Severity identification for internet gaming disorder using heart rate variability reactivity for gaming cues: a deep learning approach
BackgroundThe diminished executive control along with cue-reactivity has been suggested to play an important role in addiction. Hear rate variability (HRV), which is related to the autonomic nervous system, is a useful biomarker that can reflect cognitive-emotional responses to stimuli. In this stud...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-11-01
|
| Series: | Frontiers in Psychiatry |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1231045/full |
| _version_ | 1797635055145713664 |
|---|---|
| author | Sung Jun Hong Deokjong Lee Deokjong Lee Jinsick Park Taekyung Kim Taekyung Kim Young-Chul Jung Young-Chul Jung Young-Chul Jung Young-Min Shon Young-Min Shon Young-Min Shon In Young Kim |
| author_facet | Sung Jun Hong Deokjong Lee Deokjong Lee Jinsick Park Taekyung Kim Taekyung Kim Young-Chul Jung Young-Chul Jung Young-Chul Jung Young-Min Shon Young-Min Shon Young-Min Shon In Young Kim |
| author_sort | Sung Jun Hong |
| collection | DOAJ |
| description | BackgroundThe diminished executive control along with cue-reactivity has been suggested to play an important role in addiction. Hear rate variability (HRV), which is related to the autonomic nervous system, is a useful biomarker that can reflect cognitive-emotional responses to stimuli. In this study, Internet gaming disorder (IGD) subjects’ autonomic response to gaming-related cues was evaluated by measuring HRV changes in exposure to gaming situation. We investigated whether this HRV reactivity can significantly classify the categorical classification according to the severity of IGD.MethodsThe present study included 70 subjects and classified them into 4 classes (normal, mild, moderate and severe) according to their IGD severity. We measured HRV for 5 min after the start of their preferred Internet game to reflect the autonomic response upon exposure to gaming. The neural parameters of deep learning model were trained using time-frequency parameters of HRV. Using the Class Activation Mapping (CAM) algorithm, we analyzed whether the deep learning model could predict the severity classification of IGD and which areas of the time-frequency series were mainly involved.ResultsThe trained deep learning model showed an accuracy of 95.10% and F-1 scores of 0.995 (normal), 0.994 (mild), 0.995 (moderate), and 0.999 (severe) for the four classes of IGD severity classification. As a result of checking the input of the deep learning model using the CAM algorithm, the high frequency (HF)-HRV was related to the severity classification of IGD. In the case of severe IGD, low frequency (LF)-HRV as well as HF-HRV were identified as regions of interest in the deep learning model.ConclusionIn a deep learning model using the time-frequency HRV data, a significant predictor of IGD severity classification was parasympathetic tone reactivity when exposed to gaming situations. The reactivity of the sympathetic tone for the gaming situation could predict only the severe group of IGD. This study suggests that the autonomic response to the game-related cues can reflect the addiction status to the game. |
| first_indexed | 2024-03-11T12:15:50Z |
| format | Article |
| id | doaj.art-860078b3e84d454889fc83902aa138b9 |
| institution | Directory Open Access Journal |
| issn | 1664-0640 |
| language | English |
| last_indexed | 2024-03-11T12:15:50Z |
| publishDate | 2023-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychiatry |
| spelling | doaj.art-860078b3e84d454889fc83902aa138b92023-11-07T08:48:01ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402023-11-011410.3389/fpsyt.2023.12310451231045Severity identification for internet gaming disorder using heart rate variability reactivity for gaming cues: a deep learning approachSung Jun Hong0Deokjong Lee1Deokjong Lee2Jinsick Park3Taekyung Kim4Taekyung Kim5Young-Chul Jung6Young-Chul Jung7Young-Chul Jung8Young-Min Shon9Young-Min Shon10Young-Min Shon11In Young Kim12Biomedical Engineering Research Center, Samsung Medical Center, Seoul, Republic of KoreaDepartment of Psychiatry, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Republic of KoreaInstitute of Behavioral Sciences in Medicine, Yonsei University College of Medicine, Seoul, Republic of KoreaDivision of Research Planning, Mental Health Research Institute, National Center for Mental Health, Seoul, Republic of KoreaBiomedical Engineering Research Center, Samsung Medical Center, Seoul, Republic of KoreaDepartment of Medical Device Management and Research, Samsung Advanced Institute for Health Science and Technology, Sungkyunkwan University, Seoul, Republic of KoreaInstitute of Behavioral Sciences in Medicine, Yonsei University College of Medicine, Seoul, Republic of KoreaDepartment of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of KoreaInstitute for Innovation in Digital Healthcare, Yonsei University, Seoul, Republic of KoreaBiomedical Engineering Research Center, Samsung Medical Center, Seoul, Republic of KoreaDepartment of Medical Device Management and Research, Samsung Advanced Institute for Health Science and Technology, Sungkyunkwan University, Seoul, Republic of KoreaDepartment of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of KoreaDepartment of Biomedical Engineering, Graduate School of Biomedical Science and Engineering, Hanyang University, Seoul, Republic of KoreaBackgroundThe diminished executive control along with