Convolutional neural network for classification of nerve activity based on action potential induced neurochemical signatures
File(s)
Accepted version
Author(s)
Roever, Paul
Mirza, Khalid Baig
Nikolic, Konstantin
Toumazou, Christofer
Type
Conference Paper
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.
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.
Date Issued
2020-09-28
Date Acceptance
2020-01-05
Citation
IEEE International Symposium on Circuits & Systems (ISCAS), 2020, pp.1-5
ISSN
0271-4302
Publisher
IEEE
Start Page
1
End Page
5
Journal / Book Title
IEEE International Symposium on Circuits & Systems (ISCAS)
Copyright Statement
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Sponsor
Engineering & Physical Science Research Council (E
Commission of the European Communities
Grant Number
EP/R511547/1
825796
Source
IEEE International Symposium on Circuits and Systems (ISCAS)
Publication Status
Published
Start Date
2020-10-12
Finish Date
2020-10-14
Coverage Spatial
Seville, Spain