Accelerometry-based home monitoring for detection of nocturnal hypermotor seizures based on novelty detection.
IEEE J Biomed Health Inform
; 18(3): 1026-33, 2014 May.
Article
em En
| MEDLINE
| ID: mdl-24122607
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 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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Epilepsia
/
Acelerometria
/
Monitorização Fisiológica
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Adolescent
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Child
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Child, preschool
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Humans
Idioma:
En
Revista:
IEEE J Biomed Health Inform
Ano de publicação:
2014
Tipo de documento:
Article