A Hybrid Classification to Detect Abstinent Heroin-Addicted Individuals Using EEG Microstates

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.

Automated summary

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.