Optimized deep neural network architecture for robust detection of epileptic seizures using EEG signals

Nov 26, 2018Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology

Improved deep learning method for reliable detection of epileptic seizures from brain signals

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Abstract

The proposed seizure detection approach achieves 100% classification accuracy, sensitivity, and specificity on a benchmark clinical dataset.

A deep Long Short-Term Memory (LSTM) network is utilized to learn high-level representations of EEG patterns.
A Fully Connected (FC) layer extracts robust EEG features that are relevant to detecting epileptic seizures.
The method demonstrates resilience in noisy conditions, maintaining high detection performance despite common EEG artifacts.
The approach shows superior performance compared to existing techniques in both ideal and real-life settings.

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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.

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Optimized deep neural network architecture for robust detection of epileptic seizures using EEG signals

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