School of Mechanical and Electrical Engineering and Automation, Shanghai University, Shanghai 200444, China.
Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, China.
Math Biosci Eng. 2021 Aug 20;18(5):6926-6940. doi: 10.3934/mbe.2021344.
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.
大多数关于成瘾程度的研究都是基于统计量表、成瘾者的描述和康复医生的主观判断进行的。没有进行客观、量化的评估。本文使用基于脑电图（EEG）和前额高密度近红外光谱（NIRS）设备的同步双模态信号采集和实验范式。参考研究人员和医学专家的建议，将吸毒者分为轻度、中度和重度三组。收集了 45 名吸毒者的数据（轻度：15 名；中度：15 名；重度：15 名），然后设计了一种基于决策融合的成瘾程度测试算法。该算法用于对轻度、中度和重度成瘾进行分类。本文开创性地使用两种卷积神经网络（CNN）来抽象吸毒者的 EEG 和 NIR 数据，并将批量归一化引入 CNN 中，从而加速了训练过程，减少了参数敏感性，并增强了系统的鲁棒性。两个 CNN 输出的特征被转化为维度。两个新特征的权重分别为 50%。数据用于决策融合。在网络中，27 个样本作为训练集，9 个作为验证集，9 个作为测试集。3 类准确率仍为 63.15%，初步证明该方法是衡量成瘾程度的有效方法。并且该方法具有可用性、客观性，并能实时提供结果。
