Seizure Prediction in EEG Signals Using STFT and Domain Adaptation

Overview

Journal Front Neurosci

Date 2022 Feb 4

PMID 35115906

Authors

Peizhen Peng

Yang Song

Lu Yang

Haikun Wei

Affiliations

Soon will be listed here.

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.

Citing Articles

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Cross-patient seizure prediction via continuous domain adaptation and similar sample replay.

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Calibrating Deep Learning Classifiers for Patient-Independent Electroencephalogram Seizure Forecasting.

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A ResNet-LSTM hybrid model for predicting epileptic seizures using a pretrained model with supervised contrastive learning.

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