一个新的并行路径ConvMixer神经网络用于从步态分析中预测神经退行性疾病
Jihen Fourati1, Mohamed Othmani2, Khawla Ben Salah3
1Unit of Scientific Research, Applied College, Qassim University, Buraydah, Saudi Arabia. j.fourati@qu.edu.sa.
Medical & biological engineering & computing
|March 15, 2025
概括
早期发现神经退行性疾病 (NDD) 是至关重要的. 一种新的ConvMixer神经网络方法准确地从步态分析中分类NDD,改善早期干预和患者的结果.
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:
- 神经退行性疾病 (NDD) 逐渐损害神经功能,治疗选择有限.
- 改变步行模式,特别是足迹接触时间,是NDD的早期指标.
- 目前对NDD的诊断方法缺乏足够的早期检测能力.
研究的目的:
- 开发和验证一种新的方法来使用步态分析对神经退行性疾病进行分类.
- 通过将地面反应力信号与提取的步态特征集成,提高NDD检测的准确性.
- 在步态动力学中建立一个新的NDD分类表现的基准.
主要方法:
- 一个并行路径的ConvMixer神经网络被用于NDD分类.
- 使用了来自Physionet gaitndd数据库的步行数据.
- 为了评估模型的稳定性,实施了一种"一次排除"交叉验证策略.
主要成果:
- 拟议的ConvMixer模型实现了高性能指标:97.77%的准确性,96.37%的精度,96.5%的回忆和96.25%的F1得分.
- 使用地面反应力信号和提取的特征的综合方法显著增强了步态模式分析.
- 该模型在NDD分类中与现有的最先进方法相比,表现优越.
结论:
- 平行路径的ConvMixer神经网络提供了一个有希望的,准确和有效的工具,用于通过步态分析来早期检测神经退行性疾病.
- 整合不同的步态数据 (力信号和特征) 提高了分类性能,为更全面的诊断方法铺平了道路.
- 这种方法具有及时干预和改善神经退行性疾病管理的潜力.
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