Key result
Mamba-based dynamic graph learning achieves state-of-the-art biosignal classification with ~0.92 AUROC for EEG seizure detection.
Why the study?
Modeling multivariate biosignals like EEG and ECG is challenging due to complex temporal patterns, long-range dependencies, and computational limitations of traditional models.
Does the proposed Mamba-based model improve classification performance of EEG and ECG signals compared to traditional models?
Does the proposed Mamba-based model improve classification performance of EEG and ECG signals compared to traditional models?
A novel Mamba-based architecture with dynamic graph learning achieves state-of-the-art performance in classifying multivariate biosignals including ECGs.
Needs prospective validation before clinical use; leaves open real-world generalizability of Mamba-based biosignal models.
Multivariate biosignals such as Electroencephalography (EEG) and Electrocardiography (ECG) are widely used for understanding various pathologies that affect the brain and cardiovascular activity. However, effectively modeling such signals is challenging due to their complex temporal patterns, long-range dependencies, and dynamically evolving structures. Traditional models, such as recurrent neural networks, convolutional neural networks, and Transformers, encounter challenges with long-term temporal modeling, scalability, and computational efficiency. In this work, we propose a novel model that integrates Mamba architecture, channel attention, and dynamic graph learning for efficient and adaptive long-range temporal modeling of biosignals. Specifically, We address the biosignals modeling problem through: (i) long-range temporal modeling using parallel Mamba layers that process both time and frequency domain representations; (ii) a low-cost channel attention mechanism that identifies discriminative sensor channels with minimal computational overhead; and (iii) dynamic graph structure learning that adapts graph representations over time to capture evolving spatial relationships in biosignal data. We validate the proposed approach on three benchmark datasets: TUSZ dataset (for EEG-based epileptic seizure detection), DOD-H dataset (for EEG-based sleep stage classification), and ICBEB dataset (for ECG-based cardio disease classification). The model achieves state-of-the-art performance on these datasets. Notably, ablation studies demonstrate the effectiveness of each proposed component.
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Jiahao et al. (2025) studied Epileptic seizures, sleep disorders, and cardiovascular diseases. Mamba architecture with dynamic graph learning and channel attention vs. Existing deep learning models (e.g., GraphS4MER, LSTM, ResNet1D) was evaluated on AUROC for EEG-based epileptic seizure detection (TUSZ dataset). The proposed Mamba-based framework with dynamic graph learning achieved state-of-the-art performance for biosignal classification, including an AUROC of 0.919 for EEG-based epileptic seizure detection.
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