Electroencephalogram-Based ConvMixer Architecture for Recognizing Attention Deficit Hyperactivity Disorder in
1Nanjing Rehabilitation Medical Center, The Affiliated Brain Hospital, Nanjing Medical University, Nanjing 210029, China.
Insights
A new deep learning model, ConvMixer-ECA, accurately diagnoses Attention Deficit Hyperactivity Disorder (ADHD) using EEG signals. This AI tool shows promise for early ADHD detection in children.
Area of Science:
- Neuroscience
- Artificial Intelligence
- Medical Diagnostics
Background:
- Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder affecting 5-10% of school-aged children globally.
- Early diagnosis and intervention are critical for improving patient and family quality of life.
Purpose of the Study:
- To introduce ConvMixer-ECA, a novel deep learning architecture for accurate ADHD diagnosis using electroencephalogram (EEG) signals.
- To evaluate the efficacy of ConvMixer-ECA compared to existing deep learning models and attention-based variants.
Main Methods:
- Development and application of the ConvMixer-ECA deep learning model, integrating ConvMixer with Efficient Channel Attention (ECA) blocks.
- Training and evaluation using EEG recordings from 60 healthy children and 61 children diagnosed with ADHD.
- Performance comparison with state-of-the-art models (EEGNet, CNN, RNN, LSTM, GRU) and t-SNE visualization for feature analysis.
Main Results:
- ConvMixer-ECA achieved a high accuracy of 94.52% in recognizing ADHD from EEG data.
- The integration of ECA significantly enhanced ConvMixer's performance, outperforming other attention-based models.
- ConvMixer-ECA demonstrated superior performance compared to established deep learning architectures.
Conclusions:
- ConvMixer-ECA effectively captures distinguishing patterns between ADHD and typically developing individuals through hierarchical feature learning.
- The proposed model shows significant potential as a valuable tool for assisting clinicians in the early diagnosis and intervention of ADHD in children.
Abstract:
Attention deficit hyperactivity disorder (ADHD) is a neuro-developmental disorder that affects approximately 5-10% of school-aged children worldwide. Early diagnosis and intervention are essential to improve the quality of life of patients and their families. In this study, we propose ConvMixer-ECA, a novel deep learning architecture that combines ConvMixer with efficient channel attention (ECA) blocks for the accurate diagnosis of ADHD using electroencephalogram (EEG) signals. The model was trained and evaluated using EEG recordings from 60 healthy children and 61 children with ADHD. A series of experiments were conducted to evaluate the performance of the ConvMixer-ECA. The results showed that the ConvMixer-ECA performed well in ADHD recognition with 94.52% accuracy. The incorporation of attentional mechanisms, in particular ECA, improved the performance of ConvMixer; it outperformed other attention-based variants. In addition, ConvMixer-ECA outperformed state-of-the-art deep learning models including EEGNet, CNN, RNN, LSTM, and GRU. t-SNE visualization of the output of this model layer validated the effectiveness of ConvMixer-ECA in capturing the underlying patterns and features that separate ADHD from typically developing individuals through hierarchical feature learning. These outcomes demonstrate the potential of ConvMixer-ECA as a valuable tool to assist clinicians in the early diagnosis and intervention of ADHD in children.
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