Author

Ang-Chao Duan

View on Semantic Scholar

2 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Sep 2026

A dual-branch time-frequency fusion network for EEG-based classification of Alzheimer's disease and frontotemporal dementia.

BackgroundAlzheimer's disease (AD) and frontotemporal dementia (FTD) exhibit substantial overlap in clinical manifestations and patterns of brain functional degeneration, which poses significant challenges for automated classification based on electroencephalography (EEG).ObjectiveThis study aims to develop an EEG-based framework capable of simultaneously capturing temporal dynamics and frequency-related characteristics of EEG signals for discrimination among AD, FTD, and cognitively normal (CN) subjects.MethodsA Dual-Branch Time-Frequency Fusion Network (DBTF-Net) based on routine clinical resting-state EEG recordings acquired under eyes-closed conditions is proposed. The model employs parallel temporal and frequency branches to process raw EEG time-series signals and their corresponding time-frequency representations. A global temporal dependency construction mechanism is introduced in the temporal branch to capture both local temporal patterns and long-range temporal dependencies. Feature-level fusion is then performed across the two branches to achieve a collaborative representation of multidimensional brain functional information. The proposed method was systematically evaluated on one three-class classification task (AD versus FTD versus CN) and multiple binary classification tasks.ResultsExperimental results from five-fold cross-validation at the epoch level show the classification accuracies of DBTF-Net as 86.36%±4.28%, 83.01%±6.15%, 92.13%±10.35%, and 88.74%±7.69%% for AD versus FTD versus CN, AD versus CN, FTD versus CN, and AD versus FTD, respectively.ConclusionsThe proposed DBTF-Net leverages temporal and time-frequency information in EEG signals and provides classification of AD and FTD. Visualization analysis further indicates that the model attends to disease-relevant discriminative patterns in time-frequency representations, enhancing the interpretability of its classification decisions.

Xiao-Li Yang, Xiao Li, Chen-Chen Wang et al. · Journal of Alzheimer's Disea... · 0 citations

Open access Aug 2026

Bidirectional cross-modal attention with adaptive gating for multimodal Alzheimer’s disease classification

Alzheimer’s disease (AD) is a neurodegenerative disorder, and mild cognitive impairment (MCI) represents a transitional stage between AD and cognitively normal (CN) individuals. Early diagnosis is clinically important for delaying disease progression. To address the limitations of single-modal approaches and the insufficient modeling of complex cross-modal interactions in existing multimodal fusion methods, this paper proposes a multimodal deep learning (DL) classification framework integrating structural magnetic resonance imaging (sMRI) and clinical features. The framework employs a 3D ResNet-34 to extract imaging features and a multilayer perceptron to encode clinical data. A bidirectional cross-modal attention mechanism enhances associations between imaging and clinical modalities, followed by an adaptive gated fusion module that dynamically integrates concatenated global multimodal features with cross-modal interaction features. To evaluate the robustness of the proposed framework, all experiments were repeated using five different random seeds, and the results are reported as mean ± standard deviation. Experimental results demonstrate competitive performance across multiple classification tasks, achieving accuracies of 95.67% ± 1.70% for three-class classification (CN vs MCI vs AD) and 93.64% ± 2.36% for four-class classification (CN vs early MCI (EMCI) vs late MCI (LMCI) vs AD). For binary classification (AD vs CN, AD vs MCI, MCI vs CN, and EMCI vs LMCI), the method achieves accuracies of 96.83% ± 1.53%, 95.33% ± 1.00%, 95.83% ± 1.39%, and 93.61% ± 2.35%, respectively. The proposed framework provides an effective solution for multimodal DL-based computer-aided diagnosis of AD.

Xiao-Li Yang, Chen-Chen Wang, Xiao Li et al. · Biomedical engineering and p... · 0 citations