Title
Accelerometry-based home monitoring for detection of nocturnal hypermotor seizures based on novelty detection Accelerometry-based home monitoring for detection of nocturnal hypermotor seizures based on novelty detection
Author
Faculty/Department
Faculty of Medicine and Health Sciences
Publication type
article
Publication
Subject
Mathematics
Biology
Human medicine
Computer. Automation
Source (journal)
IEEE journal of biomedical and health informatics
Volume/pages
18(2014) :3 , p. 1026-1033
ISSN
2168-2194
ISI
000336050400035
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
Nocturnal home monitoring of epileptic children is often not feasible due to the cumbersome manner of seizure monitoring with the standard method of video/EEG-monitoring. We propose a method for hypermotor seizure detection based on accelerometers attached to the extremities. From the acceleration signals, multiple temporal, frequency, and wavelet-based features are extracted. After determining the features with the highest discriminative power, we classify movement events in epileptic and nonepileptic movements. This classification is only based on a non-parametric estimate of the probability density function of normal movements. Such approach allows us to build patient-specific models to classify movement data without the need for seizure data that are rarely available. If, in the test phase, the probability of a data point (event) is lower than a threshold, this event is considered to be an epileptic seizure; otherwise, it is considered as a normal nocturnal movement event. The mean performance over seven patients gives a sensitivity of 95.24% and a positive predictive value of 60.04%. However, there is a noticeable interpatient difference.
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