Convolutional neural network for classification of nerve activity based on action potential induced neurochemical signatures

Roever, Paul, Mirza, Khalid, Nikolic, Konstantin ORCID: https://orcid.org/0000-0002-6551-2977 and Toumazou, Chris (2020) Convolutional neural network for classification of nerve activity based on action potential induced neurochemical signatures. In: 2020 IEEE International Symposium on Circuits and Systems (ISCAS), 12-16 Oct 2020, Seville, Spain.

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Abstract

Neural activity results in chemical changes in the extracellular environment such as variation in pH or potassium/sodium ion concentration. Higher signal to noise ratio make neurochemical signals an interesting biomarker for closed-loop neuromodulation systems. For such applications, it is important to reliably classify pH signatures to control stimulation timing and possibly dosage. For example, the activity of the subdiaphragmatic vagus nerve (sVN) branch can be monitored by measuring extracellular neural pH. More importantly, gut hormone cholecystokinin (CCK)-specific activity on the sVN can be used for controllably activating sVN, in order to mimic the
gut-brain neural response to food intake. In this paper, we present a convolutional neural network (CNN) based classification system to identify CCK-specific neurochemical changes on the sVN,from non-linear background activity. Here we present a novel feature engineering approach which enables, after training, a high accuracy classification of neurochemical signals using CNN.

Item Type: Conference or Workshop Item (Lecture)
ISSN: 2158-1525
ISBN: 9781728133218
Identifier: 10.1109/ISCAS45731.2020.9180734
Page Range: pp. 1-5
Uncontrolled Keywords: Machine Learning, Artificial Neural Networks
Subjects: Computing > Intelligent systems
Computing
Depositing User: Konstantin Nikolic
Date Deposited: 01 Oct 2020 11:40
Last Modified: 06 Oct 2020 12:02
URI: http://repository.uwl.ac.uk/id/eprint/7353

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