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Epileptic Seizures Prediction Using Deep Learning Techniques
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DOI：10.1109/ACCESS.2020.2976866.png)
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摘要
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Epilepsy is a very common neurological disease that has affected more than 65 million people worldwide. In more than 30 & x0025; of the cases, people affected by this disease cannot be cured with medicines or surgery. However, predicting a seizure before it actually occurs can help in its prevention; through therapeutic intervention. Studies have observed that abnormal activity inside the brain begins a few minutes before the start of a seizure, which is known as preictal state. Many researchers have tried to find a way for predicting this preictal state of a seizure but an effective prediction in terms of high sensitivity and specificity still remains a challenge. The current study, proposes a seizure prediction system that employs deep learning methods. This method includes preprocessing of scalp EEG signals, automated features extraction; using convolution neural network and classification with the support of vector machines. The proposed method has been applied on 24 subjects of scalp EEG dataset of CHBMIT resulting in successfully achieving an average sensitivity and specificity of 92.7 & x0025; and 90.8 & x0025; respectively.
Keyword:
Electroencephalography
Feature extraction
Scalp
Machine learning
Prediction methods
Support vector machines
Electrodes
Epilepsy prediction
seizures
preictal state
scalp EEG
intracranial EEG
deep learning
CNN
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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Chaos feature study in fractional Fourier domain for preictal prediction of epileptic seizure.png)
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