Journal of rehabilitation medicine · 2025
Automated assessment of upper limb spasticity in stroke patients with fusion of multichannel surface electromyography features.
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Li Xin, Nong Feiyu, Zhang Xu, Chen Lin, Li Tian, Shi Shengliang, Long Yaobin
Аннотация
OBJECTIVE: The objective of this study was to investigate a more accurate and efficient technique for assessing spasticity in stroke patients via surface electromyography (sEMG).
METHODS: 45 hemiplegic individuals were recruited and spasticity was assessed via the modified Ashworth scale (MAS). Multichannel sEMG data were collected from 3 muscles: the long head of the biceps brachii (LB), the short head of the biceps brachii (SB), and the brachioradialis (BR). Both time-domain and frequency-domain features were extracted. A K-nearest neighbour (k-NN) classifier was used to develop a new feature vector consisting of multichannel sEMG features. Finally, a model using this new feature was constructed and evaluated for classification accuracy.
RESULTS: Data from 40 patients were analysed, revealing significant correlations between MAS scores and sEMG features. Specifically, MAS exhibited strong positive correlations with 3 time-domain features: root mean square (RMS), integral sEMG (iEMG), and envelope area (EA) (r > 0.7). In contrast, frequency-domain features were negatively correlated with the MAS score (r < -0.7). A single-channel model and a single-feature model were developed as baselines. A k-NN classifier using a novel feature vector - -integrating single-channel and single-feature data - enabled automatic spasticity grading, surpassing the performance of the baseline models. The proposed multichannel sEMG feature fusion model achieved an average accuracy of 78.7%, significantly outperforming both the single-channel model (LB: 66.0%, SB: 64.3%, BR: 70.4%) and the single-feature model (RMS 70.8%, iEMG 71.4%, and EA 63.4%).
CONCLUSIONS: Compared with single-channel and single-feature models, the k-NN model, which uses multichannel sEMG features, has superior accuracy in spasticity assessments and is a reliable tool for objective evaluation. This approach holds promise for enhancing rehabilitation strategies by enabling precise and data-driven efficacy assessments, ultimately improving patient outcomes.
METHODS: 45 hemiplegic individuals were recruited and spasticity was assessed via the modified Ashworth scale (MAS). Multichannel sEMG data were collected from 3 muscles: the long head of the biceps brachii (LB), the short head of the biceps brachii (SB), and the brachioradialis (BR). Both time-domain and frequency-domain features were extracted. A K-nearest neighbour (k-NN) classifier was used to develop a new feature vector consisting of multichannel sEMG features. Finally, a model using this new feature was constructed and evaluated for classification accuracy.
RESULTS: Data from 40 patients were analysed, revealing significant correlations between MAS scores and sEMG features. Specifically, MAS exhibited strong positive correlations with 3 time-domain features: root mean square (RMS), integral sEMG (iEMG), and envelope area (EA) (r > 0.7). In contrast, frequency-domain features were negatively correlated with the MAS score (r < -0.7). A single-channel model and a single-feature model were developed as baselines. A k-NN classifier using a novel feature vector - -integrating single-channel and single-feature data - enabled automatic spasticity grading, surpassing the performance of the baseline models. The proposed multichannel sEMG feature fusion model achieved an average accuracy of 78.7%, significantly outperforming both the single-channel model (LB: 66.0%, SB: 64.3%, BR: 70.4%) and the single-feature model (RMS 70.8%, iEMG 71.4%, and EA 63.4%).
CONCLUSIONS: Compared with single-channel and single-feature models, the k-NN model, which uses multichannel sEMG features, has superior accuracy in spasticity assessments and is a reliable tool for objective evaluation. This approach holds promise for enhancing rehabilitation strategies by enabling precise and data-driven efficacy assessments, ultimately improving patient outcomes.