INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15
Comparative study of classifiers for patient specific seizure detection
Authors Name:
Hemlata Pal
Unique Id:
IJSDR1903010
Published In:
Volume 4 Issue 3, March-2019
Abstract:
Automatic seizure detection methods basically decrease the workload of EEG monitoring units. In this study, there is considerable interest in improved offline patient specific approaches because they perform better (High sensitivity & lower false detection rate) than patient-independent ones. In this paper, we present a comparative analysis of different patient specific methods w.r.t different classification models. We consider five patient specific methods, two methods with Gaussian mixture model (GMM), next two methods with Support vector machine (SVM) and one with neural network (NN). We noted that NN based method in compare to the GMM and SVM based method had the best result applied on the same database.
Keywords:
Electroencephalogram (EEG), Patient specific epileptic seizure detection, Gaussian mixture model (GMM), Support vector machine (SVM), Neural network(NN).
Cite Article:
" Comparative study of classifiers for patient specific seizure detection", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.4, Issue 3, page no.46 - 49, March-2019, Available :http://www.ijsdr.org/papers/IJSDR1903010.pdf
Downloads:
000336258
Publication Details:
Published Paper ID: IJSDR1903010
Registration ID:190179
Published In: Volume 4 Issue 3, March-2019
DOI (Digital Object Identifier):
Page No: 46 - 49
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631
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