Electroencephalogram (EEG) based prediction of attention deficit hyperactivity disorder (ADHD) using machine learning
Nitin Ahire1, R N Awale2, Abhay Wagh3
1Department of Electronics and Telecommunication, Xavier Institute of Engineering, Mumbai, India.
Insights
Electroencephalogram (EEG) analysis reveals distinct brain patterns in children with Attention-Deficit Hyperactivity Disorder (ADHD). This study achieved 96% accuracy in identifying ADHD using EEG data, offering a potential diagnostic aid.
Area of Science:
- Neuroscience
- Pediatric Psychiatry
- Biomedical Engineering
Background:
- Attention-Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder in children.
- ADHD frequently co-occurs with learning deficits, anxiety, depression, and other behavioral disorders.
- Accurate and early diagnosis of ADHD is crucial for effective intervention.
Purpose of the Study:
- To investigate distinct Electroencephalogram (EEG) patterns in children diagnosed with ADHD.
- To evaluate the efficacy of machine learning algorithms in classifying ADHD based on EEG features.
- To identify specific electrode sites associated with ADHD-related EEG characteristics.
Main Methods:
- Resting-state, open-eye EEG signals were recorded from 61 children with ADHD and 60 healthy controls.
- Morphological and Power Spectral Density (PSD) features were extracted from EEG data.
- Principal Component Analysis (PCA) was used for dimensionality reduction, followed by classification using AdaBoost, K-Nearest Neighbour (KNN), Naive Bayes, and random forest algorithms.
Main Results:
- The Bernoulli Naive Bayes classifier achieved the highest diagnostic accuracy of 96%.
- Significant EEG characteristics for ADHD classification were identified at frontal (F), central (C), and parietal (P) electrode sites.
- Distinct EEG patterns were observed in children with ADHD compared to healthy controls.
Conclusions:
- EEG signal analysis, particularly using specific features and machine learning, shows promise for ADHD diagnosis in children.
- The findings suggest that EEG can serve as a supplementary tool to aid in the diagnosis of ADHD.
- Further research can refine EEG-based diagnostic methods for improved clinical application.
Abstract:
"Attention-Deficit Hyperactivity Disorder (ADHD)" is a neuro-developmental disorder in children under 12 years old. Learning deficits, anxiety, depression, sensory processing disorder, and oppositional defiant disorder are the most frequent comorbidities of ADHD. This research focuses on ADHD in children, considering its common occurrence and frequent coexistence with other mental disorders. The study utilizes the resting-state open-eye "Electroencephalogram" (EEG) signals of 61 children with ADHD and 60 healthy children. Morphological and "Power Spectral Density" (PSD) features associated with ADHD are analysed and "Principal Component Analysis" (PCA) is employed to reduce data dimensionality. Classification algorithms including AdaBoost, "K-Nearest Neighbour" (KNN) classifier, Naive Bayes, and random forest are utilized, with the Bernoulli Naive Bayes classifier achieving the highest accuracy of 96%. This study found some relevant characteristics for classification at the frontal (F), central (C), and parietal (P) electrode placement sites. Finally, this reveals distinct EEG patterns in children with ADHD and the study provides a potential supplementary method for ADHD diagnosis.
