College of Medical Instruments, Shanghai University of Medicine & Health Sciences, Shanghai, China.
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
Biomed Tech (Berl). 2023 Apr 27;68(5):457-468. doi: 10.1515/bmt-2023-0030. Print 2023 Oct 26.
Internet Gaming Disorder (IGD), as one of worldwide mental health issues, leads to negative effects on physical and mental health and has attracted public attention. Most studies on IGD are based on screening scales and subjective judgments of doctors, without objective quantitative assessment. However, public understanding of internet gaming disorder lacks objectivity. Therefore, the researches on internet gaming disorder still have many limitations. In this paper, a stop-signal task (SST) was designed to assess inhibitory control in patients with IGD based on prefrontal functional near-infrared spectroscopy (fNIRS). According to the scale, the subjects were divided into health and gaming disorder. A total of 40 subjects (24 internet gaming disorders; 16 healthy controls) signals were used for deep learning-based classification. The seven algorithms used for classification and comparison were deep learning algorithms (DL) and machine learning algorithms (ML), with four and three algorithms in each category, respectively. After applying hold-out method, the performance of the model was verified by accuracy. DL models outperformed traditional ML algorithms. Furthermore, the classification accuracy of the two-dimensional convolution neural network (2D-CNN) was 87.5% among all models. This was the highest accuracy out of all models that were tested. The 2D-CNN was able to outperform the other models due to its ability to learn complex patterns in data. This makes it well-suited for image classification tasks. The findings suggested that a 2D-CNN model is an effective approach for predicting internet gaming disorder. The results show that this is a reliable method with high accuracy to identify patients with IGD and demonstrate that the use of fNIRS to facilitate the development of IGD diagnosis has great potential.
网络成瘾障碍（IGD）作为全球心理健康问题之一，对身心健康产生负面影响，引起了公众关注。大多数关于 IGD 的研究都是基于筛查量表和医生的主观判断，没有客观的定量评估。然而，公众对网络成瘾障碍的理解缺乏客观性。因此，网络成瘾障碍的研究仍然存在许多局限性。本文基于前额叶功能近红外光谱（fNIRS）设计了一个停止信号任务（SST），以评估 IGD 患者的抑制控制能力。根据该量表，将受试者分为健康组和游戏障碍组。共使用了 40 名受试者（24 名网络成瘾障碍患者；16 名健康对照组）的信号进行基于深度学习的分类。用于分类和比较的七种算法分别为深度学习算法（DL）和机器学习算法（ML），每个类别分别有四个和三个算法。应用留一法后，通过准确率验证模型的性能。DL 模型优于传统的 ML 算法。此外，二维卷积神经网络（2D-CNN）在所有模型中的分类准确率为 87.5%。这是所有测试模型中最高的准确率。2D-CNN 能够优于其他模型，是因为它能够学习数据中的复杂模式。这使其非常适合图像分类任务。研究结果表明，2D-CNN 模型是预测网络成瘾障碍的有效方法。结果表明，这是一种具有高准确率的可靠方法，可用于识别 IGD 患者，并表明使用 fNIRS 促进 IGD 诊断的发展具有巨大潜力。
