Epileptic Seizure Prediction for Imbalanced Datasets

被引：3
|
作者：
Cosgun, Ercan [1 ]
Celebi, Anil [2 ]
Gullu, M. Kemal [2 ]
机构：
[1] Kirklareli Univ TBMYO, Elekt & Otomasyon Bolumu, Kirklareli, Turkey
[2] Kocaeli Univ, Muh Fak, Elekt Haberlesme Muhendisligi, Kocaeli, Turkey
来源：
2019 MEDICAL TECHNOLOGIES CONGRESS (TIPTEKNO) | 2019年
关键词：
epileptic seizure prediction; imbalanced dataset; rusboost Classifier; EMPIRICAL MODE DECOMPOSITION; PERFORMANCE;
D O I：
10.1109/tiptekno.2019.8895137
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
In this study, the methods used in the classification of imbalanced data sets were applied to EEG signals obtained from epilepsy patients and epileptic seizures were estimated. Firstly, the data set was balanced by using under-sampling, over-sampling, and synthetic minority over-sampling technique and classified with Support Vector Machines. Then, the data set was classified using the Rusboost classifier without balancing. Classification results were compared with different criteria and the advantages and disadvantage of the methods were evaluated.
引用
收藏
页码：290 / 293
页数：4
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