Abstract
Epileptic seizure prediction is one of the most used therapeutic adjuvant strategies for drug-resistant epilepsy. Conventional approaches commonly collect training and testing samples from the same patient due to inter-individual variability. However, the challenging problem of domain shift between various subjects remains unsolved, resulting in a low conversion rate to the clinic. In this work, a domain adaptation (DA)-based model is proposed to circumvent this issue. The short-time Fourier transform (STFT) is employed to extract the time-frequency features from raw EEG data, and an autoencoder is developed to map these features into high-dimensional space. By minimizing the inter-domain distance in the embedding space, this model learns the domain-invariant information, such that the generalization ability is improved by distribution alignment. Besides, to increase the feasibility of its application, this work mimics the data distribution under the clinical sampling situation and tests the model under this condition, which is the first study that adopts the assessment strategy. Experimental results on both intracranial and scalp EEG databases demonstrate that this method can minimize the domain gap effectively compared with previous approaches.
Highlights
To demonstrate the advantages over conventional methods, we select four seizure prediction researches for comparison: FTCNN (Truong et al, 2018), phase locking value (PLV) (Cho et al, 2016), spectral band power (SBP) (Ozcan and Erturk, 2019), and Wav-Convolutional neural network (CNN) (Khan et al, 2017)
The results suggest that the precision can increase by 3% for intracranial EEG and 4% for scalp EEG by applying the maximum mean discrepancy (MMD) measure, which demonstrates the advantage of MMD measure on the seizure prediction task
By combining short-time Fourier transform (STFT) with MMD-AAE, our model reduces the effects of epileptic domain variance and improves the generalization ability
Summary
Epilepsy is a brain disorder characterized by the transient occurrence of unexpected seizures, which stems from excessive, or hypersynchronous neuronal activities (Fisher et al, 2005). It affects approximately 1.0% of the world’s population (Banerjee et al, 2009), and around half of them experience severe seizures. The anti-epileptic drug (AED) administration is applied to patients, about 30% of them suffer from drug-resistant epilepsy (Kwan et al, 2011; Lin et al, 2014) These individuals might have seizures at any moment, such that their daily lives are influenced by unexpected behavioral changes, loss of muscular control and sudden faint. Many EEG-based studies regarding seizure prediction have been proposed
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