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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
Datasets
CHB-MIT
TUH EEG Seizure Corpus
Latest papers with no code
Lightweight Inference for Forward-Forward Algorithm
The human brain performs tasks with an outstanding energy-efficiency, i. e., with approximately 20 Watts.
Multimodal wearable EEG, EMG and accelerometry measurements improve the accuracy of tonic-clonic seizure detection in-hospital
The combination of wearable EEG and EMG achieved overall the most clinically useful performance in offline TCS detection with a sensitivity of 97. 7%, a FPR of 0. 4/24 h, a precision of 43. 0%, and a F1-score of 59. 7%.
Characterizing Accuracy Trade-offs of EEG Applications on Embedded HMPs
We present a combinatorial evaluation of power-performance-accuracy trade-offs of EEG applications at different approximation, power, and performance levels to provide insights into the disciplined tuning of approximation in EEG applications on embedded platforms.
Semantic segmentation for recognition of epileptiform patterns recorded via Microelectrode Arrays in vitro
To address this challenge, we here present two lightweight algorithms, the ZdensityRODE and the AMPDE, for identifying relevant events from LFPs by utilizing semantic segmentation, which involves extracting different levels of information from the LFP and relevant events from it.
Multi-Modal Machine Learning Framework for Automated Seizure Detection in Laboratory Rats
This article proposes a system that combines results from several types of models, all of which are trained on different data signals.
Epilepsy Seizure Detection and Prediction using an Approximate Spiking Convolutional Transformer
Epilepsy is a common disease of the nervous system.
EEGFormer: Towards Transferable and Interpretable Large-Scale EEG Foundation Model
Self-supervised learning has emerged as a highly effective approach in the fields of natural language processing and computer vision.
Multi-Dimensional Framework for EEG Signal Processing and Denoising Through Tensor-based Architecture
Electroencephalography (EEG) stands as a crucial tool in neuroscientific research and clinical diagnostics, providing valuable insights into the electrical activities of the brain.
An Unobtrusive and Lightweight Ear-worn System for Continuous Epileptic Seizure Detection
Epilepsy is one of the most common neurological diseases globally, affecting around 50 million people worldwide.
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.
