Department of Biomedical Engineering, Hanyang University, Seoul 04763, Korea.
Center for Bionics, Korea Institute of Science and Technology, Seoul 02792, Korea.
Sensors (Basel). 2019 Aug 9;19(16):3475. doi: 10.3390/s19163475.
Internet gaming disorder in adolescents and young adults has become an increasing public concern because of its high prevalence rate and potential risk of alteration of brain functions and organizations. Cue exposure therapy is designed for reducing or maintaining craving, a core factor of relapse of addiction, and is extensively employed in addiction treatment. In a previous study, we proposed a machine-learning-based method to detect craving for gaming using multimodal physiological signals including photoplethysmogram, galvanic skin response, and electrooculogram. Our previous study demonstrated that a craving for gaming could be detected with a fairly high accuracy; however, as the feature vectors for the machine-learning-based detection of the craving of a user were selected based on the physiological data of the user that were recorded on the same day, the effectiveness of the reuse of the machine learning model constructed during the previous experiments, without any further calibration sessions, was still questionable. This "high test-retest reliability" characteristic is of importance for the practical use of the craving detection system because the system needs to be repeatedly applied to the treatment processes as a tool to monitor the efficacy of the treatment. We presented short video clips of three addictive games to nine participants, during which various physiological signals were recorded. This experiment was repeated with different video clips on three different days. Initially, we investigated the test-retest reliability of 14 features used in a craving detection system by computing the intraclass correlation coefficient. Then, we classified whether each participant experienced a craving for gaming in the third experiment using various classifiers-the support vector machine, k-nearest neighbors (kNN), centroid displacement-based kNN, linear discriminant analysis, and random forest-trained with the physiological signals recorded during the first or second experiment. Consequently, the craving/non-craving states in the third experiment were classified with an accuracy that was comparable to that achieved using the data of the same day; thus, demonstrating a high test-retest reliability and the practicality of our craving detection method. In addition, the classification performance was further enhanced by using both datasets of the first and second experiments to train the classifiers, suggesting that an individually customized game craving detection system with high accuracy can be implemented by accumulating datasets recorded on different days under different experimental conditions.
青少年和年轻人的网络成瘾问题已经成为一个日益严重的公众关注问题，因为它的高患病率和潜在的改变大脑功能和结构的风险。线索暴露疗法旨在减少或维持成瘾复发的核心因素——渴求，并且在成瘾治疗中得到广泛应用。在之前的研究中，我们提出了一种基于机器学习的方法，使用包括光体积描记图、皮肤电反应和眼动电图在内的多模态生理信号来检测游戏渴求。我们之前的研究表明，使用基于机器学习的方法检测游戏渴求的准确率相当高；然而，由于用于基于机器学习检测用户渴求的特征向量是基于用户当天记录的生理数据选择的，因此在没有进一步校准的情况下，重新使用之前实验中构建的机器学习模型的有效性仍值得怀疑。这种“高测试-再测试可靠性”特征对于渴求检测系统的实际应用非常重要，因为该系统需要作为一种工具，在治疗过程中反复应用，以监测治疗效果。我们向九名参与者展示了三个成瘾性游戏的短视频片段，在此期间记录了各种生理信号。这个实验在三天的不同时间用不同的视频片段重复进行。首先，我们通过计算组内相关系数来研究渴求检测系统中使用的 14 个特征的测试-再测试可靠性。然后，我们使用支持向量机、k-最近邻（kNN）、基于质心位移的 kNN、线性判别分析和随机森林等各种分类器，根据参与者在第一次或第二次实验中记录的生理信号，对第三次实验中每个参与者是否经历了游戏渴求进行分类。结果，在第三次实验中，渴求/非渴求状态的分类准确率与当天数据的准确率相当，从而证明了我们的渴求检测方法具有很高的测试-再测试可靠性和实用性。此外，通过使用第一次和第二次实验的两个数据集来训练分类器，分类性能得到了进一步提高，这表明可以通过在不同实验条件下记录不同日期的数据集来实现具有高精度的个性化游戏渴求检测系统。
