Publication
Title
Adaptive nocturnal seizure detection using heart rate and low-complexity novelty detection
Author
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. (C) 2018 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.
Language
English
Source (journal)
Seizure: European journal of epilepsy. - London
Publication
London : 2018
ISSN
1059-1311
Volume/pages
59(2018), p. 48-53
ISI
000437818500010
Pubmed ID
29747021
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Project info
THE HIP TRIAL: Management of Hypotension In the Preterm Extremely Low Gestational Age Newborn
BIOTENSORS: Biomedical Data Fusion using Tensor based Blind Source Separation
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 02.08.2018
Last edited 26.07.2021
To cite this reference