Classifying developmental delays with EEG: A comparative study of machine learning and deep learning approaches

• Autorzy: Usman Muhammad , Lin Wen-Yi , Lin Yi-Yin , Hsieh Sheng-Ta , Chen Yao-Tien , Lo Yu-Chun , Lin Chun-Ling

Identyfikatory

DOI

10.1016/j.bbe.2025.04.001

• Języki publikacji: EN

Abstrakty

EN

Early detection of developmental delays is crucial for improving children’s cognitive, social, and emotional outcomes through timely interventions. This study explores the potential of machine learning (ML) and deep learning (DL) to classify Electroencephalography (EEG) data from an oddball task, distinguishing between children with and without developmental delays. Participants underwent language assessments and EEG recordings, with subsequent analysis using Event-Related Potentials (ERPs), Event-Related Spectral Perturbations (ERSPs), and functional connectivity to characterize group differences. Three methodologies were employed in this research to classify EEG data. Firstly, statistical features are extracted from the EEG data and various ML algorithms are applied for classification, with feature selection techniques utilized to identify the most relevant features and enhance classification accuracy. Secondly, brain dynamics is utilized to incorporate ERP, ERSP, and functional connectivity measures as features for developmental delay detection. Similar to the first approach, feature selection techniques are again employed to enhance classification accuracy. Lastly, DL approaches are explored by implementing multiple convolutional neural networks (CNNs), including a 2D CNN (EEGNet), various hybrid CNN architectures integrating LSTM, GRU, and attention mechanisms, and a novel 1D CNN with a standardized convolutional layer (SCL) for improved stability and training performance. The effectiveness of each approach in accurately classifying EEG data for developmental delay detection is independently analyzed. The results demonstrate that the proposed 1D convolutional neural network outperforms both EEGNet and the employed ML classifiers. This model achieves an impressive accuracy of 96.4% and an F1 score of 96.6%, underscoring its potential as a valuable tool for early and accurate developmental delay detection using EEG data.

Słowa kluczowe

EN

developmental delay; neurodevelopmental disorder; machine learning; deep learning; electroencephalography; convolutional neural network

PL

uczenie maszynowe; uczenie głębokie; elektroencefalografia; sieć neuronowa konwolucyjna

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2025
• Tom: Vol. 45, iss. 2
• Strony: 229--246
• Opis fizyczny: Bibliogr. 97 poz., rys., tab., wykr.

Twórcy

autor

Usman Muhammad

• International Ph.D. Program in Innovative Technology of Biomedical Engineering and Medical Devices, Ming Chi University of Technology, New Taipei City, Taiwan

autor

Lin Wen-Yi

• Ph.D. Program in Medical Neuroscience, College of Medical Science and Technology, Taipei Medical University and National Health Research Institutes, Taipei City, Taiwan

autor

Lin Yi-Yin

• Ph.D. Program in Medical Neuroscience, College of Medical Science and Technology, Taipei Medical University and National Health Research Institutes, Taipei City, Taiwan

autor

Hsieh Sheng-Ta

• Department of Communication Engineering, Asia Eastern University of Science and Technology, New Taipei City, Taiwan

autor

Chen Yao-Tien

• International Ph.D. Program in Innovative Technology of Biomedical Engineering and Medical Devices, Ming Chi University of Technology, New Taipei City, Taiwan

• https://orcid.org/0000-0002-4781-615X

autor

Lo Yu-Chun

• Ph.D. Program in Medical Neuroscience, College of Medical Science and Technology, Taipei Medical University, New Taipei City, Taiwan

autor

Lin Chun-Ling

• Department of Electronic Engineering, National Taipei University of Technology, Taipei City, Taiwan

• https://orcid.org/0000-0002-1947-157X

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).