Mitigating Patient-to-Patient Variation in EEG Seizure Detection using Meta Transfer Learning

Electroencephalogram (EEG) signals can be used for seizure detection, but the seizure patterns found in between patient's EEGs can have significant variations. Specifically, focal spikes in patient-specific channels as well as other patient specific patterns can strongly indicate seizure activity. Manual diagnosis on these markers leads to inconsistent interrater agreement and poor detection accuracy. Previous automation attempts have ignored patient specific approaches but fail to generalize to previously unseen patients. To reduce subjectivity in manual diagnosis, we propose an automatic seizure detection pipeline that includes quality control, preprocessing, and meta transfer learning for both feature extraction and classification. To mitigate the inter-patient seizure pattern variation, we adapt Meta UPdate Strategy (MUPS) for four-class classification on the world's largest public seizure dataset of EEGs, Temple University Seizure Corpus (TUSZ). Different from existing works on binary seizure detection, we use the non-seizure samples and the top three most frequent seizure types for seizure detection. Our experiments show that the meta transfer learning approach achieves macro-F1 of 0.5103 and AUC of 0.6792, which outperforms the baseline learners (shallow and deep) by mitigating patient-to patient variations. We demonstrate the effectiveness of meta transfer learning in feature extraction and classification for multi-class seizure detection.