{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T09:16:22Z","timestamp":1770282982485,"version":"3.49.0"},"reference-count":58,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T00:00:00Z","timestamp":1735516800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Mexico\u2019s National Technological Institute (TecNM)","award":["19292.24-P"],"award-info":[{"award-number":["19292.24-P"]}]},{"name":"National Council of Humanities, Science, and Technology (CONAHCYT)","award":["19292.24-P"],"award-info":[{"award-number":["19292.24-P"]}]},{"name":"Public Secretariat of Education (SEP)","award":["19292.24-P"],"award-info":[{"award-number":["19292.24-P"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Attention Deficit Hyperactivity Disorder (ADHD) diagnosis is often challenging due to subjective assessments and symptom variability, which can delay accurate detection and treatment. To address these limitations, this study introduces DETEC-ADHD, a web-based application that combines machine learning (ML) techniques with multi-source data to enhance diagnostic accuracy. Unlike traditional approaches, DETEC-ADHD primarily utilizes extensive personal, medical, and psychological information for its initial classification. DETEC-ADHD further refines diagnoses by identifying ADHD subtypes (inattentive, hyperactive, combined) through theta\/beta wave ratio analysis from EEG data, offering neurophysiological insights that complement its classification process. Logistic Regression, selected for its validated accuracy and reliability, served as the ML model for the app. The case studies demonstrated DETEC-ADHD\u2019s effectiveness, achieving 100% accuracy in children and 90% in adults. By integrating diverse data sources with real-time EEG analysis, DETEC-ADHD provides a scalable, cost-effective, and accessible solution for ADHD detection and subtype identification, addressing diagnostic challenges and supporting healthcare providers, particularly in resource-limited environments.<\/jats:p>","DOI":"10.3390\/bdcc9010003","type":"journal-article","created":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T12:37:19Z","timestamp":1735648639000},"page":"3","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["DETEC-ADHD: A Data-Driven Web App for Early ADHD Detection Using Machine Learning and Electroencephalography"],"prefix":"10.3390","volume":"9","author":[{"given":"Ismael","family":"Santarrosa-L\u00f3pez","sequence":"first","affiliation":[{"name":"Tecnol\u00f3gico Nacional de M\u00e9xico\/I. T. Orizaba, Av. Oriente 9, No. 852, Col. Emiliano Zapata, Orizaba C.P. 94320, Veracruz, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3296-0981","authenticated-orcid":false,"given":"Giner","family":"Alor-Hern\u00e1ndez","sequence":"additional","affiliation":[{"name":"Tecnol\u00f3gico Nacional de M\u00e9xico\/I. T. Orizaba, Av. Oriente 9, No. 852, Col. Emiliano Zapata, Orizaba C.P. 94320, Veracruz, Mexico"}]},{"given":"Maritza","family":"Bustos-L\u00f3pez","sequence":"additional","affiliation":[{"name":"Tecnol\u00f3gico Nacional de M\u00e9xico\/I. T. Orizaba, Av. Oriente 9, No. 852, Col. Emiliano Zapata, Orizaba C.P. 94320, Veracruz, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4689-5343","authenticated-orcid":false,"given":"Jonathan","family":"Hern\u00e1ndez-Capistr\u00e1n","sequence":"additional","affiliation":[{"name":"Tecnol\u00f3gico Nacional de M\u00e9xico\/I. T. Orizaba, Av. Oriente 9, No. 852, Col. 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[5th ed.]."},{"key":"ref_5","first-page":"178","article-title":"Trastorno por d\u00e9ficit de atenci\u00f3n e hiperactividad (TDAH): Factores gestacionales y perinatales asociados","volume":"7","year":"2014","journal-title":"Evid. M\u00e9d. Investig. Salud"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1177\/1550059419876525","article-title":"Phase Space Reconstruction of EEG Signals for Classification of ADHD and Control Adults","volume":"51","author":"Kaur","year":"2020","journal-title":"Clin. EEG Neurosci."