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

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 wit...

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Autores principales:	Xuelin Gu, Banghua Yang, Shouwei Gao, Lin Feng Yan, Ding Xu, Wen Wang
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	drug addiction bi-modal signal eeg-nirs machine learning 3-class accuracy degree of drug addiction Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/2eb57a73213645b68d43d1ade1ed5f54
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spelling	oai:doaj.org-article:2eb57a73213645b68d43d1ade1ed5f542021-11-23T01:19:52ZApplication of bi-modal signal in the classification and recognition of drug addiction degree based on machine learning10.3934/mbe.20213441551-0018https://doaj.org/article/2eb57a73213645b68d43d1ade1ed5f542021-08-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021344?viewType=HTMLhttps://doaj.org/toc/1551-0018Most 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.Xuelin GuBanghua YangShouwei GaoLin Feng YanDing XuWen WangAIMS Pressarticledrug addictionbi-modal signaleeg-nirsmachine learning3-class accuracydegree of drug addictionBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 5, Pp 6926-6940 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	drug addiction bi-modal signal eeg-nirs machine learning 3-class accuracy degree of drug addiction Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	drug addiction bi-modal signal eeg-nirs machine learning 3-class accuracy degree of drug addiction Biotechnology TP248.13-248.65 Mathematics QA1-939 Xuelin Gu Banghua Yang Shouwei Gao Lin Feng Yan Ding Xu Wen Wang Application of bi-modal signal in the classification and recognition of drug addiction degree based on machine learning
description	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.
format	article
author	Xuelin Gu Banghua Yang Shouwei Gao Lin Feng Yan Ding Xu Wen Wang
author_facet	Xuelin Gu Banghua Yang Shouwei Gao Lin Feng Yan Ding Xu Wen Wang
author_sort	Xuelin Gu
title	Application of bi-modal signal in the classification and recognition of drug addiction degree based on machine learning
title_short	Application of bi-modal signal in the classification and recognition of drug addiction degree based on machine learning
title_full	Application of bi-modal signal in the classification and recognition of drug addiction degree based on machine learning
title_fullStr	Application of bi-modal signal in the classification and recognition of drug addiction degree based on machine learning
title_full_unstemmed	Application of bi-modal signal in the classification and recognition of drug addiction degree based on machine learning
title_sort	application of bi-modal signal in the classification and recognition of drug addiction degree based on machine learning
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/2eb57a73213645b68d43d1ade1ed5f54
work_keys_str_mv	AT xuelingu applicationofbimodalsignalintheclassificationandrecognitionofdrugaddictiondegreebasedonmachinelearning AT banghuayang applicationofbimodalsignalintheclassificationandrecognitionofdrugaddictiondegreebasedonmachinelearning AT shouweigao applicationofbimodalsignalintheclassificationandrecognitionofdrugaddictiondegreebasedonmachinelearning AT linfengyan applicationofbimodalsignalintheclassificationandrecognitionofdrugaddictiondegreebasedonmachinelearning AT dingxu applicationofbimodalsignalintheclassificationandrecognitionofdrugaddictiondegreebasedonmachinelearning AT wenwang applicationofbimodalsignalintheclassificationandrecognitionofdrugaddictiondegreebasedonmachinelearning
_version_	1718417366642065408

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

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