• Corpus ID: 246210355

Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting

  title={Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting},
  author={Kwanhyung Lee and Hyewon Jeong and Seyun Kim and Donghwa Yang and Hoon-Chul Kang and Edward Choi},
Electroencephalogram (EEG) is an important diagnostic test that physicians use to record brain activity and detect seizures by monitoring the signals. There have been several attempts to detect seizures and abnormalities in EEG signals with modern deep learning models to reduce the clinical burden. However, they cannot be fairly compared against each other as they were tested in distinct experimental settings. Also, some of them are not trained in real-time seizure detection tasks, making it… 


A Deep Learning-Based Real-time Seizure Detection System
Clinicians require automatic seizure detection tools that provide decisions with at least 75% sensitivity and less than 1 false alarm (FA) per 24 hours, and some commercial tools recently claim to reach such performance levels.
Interpretable Seizure Classification Using Unprocessed EEG With Multi-Channel Attentive Feature Fusion
A novel deep learning architecture with attention-driven data fusion using raw scalp EEG data from a 10–20 layout, where independent shallow deep networks are trained on each channel to ensure only salient features from individual channel encoders are captured.
Real-time Inference and Detection of Disruptive EEG Networks for Epileptic Seizures
A novel dynamic learning method is introduced that first infers a time-varying network constituted by multivariate EEG signals, which represents the overall dynamics of the brain network, and subsequently quantifies its topological property using graph theory.
Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals
Efficient Epileptic Seizure Prediction Based on Deep Learning
A novel patient-specific seizure prediction technique based on deep learning and applied to long-term scalp electroencephalogram (EEG) recordings is proposed to accurately detect the preictal brain state and differentiate it from the prevailing interictal state as early as possible and make it suitable for real time usage.
EEG signal classification using frequency band analysis towards epileptic seizure prediction
  • M. Parvez, M. Paul
  • Computer Science
    16th Int'l Conf. Computer and Information Technology
  • 2014
Experimental results demonstrate that the propose method outperforms the state-of-the-art method in terms of sensitivity, specificity and accuracy to classify seizure by analyzing EEG signals to the benchmark dataset in different brain locations.
Seizure Detection Using Time Delay Neural Networks and LSTMs
A neural network system using the time-delay neural network to model temporal information (TDNN) and long short term memory (LSTM) layer to model spatial information to identify seizures from EEG signals is proposed.
Hardware Design of Real Time Epileptic Seizure Detection Based on STFT and SVM
A real-time seizure detection algorithm based on STFT and support vector machine (SVM) and its field-programmable gate array (FPGA) implementation is proposed and the possibility of integrating the proposed algorithm and FPGA implementation into a wearable seizure control device is affirm.
Epileptic Seizure Prediction With Multi-View Convolutional Neural Networks
This work proposes a multi-view convolutional neural network framework to predict the occurrence of epilepsy seizures with the goal of acquiring a shared representation of time-domain and frequency-domain features.
A Multi-View Deep Learning Framework for EEG Seizure Detection
A new autoencoder-based multi-view learning model is constructed by incorporating both inter and intra correlations of EEG channels to unleash the power of multi-channel information by adding a channel-wise competition mechanism in the training phase.