Accelerometry-Based Home Monitoring for Detection of Nocturnal Hypermotor Seizures Based on Novelty Detection
Open accessAbstract
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.
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To improve precision, patient-specific algorithms have recently been suggested in epilepsy.
“To improve precision, patient-specific algorithms have recently been suggested in epilepsy [73].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [73]
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- Authors
- Cuppens, K., Karsmakers, P., Van de Vel, A., Bonroy, B., Milosevic, M., Luca, S., Croonenborghs, T., Ceulemans, B., Lagae, L., Van Huffel, S., & Vanrumste, B.
- Affiliations
- Thomas More Kempen (BE)
- University Hospital of Antwerp , Wilrijkstraat, Belgium
- Department of Electrical Engineering (ESAT) STADIUS and iMinds Future Health Department, KU Leuven, Leuven, Belgium
- KU Leuven (BE)
- Antwerp University Hospital (BE)
- Universitair Ziekenhuis Leuven (BE)
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2014-05
- Volume
- 18
- Issue
- 3
- Pages
- 1026-1033
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- OpenAlex
- W2021350343
- Cited by
- 66
- References
- 24
- Citations by year
- 2026: 4 · 2025: 2 · 2024: 1 · 2023: 3 · 2022: 7 · 2021: 4 · 2020: 2 · 2019: 6 · 2018: 11 · 2017: 5 · 2016: 9 · 2015: 5 · 2014: 5 · 2013: 1
- FWCI
- 3.66
- Type
- journal article
- Language
- en
- Open access
- green
- lirias.kuleuven.be
- Licence
- Locations
- IEEE Journal of Biomedical and Health Informatics (publishedVersion)
- PubMed (publishedVersion)
- Lirias (submittedVersion, other-oa)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion)
- Funders
- Research Council KUL: GOA-MANET, IWT: TBM070713-Accelero, Belgian Federal Science Policy Office IUAP P6/04 (DYSCO, Dynamical systems, control and optimization, 2007-2011); EU: Neuromath (COSTBM0601)
- Agency for Innovation by Science and Technology (IWT)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Psychiatry and Mental health › Epilepsy research and treatment
- Keywords
- seizure detection, accelerometry, home monitoring, pediatric epilepsy, novelty detection, nonparametric density estimation, patient-specific models, wavelet features, frequency-domain features, interpatient variability
- MeSH terms
- Adolescent, Algorithms, Child, Child, Preschool, Electroencephalography, Epilepsy, Humans, Monitoring, Physiologic, Movement, Sensitivity and Specificity, Models, Statistical, Accelerometry
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