Motor unit firing patterns in older adults with low skeletal muscle mass

Hirono, Tetsuya; Takeda, Ryuji; Nishikawa, Taichi; Okudaira, Masamichi; Kunugi, Shun; Yoshiko, Akito; Ueda, Saeko; Yoshimura, Akane; Watanabe, Kohei

doi:10.1016/j.archger.2023.105151

Cited by 3 publications

(2 citation statements)

References 52 publications

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“…A recent study investigated the MU firing pattern in pre-sarcopenic senior individuals with low skeletal muscle mass but normal physical functions. The results showed that MUs of the pre-sarcopenic individuals exhibited an abnormal neural input pattern yet otherwise normal hierarchical pattern [ 33 ], which is consistent with the size principle proposed by Hennenman et al [ 34 ]. Hu et al [ 35 ] reported that there was no difference in MU number and mean firing rate of lower extremity muscles among individuals with risk-sarcopenia, healthy elderly, and healthy young participants, but the slope of mean MU firing rate was significantly higher in the risk-sarcopenia group compared to the young group.…”

Section: Introductionsupporting

confidence: 78%

“…A recent study investigated the MU firing pattern in pre-sarcopenic senior individuals with low skeletal muscle mass but normal physical functions. The results showed that MUs of the pre-sarcopenic individuals exhibited an abnormal neural input pattern yet otherwise normal hierarchical pattern [33], which is consistent sensitivity exceeds 75%, indicating moderate classification performance. Interpretable results obtained from the SHAP model suggest that motor unit (MU) activation mode may be a key factor affecting sarcopenia.…”

Section: Introductionmentioning

confidence: 79%

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Exploration of a machine learning approach for diagnosing sarcopenia among Chinese community-dwelling older adults using sEMG-based data

Li,

Ou,

et al. 2024

J NeuroEngineering Rehabil

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Background In the practical application of sarcopenia screening, there is a need for faster, time-saving, and community-friendly detection methods. The primary purpose of this study was to perform sarcopenia screening in community-dwelling older adults and investigate whether surface electromyogram (sEMG) from hand grip could potentially be used to detect sarcopenia using machine learning (ML) methods with reasonable features extracted from sEMG signals. The secondary aim was to provide the interpretability of the obtained ML models using a novel feature importance estimation method. Methods A total of 158 community-dwelling older residents (≥ 60 years old) were recruited. After screening through the diagnostic criteria of the Asian Working Group for Sarcopenia in 2019 (AWGS 2019) and data quality check, participants were assigned to the healthy group (n = 45) and the sarcopenic group (n = 48). sEMG signals from six forearm muscles were recorded during the hand grip task at 20% maximal voluntary contraction (MVC) and 50% MVC. After filtering recorded signals, nine representative features were extracted, including six time-domain features plus three time-frequency domain features. Then, a voting classifier ensembled by a support vector machine (SVM), a random forest (RF), and a gradient boosting machine (GBM) was implemented to classify healthy versus sarcopenic participants. Finally, the SHapley Additive exPlanations (SHAP) method was utilized to investigate feature importance during classification. Results Seven out of the nine features exhibited statistically significant differences between healthy and sarcopenic participants in both 20% and 50% MVC tests. Using these features, the voting classifier achieved 80% sensitivity and 73% accuracy through a five-fold cross-validation. Such performance was better than each of the SVM, RF, and GBM models alone. Lastly, SHAP results revealed that the wavelength (WL) and the kurtosis of continuous wavelet transform coefficients (CWT_kurtosis) had the highest feature impact scores. Conclusion This study proposed a method for community-based sarcopenia screening using sEMG signals of forearm muscles. Using a voting classifier with nine representative features, the accuracy exceeds 70% and the sensitivity exceeds 75%, indicating moderate classification performance. Interpretable results obtained from the SHAP model suggest that motor unit (MU) activation mode may be a key factor affecting sarcopenia.

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Section: Introductionsupporting

confidence: 78%

“…A recent study investigated the MU firing pattern in pre-sarcopenic senior individuals with low skeletal muscle mass but normal physical functions. The results showed that MUs of the pre-sarcopenic individuals exhibited an abnormal neural input pattern yet otherwise normal hierarchical pattern [33], which is consistent sensitivity exceeds 75%, indicating moderate classification performance. Interpretable results obtained from the SHAP model suggest that motor unit (MU) activation mode may be a key factor affecting sarcopenia.…”

Section: Introductionmentioning

confidence: 79%

Exploration of a machine learning approach for diagnosing sarcopenia among Chinese community-dwelling older adults using sEMG-based data

Li,

Ou,

et al. 2024

J NeuroEngineering Rehabil

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Prediction of 1-year change in knee extension strength by neuromuscular properties in older adults

Hirono,

Takeda,

Nishikawa

et al. 2023

GeroScience

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Multi-channel EMG manifestations of upper-extremity muscle coordination imbalance among community-dwelling sarcopenic seniors

He,

Wu,

et al. 2024

BioMed Eng OnLine

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Background Sarcopenia is an age-related, insidious, crippling but curable degenerative disease if diagnosed and treated early. However, no accessible and accurate early screening method is available for community settings that does not require specialized personnel. One of the hallmarks of sarcopenia is the pathological changes of muscle fiber type composition and motor unit firing patterns. Surface electromyography (sEMG) may serve as an effective tool for detecting differences between healthy and sarcopenic individuals due to its superior wearability and accessibility compared to other screening methods such as medical imaging and bioimpedance measurements, making it ideal for community-based sarcopenic screening. Our study aims to explore sEMG biomarkers that can be used for screening or diagnosis of sarcopenia. Results We collected multi-channel sEMG signals from six forearm muscles of 98 healthy and 55 sarcopenic community-dwelling older adults. Participants performed grasp tasks at 20% and 50% of maximum voluntary contraction (MVC). Hexagons created by various EMG features, normalized with respect to respective MVC, and symmetry analyses were performed to estimate multi-muscle coordination patterns. An innovative index, namely incenter–circumcenter distance of muscle coordination (ICDMC), is proposed to discriminate between the healthy and sarcopenic groups. We utilized non-parametric tests to compare the ICDMC between the two groups, considering a p -value less than 0.05 statistically significant. The results showed that at 20% MVC, ICDMCs from root mean square (RMS), mean absolute value (MAV), slope sign changes (SSC) and wavelength (WL) showed statistically significant differences. More insights of this sEMG manifestation of sarcopenia were revealed by gender- and age-stratifications analyses. Conclusions Our results demonstrated that there are clear sEMG manifestations of altered muscle coordination in sarcopenic patients. More consistent force generation patterns were observed in the sarcopenic group, especially at lower contraction intensities. The novel ICDMC can quantify differences between sarcopenic and healthy muscle. These results warrant further research to further develop more accessible sarcopenia screening strategies in community settings based on electrophysiological measurements such as sEMG.

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Motor unit firing patterns in older adults with low skeletal muscle mass

Cited by 3 publications

References 52 publications

Exploration of a machine learning approach for diagnosing sarcopenia among Chinese community-dwelling older adults using sEMG-based data

Exploration of a machine learning approach for diagnosing sarcopenia among Chinese community-dwelling older adults using sEMG-based data

Prediction of 1-year change in knee extension strength by neuromuscular properties in older adults

Multi-channel EMG manifestations of upper-extremity muscle coordination imbalance among community-dwelling sarcopenic seniors

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