Institute of Biomedical Engineering, University of Toronto, 164 College St Room 407, Toronto, ON, M5S 3G9, Canada.
Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Rd, Toronto, ON, M5S 3G9, Canada.
Sci Rep. 2021 May 21;11(1):10682. doi: 10.1038/s41598-021-89624-3.
Electrical signals from the peripheral nervous system have the potential to provide the necessary motor, sensory or autonomic information for implementing closed-loop control of neuroprosthetic or neuromodulatory systems. However, developing methods to recover information encoded in these signals is a significant challenge. Our goal was to test the feasibility of measuring physiologically generated nerve action potentials that can be classified as sensory or motor signals. A tetrapolar recording nerve cuff electrode was used to measure vagal nerve (VN) activity in a rodent model of upper airway obstruction. The effect of upper airway occlusions on VN activity related to respiration (RnP) was calculated and compared for 4 different cases: (1) intact VN, (2) VN transection only proximal to recording electrode, (3) VN transection only distal to the recording electrode, and (4) transection of VN proximal and distal to electrode. We employed a Support Vector Machine (SVM) model with Gaussian Kernel to learn a model capable of classifying efferent and afferent waveforms obtained from the tetrapolar electrode. In vivo results showed that the RnP values decreased significantly during obstruction by 91.7% ± 3.1%, and 78.2% ± 3.4% for cases of intact VN or proximal transection, respectively. In contrast, there were no significant changes for cases of VN transection at the distal end or both ends of the electrode. The SVM model yielded an 85.8% accuracy in distinguishing motor and sensory signals. The feasibility of measuring low-noise directionally-sensitive neural activity using a tetrapolar nerve cuff electrode along with the use of an SVM classifier was shown. Future experimental work in chronic implant studies is needed to support clinical translatability.
外周神经系统的电信号有可能提供实现神经假体或神经调节系统闭环控制所需的运动、感觉或自主信息。然而，开发从这些信号中恢复信息的方法是一个重大挑战。我们的目标是测试测量生理产生的神经动作电位的可行性，这些电位可以被分类为感觉或运动信号。四极记录神经袖带电极用于测量上气道阻塞啮齿动物模型中的迷走神经 (VN) 活动。计算并比较了上气道阻塞对上气道阻塞与呼吸相关的 VN 活动 (RnP) 的影响，对于 4 种不同情况：(1) 完整的 VN，(2) 仅在记录电极近端的 VN 横切，(3) 仅在记录电极远端的 VN 横切，以及 (4) 电极近端和远端的 VN 横切。我们采用了带有高斯核的支持向量机 (SVM) 模型来学习能够对从四极电极获得的传出和传入波形进行分类的模型。体内结果表明，在阻塞期间，RnP 值分别显著下降了 91.7%±3.1%和 78.2%±3.4%，对于完整 VN 或近端横切的情况。相比之下，在 VN 横切在电极的远端或两端的情况下，没有发生显著变化。SVM 模型在区分运动和感觉信号方面的准确率为 85.8%。使用四极神经袖带电极测量低噪声定向敏感神经活动的可行性以及使用 SVM 分类器的可行性已得到证明。需要进行慢性植入研究的进一步实验工作来支持临床转化。
