Adaptive nocturnal seizure detection using heart rate and low-complexity novelty detection. (July 2018)

• Record Type: Journal Article
• Authors: De Cooman, Thomas; Varon, Carolina; Van de Vel, Anouk; Jansen, Katrien; Ceulemans, Berten; Lagae, Lieven; Van Huffel, Sabine
• Is Part Of: Seizure. Volume 59(2018)
• Page Start: 48
• Page End: 53
• Publication Date: 2018-07
• Subjects: Seizure detection -- ECG -- Heart rate -- Personalization; Epilepsy -- Periodicals; Epilepsy -- Periodicals; Seizures -- Periodicals; Épilepsie -- Périodiques; Electronic journals; Electronic journals; 616.853
• DOI: 10.1016/j.seizure.2018.04.020
• Languages: English
• ISSNs: 1059-1311

Abstract

Highlights: Personalized heart rate based seizure detection is required for good performance. A personalized seizure detection algorithm is proposed using only heart rate data. Algorithm automatically adapts to patients without requiring seizure annotations. Adaptation to patient heart rate characteristics after a couple of hours. Good performance for nocturnal monitoring of partial and convulsive seizures. Abstract: Purpose: Automated seizure detection at home is mostly done using either patient-independent algorithms or manually personalized algorithms. Patient-independent algorithms, however, lead to too many false alarms, whereas the manually personalized algorithms typically require manual input from an experienced clinician for each patient, which is a costly and unscalable procedure and it can only be applied when the patient had a sufficient amount of seizures. We therefore propose a nocturnal heart rate based seizure detection algorithm that automatically adapts to the patient without requiring seizure labels. Methods: The proposed method initially starts with a patient-independent algorithm. After a very short initialization period, the algorithm already adapts to the patients' characteristics by using a low-complex novelty detection classifier. The algorithm is evaluated on 28 pediatric patients with 107 convulsive and clinical subtle seizures during 695 h of nocturnal multicenter data in a retrospective study that mimics a real-time analysis. Results: By using the adaptive seizure detection algorithm, the overall performance was 77.6% sensitivity with on average 2.56 false alarms per night. This is 57% less false alarms than a patient-independent algorithm with a similar sensitivity. Patients with tonic–clonic seizures showed a 96% sensitivity with on average 1.84 false alarms per night. Conclusion: The proposed method shows a strongly improved detection performance over patient-independent performance, without requiring manual adaptation by a clinician. Due to the low-complexity of the algorithm, it can be easily implemented on wearables as part of a (multimodal) seizure alarm system.