Classification of internet addiction using machine learning on electroencephalography synchronization and functional connectivity.
Background Internet addiction (IA) refers to excessive internet use that causes cognitive impairment or distress. Understanding the neurophysiological mechanisms underpinning IA is crucial for enabling an accurate diagnosis and informing treatment and prevention strategies. Despite the recent increa...
| Publicado en: | Psychological Medicine Vol. 55; pp. 1 - 12 |
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| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Cambridge University Press
2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=191245217&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 191245217 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00332917 6Q3 jtl: Psychological Medicine issn: 00332917 maglogo: N pubinfo: dt: 2025 vid: 55 pid: 15979 pub: Cambridge University Press artinfo: ui: 191245217 191245217 191245217 10.1017/S0033291725001035 191245217 ppf: 1 ppct: 11 formats: tig: atl: Classification of internet addiction using machine learning on electroencephalography synchronization and functional connectivity. aug: sug: subj: Internet Addiction Diagnosis Machine Learning Electroencephalography Functional Connectivity Biological Markers Diagnosis, Computer Assisted Human Young Adult Neurophysiology Support Vector Machine Descriptive Statistics T-Tests Self Report Random Forest Students, College Male Female Chi Square Test Hong Kong Data Analysis Software Adolescence Adult Funding Source Adolescent: 13-18 years Adult: 19-44 years Male Female ab: Background Internet addiction (IA) refers to excessive internet use that causes cognitive impairment or distress. Understanding the neurophysiological mechanisms underpinning IA is crucial for enabling an accurate diagnosis and informing treatment and prevention strategies. Despite the recent increase in studies examining the neurophysiological traits of IA, their findings often vary. To enhance the accuracy of identifying key neurophysiological characteristics of IA, this study used the phase lag index (PLI) and weighted PLI (WPLI) methods, which minimize volume conduction effects, to analyze the resting-state electroencephalography (EEG) functional connectivity. We further evaluated the reliability of the identified features for IA classification using various machine learning methods. Methods Ninety-two participants (42 with IA and 50 healthy controls (HCs)) were included. PLI and WPLI values for each participant were computed, and values exhibiting significant differences between the two groups were selected as features for the subsequent classification task. Results Support vector machine (SVM) achieved an 83% accuracy rate using PLI features and an improved 86% accuracy rate using WPLI features. t -test results showed analogous topographical patterns for both the WPLI and PLI. Numerous connections were identified within the delta and gamma frequency bands that exhibited significant differences between the two groups, with the IA group manifesting an elevated level of phase synchronization. Conclusions Functional connectivity analysis and machine learning algorithms can jointly distinguish participants with IA from HCs based on EEG data. PLI and WPLI have substantial potential as biomarkers for identifying the neurophysiological traits of IA. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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