Source-Free Domain Adaptation (SFDA) for Privacy-Preserving Seizure Subtype Classification

Electroencephalogram (EEG) based seizure subtype classification is very important in clinical diagnostics. Source-free domain adaptation (SFDA) uses a pre-trained source model, instead of the source data, for privacy-preserving transfer learning. SFDA is useful in seizure subtype classification, whi...

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Bibliographic Details
Published in:IEEE transactions on neural systems and rehabilitation engineering 2023, Vol.31, p.2315-2325
Main Authors: Zhao, Changming, Peng, Ruimin, Wu, Dongrui
Format: Article
Language:English
Subjects:
Adaptation
Adaptation models
Algorithms
Boosting
Brain modeling
Calibration
Classification
Data models
Datasets
Domains
EEG
Electroencephalography
ensemble learning
Feature extraction
Humans
Machine Learning
Privacy
seizure subtype classification
Seizures
Seizures - diagnosis
source-free domain adaptation
Training
Transfer learning
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Summary:Electroencephalogram (EEG) based seizure subtype classification is very important in clinical diagnostics. Source-free domain adaptation (SFDA) uses a pre-trained source model, instead of the source data, for privacy-preserving transfer learning. SFDA is useful in seizure subtype classification, which can protect the privacy of the source patients, while reducing the amount of labeled calibration data for a new patient. This paper introduces semi-supervised transfer boosting (SS-TrBoosting), a boosting-based SFDA approach for seizure subtype classification. We further extend it to unsupervised transfer boosting (U-TrBoosting) for unsupervised SFDA, i.e., the new patient does not need any labeled EEG data. Experiments on three public seizure datasets demonstrated that SS-TrBoosting and U-TrBoosting outperformed multiple classical and state-of-the-art machine learning approaches in cross-dataset/cross-patient seizure subtype classification.
ISSN:1534-4320
1558-0210
DOI:10.1109/TNSRE.2023.3274563