Sarcopenia Diagnostic Technique Based on Artificial Intelligence Using Bio-signal of Neuromuscular System: A Proof-of-Concept Study
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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.
Abstract
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.
참고문헌 (29)
- Landi F, "Sarcopenia as a risk factor for falls in elderly individuals : results from the ilSIRENTE study", Clin Nutr, 31, pp.652~658 (2012)
- Gadelha AB, "Severity of sarcopenia is associated with postural balance and risk of falls in community-dwelling older women", Exp Aging Res, 44, pp.258~269 (2018)
- DeAndrade J, "Sarcopenia is a risk factor for complications and an independent predictor of hospital length of stay in trauma patients", J Surg Res, 221, pp.161~166 (2018)
- Huang DD, "Sarcopenia, as defined by low muscle mass, strength and physical performance, predicts complications after surgery for colorectal cancer", Colorectal Dis, 17, pp.O256~O264 (2015)
- Hendrickson NR, "Sarcopenia is associated with increased mortality but not complications following resection and reconstruction of sarcoma of the extremities", J Surg Oncol, 121, pp.1241~1248 (2020)
- Tang T, "A sarcopenia screening test predicts mortality in hospitalized older adults", Sci Rep, 8, pp.2923~ (2018)
- Kaltenhauser S, "Diagnosis of sarcopenia on thoracic computed tomography and its association with postoperative survival after anatomic lung cancer resection", Sci Rep, 13, pp.18450~ (2023)
- Cruz-Jentoft AJ, "Sarcopenia", Lancet, 393, pp.2636~2646 (2019)
- Chen LK, "Asian working group for sarcopenia : 2019 consensus update on sarcopenia diagnosis and treatment", J Am Med Dir Assoc, 21, pp.300~307 (2020)
- Mas MF, "Stroke and sarcopenia", Curr Phys Med Rehabil Rep, 8, pp.452~460 (2020)
- Su Y, "Prevalence of stroke-related sarcopenia : a systematic review and meta-analysis", J Stroke Cerebrovasc Dis, 29, pp.105092~ (2020)
- Krivickas LS, "Relationship between force and size in human single muscle fibres", Exp Physiol, 96, pp.539~547 (2011)
- Methenitis S, "Intramuscular fiber conduction velocity, isometric force and explosive performance", J Hum Kinet, 51, pp.93~101 (2016)
- Rubin DI, "Needle electromyography : basic concepts", Handb Clin Neurol, 160, pp.243~256 (2019)
- Shipe C, "Electrodiagnostic evaluation of motor neuron disorders", Am J Electroneurodiagn Technol, 44, pp.30~36 (2004)
- Kouzi I, "Motor unit number estimation and quantitative needle electromyography in stroke patients", J Electromyogr Kinesiol, 24, pp.910~916 (2014)
- De Luca CJ, "Behaviour of human motor units in different muscles during linearly varying contractions", J Physiol, 329, pp.113~128 (1982)
- Del Vecchio A, "Tutorial : analysis of motor unit discharge characteristics from high-density surface EMG signals", J Electromyogr Kinesiol, 53, pp.102426~ (2020)
- Merletti R, "Surface electromyography : physiology, engineering, and applications" (2016), John Wiley & Sons Hoboken
- Piasecki M, "Motor unit number estimates and neuromuscular transmission in the tibialis anterior of master athletes : evidence that athletic older people are not spared from age-related motor unit remodeling", Physiol Rep, 4, pp.e12987~ (2016)
- de Carvalho M, "Diaphragm motor responses to phrenic nerve stimulation in ALS : surface and needle recordings", Clin Neurophysiol, 129, pp.349~353 (2018)
- Nakagawa K, "Neuromuscular recruitment pattern in motor point stimulation", Artif Organs, 47, pp.537~546 (2023)
- Peng Y, "Motor unit number estimation based on high-density surface electromyography decomposition", Clin Neurophysiol, 127, pp.3059~3065 (2016)
- Yamada Y, "Membrane capacitance and characteristic frequency are associated with contractile properties of skeletal muscle", Med Eng Phys, 106, pp.103832~ (2022)
- Ghaderyan P, "A novel cepstral-based technique for automatic cognitive load estimation", Biomed Signal Process Control, 39, pp.396~404 (2018)
- Rabiner LR, "Introduction to digital speech processing", Found Trends Signal Process, 1, pp.1~194 (2007)
- Meza-Valderrama D, "Sarcopenia, malnutrition, and cachexia : adapting definitions and terminology of nutritional disorders in older people with cancer", Nutrients, 13, pp.761~ (2021)
- Fukuma K, "Pre-existing sarcopenia and swallowing outcomes in acute stroke patients", Clin Nutr, 42, pp.1454~1461 (2023)
- Chai KC, "Association between preexisting sarcopenia and stroke in patients with type 2 diabetes mellitus", J Nutr Health Aging, 26, pp.936~944 (2022)