Year: 2020
Type: dissertation
Abstract: Differential measurement of efferent and afferent peripheral nerve activity offers a promising means of improving the clinical utility of implantable neuroprostheses. The tripolar nerve cuff electrode has historically served as the gold standard for achieving good signal-to-noise (SNR) recordings and suitable for chronic implant. However, the symmetrical geometry of this electrode array – electrically-shorted side contacts – precludes it from measuring electrical signals that can be used to obtain directional information (e.g., differentiate efferent and afferent neural activity). To this purpose, the focal point of this study will be to integrate a minimum number of electrode contacts nerve cuff electrode (e.g., tetrapolar) and a robust machine learning classifier to differentiate efferent and afferent physiological neural activity changes. This thesis presents the design of a tetrapolar cuff electrode that can achieve sufficient SNR neural recordings, comparable to that of conventional tripolar cuff electrodes. The rational of this design was based on the use of a bipolar nerve cuff electrode for differentially recording efferent/afferent neural activity. However, we have replaced each contact with a (lower noise) tripolar recording configurations. In this manner, we are obtaining two low-noise signals that are subtracted to differentiate efferent and afferent neural activity. Based on the obtained tetrapolar measures, a robust classifier was achieved that can differentiate afferent and efferent data. This bi-directionally feature is particularly noteworthy, because the design constraints of conventional state-of-the-art nerve cuff electrodes (i.e., tripolar configuration) made it impossible to achieve bi-directionally nerve recording. Furthermore, the recording of neural activity related to specific physiological parameters will be extremely useful for translating this technology to clinical disorders such as VNS for epilepsy and resistant hypertension treatment, investigating respiratory disturbance related to neural recording (e.g., obstructive sleep apnea).
Source: TSpace (University of Toronto)
Author: Parisa Sabetian
Language: English
Subfield: Cellular and Molecular Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W3095708577