Research on epileptic EEG recognition based on improved residual networks of 1-D CNN and indRNN

BMC Medical Informatics and Decision Making

Yi Zhou¹  Ziyi Chen²  Xiaoyan Wei³  Mengnan Ma⁴  Yinlin Cheng⁴ 

[1] Department of Medical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, No.74 Zhongshan 2nd Road, 510080, Guangzhou, China;Key Laboratory of Tropical Disease Control (Sun Yat-sen University), Ministry of Education, No.74 Zhongshan 2nd Road, 510080, Guangzhou, China;Department of Neurology, First Affiliated Hospital, Sun Yat-sen University, No.58 Zhongshan 2nd Road, 510080, Guangzhou, China;Minister of Science, Education and Data Management Department, Guangzhou Women and Children’s Medical Center, National Children’s Medical Center for South Central Region, Guangzhou Medical University, No.9 Jinsui Road, 510623, Guangzhou, China;School of Biomedical Engineering, Sun Yat-sen University, No.132 Waihuan East Road, 510006, Guangzhou, China;Department of Medical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, No.74 Zhongshan 2nd Road, 510080, Guangzhou, China;

关键词: Epilepsy;    Residual network;    CNN;    indRNN;    RCNN;   

DOI  :  10.1186/s12911-021-01438-5

来源: Springer

【 摘 要 】

BackgroundEpilepsy is one of the diseases of the nervous system, which has a large population in the world. Traditional diagnosis methods mostly depended on the professional neurologists’ reading of the electroencephalogram (EEG), which was time-consuming, inefficient, and subjective. In recent years, automatic epilepsy diagnosis of EEG by deep learning had attracted more and more attention. But the potential of deep neural networks in seizure detection had not been fully developed.MethodsIn this article, we used a one-dimensional convolutional neural network (1-D CNN) to replace the residual network architecture’s traditional convolutional neural network (CNN). Moreover, we combined the Independent recurrent neural network (indRNN) and CNN to form a new residual network architecture-independent convolutional recurrent neural network (RCNN). Our model can achieve an automatic diagnosis of epilepsy EEG. Firstly, the important features of EEG were learned by using the residual network architecture of 1-D CNN. Then the relationship between the sequences were learned by using the recurrent neural network. Finally, the model outputted the classification results.ResultsOn the small sample data sets of Bonn University, our method was superior to the baseline methods and achieved 100% classification accuracy, 100% classification specificity. For the noisy real-world data, our method also exhibited powerful performance.ConclusionThe model we proposed can quickly and accurately identify the different periods of EEG in an ideal condition and the real-world condition. The model can provide automatic detection capabilities for clinical epilepsy EEG detection. We hoped to provide a positive significance for the prediction of epileptic seizures EEG.

【 授权许可】

CC BY