The National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, China.
Medical Equipment Innovation Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, China.
J Neuroeng Rehabil. 2024 May 9;21(1):69. doi: 10.1186/s12984-024-01369-y.
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.
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.
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.
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.
在肌少症筛查的实际应用中，需要更快、更省时且便于在社区开展的检测方法。本研究的主要目的是使用机器学习（ML）方法，从表面肌电（sEMG）信号中提取合理的特征，对社区居住的老年人进行肌少症筛查，并探讨 hand grip 中的 sEMG 是否可以用于检测肌少症。次要目的是使用新的特征重要性估计方法为获得的 ML 模型提供可解释性。
共招募了 158 名社区居住的老年人（≥60 岁）。根据 2019 年亚洲肌少症工作组（AWGS 2019）的诊断标准和数据质量检查进行筛选后，将参与者分为健康组（n=45）和肌少症组（n=48）。在 20%最大自主收缩（MVC）和 50% MVC 下，记录 hand grip 任务中 6 块前臂肌肉的 sEMG 信号。对记录的信号进行滤波后，提取了 9 个有代表性的特征，包括 6 个时域特征和 3 个时频域特征。然后，实现了一个由支持向量机（SVM）、随机森林（RF）和梯度提升机（GBM）组成的投票分类器，用于对健康组与肌少症组进行分类。最后，使用 SHapley Additive exPlanations（SHAP）方法来研究分类过程中的特征重要性。
在 20%和 50% MVC 测试中，健康组和肌少症组参与者的 9 个特征中有 7 个具有统计学差异。通过 5 折交叉验证，使用这些特征的投票分类器实现了 80%的灵敏度和 73%的准确率。这种性能优于单独的 SVM、RF 和 GBM 模型。最后，SHAP 结果表明，波长（WL）和连续小波变换系数的峰度（CWT_kurtosis）具有最高的特征影响评分。
本研究提出了一种使用前臂肌肉表面肌电信号进行基于社区的肌少症筛查的方法。使用具有 9 个有代表性特征的投票分类器，准确率超过 70%，灵敏度超过 75%，表明分类性能中等。从 SHAP 模型中获得的可解释结果表明，运动单位（MU）激活模式可能是影响肌少症的关键因素。
