Adaptive long-range modeling of EEG and ECG with Mamba and dynamic graph learning

Abstract

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.