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Maniruzzaman, M., Al Mehedi Hasan, M., Suzuki, T., and Shin, J. (2023, January 11\u201314). Identification of Children with ADHD from EEG Signals Based on Entropy Measures and Support Vector Machine. Proceedings of the 2023 11th European Workshop on Visual Information Processing (EUVIP), Gj\u00f8vik, Norwa.","DOI":"10.1109\/EUVIP58404.2023.10323044"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Stock, S., Hausberg, J., Armengol-Urpi, A., Kaufmann, T., Schinle, M., Gerdes, M., and Stork, W. (2023, January 29\u201331). Towards EEG-based objective ADHD diagnosis support using convolutional neural networks. Proceedings of the 2023 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Eindhoven, The Netherlands.","DOI":"10.1109\/CIBCB56990.2023.10264876"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"193","DOI":"10.3390\/signals4010010","article-title":"Automatic Identification of Children with ADHD from EEG Brain Waves","volume":"4","author":"Alim","year":"2023","journal-title":"Signals"},{"key":"ref_10","first-page":"3948","article-title":"Teacher Assistant-Based Knowledge Distillation Extracting Multi-level Features on Single Channel Sleep EEG","volume":"4","author":"Liang","year":"2023","journal-title":"IJCAI Int. Jt. Conf. Artif. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Jia, Z., Liang, H., Liu, Y., Wang, H., and Jiang, T. (2024). DistillSleepNet: Heterogeneous Multi-Level Knowledge Distillation via Teacher Assistant for Sleep Staging. IEEE Transactions on Big Data, IEEE.","DOI":"10.1109\/TBDATA.2024.3453763"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ahire, N., Awale, R.N., and Wagh, A. (2023). Electroencephalogram (EEG) based prediction of attention deficit hyperactivity disorder (ADHD) using machine learning. Appl. Neuropsychol. Adult, 1\u201312.","DOI":"10.1080\/23279095.2023.2247702"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Miao, B., and Zhang, Y. (2017). A Feature Selection Method for Classification of ADHD. 4th International Conference on Information, Cybernetics and Computational Social Systems (ICCSS), IEEE. [4th ed.].","DOI":"10.1109\/ICCSS.2017.8091376"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1109\/TCYB.2015.2396635","article-title":"A Gesture Recognition System for Detecting Behavioral Patterns of ADHD","volume":"46","author":"Bautista","year":"2014","journal-title":"IEEE Trans. Cybern."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.eswa.2017.08.044","article-title":"A multi-level classification framework for multi-site medical data: Application to the ADHD-200 collection","volume":"91","author":"Itani","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Khanna, S., and Das, W. (2020). A Novel Application for the Efficient and Accessible Diagnosis of ADHD Using Machine Learning (Extended Abstract). 2020 IEEE\/ITU International Conference on Artificial Intelligence for Good, AI4G 2020, Institute of Electrical and Electronics Engineers Inc.","DOI":"10.1109\/AI4G50087.2020.9311012"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Uluyagmur-Ozturk, M., Arman, A.R., Yilmaz, S.S., Findik, O.T.P., Genc, H.A., Carkaxhiu-Bulut, G., Yazgan, M.Y., Teker, U., and Cataltepe, Z. (2017). ADHD and ASD Classification Based on Emotion Recognition Data. 15th IEEE International Conference on Machine Learning and Applications (ICMLA), Institute of Electrical and Electronics Engineers (IEEE).","DOI":"10.1109\/ICMLA.2016.0145"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2811","DOI":"10.1007\/s11831-021-09682-8","article-title":"Artificial Intelligence Based Techniques for the Detection of Socio-Behavioral Disorders: A Systematic Review","volume":"29","author":"Mengi","year":"2021","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Slater, J., Joober, R., Koborsy, B.L., Mitchell, S., Sahlas, E., and Palmer, C. (2022). Can electroencephalography (EEG) identify ADHD subtypes? A systematic review. Neurosci. Biobehav. Rev., 139.","DOI":"10.1016\/j.neubiorev.2022.104752"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Mohd, A., Ali, A.M., and Halim, S.A. (2022). Detecting ADHD Subjects Using Machine Learning Algorithm. 2022 IEEE International Conference on Computing (ICOCO), IEEE.","DOI":"10.1109\/ICOCO56118.2022.10031796"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Attallah, O. (2024). ADHD-AID: Aiding Tool for Detecting Children\u2019s Attention Deficit Hyperactivity Disorder via EEG-Based Multi-Resolution Analysis and Feature Selection. Biomimetics, 9.","DOI":"10.3390\/biomimetics9030188"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Bakhtyari, M., and Mirzaei, S. (2022). ADHD detection using dynamic connectivity patterns of EEG data and ConvLSTM with attention framework. Biomed. Signal Process. Control, 76.","DOI":"10.1016\/j.bspc.2022.103708"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Tor, H.T., Ooi, C.P., Lim-Ashworth, N.S., Wei, J.K.E., Jahmunah, V., Oh, S.L., Acharya, U.R., and Fung, D.S.S. (2021). Automated detection of conduct disorder and attention deficit hyperactivity disorder using decomposition and