provide preliminar y proof that SMCS could be a potential surrogate biomarker to reflect sarcopenia in stroke patients.
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