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
Latest papers with no code
Real-Time Diagnostic Integrity Meets Efficiency: A Novel Platform-Agnostic Architecture for Physiological Signal Compression
Head-based signals such as EEG, EMG, EOG, and ECG collected by wearable systems will play a pivotal role in clinical diagnosis, monitoring, and treatment of important brain disorder diseases.
Deep Learning Approaches for Seizure Video Analysis: A Review
Historically, these approaches have been used for disease detection, classification, and prediction using diagnostic data; however, there has been limited exploration of their application in evaluating video-based motion detection in the clinical epileptology setting.
Seizure detection from Electroencephalogram signals via Wavelets and Graph Theory metrics
Here we apply the Maximum Overlap Discrete Wavelet Transform to both reduce signal \textit{noise} and use signal variance exhibited at differing inherent frequency levels to develop various metrics of connection between the electrodes placed upon the scalp.
READS-V: Real-time Automated Detection of Epileptic Seizures from Surveillance Videos via Skeleton-based Spatiotemporal ViG
An accurate and efficient epileptic seizure onset detection system can significantly benefit patients.
Enhancing Epileptic Seizure Detection with EEG Feature Embeddings
Here, we propose to enhance the seizure detection performance by learning informative embeddings of the EEG signal.
Privacy-preserving Early Detection of Epileptic Seizures in Videos
In this work, we contribute towards the development of video-based epileptic seizure classification by introducing a novel framework (SETR-PKD), which could achieve privacy-preserved early detection of seizures in videos.
EpiDeNet: An Energy-Efficient Approach to Seizure Detection for Embedded Systems
The EpiDeNet-SSWCE method demonstrates effective and accurate seizure detection performance on heavily imbalanced datasets, while being suited for implementation on energy-constrained platforms.
Protecting the Future: Neonatal Seizure Detection with Spatial-Temporal Modeling
A timely detection of seizures for newborn infants with electroencephalogram (EEG) has been a common yet life-saving practice in the Neonatal Intensive Care Unit (NICU).
Ongoing EEG artifact correction using blind source separation
Significance: The presented algorithm may be useful for ongoing correction of artifacts, e. g., in online systems for epileptic spike and seizure detection or brain-computer interfaces.
Reporting existing datasets for automatic epilepsy diagnosis and seizure detection
More than 50 million individuals are affected by epilepsy, a chronic neurological disorder characterized by unprovoked, recurring seizures and psychological symptoms.
