Optimized deep neural network architecture for robust detection of epileptic seizures using EEG signals
| Autor: | Ramy Hussein, Hamid Palangi, Rabab K. Ward, Z. Jane Wang |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Electroencephalography 050105 experimental psychology Background noise 03 medical and health sciences Epilepsy Deep Learning 0302 clinical medicine Seizures Robustness (computer science) Physiology (medical) medicine Humans 0501 psychology and cognitive sciences Sensitivity (control systems) medicine.diagnostic_test business.industry Deep learning 05 social sciences Signal Processing Computer-Assisted Pattern recognition medicine.disease Sensory Systems ComputingMethodologies_PATTERNRECOGNITION Neurology Softmax function Benchmark (computing) Neural Networks Computer Neurology (clinical) Artificial intelligence business 030217 neurology & neurosurgery |
| Zdroj: | Clinical Neurophysiology. 130:25-37 |
| ISSN: | 1388-2457 |
| Popis: | Objective Automatic detection of epileptic seizures based on deep learning methods received much attention last year. However, the potential of deep neural networks in seizure detection has not been fully exploited in terms of the optimal design of the model architecture and the detection power of the time-series brain data. In this work, a deep neural network architecture is introduced to learn the temporal dependencies in Electroencephalogram (EEG) data for robust detection of epileptic seizures. Methods A deep Long Short-Term Memory (LSTM) network is first used to learn the high-level representations of different EEG patterns. Then, a Fully Connected (FC) layer is adopted to extract the most robust EEG features relevant to epileptic seizures. Finally, these features are supplied to a softmax layer to output predicted labels. Results The results on a benchmark clinical dataset reveal the prevalence of the proposed approach over the baseline techniques; achieving 100% classification accuracy, 100% sensitivity, and 100% specificity. Our approach is additionally shown to be robust in noisy and real-life conditions. It maintains high detection performance in the existence of common EEG artifacts (muscle activities and eye movement) as well as background noise. Conclusions We demonstrate the clinical feasibility of our seizure detection approach achieving superior performance over the cutting-edge techniques in terms of seizure detection performance and robustness. Significance Our seizure detection approach can contribute to accurate and robust detection of epileptic seizures in ideal and real-life situations. |
| Databáze: | OpenAIRE |
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