Anastasiev Alexey, Kadone Hideki, Marushima Aiki, Watanabe Hiroki, Zaboronok Alexander, Watanabe Shinya, Matsumura Akira, Suzuki Kenji, Matsumaru Yuji, Nishiyama Hiroyuki, Ishikawa Eiichi
Department of Neurosurgery, University of Tsukuba Hospital, University of Tsukuba, 2-1-1 Amakubo, Tsukuba 305-8576, Ibaraki, Japan.
Center for Cybernics Research (CCR), Institute of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8575, Ibaraki, Japan.
Sensors (Basel). 2025 Jun 11;25(12):3664. doi: 10.3390/s25123664.
We introduce a hybrid deep learning model for recognizing hand gestures from electromyography (EMG) signals in subacute stroke patients: the one-dimensional convolutional long short-term memory neural network (CNN-LSTM). The proposed network was trained, tested, and cross-validated on seven hand gesture movements, collected via EMG from 25 patients exhibiting clinical features of paresis. EMG data from these patients were collected twice post-stroke, at least one week apart, and divided into datasets A and B to assess performance over time while balancing subject-specific content and minimizing training bias. Dataset A had a median post-stroke time of 16.0 ± 8.6 days, while dataset B had a median of 19.2 ± 13.7 days. In classification tests based on the number of gesture classes (ranging from two to seven), the hybrid model achieved accuracies ranging from 85.66% to 82.27% in dataset A and from 88.36% to 81.69% in dataset B. To address the limitations of deep learning with small datasets, we developed a novel bilateral data fusion approach that incorporates EMG signals from the non-paretic limb during training. This approach significantly enhanced model performance across both datasets, as evidenced by improvements in sensitivity, specificity, accuracy, and F1-score metrics. The most substantial gains were observed in the three-gesture subset, where classification accuracy increased from 73.01% to 78.42% in dataset A, and from 77.95% to 85.69% in dataset B. In conclusion, although these results may be slightly lower than those of traditional supervised learning algorithms, the combination of bilateral data fusion and the absence of feature engineering offers a novel perspective for neurorehabilitation, where every data segment is critically significant.
我们介绍了一种用于识别亚急性中风患者肌电图（EMG）信号中手势的混合深度学习模型：一维卷积长短期记忆神经网络（CNN-LSTM）。该网络在七种手势动作上进行了训练、测试和交叉验证，这些手势动作是通过肌电图从25名表现出轻瘫临床特征的患者中收集的。这些患者的肌电图数据在中风后收集了两次，间隔至少一周，并分为数据集A和B，以评估随时间的性能，同时平衡个体特异性内容并最小化训练偏差。数据集A的中风后中位时间为16.0±8.6天，而数据集B的中位时间为19.2±13.7天。在基于手势类别数量（从两个到七个）的分类测试中，混合模型在数据集A中的准确率范围为85.66%至82.27%，在数据集B中的准确率范围为88.36%至81.69%。为了解决小数据集深度学习的局限性，我们开发了一种新颖的双边数据融合方法，该方法在训练期间纳入了非瘫痪肢体的肌电图信号。这种方法显著提高了两个数据集的模型性能，敏感性、特异性、准确率和F1分数指标的提高证明了这一点。在三手势子集中观察到了最大的提升，其中数据集A的分类准确率从73.01%提高到78.42%，数据集B的分类准确率从77.95%提高到85.69%。总之，尽管这些结果可能略低于传统监督学习算法的结果，但双边数据融合与无特征工程的结合为神经康复提供了一个新的视角，在神经康复中每个数据段都至关重要。
