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Journal of Computer Science & Systems Biology | ISSN: 0974-7230 | Volume: 11
&
Biostatistics and Bioinformatics
Big Data Analytics & Data Mining
7
th
International Conference on
7
th
International Conference on
September 26-27, 2018 | Chicago, USA
Classification of multi-channel electroencephalogram time series with a linear discriminant analysis
Yongxiang Gao
and
Jinxin Zhang
Sun Yat-sen University, China
E
pilepsy is a chronic neurological disease characterized by epileptic seizures that affect approximately 50 million people
worldwide. An electroencephalogram is the most dominant method to detect epileptic seizures, it contains information
about brain activity. Therefore, an automatic diagnostic method needed to be proposed to help the doctor make the correct
decision, many methods have also been developed during the past years but there is no unanimous opinion. In this work, a
strategy has been proposed to differential EEG as normal, epileptic seizures and interictal. Maximal overlap wavelet transform
was used to extract wavelet coefcients, five features (variance, Pearson correlation coefcient, Hoeffdings’Dmeasure, Shannon
entropy, interquartile range) were calculated from EEG and then input to the linear discriminant classifier for the classification
purpose. Data were collected from the Department of Neurology, the Second Afliated Hospital of Guangzhou Medical
University containing 34 healthy people, 30 epileptic seizures patients and 21 interictal patients. Here only db4 was used. The
performance of classifiers was evaluated use leave-one-out cross-validation in terms of accuracy and auc. Results show that the
accuracy of healthy and epileptic seizures is 1 and auc is 1. The accuracy of interictal and epileptic seizures is 92.16% and auc
is 0.96. The method we proposed can extract information from EEG.
Biography
Yongxiang Gao has been studying full-time in the graduate program for Master’s Degree on Epidemiology and Health Statistics in the School of Public Health, Sun
Yat-sen University from September 2016 to now. The normal study period is three years.
gao_yongxiang@163.comYongxiang Gao et al., J Comput Sci Syst Biol 2018, Volume: 11
DOI: 10.4172/0974-7230-C1-021