Title
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Accelerometry-based home monitoring for detection of nocturnal hypermotor seizures based on novelty detection
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Author
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Abstract
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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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Language
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English
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Source (journal)
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IEEE journal of biomedical and health informatics
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Publication
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2014
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ISSN
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2168-2194
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DOI
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10.1109/JBHI.2013.2285015
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Volume/pages
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18
:3
(2014)
, p. 1026-1033
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ISI
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000336050400035
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Full text (Publisher's DOI)
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