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Feature selection methods for accelerometry-based seizure detection in children

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Abstract

We investigate the application of feature selection methods and their influence on distinguishing nocturnal motor seizures in epileptic children from normal nocturnal movements using accelerometry signals. We studied two feature selection methods applied one after the other to reduce the complexity and computation costs of least-squares support vector machine (LS-SVM) models. Simultaneous feature selection analyses were performed for each seizure type individually and jointly. Starting from 140 features, a filter method based on mutual information was applied to remove irrelevant and redundant features. The obtained subset was further reduced through a wrapper feature selection strategy using an LS-SVM classifier with both forward search and backward elimination. The discriminative power of each feature subset was evaluated on the test data in terms of the area under the receiver operating characteristic curve, sensitivity, and false detection rate per hour. We showed that, by using only a filter method for feature selection, it was possible to obtain classification results of comparable or slightly reduced performance with respect to the complete feature set. The attained results could facilitate further development of accelerometry-based seizure detection and alarm systems.

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Acknowledgments

The research was supported by Bijzonder Onderzoeksfonds KU Leuven (BOF); Center of Excellence (CoE): PFV/10/002 (OPTEC); Fonds voor Wetenschappelijk Onderzoek-Vlaanderen (FWO): G.0108.11 (Compressed Sensing), G.0869.12N (Tumor imaging), and G.0A5513N (Deep brain stimulation); iMinds Medical Information Technologies: Dotatie-Strategisch basis onderzoek (SBO-2015); Belgian Federal Science Policy Office: IUAP P7/19/(DYSCO, ‘Dynamical systems, control and optimization’, 2012–2017); EU: European Union’s Seventh Framework Programme (FP7/2007–2013): EU MC ITN TRANSACT 2012, 316679, ERASMUS EQR: Community service engineer, 539642-LLP-1-2013; and European Research Council: ERC Advanced Grant, 339804 BIOTENSORS. This paper reflects only the authors’ views, and the Union is not liable for any use that may be made of the contained information.

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Authors and Affiliations

  1. Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics/iMinds Medical IT, KU Leuven, 3001, Leuven, Belgium

    Milica Milošević, Bart Vanrumste & Sabine Van Huffel

  2. ESAT, Advanced Integrated Sensing (AdvISe), KU Leuven, 2440, Geel, Belgium

    Bart Vanrumste

  3. Department of Neurology-Paediatric Neurology, University Hospital Antwerp, 2650, Edegem, Belgium

    Anouk Van de Vel & Berten Ceulemans

  4. MOBILAB, Thomas More Kempen, 2440, Geel, Belgium

    Kris Cuppens & Bert Bonroy

  5. Rehabilitation Center for Children and Youth Pulderbos, 2242, Pulderbos, Belgium

    Berten Ceulemans & Lieven Lagae

  6. Department of Child Neurology, University Hospitals KU Leuven, 3000, Leuven, Belgium

    Lieven Lagae

Authors
  1. Milica Milošević
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  2. Anouk Van de Vel
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  3. Kris Cuppens
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  4. Bert Bonroy
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  5. Berten Ceulemans
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  6. Lieven Lagae
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  7. Bart Vanrumste
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  8. Sabine Van Huffel
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Corresponding author

Correspondence to Sabine Van Huffel.

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Milošević, M., Van de Vel, A., Cuppens, K. et al. Feature selection methods for accelerometry-based seizure detection in children. Med Biol Eng Comput 55, 151–165 (2017). https://doi.org/10.1007/s11517-016-1506-9

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  • DOI: https://doi.org/10.1007/s11517-016-1506-9

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