SOTAVerified

Seizure Detection

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–60 of 175 papers

TitleStatusHype
Augmentation of EEG and ECG Time Series for Deep Learning Applications: Integrating Changepoint Detection into the iAAFT Surrogates—0
Deep density ratio estimation for change point detection—0
Audio-Based Epileptic Seizure Detection—0
Deep Learning for EEG Seizure Detection in Preterm Infants—0
Shorter Latency of Real-time Epileptic Seizure Detection via Probabilistic Prediction—0
Deep Recurrent Neural Networks for seizure detection and early seizure detection systems—0
Designing Pre-training Datasets from Unlabeled Data for EEG Classification with Transformers—0
Novel Epileptic Seizure Detection Techniques and their Empirical Analysis—0
An Efficient Epileptic Seizure Detection Technique using Discrete Wavelet Transform and Machine Learning Classifiers—0
Deep Classification of Epileptic Signals—0
Show:102550
← PrevPage 6 of 18Next →
All datasetsTUH EEG Seizure CorpusCHB-MIT

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet+ LSTMAUROC0.92—Unverified
2CNN2D+LSTMAUROC0.92—Unverified
#ModelMetricClaimedVerifiedStatus
1TF-Tensor-CNNAccuracy89.63—Unverified