A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection
Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …
generated in great volume by both physical sensors and online processes (virtual sensors) …
Deep learning for time series classification and extrinsic regression: A current survey
Time Series Classification and Extrinsic Regression are important and challenging machine
learning tasks. Deep learning has revolutionized natural language processing and computer …
learning tasks. Deep learning has revolutionized natural language processing and computer …
Hungry hungry hippos: Towards language modeling with state space models
State space models (SSMs) have demonstrated state-of-the-art sequence modeling
performance in some modalities, but underperform attention in language modeling …
performance in some modalities, but underperform attention in language modeling …
Contrast everything: A hierarchical contrastive framework for medical time-series
Y Wang, Y Han, H Wang… - Advances in Neural …, 2024 - proceedings.neurips.cc
Contrastive representation learning is crucial in medical time series analysis as it alleviates
dependency on labor-intensive, domain-specific, and scarce expert annotations. However …
dependency on labor-intensive, domain-specific, and scarce expert annotations. However …
Learning topology-agnostic eeg representations with geometry-aware modeling
Large-scale pre-training has shown great potential to enhance models on downstream tasks
in vision and language. Developing similar techniques for scalp electroencephalogram …
in vision and language. Developing similar techniques for scalp electroencephalogram …
Self-supervised contrastive learning for medical time series: A systematic review
Medical time series are sequential data collected over time that measures health-related
signals, such as electroencephalography (EEG), electrocardiography (ECG), and intensive …
signals, such as electroencephalography (EEG), electrocardiography (ECG), and intensive …
Brant: Foundation model for intracranial neural signal
We propose a foundation model named Brant for modeling intracranial recordings, which
learns powerful representations of intracranial neural signals by pre-training, providing a …
learns powerful representations of intracranial neural signals by pre-training, providing a …
Learning decomposed spatial relations for multi-variate time-series modeling
Modeling multi-variate time-series (MVTS) data is a long-standing research subject and has
found wide applications. Recently, there is a surge of interest in modeling spatial relations …
found wide applications. Recently, there is a surge of interest in modeling spatial relations …
[HTML][HTML] Epileptic multi-seizure type classification using electroencephalogram signals from the Temple University Hospital Seizure Corpus: A review
Epilepsy is one of the most paramount neurological diseases, affecting about 1% of the
world's population. Seizure detection and classification are difficult tasks and are ongoing …
world's population. Seizure detection and classification are difficult tasks and are ongoing …
Self-supervised learning for anomalous channel detection in EEG graphs: Application to seizure analysis
TKK Ho, N Armanfard - Proceedings of the AAAI conference on artificial …, 2023 - ojs.aaai.org
Electroencephalogram (EEG) signals are effective tools towards seizure analysis where one
of the most important challenges is accurate detection of seizure events and brain regions in …
of the most important challenges is accurate detection of seizure events and brain regions in …