Feature selection methods for accelerometry-based seizure detection in children.
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...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 55; no. 1; pp. 151 - 166 |
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| Autores principales: | , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jan2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=120629448&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 120629448 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jan2017 vid: 55 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 120629448 120629448 NLM27106758 10.1007/s11517-016-1506-9 NLM27106758 120629448 ppf: 151 ppct: 15 formats: fmt: @attributes: type: P tig: atl: Feature selection methods for accelerometry-based seizure detection in children. aug: au: Milošević, Milica Vel, Anouk Cuppens, Kris Bonroy, Bert Ceulemans, Berten Lagae, Lieven Vanrumste, Bart Huffel, Sabine Milošević, Milica Van de Vel, Anouk Van Huffel, Sabine affil: Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics/iMinds Medical IT , KU Leuven , 3001 Leuven Belgium sug: subj: Algorithms Accelerometry Methods Seizures Diagnosis ROC Curve Adolescence Child Adolescent: 13-18 years Child: 6-12 years ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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