Title
Online detection of tonic-clonic seizures in pediatric patients using ECG and low-complexity incremental novelty detection Online detection of tonic-clonic seizures in pediatric patients using ECG and low-complexity incremental novelty detection
Author
Faculty/Department
Faculty of Medicine and Health Sciences
Publication type
conferenceObject
Publication
New york :Ieee ,
Source (journal)
MEDICINE AND BIOLOGY SOCIETY (EMBC)
IEEE Engineering in medicine and biology society conference proceedings
Source (book)
37th Annual International Conference of the IEEE Engineering in Medicine, and Biology Society (EMBC), AUG 25-29, 2015, Milan, ITALY
Volume/pages
(2015) , p. 5597-5600
ISSN
1557-170X
ISBN
978-1-4244-9270-1
ISI
000371717205214
Carrier
E
Target language
English (eng)
Affiliation
University of Antwerp
Abstract
Home monitoring of refractory epilepsy patients has become of more interest the last couple of decades. A biomedical signal that can be used for online seizure detection at home is the electrocardiogram. Previous studies have shown that tonic-clonic seizures are most often accompanied with a strong heart rate increase. The main issue however is the strong patient-specific behavior of the ictal heart rate features, which makes it hard to make a patient-independent seizure detection algorithm. A patient-specific algorithm might be a solution, but existing methods require the availability of data of several seizures, which would make them inefficient in case the first seizure only occurs after a couple of days. Therefore an online method is described here that automatically converts from a patient-independent towards a patient-specific algorithm as more patient-specific data become available. This is done by using online feedback from the users to previously given alarms. By using a simplified one-class classifier, no seizure training data needs to be available for a good performance. The method is already able to adapt to the patient-specific characteristics after a couple of hours, and is able to detect 23 of 24 seizures longer than 10s, with an average of 0.38 false alarms per hour. Due to its low-complexity, it can be easily used for wearable seizure detection at home.
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