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AlCheNet: A Pathology-informed Graph Representation of Channel-wise EEG for Alzheimer’s Disease Classification
논문 기본 정보
- 저자정보
- 저널정보
- 한국정보통신학회 INTERNATIONAL CONFERENCE ON FUTURE INFORMATION & COMMUNICATION ENGINEERING 2025 INTERNATIONAL CONFERENCE ON FUTURE INFORMATION & COMMUNICATION ENGINEERING Vo.16 No.1
- 발행연도
- 2025.7
- 수록면
- 84 - 88 (5page)
피인용 0
AlCheNet: A Pathology-informed Graph Representation of Channel-wise EEG for Alzheimer’s Disease Classification
초록·키워드
Electroencephalogram (EEG) is widely used for clinical diagnosis and brain-computer interface (BCI) applications. In Alzheimer's disease (AD), disrupted functional connec-tivity between brain regions is a well-established biomarker. However, conventional EEG analysis methods often overlook inter-channel dependencies and are susceptible to noise due to the inherently low signal-to-noise ratio (SNR) of EEG signals, which limits robust feature extraction.
To address these limitations, we propose AlCheNet, a hybrid graph-based neural network that learns frequency-specific features and inter-channel dependencies in EEG data. The model first applies one-dimensional convolutions to extract frequency-specific features, then constructs a graph where each node corresponds to an EEG channel. A graph-based neural network is then employed to learn inter-channel relationships and denoise the signal by enforcing inter-channel smoothness. To avoid over-smoothing and preserve disease-relevant regional patterns, we used pathology-informed graph to guide adjacency matrix construction. We evaluated AlCheNet on a clinical EEG dataset comprising patients with AD, frontotemporal dementia, and healthy controls. AlCheNet, built upon the ChebNet architecture, outperformed a comparative ChebNet-based model by 1.82% in accuracy and 4.39% in F1-score, demonstrating superior representation learning capability
To address these limitations, we propose AlCheNet, a hybrid graph-based neural network that learns frequency-specific features and inter-channel dependencies in EEG data. The model first applies one-dimensional convolutions to extract frequency-specific features, then constructs a graph where each node corresponds to an EEG channel. A graph-based neural network is then employed to learn inter-channel relationships and denoise the signal by enforcing inter-channel smoothness. To avoid over-smoothing and preserve disease-relevant regional patterns, we used pathology-informed graph to guide adjacency matrix construction. We evaluated AlCheNet on a clinical EEG dataset comprising patients with AD, frontotemporal dementia, and healthy controls. AlCheNet, built upon the ChebNet architecture, outperformed a comparative ChebNet-based model by 1.82% in accuracy and 4.39% in F1-score, demonstrating superior representation learning capability
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