Affiliations: [a] Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China | [b] Department of Biostatistics, University of Oslo, Oslo, Norway
Correspondence:
[*]
Corresponding author: Jinxin Zhang, Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, No. 74 Zhongshan Rd. 2, Guangzhou, Guangdong, China. Tel.: +86 20 87332453; E-mail: [email protected].
Abstract: BACKGROUND: An electroencephalogram (EEG) is the most dominant method for detecting epileptic seizures. However, the existing techniques use single-channel EEGs from public databases and the sample size is small. OBJECTIVE: This study proposes a strategy to distinguish multichannel EEGs for health control, particularly the interictal and ictal multichannel EEGs of epileptic patients. METHOD: We calculated five features (variance, Pearson correlation coefficient, Hoeffding’s D measure, Shannon entropy, inter-quartile range), which are based on maximal overlap discrete wavelet transform. These features were then fed into linear discriminant analysis for classification purposes. Finally, the proposed method was tested on data on 34 healthy people, 21 interictal patients and 30 ictal patients taken from a hospital. RESULTS: Our experimental results show that the accuracy between healthy and epileptic seizures was 96.88% and the area under the curve (AUC) is 1. The accuracy between interictal and epileptic seizures was 94.12% and the AUC was 0.97. We also obtained an accuracy and AUC equal to 1 for discrimination of interictal EEGs from normal. Finally, we obtained an AUC of 0.83 and an accuracy of 85.88% for discrimination in these three classes. Therefore, our study achieves sufficient performance. CONCLUSIONS: Our proposed method can serve as an auxiliary tool for clinicians who wish to make clinical decisions and reduces the burden of detecting epileptic seizures.
