Identifying abstinent heroin addicts on the basis of single channel's ERP and behavioral data in the gambling task - Liang, Xiaozhe; Hao, Yanrong; Xu, Zhihao; Li, Na; Zhao, Qinglin (2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2020)
In the attentional bias and cognitive processing relating to the abstinent heroin addicts (AHAs), there were considerable studies about event related potentials (ERP) and behavioral data. However, the large amount of data lead to longer data processing time, and few studies were done on single channel data about AHA. This study investigated whether single channel's data can be used to identify AHAs from healthy controls (HCs) accurately. Two groups of age-, education-, and gender-matched adults (22 AHAs, 21 HCs) performed on the gambling task. ERP features and behavior features were used to classify. For discriminating AHAs and HCs, ReliefF and SVM-RFE were applied for feature selection, and Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) were used to search the optimal classification model of Support Vector Machine (SVM). We analyzed the statistical significance of all the features and obtained the classification result of different stimulation conditions. In statistics, we found that AHAs were significantly different from HCs in the amplitude of P300, ERP's mean value and ERP's variance under the monetary stimulation. For large money stimulation, P300 power in delta band and N100 power in delta band had significant difference between AHAs and HCs. Combining feature sorting algorithms and optimization algorithms, the results indicated that optimal performance was achieved by using ReliefF and GA. Use the above method, the best accuracy is 86.04% in four kind (+99, +9, -9, -99) of stimulation. This is the first study that used single channel's ERP data to identify AHAs with HCs, our study provided a new insight and objective method for the rapid diagnosis of AHAs.