Supervised Transfer Learning for Personalized Heart Rate Based Epileptic Seizure Detection

Seizure alarm systems can improve the quality of life of refractory epilepsy patients, but require seizure detection algorithms. State-of-the-art heart rate-based algorithms often use a patient-independent approach due to insufficient annotated patient data, which does not allow a robust personalization. Ictal heart rate changes are however patient-dependent and could benefit from personalized algorithms. In this study, we propose to personalize seizure detection by using supervised transfer learning, which allows to train a classifier with a limited amount of data by using a reference classifier. It is evaluated on 207 hours of data including 74 seizures from 6 patients. An optimal performance of 89.8% sensitivity was achieved with on average 1.1 false alarms per hour, which is 54% false alarms less than the reference patient-independent classifier by using a limited amount of patient data. This shows that transfer learning can be used for a fast and robust personalization of detection algorithms.