nonlinear techniques with EEG signals. Comput. Methods Progr. Biomed., 200.","DOI":"10.1016\/j.cmpb.2021.105941"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Barua, P.D., Dogan, S., Baygin, M., Tuncer, T., Palmer, E.E., Ciaccio, E.J., and Acharya, U.R. (2022). TMP19: A Novel Ternary Motif Pattern-Based ADHD Detection Model Using EEG Signals. Diagnostics, 12.","DOI":"10.3390\/diagnostics12102544"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Vimalajeewa, D., McDonald, E., Bruce, S.A., and Vidakovic, B. (2022). Wavelet-based approach for diagnosing attention deficit hyperactivity disorder (ADHD). Sci. Rep., 12.","DOI":"10.1038\/s41598-022-26077-2"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1080\/08839514.2021.1933761","article-title":"Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Using Machine Learning","volume":"35","author":"Chen","year":"2021","journal-title":"Appl. Artif. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"67","DOI":"10.9758\/cpn.23.1054","article-title":"The Effect of Mobile Neurofeedback Training in Children with Attention Deficit Hyperactivity Disorder: A Randomized Controlled Trial","volume":"22","author":"Kwon","year":"2024","journal-title":"Clin. Psychopharmacol. Neurosci."},{"key":"ref_28","first-page":"2651","article-title":"Reducing symptoms of attention deficit\/hyperactivity disorder (ADHD) in elementary students: The effectiveness of neurofeedback","volume":"86","author":"Shojaei","year":"2024","journal-title":"Ann. Med. Surg."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"108915","DOI":"10.1016\/j.isci.2024.108915","article-title":"Enhanced attention-related alertness following right anterior insular cortex neurofeedback training","volume":"27","author":"Popovova","year":"2024","journal-title":"iScience"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1037\/neu0000932","article-title":"A diffusion decision model analysis of the cognitive effects of neurofeedback for ADHD","volume":"38","author":"Painter","year":"2024","journal-title":"Neuropsychology"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1080\/13554794.2022.2027456","article-title":"Is neurofeedback effective in children with ADHD? A systematic review and meta-analysis","volume":"28","author":"Rahmani","year":"2022","journal-title":"Neurocase"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1007\/s10484-020-09455-2","article-title":"Neurofeedback and Attention-Deficit\/Hyperactivity-Disorder (ADHD) in Children: Rating the Evidence and Proposed Guidelines","volume":"45","author":"Arns","year":"2020","journal-title":"Appl. Psychophysiol. Biofeedback"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"637","DOI":"10.2147\/NDT.S251547","article-title":"Treatment Efficacy and Clinical Effectiveness of EEG Neurofeedback as a Personalized and Multimodal Treatment in ADHD: A Critical Review","volume":"17","author":"Brown","year":"2021","journal-title":"Neuropsychiatr. Dis. Treat."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"116","DOI":"10.30765\/er.40.3.12","article-title":"EEG data processing in ADHD diagnosis and neurofeedback","volume":"40","year":"2020","journal-title":"Eng. Rev."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1332","DOI":"10.1016\/j.clinph.2020.02.020","article-title":"Long-term effects of theta\/beta neurofeedback on EEG power spectra in children with attention deficit hyperactivity disorder","volume":"131","author":"Janssen","year":"2020","journal-title":"Clin. Neurophysiol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1177\/15500594241279580","article-title":"Neurofeedback Training in Children with ADHD: A Systematic Review of Personalization and Methodological Features Facilitating Training Conditions","volume":"55","author":"Himmelmeier","year":"2024","journal-title":"Clin. EEG Neurosci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"47","DOI":"10.3991\/ijes.v10i01.29079","article-title":"Neurofeedback and ADHD","volume":"10","author":"Vlachou","year":"2022","journal-title":"Int. J. Recent Contrib. Eng. Sci. IT"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Hicks, S.A., Stautland, A., Fasmer, O.B., F\u00f8rland, W., Hammer, H.L., Halvorsen, P., Mjeldheim, K., Oedegaard, K.J., Osnes, B., and Gi\u00e6ver Syrstad, V.E. (2021). HYPERAKTIV, Proceedings of the 12th ACM Multimedia Systems Conference, Istanbul, Turkey, 28 September\u20131 October 2021, ACM.","DOI":"10.1145\/3458305.3478454"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Kaur, A., and Kahlon, K.S. (2022). Accurate Identification of ADHD among Adults Using Real-Time Activity Data. Brain Sci., 12.","DOI":"10.3390\/brainsci12070831"},{"key":"ref_40","unstructured":"Nichols, N. (2023, February 14). ADHD200. Available online: http:\/\/preprocessed-connectomes-project.org\/adhd200\/."