Publication
Title
Accelerometry-based home monitoring for detection of nocturnal hypermotor seizures based on novelty detection
Author
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.
Language
English
Source (journal)
IEEE journal of biomedical and health informatics
Publication
2014
ISSN
2168-2194
DOI
10.1109/JBHI.2013.2285015
Volume/pages
18 :3 (2014) , p. 1026-1033
ISI
000336050400035
Full text (Publisher's DOI)
UAntwerpen
Faculty/Department
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identifier
Creation 03.07.2014
Last edited 04.03.2024
To cite this reference