Personalizing Heart Rate-Based Seizure Detection Using Supervised SVM Transfer Learning

• Main Authors: Thomas De Cooman, Kaat Vandecasteele, Carolina Varon, Borbála Hunyadi, Evy Cleeren, Wim Van Paesschen, Sabine Van Huffel
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2020-02-01
• Series: Frontiers in Neurology
• Subjects: epilepsy; transfer learning; seizure detection; personalization; heart rate analysis; SVM
• Online Access: https://www.frontiersin.org/article/10.3389/fneur.2020.00145/full

Objective: Automated seizure detection is a key aspect of wearable seizure warning systems. As a result, the quality of life of refractory epilepsy patients could be improved. Most state-of-the-art algorithms for heart rate-based seizure detection use a so-called patient-independent approach, which do not take into account patient-specific data during algorithm training. Although such systems are easy to use in practice, they lead to many false detections as the ictal heart rate changes are patient-dependent. In practice, only a limited amount of accurately annotated patient data is typically available, which makes it difficult to create fully patient-specific algorithms.Methods: In this context, this study proposes for the first time a new transfer learning approach that allows to personalize heart rate-based seizure detection by using only a couple of days of data per patient. The algorithm was evaluated on 2,172 h of single-lead ECG data from 24 temporal lobe epilepsy patients including 227 focal impaired awareness seizures.Results: The proposed personalized approach resulted in an overall sensitivity of 71% with 1.9 false detections per hour. This is an average decrease in false detection rate of 37% compared to the reference patient-independent algorithm using only a limited amount of personal seizure data. The proposed transfer learning approach adapts faster and more robustly to patient-specific characteristics than other alternatives for personalization in the literature.Conclusion: The proposed method allows an easy implementable solution to personalize heart rate-based seizure detection, which can improve the quality of life of refractory epilepsy patients when used as part of a multimodal seizure detection system.