使用深度学习的多通道神经带记录的时间序列分类.
Aseem Partap Singh Gill1,2, Jose Zariffa1,3,4,5
1Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada.
PloS one
|March 12, 2024
概括
深度学习增强了神经带电极的神经记录选择性,改善了残疾人的神经假体. 卷积神经网络 (CNN) 在分类神经信号方面取得了高精度,为更好的功能恢复铺平了道路.
科学领域:
- 生物医学工程 生物医学工程
- 神经科学是一个神经科学.
- 机器学习 机器学习
背景情况:
- 神经假肢依赖于有效的神经记录,但慢性应用面临信号质量和解释方面的挑战.
- 神经袖子电极提供长期植入,但信号噪声比较低,限制神经通路的选择性.
研究的目的:
- 研究深度学习的有效性,特别是时间序列定制的神经网络,在增强神经记录选择性时使用多接触神经袖口电极.
- 为了比较不同的神经网络架构,评估窗口长度的影响,并评估数据增强的好处,以改善信号分类.
主要方法:
- 利用了来自老鼠坐骨神经的56通道神经带记录的数据集,其中包括来自机械刺激的唤起的 afferent信号.
- 对比了针对时间序列数据设计的各种卷积神经网络 (CNN) 架构.
- 分析了分类性能和数据窗口长度之间的权衡,并评估了数据增强的影响.
主要成果:
- 性能最好的深度学习模型实现了0.936 ± 0.084的分类精度和0.917 ± 0.103.3的F1得分.
- 使用50毫秒的数据窗口和增强训练数据集观察到最佳性能.
- 对外围神经记录的选择性显著改善.
结论:
- 深度学习技术,特别是时间序列数据的CNN,有效地提高了神经带电极对外围神经记录的选择性.
- 这项研究为优化窗口持续时间和利用数据增强用于神经假肢应用中增强神经信号分类提供了有价值的见解.
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