Papers › Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models

Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models

21 Nov 2022arXiv:2211.11176archive 2025-07-28

Siyi Tang, Jared A. Dunnmon, Liangqiong Qu, Khaled K. Saab, Tina Baykaner, Christopher Lee-Messer, Daniel L. Rubin

Multivariate biosignals are prevalent in many medical domains, such as electroencephalography, polysomnography, and electrocardiography. Modeling spatiotemporal dependencies in multivariate biosignals is challenging due to (1) long-range temporal dependencies and (2) complex spatial correlations between the electrodes. To address these challenges, we propose representing multivariate biosignals as time-dependent graphs and introduce GraphS4mer, a general graph neural network (GNN) architecture that improves performance on biosignal classification tasks by modeling spatiotemporal dependencies in biosignals. Specifically, (1) we leverage the Structured State Space architecture, a state-of-the-art deep sequence model, to capture long-range temporal dependencies in biosignals and (2) we propose a graph structure learning layer in GraphS4mer to learn dynamically evolving graph structures in the data. We evaluate our proposed model on three distinct biosignal classification tasks and show that GraphS4mer consistently improves over existing models, including (1) seizure detection from electroencephalographic signals, outperforming a previous GNN with self-supervised pre-training by 3.1 points in AUROC; (2) sleep staging from polysomnographic signals, a 4.1 points improvement in macro-F1 score compared to existing sleep staging models; and (3) 12-lead electrocardiogram classification, outperforming previous state-of-the-art models by 2.7 points in macro-F1 score.

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Activation tsy935/graphs4mer/model/s4.py official repository ran · our draft was wrong MIT (permissive) · 89b10a8b26bbb7ed · report
LinearActivation tsy935/graphs4mer/model/s4.py official repository ran · our draft was wrong MIT (permissive) · 400c752a0d8d06f7 · report
get_logger tsy935/graphs4mer/model/s4.py official repository ran · our draft was wrong MIT (permissive) · 61139ec62260b411 · report
Constant tsy935/graphs4mer/utils/schedulers.py official repository unverified MIT (permissive) · 1e0febe289a36b3a · report
InvSqrt tsy935/graphs4mer/utils/schedulers.py official repository unverified MIT (permissive) · 1c54b8619226bf7e · report
calculate_cosine_decay_weight tsy935/graphs4mer/model/graphs4mer.py official repository unverified MIT (permissive) · 751d8cfeff34eb71 · report
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eval_dict tsy935/graphs4mer/utils/utils.py official repository unverified MIT (permissive) · 466fd9ef12df241b · report
feature_smoothing tsy935/graphs4mer/model/graphs4mer.py official repository unverified MIT (permissive) · 52d6eda252b11940 · report
get_save_dir tsy935/graphs4mer/utils/utils.py official repository unverified MIT (permissive) · c1aba344be9c7d80 · report
masked_rmse_np tsy935/graphs4mer/utils/utils.py official repository unverified MIT (permissive) · 53f3b296ffe2e5e2 · report
str2bool tsy935/graphs4mer/args.py official repository unverified MIT (permissive) · facb9be7b42eb54b · report

Tasks

ClassificationGraph Neural NetworkGraph structure learningSeizure DetectionSleep StagingState Space Models

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Methods

Graph Neural Network

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