cue-reactivity has been suggested to play an important role in addiction. Hear rate variability (HRV), which is related to the autonomic nervous system, is a useful biomarker that can reflect cognitive-emotional responses to stimuli. In this study, Internet gaming disorder (IGD) subjects’ autonomic response to gaming-related cues was evaluated by measuring HRV changes in exposure to gaming situation. We investigated whether this HRV reactivity can significantly classify the categorical classification according to the severity of IGD.MethodsThe present study included 70 subjects and classified them into 4 classes (normal, mild, moderate and severe) according to their IGD severity. We measured HRV for 5 min after the start of their preferred Internet game to reflect the autonomic response upon exposure to gaming. The neural parameters of deep learning model were trained using time-frequency parameters of HRV. Using the Class Activation Mapping (CAM) algorithm, we analyzed whether the deep learning model could predict the severity classification of IGD and which areas of the time-frequency series were mainly involved.ResultsThe trained deep learning model showed an accuracy of 95.10% and F-1 scores of 0.995 (normal), 0.994 (mild), 0.995 (moderate), and 0.999 (severe) for the four classes of IGD severity classification. As a result of checking the input of the deep learning model using the CAM algorithm, the high frequency (HF)-HRV was related to the severity classification of IGD. In the case of severe IGD, low frequency (LF)-HRV as well as HF-HRV were identified as regions of interest in the deep learning model.ConclusionIn a deep learning model using the time-frequency HRV data, a significant predictor of IGD severity classification was parasympathetic tone reactivity when exposed to gaming situations. The reactivity of the sympathetic tone for the gaming situation could predict only the severe group of IGD. This study suggests that the autonomic response to the game-related cues can reflect the addiction status to the game.https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1231045/fulldeep learning modelheart rate variabilityinternet gaming disorderbehavioral addictionaddiction |
| spellingShingle | Sung Jun Hong Deokjong Lee Deokjong Lee Jinsick Park Taekyung Kim Taekyung Kim Young-Chul Jung Young-Chul Jung Young-Chul Jung Young-Min Shon Young-Min Shon Young-Min Shon In Young Kim Severity identification for internet gaming disorder using heart rate variability reactivity for gaming cues: a deep learning approach Frontiers in Psychiatry deep learning model heart rate variability internet gaming disorder behavioral addiction addiction |
| title | Severity identification for internet gaming disorder using heart rate variability reactivity for gaming cues: a deep learning approach |
| title_full | Severity identification for internet gaming disorder using heart rate variability reactivity for gaming cues: a deep learning approach |
| title_fullStr | Severity identification for internet gaming disorder using heart rate variability reactivity for gaming cues: a deep learning approach |
| title_full_unstemmed | Severity identification for internet gaming disorder using heart rate variability reactivity for gaming cues: a deep learning approach |
| title_short | Severity identification for internet gaming disorder using heart rate variability reactivity for gaming cues: a deep learning approach |
| title_sort | severity identification for internet gaming disorder using heart rate variability reactivity for gaming cues a deep learning approach |
| topic | deep learning model heart rate variability internet gaming disorder behavioral addiction addiction |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1231045/full |
| work_keys_str_mv | AT sungjunhong severityidentificationforinternetgamingdisorderusingheartratevariabilityreactivityforgamingcuesadeeplearningapproach AT deokjonglee severityidentificationforinternetgamingdisorderusingheartratevariabilityreactivityforgamingcuesadeeplearningapproach AT deokjonglee severityidentificationforinternetgamingdisorderusingheartratevariabilityreactivityforgamingcuesadeeplearningapproach AT jinsickpark severityidentificationforinternetgamingdisorderusingheartratevariabilityreactivityforgamingcuesadeeplearningapproach AT taekyungkim severityidentificationforinternetgamingdisorderusingheartratevariabilityreactivityforgamingcuesadeeplearningapproach AT taekyungkim severityidentificationforinternetgamingdisorderusingheartratevariabilityreactivityforgamingcuesadeeplearningapproach AT youngchuljung severityidentificationforinternetgamingdisorderusingheartratevariabilityreactivityforgamingcuesadeeplearningapproach AT youngchuljung severityidentificationforinternetgamingdisorderusingheartratevariabilityreactivityforgamingcuesadeeplearningapproach AT youngchuljung severityidentificationforinternetgamingdisorderusingheartratevariabilityreactivityforgamingcuesadeeplearningapproach AT youngminshon severityidentificationforinternetgamingdisorderusingheartratevariabilityreactivityforgamingcuesadeeplearningapproach AT youngminshon severityidentificationforinternetgamingdisorderusingheartratevariabilityreactivityforgamingcuesadeeplearningapproach AT youngminshon severityidentificationforinternetgamingdisorderusingheartratevariabilityreactivityforgamingcuesadeeplearningapproach AT inyoungkim severityidentificationforinternetgamingdisorderusingheartratevariabilityreactivityforgamingcuesadeeplearningapproach |