Accelerometry-Based Home Monitoring for Detection of Nocturnal Hypermotor Seizures Based on Novelty Detection

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 s...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2014-05, Vol.18 (3), p.1026-1033
Main Authors: Cuppens, Kris, Karsmakers, Peter, Van de Vel, Anouk, Bonroy, Bert, Milosevic, Milica, Luca, Stijn, Croonenborghs, Tom, Ceulemans, Berten, Lagae, Lieven, Van Huffel, Sabine, Vanrumste, Bart
Format: Article
Language:English
Subjects:
Accelerometers
Accelerometry - methods
Adolescent
Algorithms
Brain models
Child
Child, Preschool
Data models
Electroencephalography - methods
Epilepsy
Epilepsy - diagnosis
Feature extraction
home monitoring
Humans
hypermotor seizures
Models, Statistical
Monitoring
Monitoring, Physiologic - methods
Movement - physiology
novelty detection
Probability density function
Sensitivity and Specificity
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Summary: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 nonparametric 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.
ISSN:2168-2194
2168-2208
DOI:10.1109/JBHI.2013.2285015