{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T04:44:00Z","timestamp":1774932240992,"version":"3.50.1"},"reference-count":33,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers in Biology and Medicine"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1016\/j.compbiomed.2026.111625","type":"journal-article","created":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T11:02:34Z","timestamp":1774350154000},"page":"111625","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Optimized Complex-Valued Spatio-Temporal Graph Convolutional Networks for attention deficit hyperactivity disorder detection in pediatric EEG signals"],"prefix":"10.1016","volume":"207","author":[{"given":"R.","family":"Lakshmi","sequence":"first","affiliation":[]},{"given":"Vanathi","family":"Balasubramanian","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.compbiomed.2026.111625_bib1","first-page":"1","article-title":"Automatic detection of attention deficit hyperactivity disorder using machine learning algorithms based on short time fourier transform and discrete cosine transform","author":"Deshmukh","year":"2025","journal-title":"Appl. Neuropsychol.: Child"},{"key":"10.1016\/j.compbiomed.2026.111625_bib2","doi-asserted-by":"crossref","first-page":"271","DOI":"10.2147\/NDT.S509094","article-title":"Electroencephalogram (EEG) based prediction of attention deficit hyperactivity disorder (ADHD) using machine learning","author":"Kim","year":"2025","journal-title":"Neuropsychiatric Dis. Treat."},{"key":"10.1016\/j.compbiomed.2026.111625_bib3","first-page":"1","article-title":"Impact of brain regions on attention deficit hyperactivity disorder (ADHD) electroencephalogram (EEG) signals: Comparison of machine learning algorithms with empirical mode decomposition and time domain analysis","author":"Deshmukh","year":"2025","journal-title":"Appl. Neuropsychol.: Child"},{"key":"10.1016\/j.compbiomed.2026.111625_bib4","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2025.108197","article-title":"Application of artificial intelligence in attention-deficit hyperactivity disorder deteaction and response to treatment: a systematic review","volume":"110","author":"Hoseini","year":"2025","journal-title":"Biomed. Signal Process Control"},{"issue":"4","key":"10.1016\/j.compbiomed.2026.111625_bib5","doi-asserted-by":"crossref","first-page":"1365","DOI":"10.1007\/s10803-024-06290-w","article-title":"Detecting autism spectrum disorder and attention deficit hyperactivity disorder using multimodal time-frequency analysis with machine learning using the electroretinogram from two flash strengths","volume":"55","author":"Manjur","year":"2025","journal-title":"J. Autism Dev. Disord."},{"key":"10.1016\/j.compbiomed.2026.111625_bib6","article-title":"Identifying neuroimaging biomarkers of attention-deficit hyperactivity disorder (ADHD) from cortical hemodynamic responses during Go\/NoGo task using machine learning approaches","volume":"140","author":"Li","year":"2025","journal-title":"Prog. Neuro Psychopharmacol. Biol. Psychiatr."},{"issue":"1","key":"10.1016\/j.compbiomed.2026.111625_bib7","article-title":"Detection and classification of ADHD from EEG signals using tunable q\u2010factor wavelet transform","volume":"2022","author":"Joy","year":"2022","journal-title":"J. Sens."},{"key":"10.1016\/j.compbiomed.2026.111625_bib8","first-page":"4411","article-title":"Big data analytics for uncovering voxel connectivity patterns in attention deficit hyperactivity disorder","author":"Caraka","year":"2025","journal-title":"J. Multidiscip. Healthc."},{"issue":"1","key":"10.1016\/j.compbiomed.2026.111625_bib9","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1007\/s42600-025-00406-6","article-title":"Investigation of electroencephalography in attention-deficit hyperactivity disorder subtype classification with machine learning","volume":"41","author":"Pedrollo","year":"2025","journal-title":"Res. Biomed. Eng."},{"issue":"4","key":"10.1016\/j.compbiomed.2026.111625_bib10","doi-asserted-by":"crossref","DOI":"10.1002\/kjm2.12931","article-title":"Identification of diagnostic and therapeutic biomarkers for attention\u2010deficit\/hyperactivity disorder","volume":"41","author":"Lee","year":"2025","journal-title":"Kaohsiung J. Med. Sci."