Epileptic Seizure Detection Based on Stockwell Transform and Bidirectional Long Short-Term Memory

In recent decades, an epileptic seizure is a neurological disorder, which is commonly detected from intracranial Electroencephalogram (iEEG) signals. However, the visual interpretation and inspection of iEEG signal is subjective variability, a time-consuming mechanism, slow and vulnerable to errors. In this research article, an automated epileptic seizure detection model is proposed to highlight the above-mentioned concerns. The proposed automated model integrates the Reconstruction Independent Component Analysis (RICA) and Long Short Term Memory (LSTM) for seizure detection. In the proposed model, RICA is utilized to extract the features from the normalized iEEG signals, and then the obtained feature vectors are fed to the LSTM network for classification, which effectively classifies inter-ictal and ictal iEEG signals. This experimental outcome showed that the proposed RICA-LSTM model achieved an accuracy of 98.92%, sensitivity of 99.01%, specificity of 98.68%, balanced accuracy of 99.24%, and f-score of 98.25% in epileptic seizure recognition on the SWEC-ETHZ iEEG database, which is better compared to the conventional machine learning classifiers.

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Automatic epileptic seizure recognition using reliefF feature selection and long short term memory classifier

Journal of Ambient Intelligence and Humanized Computing ◽

10.1007/s12652-020-02185-7 ◽

2020 ◽

Author(s):

Hirald Dwaraka Praveena ◽

C. Subhas ◽

K. Rama Naidu

Keyword(s):

Feature Selection ◽

Epileptic Seizure ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

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Patient-Independent Seizure Detection Based on Channel-Perturbation Convolutional Neural Network and Bidirectional Long Short-Term Memory

International Journal of Neural Systems ◽

10.1142/s0129065721500519 ◽

2021 ◽

Author(s):

Guoyang Liu ◽

Lan Tian ◽

Weidong Zhou

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Short Term Memory ◽

Perturbation Technique ◽

Seizure Detection ◽

Detection Methods ◽

Short Term ◽

Term Memory ◽

Scalp Eeg ◽

Long Short Term Memory

Automatic seizure detection is of great significance for epilepsy diagnosis and alleviating the massive burden caused by manual inspection of long-term EEG. At present, most seizure detection methods are highly patient-dependent and have poor generalization performance. In this study, a novel patient-independent approach is proposed to effectively detect seizure onsets. First, the multi-channel EEG recordings are preprocessed by wavelet decomposition. Then, the Convolutional Neural Network (CNN) with proper depth works as an EEG feature extractor. Next, the obtained features are fed into a Bidirectional Long Short-Term Memory (BiLSTM) network to further capture the temporal variation characteristics. Finally, aiming to reduce the false detection rate (FDR) and improve the sensitivity, the postprocessing, including smoothing and collar, is performed on the outputs of the model. During the training stage, a novel channel perturbation technique is introduced to enhance the model generalization ability. The proposed approach is comprehensively evaluated on the CHB-MIT public scalp EEG database as well as a more challenging SH-SDU scalp EEG database we collected. Segment-based average accuracies of 97.51% and 93.70%, event-based average sensitivities of 86.51% and 89.89%, and average AUC-ROC of 90.82% and 90.75% are yielded on the CHB-MIT database and SH-SDU database, respectively.

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