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An approach to arousal disorder classification using deformable convolution and adaptive multiscale features in EEG
Andia Foroughi1, Fardad Farokhi1, Fereidoun Nowshiravan Rahatabad1
1Department of Biomedical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Brain Research Bulletin
|July 17, 2025
Summary
Automated arousal disorder classification using Electroencephalography (EEG) and a novel deformable convergence network achieved over 96% accuracy. This method offers efficient and precise detection, aiding early diagnosis of neuropathologies like Parkinson's and Alzheimer's disease.
Area of Science:
- Neuroscience
- Medical Technology
- Artificial Intelligence
Background:
- Diagnosing sleep disorders via Polysomnography (PSG) is time-consuming.
- Automated analysis of sleep events, particularly arousal disorders, is crucial for early detection of neuropathologies.
- Electroencephalography (EEG) data is underutilized due to manual analysis, despite its diagnostic potential.
Purpose of the Study:
- To develop and evaluate a novel automated method for classifying arousal disorders from EEG data.
- To introduce a hierarchical multiscale deformable attention module for analyzing complex EEG patterns.
- To assess the model's performance in handling imbalanced data and reducing false positive rates in arousal detection.
Main Methods:
- EEG data segmented into 30-second windows and converted to spectrogram images.
- Application of a novel hierarchical multiscale deformable attention module for classification.
- Analysis of data from 994 participants in the 2018 PhysioNet Challenge, including multimodal signal fusion (EEG + ECG).
Main Results:
- The proposed deformable convergence network achieved an accuracy exceeding 96%, outperforming existing multi-scale channel attention modules.
- The model demonstrated effectiveness in handling imbalanced classification and reducing false positive rates.
- Integrating EEG with ECG (multimodal fusion) significantly enhanced classification performance.
Conclusions:
- The novel automated method provides an objective, efficient, and precise approach for examining arousal disorders from EEG data.
- Early detection of arousal disorders through this method can aid in the timely intervention for neurodegenerative diseases.
- Combining cortical (EEG) and autonomic (ECG) information improves the accuracy of arousal disorder detection.
