Comparative study of classifiers for patient specific seizure detection
Electroencephalogram (EEG), Patient specific epileptic seizure detection, Gaussian mixture model (GMM), Support vector machine (SVM), Neural network(NN).
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.
" Comparative study of classifiers for patient specific seizure detection", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.4, Issue 3, page no.46 - 49, March-2019, Available :https://ijsdr.org/papers/IJSDR1903010.pdf
Volume 4
Issue 3,
March-2019
Pages : 46 - 49
Paper Reg. ID: IJSDR_190179
Published Paper Id: IJSDR1903010
Downloads: 000347196
Research Area: Engineering
Country: Indore, MADHYA PRADESH, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
