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Deep neural network to differentiate internet gaming disorder from healthy controls during stop-signal task: a multichannel near-infrared spectroscopy study

  • Qiwen Wang ORCID logo , Yongkang Li , Hang Su , Na Zhong , Qi Xu and Xiaoou Li EMAIL logo
Published/Copyright: April 27, 2023
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Biomedical Engineering / Biomedizinische Technik
From the journal Biomedical Engineering / Biomedizinische Technik Volume 68 Issue 5

Abstract

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.

Keywords: 2D-CNN; fNIRS; internet gaming disorder; machine learning
Corresponding author: Xiaoou Li, College of Medical Instruments, Shanghai University of Medicine & Health Sciences, Shanghai, 201318, China; and School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China, E-mail:

Acknowledgments

We would like to express our gratitude to Shanghai University of Medicine & Health Sciences and Shanghai Mental Health Center for providing the data collection site. We also thank the volunteers for participating in this study.

  1. Research funding: This article was supported by the Foundation of Shanghai Intelligent Medical Devices and Active Health Collaborative Innovation Center and a three-year action plan for the Key Discipline Construction Project of Shanghai Public Health System Construction (project no. GWV-10.1-XK05), Shanghai Municipal Science and Technology Plan Project (project no. 22010502400), Brain Science and Brain-Like Intelligence Technology (2022ZD0211100), Shanghai Mental Health Center Hospital Project (2022zd01), Shanghai Mental Health Center Flying Talent Project (2022-FX-01).

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: Authors state no conflict of interest. Informed consent: Informed consent was obtained from all individuals included in this study.

  4. Ethical approval: The ethical approval for this study was obtained from the Ethics Committee of Shanghai Mental Health Center. All study procedures were conducted in accordance with the Declaration of Helsinki.

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Received: 2023-01-18
Accepted: 2023-03-27
Published Online: 2023-04-27
Published in Print: 2023-10-26

© 2023 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/bmt-2023-0030
Keywords for this article
2D-CNN; fNIRS; internet gaming disorder; machine learning

Articles in the same Issue

  1. Frontmatter
  2. Review
  3. Research frontiers of electroporation-based applications in cancer treatment: a bibliometric analysis
  4. Research Articles
  5. Deep neural network to differentiate internet gaming disorder from healthy controls during stop-signal task: a multichannel near-infrared spectroscopy study
  6. A low power respiratory sound diagnosis processing unit based on LSTM for wearable health monitoring
  7. Effective deep learning classification for kidney stone using axial computed tomography (CT) images
  8. De- and recellularized urethral reconstruction with autologous buccal mucosal cells implanted in an ovine animal model
  9. The impact of right ventricular hemodynamics on the performance of a left ventricular assist device in a numerical simulation model
  10. Optimal assist strategy exploration for a direct assist device under stress‒strain dynamics
  11. Revisiting SFA stent technology: an updated overview on mechanical stent performance
  12. Parameter-based patient-specific restoration of physiological knee morphology for optimized implant design and matching
  13. Influences of smart glasses on postural control under single- and dual-task conditions for ergonomic risk assessment
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