Optimal training dataset composition for SVM-based, age-independent, automated epileptic seizure detection.
Automated seizure detection is a valuable asset to health professionals, which makes adequate treatment possible in order to minimize brain damage. Most research focuses on two separate aspects of automated seizure detection: EEG feature computation and classification methods. Little research has be...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 54; no. 8; pp. 1285 - 1294 |
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| Autores principales: | , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Aug2016
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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=117360494&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 117360494 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Aug2016 vid: 54 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 117360494 117360494 NLM27032931 10.1007/s11517-016-1468-y NLM27032931 PMC4958398 117360494 ppf: 1285 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Optimal training dataset composition for SVM-based, age-independent, automated epileptic seizure detection. aug: au: Bogaarts, J. Gommer, E. Hilkman, D. Kranen-Mastenbroek, V. Reulen, J. Bogaarts, J G Gommer, E D Hilkman, D M W van Kranen-Mastenbroek, V H J M Reulen, J P H affil: Department of Clinical Neurophysiology , AZM Maastricht , P. Debyelaan 25 6229 HX Maastricht Netherlands sug: subj: Diagnosis, Computer Assisted Methods Epilepsy Diagnosis Resource Databases Electroencephalography Aged, 80 and Over ROC Curve Infant Adult Signal Processing, Computer Assisted Aged Middle Age Aged, 80 & over Infant: 1-23 months Adult: 19-44 years Aged: 65+ years Middle Aged: 45-64 years ab: Automated seizure detection is a valuable asset to health professionals, which makes adequate treatment possible in order to minimize brain damage. Most research focuses on two separate aspects of automated seizure detection: EEG feature computation and classification methods. Little research has been published regarding optimal training dataset composition for patient-independent seizure detection. This paper evaluates the performance of classifiers trained on different datasets in order to determine the optimal dataset for use in classifier training for automated, age-independent, seizure detection. Three datasets are used to train a support vector machine (SVM) classifier: (1) EEG from neonatal patients, (2) EEG from adult patients and (3) EEG from both neonates and adults. To correct for baseline EEG feature differences among patients feature, normalization is essential. Usually dedicated detection systems are developed for either neonatal or adult patients. Normalization might allow for the development of a single seizure detection system for patients irrespective of their age. Two classifier versions are trained on all three datasets: one with feature normalization and one without. This gives us six different classifiers to evaluate using both the neonatal and adults test sets. As a performance measure, the area under the receiver operating characteristics curve (AUC) is used. With application of FBC, it resulted in performance values of 0.90 and 0.93 for neonatal and adult seizure detection, respectively. For neonatal seizure detection, the classifier trained on EEG from adult patients performed significantly worse compared to both the classifier trained on EEG data from neonatal patients and the classier trained on both neonatal and adult EEG data. For adult seizure detection, optimal performance was achieved by either the classifier trained on adult EEG data or the classifier trained on both neonatal and adult EEG data. Our results show that age-independent seizure detection is possible by training one classifier on EEG data from both neonatal and adult patients. Furthermore, our results indicate that for accurate age-independent seizure detection, it is important that EEG data from each age category are used for classifier training. This is particularly important for neonatal seizure detection. Our results underline the under-appreciated importance of training dataset composition with respect to accurate age-independent seizure detection. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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