Cross-Subject Seizure Detection via Unsupervised Domain-Adaptation

Automatic seizure detection from Electroencephalography (EEG) is of great importance in aiding the diagnosis and treatment of epilepsy due to the advantages of convenience and economy. Existing seizure detection methods are usually patient-specific, the training and testing are carried out on the sa...

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Bibliographic Details
Published inInternational journal of neural systems Vol. 34; no. 10; p. 2450055
Main Authors Wang, Shuai, Feng, Hailing, Lv, Hongbin, Nie, Chenxi, Feng, Wenqian, Peng, Hao, Zhang, Lin, Zhao, Yanna
Format Journal Article
LanguageEnglish
Published Singapore 01.10.2024
Subjects
Deep Learning
Electroencephalography - methods
Humans
Neural Networks, Computer
Seizures - diagnosis
Seizures - physiopathology
Signal Processing, Computer-Assisted
Unsupervised Machine Learning
feature alignment
adversarial learning
seizure detection
domain adaptation
EEG
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Summary:Automatic seizure detection from Electroencephalography (EEG) is of great importance in aiding the diagnosis and treatment of epilepsy due to the advantages of convenience and economy. Existing seizure detection methods are usually patient-specific, the training and testing are carried out on the same patient, limiting their scalability to other patients. To address this issue, we propose a cross-subject seizure detection method via unsupervised domain adaptation. The proposed method aims to obtain seizure specific information through shallow and deep feature alignments. For shallow feature alignment, we use convolutional neural network (CNN) to extract seizure-related features. The distribution gap of the shallow features between different patients is minimized by multi-kernel maximum mean discrepancies (MK-MMD). For deep feature alignment, adversarial learning is utilized. The feature extractor tries to learn feature representations that try to confuse the domain classifier, making the extracted deep features more generalizable to new patients. The performance of our method is evaluated on the CHB-MIT and Siena databases in epoch-based experiments. Additionally, event-based experiments are also conducted on the CHB-MIT dataset. The results validate the feasibility of our method in diminishing the domain disparities among different patients.
ISSN:1793-6462
DOI:10.1142/S0129065724500552