A Novel Cue-Induced Abdominal Reaction Analysis for Internet Gaming Disorder

Ji, Hong Ming; Hsiao, Tzu‐Chien

doi:10.1007/s10916-019-1221-9

Cited by 6 publications

(3 citation statements)

References 28 publications

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“…Our findings, however, do not fully correspond to those of previous studies. Relevant studies have reported the following: posttraumatic stress disorder symptom is negatively associated with HF PRV during rest [20], gamers with GD tend to exhibit an increased LF/HF HRV ratio [10] and respiratory rates [22] and decreased natural logarithm of LF HRV [12] when playing online games, gamers with GD exhibit increased respiratory rates when watching gameplay videos [30], and individuals exhibit increased total peripheral resistance when performing negative memory recall [31] and experiencing positive video stimuli [32]. This inconsistency can be attributed to the varying respiratory and cardiovascular responses triggered by varying emotional stimuli, particularly in the HGD group.…”

Section: Resultsmentioning

confidence: 99%

Extended classifier system with continuous real-coded variables for feature extraction of instantaneous pulse-rate variability and respiration of individuals with gaming disorder

Ming

Hsiao

2021

BioMed Eng OnLine

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Background Individuals with gaming disorder (GD) exhibit autonomic nervous system responses that indicate dysfunctional emotion regulation. Pulse rate variability (PRV) is a valuable biomarker for investigating the autonomic function of patients with mental disorders. Because individuals with GD dynamically regulate emotions during gaming, the PRV response relating to GD is not well understood. To investigate the dynamic PRV responses of individuals with GD, this study proposed the indexes of instantaneous PRV (iPRV) and instantaneous respiratory frequency (IFresp) of arterial blood pressure signals using empirical mode decomposition and normalized direct-quadrature algorithms. iPRV consists of low-frequency (LF), high-frequency (HF), and very high-frequency (VHF) bands. Moreover, a novel method of extended classifier system with continuous real-coded variables (XCSR) was used to detect GD and extract GD-related iPRV features using iPRV and IFresp as input data. Results A total of 32 college students without depressive and anxiety symptoms or cardiovascular diseases were recruited in this study. Participants were grouped into the high-risk GD and low-risk GD using both Chen Internet Addiction Scale and Internet Gaming Disorder Questionnaire. Their arterial blood pressures signals were measured while they watched gameplay videos with negative or positive emotional stimuli. Seven participants with high-risk GD exhibited significantly increased normalized VHF (nVHF) PRV and IFresp readings and significantly decreased normalized LF (nLF) PRV readings and LF/HF PRV ratios (from baseline) during negative or positive gameplay videos stimuli. These participants also exhibited higher nVHF PRV and lower nLF PRV readings and LF/HF PRV ratios when they experienced negative gameplay video stimuli relative to 17 participants with low-risk GD. The classification accuracy of the XCSR reached 90% for both negative and positive video stimuli, and nVHF PRV was most frequently used to detect GD risk. Conclusions iPRV and IFresp can be used to detect GD and analyze the autonomic mechanism of individuals with GD.

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Section: Resultsmentioning

confidence: 99%

Extended classifier system with continuous real-coded variables for feature extraction of instantaneous pulse-rate variability and respiration of individuals with gaming disorder

Ming

Hsiao

2021

BioMed Eng OnLine

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“…Inhalation and exhalation change the intrathoracic pressure, which in turn affects the heart rate and blood pressure. Individuals with high-risk IGD reduced their respiratory rates by using slow and deep breathing exercises while watching negative films [31]. Therefore, the respiration response is a key feature for detecting IA risk, and respiratory adjustments constitute a valuable means of affecting psychophysiological responses.…”

Section: Physiological Response Of Iamentioning

confidence: 99%

“…The XCSR method was then applied to verify the major psychological symptoms of IA from the CIAS data. To investigate the instantaneous physiological responses of IA, the ensemble empirical mode decomposition (EEMD) method [31] was used to decompose a physiological signal into instantaneous components, and the XCSR method was employed to extract the IA-related physiological features from these components. This study has two main contributions, namely the use of a rule-based machine learning system to classify the risk of IA, and the extraction of the major symptoms and instantaneous physiological features of IA from a questionnaire and from physiological components, respectively.…”

Section: Introductionmentioning

confidence: 99%

Extraction of Psychological Symptoms and Instantaneous Respiratory Frequency as Indicators of Internet Addiction Using Rule-Based Machine Learning

Chi¹,

Chen²,

Hsiao³

2021

Adv. sci. technol. eng. syst. j.

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Internet addiction (IA) has adverse effects on psychophysiological responses, interpersonal relationships, and academic and occupational performance. IA detection has received increasing attention. Although questionnaires enable long-term assessment (over 6 months) and physiological measurements to aid the short-term evaluation (over 2 min) of IA, the lack of algorithms results in an inability to detect IA in real time. A computer-aided system can address this problem. This study used the extended classifier system with continuous realcoded variables (XCSR) for rule-based machine learning to classify IA risk. Chen Internet Addiction Scale (CIAS) items were verified and instantaneous respiratory features of IA were extracted with "don't care" attribute values. The result demonstrated that the XCSR model achieved more than 95% classification accuracy. Using the "don't care" attribute values, the CIAS items were reduced from 26 to 19, and the instantaneous frequency (IF) of respiratory muscle contractions, respiratory wall movements, and body movements were extracted as IA-related features. These findings suggested that the XCSR model is a potentially useful system for detecting IA. The modified 19-item CIAS and IF of respiration can be adopted to assist in the real-time detection of IA and explore the psychophysiological developments of IA users. In future studies, more samples must be collected to validate these findings and instantaneous physiological responses investigated with different window sizes while participants with IA engage in active online gameplay.

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Effect of short-term abdominal breathing on heart rate variability as an indicator of emotional regulation in college student with internet gaming disorder

Chi

Hsiao

2023

Cogent Psychology

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A Novel Cue-Induced Abdominal Reaction Analysis for Internet Gaming Disorder

Cited by 6 publications

References 28 publications

Extended classifier system with continuous real-coded variables for feature extraction of instantaneous pulse-rate variability and respiration of individuals with gaming disorder

Extended classifier system with continuous real-coded variables for feature extraction of instantaneous pulse-rate variability and respiration of individuals with gaming disorder

Extraction of Psychological Symptoms and Instantaneous Respiratory Frequency as Indicators of Internet Addiction Using Rule-Based Machine Learning

Effect of short-term abdominal breathing on heart rate variability as an indicator of emotional regulation in college student with internet gaming disorder

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