Journal
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
Volume 9, Issue 3, Pages 700-709Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSS.2021.3135425
Keywords
Electroencephalography; Feature extraction; Drugs; Electrodes; Topology; Time series analysis; Support vector machines; Binary classification; electroencephalography (EEG); heroin addiction; microstate
Funding
- National Key Research and Development Program of China [2019YFA0706200]
- National Natural Science Foundation of China [61632014, 61627808]
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In this study, a hybrid classifier based on EEG signals was proposed to objectively and effectively identify heroin-addicted individuals and healthy controls. The results showed that the classifier achieved high accuracy in distinguishing the two groups, and microstate features were identified as potential biomarkers for identifying heroin-addicted individuals.
Objective: Diagnosis of the severity of heroin addiction with electroencephalography (EEG) signals is a challenging problem. It has been shown that brain microstates are associated with brain status and healthy condition. However, there is no study on how heroin addiction affects brain microstates. Approach: We propose a hybrid classifier based on the microstate features, extracting from resting state EEGs, to objectively and effectively identify abstinent heroin-addicted individuals (AHAIs) and healthy controls (HCs). In addition to the commonly used features such as duration, occurrence, and transition, we calculated three new features. Main Results: The results showed that the support vector machine (SVM), which allows classification of the AHAIs and HCs with a 73% accuracy rate, was an optimal classifier. Moreover, the weight setting-based genetic algorithm (GA) further improved the accuracy rate to 81%. The hybrid classification not only provides direct evidence showing the differences in EEG microstate features between AHAIs and HCs, but also offers a method to distinguish the heroin brain states of people addicted to heroin and healthy individuals and demonstrates that microstate features could serve as potential bio-markers for identifying AHAIs. Significance: our methods and the selected features may provide electrophysiological insights for the assessment of the heroin withdrawal treatment effects.
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