},{"key":"10.1016\/j.compbiomed.2026.111625_bib11","series-title":"International Journal of High Speed Electronics and Systems","article-title":"Classification method of children's attention deficit hyperactivity disorder based on double-layer twin neural network","author":"Hanzi","year":"2025"},{"key":"10.1016\/j.compbiomed.2026.111625_bib12","series-title":"Generative Artificial Intelligence for Biomedical and Smart Health Informatics","first-page":"103","article-title":"Temporal normalization and brain image analysis for early\u2010stage prediction of Attention Deficit Hyperactivity Disorder (ADHD)","author":"Kchaudhary","year":"2025"},{"key":"10.1016\/j.compbiomed.2026.111625_bib13","article-title":"Novel neural activity profiles underlying inhibitory control deficits of clinical relevance in attention-Deficit\/Hyperactivity disorder: insights from electroencephalography tensor decomposition","author":"Gholamipourbarogh","year":"2025","journal-title":"Biol. Psychiatry Cogn. Neurosci. Neuroimaging"},{"issue":"4","key":"10.1016\/j.compbiomed.2026.111625_bib14","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1080\/21622965.2024.2336019","article-title":"Prediction of attention deficit hyperactivity disorder based on explainable artificial intelligence","volume":"14","author":"Navarro-Soria","year":"2025","journal-title":"Appl. Neuropsychol.: Child"},{"issue":"14","key":"10.1016\/j.compbiomed.2026.111625_bib15","doi-asserted-by":"crossref","first-page":"12901","DOI":"10.1007\/s11042-024-19460-w","article-title":"Attention deficit hyperactivity disorder subtypes classification: a machine learning approach with phenotypic information and brain tissue volume","volume":"84","author":"UshaRupni","year":"2025","journal-title":"Multimed. Tool. Appl."},{"issue":"1","key":"10.1016\/j.compbiomed.2026.111625_bib16","first-page":"209","article-title":"Empirically derived symptom profiles in adults with attention-deficit\/hyperactivity disorder: an unsupervised machine learning approach","volume":"33","author":"Rodriguez","year":"2026","journal-title":"Appl. Neuropsychol.: Adult"},{"issue":"12","key":"10.1016\/j.compbiomed.2026.111625_bib17","doi-asserted-by":"crossref","first-page":"1042","DOI":"10.1007\/s11760-025-04589-4","article-title":"Generalizable temporal-spectral-spatial quantitative electroencephalogram based diagnosis of attention-deficit hyperactivity disorder in children","volume":"19","author":"Holker","year":"2025","journal-title":"Signal, Image and Video Processing"},{"issue":"3","key":"10.1016\/j.compbiomed.2026.111625_bib18","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1007\/s41030-025-00299-x","article-title":"Obstructive sleep apnea and sleep disorders in children with attention deficit hyperactivity disorder","volume":"11","author":"Nguyen-Thi-Phuong","year":"2025","journal-title":"Pulmonary Therapy"},{"key":"10.1016\/j.compbiomed.2026.111625_bib19","article-title":"A concrete damage plasticity model for predicting the effects of compressive high-strength concrete under static and dynamic loads","volume":"44","author":"Minh","year":"2021","journal-title":"J. Build. Eng."},{"key":"10.1016\/j.compbiomed.2026.111625_bib20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TNSRE.2020.3019063","article-title":"Deep spatio-temporal representation and ensemble classification for attention deficit\/hyperactivity disorder","volume":"29","author":"Liu","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10.1016\/j.compbiomed.2026.111625_bib21","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2025.110826","article-title":"A decision support system based on multi-head convolutional and recurrent neural networks for assisting physicians in diagnosing ADHD","volume":"196","author":"Sanchis","year":"2025","journal-title":"Comput. Biol. Med."},{"issue":"4","key":"10.1016\/j.compbiomed.2026.111625_bib22","doi-asserted-by":"crossref","DOI":"10.1016\/j.pes.2025.100172","article-title":"Common spatial pattern based feature extractor with hybrid Linknet-SqueezeNet for ADHD detection from EEG signal","volume":"2","author":"Sindhu","year":"2025","journal-title":"Progress Eng. Sci."