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Application of bi-modal signal in the classification and recognition of drug addiction degree based on machine learning

Authors :
Xuelin Gu
Banghua Yang
Shouwei Gao
Lin Feng Yan
Ding Xu
Wen Wang
Source :
Mathematical Biosciences and Engineering, Vol 18, Iss 5, Pp 6926-6940 (2021)
Publication Year :
2021
Publisher :
AIMS Press, 2021.

Abstract

Most studies on drug addiction degree are made based on statistical scales, addicts' account, and subjective judgement of rehabilitation doctors. No objective, quantified evaluation has been made. This paper uses devises the synchronous bimodal signal collection and experimentation paradigm with electroencephalogram (EEG) and forehead high-density near-infrared spectroscopy (NIRS) device. The drug addicts are classified into mild, moderate and severe groups with reference to the suggestions of researchers and medical experts. Data of 45 drug addicts (mild: 15; moderate: 15; and severe: 15) is collected, and then used to design an addiction degree testing algorithm based on decision fusion. The algorithm is used to classify mild, moderate and severe addiction. This paper pioneers to use two types of Convolutional Neural Network (CNN) to abstract the EEG and NIR data of drug addicts, and introduces batch normalization to CNN, thus accelerating training process, reducing parameter sensitivity, and enhancing system robustness. The characteristics output by two CNNs are transformed into dimensions. Two new characteristics are assigned with a weight of 50% each. The data is used for decision fusion. In the networks, 27 subjects are used as training sets, 9 as validation sets, and 9 as testing sets. The 3-class accuracy remains to be 63.15%, preliminarily justifying this method as an effective approach to measure drug addiction degree. And the method is ready to use, objective, and offers results in real time.

Details

Language :
English
ISSN :
15510018
Volume :
18
Issue :
5
Database :
Directory of Open Access Journals
Journal :
Mathematical Biosciences and Engineering
Publication Type :
Academic Journal
Accession number :
edsdoj.2eb57a73213645b68d43d1ade1ed5f54
Document Type :
article
Full Text :
https://doi.org/10.3934/mbe.2021344?viewType=HTML