Key result
An imbalance learning approach using personalized Artificial Neural Networks achieved an average F-measure accuracy above 86% for classifying seizure events in the Physionet MIT dataset.
An imbalance learning approach using personalized neural networks achieves high accuracy for seizure detection in EEG data.
Does not support immediate clinical adoption for seizure monitoring; extends personalized neural network methods in EEG research.
Around 1% of world's population suffer from epileptic seizures which can lead to injuries and even unexpected death. Making use of EEG signals, which are proven to be the best indicators of seizures, we aim to build an Artificial Neural Networks to classify seizure and non-seizure events. However, the limited availability of seizure events in the EEG data makes it difficult for the automatic classifiers in general to accurately classify seizure events. To improve this, we propose an imbalance learning approach to improve accuracy of highly imbalanced seizure dataset. Since each patient provides a different response to the seizure, we personalize the classification models in terms of training data and model parameters. The proposed imbalance learning method provides an average F-measure accuracy above 86% for Physionet MIT dataset.
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Birjandtalab et al. (2018) studied Epileptic seizures. Imbalance learning approach using Artificial Neural Networks was evaluated on Average F-measure accuracy for seizure detection. An imbalance learning approach using personalized Artificial Neural Networks achieved an average F-measure accuracy above 86% for classifying seizure events in the Physionet MIT dataset.
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