},{"key":"10.1016\/j.compbiomed.2026.111625_bib23","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1016\/j.procs.2025.03.235","article-title":"ADHD detection using artificial neural network","volume":"260","author":"Hassan","year":"2025","journal-title":"Procedia Comput. Sci."},{"key":"10.1016\/j.compbiomed.2026.111625_bib24","doi-asserted-by":"crossref","DOI":"10.1016\/j.dscb.2025.100198","article-title":"Attention deficit hyperactivity disorder identification: FMRI data analyzed with CNN and seed-based approach","volume":"17","author":"Oyshi","year":"2025","journal-title":"Brain Disorders"},{"key":"10.1016\/j.compbiomed.2026.111625_bib25","doi-asserted-by":"crossref","DOI":"10.1109\/TCDS.2025.3556888","article-title":"Electroencephalogram-based unified approach for multiple neurodevelopmental disorders detection in children using successive multivariate variational mode decomposition","author":"Chandela","year":"2025","journal-title":"IEEE Trans. Cognitive Developmental Syst."},{"key":"10.1016\/j.compbiomed.2026.111625_bib26","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106913","article-title":"An explainable spatio-temporal graph convolutional network for the biomarkers identification of ADHD","volume":"99","author":"Chen","year":"2025","journal-title":"Biomed. Signal Process Control"},{"key":"10.1016\/j.compbiomed.2026.111625_bib27","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113175","article-title":"Multiscale spectral augmentation for graph contrastive learning for fMRI analysis to diagnose psychiatric disease","volume":"314","author":"Hu","year":"2025","journal-title":"Knowl. Base Syst."},{"key":"10.1016\/j.compbiomed.2026.111625_bib28","first-page":"1","article-title":"Attention-driven deep learning framework for EEG analysis in ADHD detection","author":"Ahire","year":"2025","journal-title":"Appl. Neuropsychol.: Child"},{"key":"10.1016\/j.compbiomed.2026.111625_bib29","doi-asserted-by":"crossref","DOI":"10.1109\/ACCESS.2025.3539706","article-title":"Optimized temporal denoised convolutional autoencoder for enhanced ADHD classification using fMRI data","author":"Begum","year":"2025","journal-title":"IEEE Access"},{"key":"10.1016\/j.compbiomed.2026.111625_bib32","article-title":"Smooth deep learning magnetotelluric inversion based on physics-informed swin transformer and multi-window savitzky-golay filter","author":"Liu","year":"2023","journal-title":"IEEE Trans. Geosci. Rem. Sens."},{"key":"10.1016\/j.compbiomed.2026.111625_bib33","article-title":"Synchro-transient-extracting transform for the analysis of signals with both harmonic and impulsive components","author":"Ma","year":"2024","journal-title":"IEEE Trans. Ind. Electron."},{"key":"10.1016\/j.compbiomed.2026.111625_bib34","article-title":"Complex-value spatio-temporal graph convolutional neural networks and its applications to electric power systems AI","author":"Wu","year":"2023","journal-title":"IEEE Trans. Smart Grid"},{"issue":"6","key":"10.1016\/j.compbiomed.2026.111625_bib35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10462-024-10716-3","article-title":"Red-billed blue magpie optimizer: a novel metaheuristic algorithm for 2D\/3D UAV path planning and engineering design problems","volume":"57","author":"Fu","year":"2024","journal-title":"Artif. Intell. Rev."}],"container-title":["Computers in Biology and Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0010482526001885?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0010482526001885?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T03:09:52Z","timestamp":1774926592000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0010482526001885"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":33,"alternative-id":["S0010482526001885"],"URL":"https:\/\/doi.org\/10.1016\/j.compbiomed.2026.111625","relation":{},"ISSN":["0010-4825"],"issn-type":[{"value":"0010-4825","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Optimized Complex-Valued Spatio-Temporal Graph Convolutional Networks for attention deficit hyperactivity disorder detection in pediatric EEG signals","name":"articletitle","label":"Article Title"},{"value":"Computers in Biology and Medicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compbiomed.2026.111625","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"111625"}}