Unsupervised Domain Adaptation for Cross-Subject Few-Shot Neurological Symptom Detection
This work addresses the challenge of few-shot symptom detection for epilepsy patients, offering a domain-specific incremental improvement over existing subject-specific training schemes.
The paper tackled the problem of detecting neurological symptoms in new patients with limited recordings by introducing an unsupervised domain adaptation approach for cross-subject few-shot epileptic seizure detection, achieving a 9.4% improvement in 1-shot classification accuracy compared to subject-specific methods.
Modern machine learning tools have shown promise in detecting symptoms of neurological disorders. However, current approaches typically train a unique classifier for each subject. This subject-specific training scheme requires long labeled recordings from each patient, thus failing to detect symptoms in new patients with limited recordings. This paper introduces an unsupervised domain adaptation approach based on adversarial networks to enable few-shot, cross-subject epileptic seizure detection. Using adversarial learning, features from multiple patients were encoded into a subject-invariant space and a discriminative model was trained on subject-invariant features to make predictions. We evaluated this approach on the intracranial EEG (iEEG) recordings from 9 patients with epilepsy. Our approach enabled cross-subject seizure detection with a 9.4\% improvement in 1-shot classification accuracy compared to the conventional subject-specific scheme.