Seizure Prediction in EEG Signals Using STFT and Domain Adaptation

Frontiers in Neuroscience

Neuroscience

Lu Yang¹  Peizhen Peng²  Haikun Wei²  Yang Song³ 

[1] Epilepsy Center, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China;Key Laboratory of Measurement and Control of Control Science and Engineering (CSE), Ministry of Education, School of Automation, Southeast University, Nanjing, China;State Grid Nanjing Power Supply Company, Nanjing, China;

关键词: seizure prediction;    feature extraction;    neuropsychiatric disorders;    domain adaptation;    STFT;    EEG;   

DOI  :  10.3389/fnins.2021.825434

 received in 2021-11-30, accepted in 2021-12-22,  发布年份 2022

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

Unknown   
Copyright © 2022 Peng, Song, Yang and Wei.