根据变压器长短记忆模型,识别中风患者膝盖过度延伸的特征
Aoli Shen1, Xinpeng Chen2, Qing Xia3
1Fujian University of Technology, Fuzhou, 350000, China.
Biomedical engineering online
|January 24, 2026
概括
一种新的变压器长短记忆 (变压器-LSTM) 模型使用表面电肌图 (sEMG) 数据准确地检测中风患者的膝盖过延伸. 这种简化方法提供了一个具有成本效益的临床查解决方案.
科学领域:
- 生物医学工程 生物医学工程
- 康复技术 康复技术 康复技术
- 步态分析 步态分析
背景情况:
- 在中风患者中,膝盖过延的临床识别具有挑战性和资源密集.
- 现有的方法需要繁而昂贵的程序.
- 表面电肌图 (sEMG) 提供了一个潜在的非侵入性数据源.
研究的目的:
- 开发一种简化的算法模型,用于在中风幸存者中识别膝盖过延伸.
- 为了减少与当前选方法相关的成本和复杂性.
- 利用先进的机器学习来提高诊断能力.
主要方法:
- 利用一个包括50名中风患者的开源步行数据库.
- 从特定的肌肉对 (BF/RF,TA/GAS) 导出输入共收缩指数 (CCI) 值,将其导入变压器长短记忆 (变压器-LSTM) 模型中.
- 使用精度,F1分数,接收器运行特征 (ROC) 曲线和损失函数趋同来评估模型性能.
主要成果:
- 变压器-LSTM模型实现了83.38%的高识别精度 (F1得分:0.8335).
- 这种表现明显优于其他机器学习模型,包括线性回归,SVM,CNN,GRU,LSTM和变压器.
- 该模型显示了快速损失函数的收和0.99.9的ROC曲线下的面积.
结论:
- 变压器-LSTM模型为查中风患者膝盖过延伸提供了强大而准确的解决方案.
- 通过实现更高的识别率,它具有实际临床应用的巨大潜力.
- 这种方法简化了诊断过程,并降低了相关成本.
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