Modeling multivariate biosignals with graph neural networks and structured state space models
Multivariate biosignals are prevalent in many medical domains, such as
electroencephalography, polysomnography, and electrocardiography. Modeling …
electroencephalography, polysomnography, and electrocardiography. Modeling …
Spatiotemporal modeling of multivariate signals with graph neural networks and structured state space models
Multivariate signals are prevalent in various domains, such as healthcare, transportation
systems, and space sciences. Modeling spatiotemporal dependencies in multivariate …
systems, and space sciences. Modeling spatiotemporal dependencies in multivariate …
DG-ECG: Multi-stream deep graph learning for the recognition of disease-altered patterns in electrocardiogram
Abstract Representation learning of electrocardiogram (ECG) has been an active research
field for the automated detection of cardiac disease. In addition to extracting time and …
field for the automated detection of cardiac disease. In addition to extracting time and …
Self-supervised graph neural networks for improved electroencephalographic seizure analysis
Automated seizure detection and classification from electroencephalography (EEG) can
greatly improve seizure diagnosis and treatment. However, several modeling challenges …
greatly improve seizure diagnosis and treatment. However, several modeling challenges …
[HTML][HTML] Graph-based deep learning for medical diagnosis and analysis: past, present and future
With the advances of data-driven machine learning research, a wide variety of prediction
problems have been tackled. It has become critical to explore how machine learning and …
problems have been tackled. It has become critical to explore how machine learning and …
Classify EEG and reveal latent graph structure with spatio-temporal graph convolutional neural network
X Li, B Qian, J Wei, A Li, X Liu… - 2019 IEEE International …, 2019 - ieeexplore.ieee.org
Electroencephalogram (EEG) is a test that detect brain activities using multiple electrodes
placed on the scalp. Multiple channels of EEG signals are recorded through the electrodes …
placed on the scalp. Multiple channels of EEG signals are recorded through the electrodes …
Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications
Graphs are a powerful tool for representing and analyzing unstructured, non-Euclidean data
ubiquitous in the healthcare domain. Two prominent examples are molecule property …
ubiquitous in the healthcare domain. Two prominent examples are molecule property …
Ptgb: Pre-train graph neural networks for brain network analysis
The human brain is the central hub of the neurobiological system, controlling behavior and
cognition in complex ways. Recent advances in neuroscience and neuroimaging analysis …
cognition in complex ways. Recent advances in neuroscience and neuroimaging analysis …
EEG-GAT: graph attention networks for classification of electroencephalogram (EEG) signals
Graph neural networks (GNN) are an emerging framework in the deep learning community.
In most GNN applications, the graph topology of data samples is provided in the dataset …
In most GNN applications, the graph topology of data samples is provided in the dataset …
Spatio-temporal-spectral hierarchical graph convolutional network with semisupervised active learning for patient-specific seizure prediction
Graph theory analysis using electroencephalogram (EEG) signals is currently an advanced
technique for seizure prediction. Recent deep learning approaches, which fail to fully …
technique for seizure prediction. Recent deep learning approaches, which fail to fully …