One and Two Dimensional Convolutional Neural Networks for Seizure Detection Using EEG Signals
Wang, X., Ristaniemi, T., & Cong, F. (2020). One and Two Dimensional Convolutional Neural Networks for Seizure Detection Using EEG Signals. In EUSIPCO 2020 : 28th European Signal Processing Conference (pp. 1387-1391). IEEE. European Signal Processing Conference. https://doi.org/10.23919/Eusipco47968.2020.9287640
Published inEuropean Signal Processing Conference
© Authors, 2020
Deep learning for the automated detection of epileptic seizures has received much attention during recent years. In this work, one dimensional convolutional neural network (1D-CNN) and two dimensional convolutional neural network (2D-CNN) are simultaneously used on electroencephalogram (EEG) data for seizure detection. Firstly, using sliding windows without overlap on raw EEG to obtain the definite one-dimension time EEG segments (1D-T), and continuous wavelet transform (CWT) for 1D-T signals to obtain the two-dimension time-frequency representations (2D-TF). Then, 1D-CNN and 2D-CNN model architectures are used on 1D-T and 2D-TF signals for automatic classification, respectively. Finally, the classification results from 1D-CNN and 2D-CNN are showed. In the two-classification and three-classification problems of seizure detection, the highest accuracy can reach 99.92% and 99.55%, respectively. It shows that the proposed method for a benchmark clinical dataset can achieve good performance in terms of seizure detection. ...
Parent publication ISBN978-1-7281-5001-7
Is part of publicationEUSIPCO 2020 : 28th European Signal Processing Conference
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Additional information about fundingThis work was supported by the National Natural Science Foundationof China (Grant No. 91748105&81471742), the Fundamental ResearchFunds for the Central Universities [DUT2019] in Dalian University ofTechnology in China and the scholarships from China Scholarship Council(No. 201806060166).
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