Comparative analysis of electroencephalogram (EEG) data gathered from the frontal region with other brain regions affected by attention deficit hyperactivity disorder (ADHD) through multiresolution analysis and machine learning techniques

• Published in: Applied neuropsychology. Child pp. 1 - 15
• Main Authors: Deshmukh, Manjusha, Khemchandani, Mahi, Thakur, Paramjit Mahesh
• Format: Journal Article
• Language: English
• Published: United States 01.10.2024
• Subjects: electroencephalography; discrete wavelet transform; empirical mode decomposition; machine learning; Attention deficit hyperactivity disorder
• DOI: 10.1080/21622965.2024.2405719
• ISSN: 2162-2965; 2162-2973

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by repeated patterns of hyperactivity, impulsivity, and inattention that limit daily functioning and development. Electroencephalography (EEG) anomalies correspond to changes in brain connection and activity. The authors propose utilizing empirical mode decomposition (EMD) and discrete wavelet transform (DWT) for feature extraction and machine learning (ML) algorithms to categorize ADHD and control subjects. For this study, the authors considered freely accessible ADHD data obtained from the IEEE data site. Studies have demonstrated a range of EEG anomalies in ADHD patients, such as variations in power spectra, coherence patterns, and event-related potentials (ERPs). Some of the studies claimed that the brain's prefrontal cortex and frontal regions collaborate in intricate networks, and disorders in either of them exacerbate the symptoms of ADHD. , Based on the research that claimed the brain's prefrontal cortex and frontal regions collaborate in intricate networks, and disorders in either of them exacerbate the symptoms of ADHD, the proposed study examines the optimal position of EEG electrode for identifying ADHD and in addition to monitoring accuracy on frontal/ prefrontal and other regions of brain our study also investigates the position groupings that have the highest effect on accurateness in identification of ADHD. The results demonstrate that the dataset classified with AdaBoost provided values for accuracy, precision, specificity, sensitivity, and F1-score as 1.00, 0.70, 0.70, 0.75, and 0.71, respectively, whereas using random forest (RF) it is 0.98, 0.64, 0.60, 0.81, and 0.71, respectively, in detecting ADHD. After detailed analysis, it is observed that the most accurate results included all electrodes. The authors believe the processes can detect various neurodevelopmental problems in children utilizing EEG signals.