TABLE III.

A summary of contributions describing the application of RNNS for epileptic seizure detection.

Author	Learning taska	Model architecture	Performance	Datasetb
Thodoroff et al. 2016[69]	Whether a 30-second segment of EEG signal contains a seizure or not.	• In each 30s segment, the multi-channel signal was first coded into 3-channel images with 1s windows by spatial 2D projection and fast Fourier transform. • The image sequences were fed into CNN layers to extract local features. • The feature sequence was fed into a bidirectional LSTM, and a dense layer was applied on the RNN output through all the time steps for final decision-making.	Average sensitivity was 85% and positive rate was 0.8/hour.	CHB-MIT
Raghu et al. 2017 [70]	Three tasks: 1. Normal vs. pre-ictal; 2. normal vs. epileptic; 3. pre-ictal vs. epileptic.	• A Weiner filter first removed the 50Hz power line noise. • In the 1s segment, log energy entropy, wavelet packet log energy entropy, and wavelet packet norm entropy were calculated as input features. • Elman RNN with two hidden layers was used as a classifier.	The accuracies of three tasks were: 99.70%, 99.70% and 99.85%.	Uni Bonn
Ahmedt-Aristizabal et al. 2018 [71]	normal vs. inter-ictal vs. ictal	The raw EEG signals were directly fed to 2-layer LSTM, in which the output at the last step was fed into a dense layer for final decision-making.	The accuracy and average AUC were 95.54% and 0.9582.	Uni Bonn
Tsiouris et al. 2018[27]	pre-ictal vs. inter-ictal.	• First extracted 643 features in 5s chunk and then formed an input feature sequence for each signal segment. • Fed the sequence into 2-layer LSTM, followed by fully connected layers for final output.	The sensitivity and specificity were 99.28% and 99.60%.	CHB-MIT
Daoud et al. 2019[48]	pre-ictal vs. inter-ictal.	• Multi-channel raw signals were fed into the CNN layers or a pre-trained encoder to extract features. • The feature sequences were then fed into bidirectional LSTM, and the outputs at last step were concatenated for final decision-making.	Both models obtained 99.66% accuracy, 99.72% sensitivity, and 99.60% specificity.	CHB-MIT
Huang et al. 2019[72]	Seizure or not seizure.	• A channel dropout layer was first applied to the multi-channel EEG signals. The signals were then fed to the multi-scale CNN layers to extract multi-scale features, followed by an attention model. • The feature sequences were fed into bidirectional LSTM and GRU as two streams. The outputs of the RNN were concatenated as inputs for a dense layer at each time step. Then, the dense layer’s output sequences were fed into a global average pooling layer for final output.	Specificity and Sensitivity were 93.94% and 92.88%, respectively.	CHB-MIT
Abbasi et al. 2019[73]	Four tasks: 1. pre-ictal vs. ictal; 2. pre-ictal vs. inter-ictal; 3. inter-ictal vs. ictal; 4. pre-ictal vs. inter-ictal vs. ictal.	• The raw EEG signal was divided into five components by discrete cosine transform. • Hurst exponent and auto-regressive–moving-average features were extracted for each component. • The features were used as input of 2-layer LSTM for final output.	The accuracies for the four tasks were 99.17%, 97.78%, 97.78% and 94.81%.	Uni Bonn
Hussein et al. 2019[74]	Four tasks: 1. normal vs. ictal; 2. non-ictal vs. ictal; 3. normal vs. inter-ictal vs. ictal; 4. 5 type ictal states classificationc.	• The 4096*1 raw signal was first reshaped into 2048*2, and then fed into an LSTM with 2048 steps. • A dense layer was applied to the RNN output at all the time steps. • The output of the dense layer was fed into an averaging pooling layer, and a softmax layer was used for final decision-making.	All tasks reached 100% accuracy	Uni Bonn

^aFor the epileptic seizure studies, there are several states: normal state, which describes healthy subjects’ EEG signal without any seizure history; pre-ictal state, which is defined by the period just before the seizure; ictal state, which is during the seizure occurrence, post-ictal state, that is assigned to the period after the seizure took place; and post-ictal state, that is assigned to the period after the seizure took place.[75].

^bCHB-MIT: CHB( Children’s Hospital Boston)-MIT(Massachusetts Institute of Technology) Scalp EEG Database, contains 23 patients divided among 24 cases (a patient has two recordings). The dataset consists of 969 Hours of scalp EEG recordings with 173 seizures[15]. Uni Bonn: data samples were collected in the Department of Epileptology at Bonn University[76]. This database was divided into five sets named A, B, C, D, and E. Sets A and B included surface EEG signals collected from five healthy participants. Set A was recorded from the five participants when they were awake and rested with their eyes open, while set B was recorded when their eyes were closed. Sets C, D, and E included signals collected from the cerebral cortex of five epileptic patients. Set E was taken from those patients while experiencing active seizures, and sets C and D were recorded throughout the seizure-free interims. The electrodes of set D and set C were implanted within the brain epileptogenic zone and the hippocampal formation of the obverse cerebral hemisphere, respectively.

^cThe 5 types were A∼E sets provided by Uni Bonn dataset.