Gill Aseem Partap Singh, Zariffa Jose
Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada.
Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada.
PLoS One. 2024 Mar 12;19(3):e0299271. doi: 10.1371/journal.pone.0299271. eCollection 2024.
Neurostimulation and neural recording are crucial to develop neuroprostheses that can restore function to individuals living with disabilities. While neurostimulation has been successfully translated into clinical use for several applications, it remains challenging to robustly collect and interpret neural recordings, especially for chronic applications. Nerve cuff electrodes offer a viable option for recording nerve signals, with long-term implantation success. However, nerve cuff electrodes' signals have low signal-to-noise ratios, resulting in reduced selectivity between neural pathways. The objective of this study was to determine whether deep learning techniques, specifically networks tailored for time series applications, can increase the recording selectivity achievable using multi-contact nerve cuff electrodes. We compared several neural network architectures, the impact and trade-off of window length on classification performance, and the benefit of data augmentation. Evaluation was carried out using a previously collected dataset of 56-channel nerve cuff recordings from the sciatic nerve of Long-Evans rats, which included afferent signals evoked using three types of mechanical stimuli. Through this study, the best model achieved an accuracy of 0.936 ± 0.084 and an F1-score of 0.917 ± 0.103, using 50 ms windows of data and an augmented training set. These results demonstrate the effectiveness of applying CNNs designed for time-series data to peripheral nerve recordings, and provide insights into the relationship between window duration and classification performance in this application.
神经刺激和神经记录对于开发能够为残疾人士恢复功能的神经假体至关重要。虽然神经刺激已成功转化为多种临床应用，但可靠地收集和解释神经记录仍然具有挑战性，特别是对于长期应用而言。神经袖套电极提供了一种记录神经信号的可行选择，并在长期植入方面取得了成功。然而，神经袖套电极的信号信噪比很低，导致神经通路之间的选择性降低。本研究的目的是确定深度学习技术，特别是为时间序列应用量身定制的网络，是否可以提高使用多触点神经袖套电极可实现的记录选择性。我们比较了几种神经网络架构、窗口长度对分类性能的影响和权衡，以及数据增强的益处。使用先前收集的来自Long-Evans大鼠坐骨神经的56通道神经袖套记录数据集进行评估，该数据集包括使用三种类型的机械刺激诱发的传入信号。通过这项研究，最佳模型使用50毫秒的数据窗口和增强训练集，实现了0.936±0.084的准确率和0.917±0.103的F1分数。这些结果证明了将为时间序列数据设计的卷积神经网络应用于外周神经记录的有效性，并提供了对该应用中窗口持续时间与分类性能之间关系的见解。
