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Seizure Detection
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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
논문
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A foundation model with multi-variate parallel attention to generate neuronal activity
EEG-Based Inter-Patient Epileptic Seizure Detection Combining Domain Adversarial Training with CNN-BiLSTM Network
Time series saliency maps: explaining models across multiple domains
SzCORE as a benchmark: report from the seizure detection challenge at the 2025 AI in Epilepsy and Neurological Disorders Conference
Machine-Learning-Powered Neural Interfaces for Smart Prosthetics and Diagnostics
The use of Multi-domain Electroencephalogram Representations in the building of Models based on Convolutional and Recurrent Neural Networks for Epilepsy Detection
Augmentation of EEG and ECG Time Series for Deep Learning Applications: Integrating Changepoint Detection into the iAAFT Surrogates
SeizureTransformer: Scaling U-Net with Transformer for Simultaneous Time-Step Level Seizure Detection from Long EEG Recordings
Improving Neonatal Care: An Active Dry-Contact Electrode-based Continuous EEG Monitoring System with Seizure Detection
Automated Video-EEG Analysis in Epilepsy Studies: Advances and Challenges
BioSerenity-E1: a self-supervised EEG model for medical applications
MT-NAM: An Efficient and Adaptive Model for Epileptic Seizure Detection
The Case for Cleaner Biosignals: High-fidelity Neural Compressor Enables Transfer from Cleaner iEEG to Noisier EEG
SeizeIT2: Wearable Dataset Of Patients With Focal Epilepsy
CEReBrO: Compact Encoder for Representations of Brain Oscillations Using Efficient Alternating Attention
Automated Detection of Epileptic Spikes and Seizures Incorporating a Novel Spatial Clustering Prior
A Systematic Review of Machine Learning Methods for Multimodal EEG Data in Clinical Application
Canine EEG Helps Human: Cross-Species and Cross-Modality Epileptic Seizure Detection via Multi-Space Alignment
Real-time Sub-milliwatt Epilepsy Detection Implemented on a Spiking Neural Network Edge Inference Processor
BUNDL: Bayesian Uncertainty-aware Deep Learning with Noisy training Labels for Seizure Detection in EEG
From Epilepsy Seizures Classification to Detection: A Deep Learning-based Approach for Raw EEG Signals
Designing Pre-training Datasets from Unlabeled Data for EEG Classification with Transformers
MetaWearS: A Shortcut in Wearable Systems Lifecycle with Only a Few Shots
Efficient Patient Fine-Tuned Seizure Detection with a Tensor Kernel Machine
EEGMamba: Bidirectional State Space Model with Mixture of Experts for EEG Multi-task Classification
BISeizuRe: BERT-Inspired Seizure Data Representation to Improve Epilepsy Monitoring
SincVAE: a New Approach to Improve Anomaly Detection on EEG Data Using SincNet and Variational Autoencoder
Energy-Efficient Seizure Detection Suitable for low-power Applications
Residual and bidirectional LSTM for epileptic seizure detection
Using Explainable AI for EEG-based Reduced Montage Neonatal Seizure Detection
Learning Robust Representations of Tonic-Clonic Seizures With Cyclic Transformer
EPIDetect: Video-based convulsive seizure detection in chronic epilepsy mouse model for anti-epilepsy drug screening
Deep Latent Variable Modeling of Physiological Signals
Study on spike-and-wave detection in epileptic signals using t-location-scale distribution and the K-nearest neighbors classifier
Scaling convolutional neural networks achieves expert-level seizure detection in neonatal EEG
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