Automatic Seizure Detection via an Optimized Image-Based Deep Feature Learning

In this paper, our goal is to find an optimized approach that can learn features from multichannel EEG time-series data to perform automatic seizure detection. In general, it is not easy to learn robust features from EEG signals due to the variations in both intra and inter-patient variability. However, to achieve good generalization, we use an algorithm that tries to capture spectral, temporal and spatial information, in contrast to standard EEG analysis techniques that ignore spatial aspects. The first stage of this algorithm is to transform EEG signals into a sequence of topology-preserving multi-spectral and temporal images. After that, these generated images are fed as inputs to a convolutional neural network. By overcoming the lack of data, especially the positive samples, and creating a process to deal with unbalanced datasets and optimizing the complexity of the network, our convolutional neural network learns a general spatially invariant representation of a seizure in a reasonable time and improves sensitivity, specificity and accuracy result comparable to the state-of-the-art results.

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