An automated detection of epileptic seizures EEG using CNN classifier based on feature fusion with high accuracy

Wenna Chen; Yixing Wang; Yuhao Ren; Hongwei Jiang; Ganqin Du; Jincan Zhang; Jinghua Li

doi:10.1186/s12911-023-02180-w

An automated detection of epileptic seizures EEG using CNN classifier based on feature fusion with high accuracy

BMC Med Inform Decis Mak. 2023 May 22;23(1):96. doi: 10.1186/s12911-023-02180-w.

Authors

Wenna Chen^#¹, Yixing Wang^#², Yuhao Ren², Hongwei Jiang³, Ganqin Du⁴, Jincan Zhang⁵, Jinghua Li²

Affiliations

¹ The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China.
² College of Information Engineering, Henan University of Science and Technology, Luoyang, China.
³ The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China. jianghw@haust.edu.cn.
⁴ The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China. dgq99@163.com.
⁵ College of Information Engineering, Henan University of Science and Technology, Luoyang, China. zjc850126@163.com.

^# Contributed equally.

Abstract

Background: Epilepsy is a neurological disorder that is usually detected by electroencephalogram (EEG) signals. Since manual examination of epilepsy seizures is a laborious and time-consuming process, lots of automatic epilepsy detection algorithms have been proposed. However, most of the available classification algorithms for epilepsy EEG signals adopted a single feature extraction, in turn to result in low classification accuracy. Although a small account of studies have carried out feature fusion, the computational efficiency is reduced due to too many features, because there are also some poor features that interfere with the classification results.

Methods: In order to solve the above problems, an automatic recognition method of epilepsy EEG signals based on feature fusion and selection is proposed in this paper. Firstly, the Approximate Entropy (ApEn), Fuzzy Entropy (FuzzyEn), Sample Entropy (SampEn), and Standard Deviation (STD) mixed features of the subband obtained by the Discrete Wavelet Transform (DWT) decomposition of EEG signals are extracted. Secondly, the random forest algorithm is used for feature selection. Finally, the Convolutional Neural Network (CNN) is used to classify epilepsy EEG signals.

Results: The empirical evaluation of the presented algorithm is performed on the benchmark Bonn EEG datasets and New Delhi datasets. In the interictal and ictal classification tasks of Bonn datasets, the proposed model achieves an accuracy of 99.9%, a sensitivity of 100%, a precision of 99.81%, and a specificity of 99.8%. For the interictal-ictal case of New Delhi datasets, the proposed model achieves a classification accuracy of 100%, a sensitivity of 100%, a specificity of 100%, and a precision of 100%.

Conclusion: The proposed model can effectively realize the high-precision automatic detection and classification of epilepsy EEG signals. This model can provide high-precision automatic detection capability for clinical epilepsy EEG detection. We hope to provide positive implications for the prediction of seizure EEG.

Keywords: Convolutional neural networks; Epilepsy detection; Epileptic EEG signal classification; Feature selection; Random forest.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Electroencephalography / methods
Epilepsy* / diagnosis
Humans
Neural Networks, Computer
Seizures / diagnosis
Signal Processing, Computer-Assisted*