使用时空深度学习分类器中的步态信号对帕金森病严重程度的分类
Brenda G Muñoz-Mata1, Guadalupe Dorantes-Méndez2, Omar Piña-Ramírez3
1Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, Av. Parque Chapultepec 1570, San Luis Potosí, 78295, San Luis Potosí, México.
Medical & biological engineering & computing
|June 17, 2024
概括
这项研究开发了一种新的算法,使用垂直地面反应力 (VGRF) 信号来分类帕金森病 (PD) 严重程度. 人工智能模型准确地区分了步行模式,有助于更精确的PD诊断和管理.
科学领域:
- 神经学 神经学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 帕金森病 (PD) 是一种神经退行性疾病,其特点是运动障碍,包括改变的步态模式.
- 由于依赖临床专业知识和与正常衰老或其他疾病相似的步态相似,准确的PD严重程度评级具有挑战性.
- 现有的PD诊断分类系统需要加强,特别是在早期阶段.
研究的目的:
- 开发和验证帕金森病步态严重程度分类算法.
- 为了利用垂直地面反应力 (VGRF) 信号进行客观的步态分析.
- 提高PD严重程度评估的准确性和可靠性.
主要方法:
- 修改后的卷积长深神经网络 (CLDNN) 架构被用于从VGRF数据中建模步态相信号.
- 使用来自公共数据库的数据,包括93名PD患者和72名健康对照.
- 应用了十倍交叉验证方法来评估分类器的性能.
主要成果:
- 开发的算法实现了高权重准确率:霍恩-雅尔尺度为99.296~0.128%,UPDRS尺度为99.343~0.182%.
- 与文献中之前的研究相比,分类器表现出了优越的性能.
- 该模型有效地区分了PD严重程度的不同级别的步态模式.
结论:
- 拟议的算法提供了一种有效和客观的方法,用于使用VGRF信号对PD步行严重程度进行分类.
- 这种方法有可能提高PD诊断和监测的准确性.
- 基于CLDNN的模型在运动障碍评估中显示出临床应用的前景.
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