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"23626","DOI":"10.1109\/ACCESS.2017.2762703","article-title":"3D CNN Based Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Using Functional and Structural MRI","volume":"5","author":"Zou","year":"2017","journal-title":"IEEE Access"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"e190012","DOI":"10.1148\/ryai.2019190012","article-title":"A Multichannel Deep Neural Network Model Analyzing Multiscale Functional Brain Connectome Data for Attention Deficit Hyperactivity Disorder Detection","volume":"2","author":"Chen","year":"2019","journal-title":"Radiol. Artif. Intell."},{"key":"ref_43","unstructured":"Nasrabadi, A.M., Allahverdy, A., Samavati, M., and Reza Mohammadi, M. (2023, February 14). Eeg Data for ADHD\/Control Children. Available online: https:\/\/ieee-dataport.org\/open-access\/eeg-data-adhd-control-children."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1007\/s13534-016-0218-2","article-title":"EEG classification of ADHD and normal children using non-linear features and neural network","volume":"6","author":"Mohammadi","year":"2016","journal-title":"Biomed. Eng. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Moghaddari, M., Lighvan, M.Z., and Danishvar, S. (2020). Diagnose ADHD disorder in children using convolutional neural network based on continuous mental task EEG. Comput. Methods Programs Biomed., 197.","DOI":"10.1016\/j.cmpb.2020.105738"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"590","DOI":"10.3390\/econometrics3030590","article-title":"A Kolmogorov-Smirnov Based Test for Comparing the Predictive Accuracy of Two Sets of Forecasts","volume":"3","author":"Hassani","year":"2015","journal-title":"Econometrics"},{"key":"ref_47","unstructured":"(2024, November 23). Python Software Foundation \u00bfQu\u00e9 es Python?. Available online: https:\/\/docs.python.org\/es\/3\/faq\/general.html#what-is-python."},{"key":"ref_48","unstructured":"(2023, October 20). FastAPI. Available online: https:\/\/fastapi.tiangolo.com\/."},{"key":"ref_49","unstructured":"(2023, October 19). Scikit-Learn. Available online: https:\/\/scikit-learn.org\/stable\/."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1016\/j.braindev.2012.02.013","article-title":"EEG characteristics and visual cognitive function of children with attention deficit hyperactivity disorder (ADHD)","volume":"34","author":"Shi","year":"2012","journal-title":"Brain Dev."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.jneumeth.2017.10.015","article-title":"Online detection of amplitude modulation of motor-related EEG desynchronization using a lock-in amplifier: Comparison with a fast Fourier transform, a continuous wavelet transform, and an autoregressive algorithm","volume":"293","author":"Kato","year":"2018","journal-title":"J. Neurosci. Methods"},{"key":"ref_52","unstructured":"Interaxon Inc. (2023, October 29). Muse Band. Available online: https:\/\/choosemuse.com\/."},{"key":"ref_53","unstructured":"(2023, October 29). Emotiv. Available online: https:\/\/www.emotiv.com\/."},{"key":"ref_54","unstructured":"(2024, November 23). NeroSky MindWave Mobile 2. Available online: https:\/\/neurosky.com\/."},{"key":"ref_55","unstructured":"(2024, November 23). tec g.NAUTILUS PRO. Available online: https:\/\/www.gtec.at\/."},{"key":"ref_56","unstructured":"(2024, November 23). Focus Calm. Available online: https:\/\/focuscalm.com\/."},{"key":"ref_57","unstructured":"(2023, October 29). Mendi.io Mendi. Available online: https:\/\/www.mendi.io\/."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"29","DOI":"10.20473\/iapl.v4i1.48153","article-title":"Application of ANFIS-based Non-Linear Regression Modelling to Predict Concentration Level in Concentration Grid Test as Early Detection of ADHD in Children","volume":"4","author":"Ittaqillah","year":"2023","journal-title":"Indones. Appl. Phys. Lett."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/1\/3\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:56:13Z","timestamp":1760115373000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/1\/3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,30]]},"references-count":58,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["bdcc9010003"],"URL":"https:\/\/doi.org\/10.3390\/bdcc9010003","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,30]]}}}