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Epileptic seizure prediction using scalp electroencephalogram signals
- Source :
- Biocybernetics and Biomedical Engineering. 41:211-220
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- Epilepsy is a brain disorder in which patients undergo frequent seizures. Around 30% of patients affected with epilepsy cannot be treated with medicines/surgical procedures. Abnormal activity, known as the preictal state starts few minutes before the seizure actually occurs. Therefore, it may be possible to deliver medication prior to the occurrence of a seizure if initiation of the preictal state can predicted before the seizure onset. We propose an epileptic seizure prediction method that predicts the preictal state before the seizure onset using electroencephalogram (EEG) monitoring of brain activity. It involves three steps including preprocessing of EEG signals, feature extraction classification of preictal and interictal states. In our proposed method, we have used (i) Empirical model decomposition to remove noise from the EEG signals and Generative Adversarial Networks to generate preictal samples to deal with the class imbalance problem; (ii) Automated features have been extracted with three layer Convolutional Neural Networks and (iii) Classification between preictal and interictal states is done with Long Short Term Memory units. In this study, we have used CHBMIT dataset of scalp EEG signals and have validated our proposed method on 22 subjects of dataset. Our proposed seizure prediction method is able to achieve 93% sensitivity and 92.5% specificity with average time of 32 min to predict the seizure's onset. Results obtained from our method have been compared with recent state-of-the-art epileptic seizure prediction methods. Our proposed method performs better in terms of sensitivity, specificity and average anticipation time.
- Subjects :
- medicine.diagnostic_test
Brain activity and meditation
business.industry
Computer science
0206 medical engineering
Feature extraction
Biomedical Engineering
Pattern recognition
02 engineering and technology
Scalp electroencephalogram
Electroencephalography
medicine.disease
020601 biomedical engineering
Convolutional neural network
Epilepsy
0202 electrical engineering, electronic engineering, information engineering
medicine
020201 artificial intelligence & image processing
Ictal
Artificial intelligence
Epileptic seizure
medicine.symptom
business
Subjects
Details
- ISSN :
- 02085216
- Volume :
- 41
- Database :
- OpenAIRE
- Journal :
- Biocybernetics and Biomedical Engineering
- Accession number :
- edsair.doi...........a098aef0efa06a0ab686e07f5280fbe7