A Decision Support System Based on multi-head convolutional and Recurrent Neural Networks for assisting physicians in diagnosing ADHD.

Journal: Computers in biology and medicine
Published Date: Aug 1, 2025

Abstract

BACKGROUND: Attention-Deficit Hyperactivity Disorder (ADHD) is highly prevalent among children and adolescents. Traditional diagnostic methods are subjective and time-consuming, underscoring the need for more objective diagnostic tools. Electroencephalography (EEG) has emerged as a promising biomarker for detecting ADHD. This study proposes MCRNet, a Multi-head Convolutional and Recurrent Neural Network, for aiding in ADHD detection using EEG and Deep Learning (DL) techniques.

Authors

  • Javier Sanchis
    Lucentia Research Group, Department of Software and Computing Systems, University of Alicante, Carretera San Vicent del Raspeig, s/n, San Vicent del Raspeig, 03690, Alicante, Spain; XSB Disseny i Multimèdia, Carrer del Mercat, 21, Onil, 03430, Alicante, Spain. Electronic address: javier.sanchis@ua.es.
  • Miguel A Teruel
    LoUISE Research Group, Research Institute of Informatics, University of Castilla-La Mancha, 02071 Albacete, Spain. miguel@dsi.uclm.es.
  • Juan Trujillo
    Lucentia Research Group, Department of Software and Computing Systems, University of Alicante, Carretera San Vicent del Raspeig, s/n, San Vicent del Raspeig, 03690, Alicante, Spain.

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