Seizure Detection
27 papers with code • 2 benchmarks • 8 datasets
Seizure Detection is a binary supervised classification problem with the aim of classifying between seizure and non-seizure states of a patient.
Source: ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification
Benchmarks
These leaderboards are used to track progress in Seizure Detection
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Datasets
CHB-MIT
TUH EEG Seizure Corpus
Most implemented papers
Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis
tsy935/eeg-gnn-ssl
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ICLR 2022
Automated seizure detection and classification from electroencephalography (EEG) can greatly improve seizure diagnosis and treatment.
Systematic Assessment of Hyperdimensional Computing for Epileptic Seizure Detection
Furthermore, we evaluate a post-processing strategy to adjust the predictions to the dynamics of epileptic seizures, showing that performance is significantly improved in all the approaches and also that after post-processing, differences in performance are much smaller between approaches.
Efficient Epileptic Seizure Detection Using CNN-Aided Factor Graphs
We propose a computationally efficient algorithm for seizure detection.
Multi-Centroid Hyperdimensional Computing Approach for Epileptic Seizure Detection
At the same time, the total number of sub-classes is not significantly increased compared to the balanced dataset.
Scalable Machine Learning Architecture for Neonatal Seizure Detection on Ultra-Edge Devices
vishaln15/NeonatalSeizureDetection
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Since there is a need for the classification of bio-signals to be computationally inexpensive in the case of seizure detection, this research presents a machine learning (ML) based architecture that operates with comparable predictive performance as previous models but with minimum level configuration.
Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting
aitrics/eeg_real_time_seizure_detection
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Electroencephalogram (EEG) is an important diagnostic test that physicians use to record brain activity and detect seizures by monitoring the signals.
Exploration of Hyperdimensional Computing Strategies for Enhanced Learning on Epileptic Seizure Detection
Yet, most of them have not been tested on the challenging task of epileptic seizure detection, and it stays unclear whether they can increase the HD computing performance to the level of the current state-of-the-art algorithms, such as random forests.
Ensemble learning using individual neonatal data for seizure detection
anaborovac/distributed-nsda
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The weighted mean aggregation scheme showed best performance, it was only marginally outperformed by the Dawid--Skene method when local detectors approach performance of a single detector trained on all available data.
MICAL: Mutual Information-Based CNN-Aided Learned Factor
Since the soft estimates obtained as the combined features from the neural MI estimator and the CNN do not capture the temporal correlation between different EEG blocks, we use them not as estimates of the seizure state, but to compute the function nodes of a factor graph.
Seizure Detection and Prediction by Parallel Memristive Convolutional Neural Networks
coreylammie/memristive-seizure-detection-and-prediction-by-parallel-convolutional-neural-networks
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During the past two decades, epileptic seizure detection and prediction algorithms have evolved rapidly.
