A4 Article in conference proceedings
One and Two Dimensional Convolutional Neural Networks for Seizure Detection Using EEG Signals (2020)
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
JYU authors or editors
Publication details
All authors or editors: Wang, Xiaoshuang; Ristaniemi, Tapani; Cong, Fengyu
Parent publication: EUSIPCO 2020 : 28th European Signal Processing Conference
Place and date of conference: Amsterdam, Netherlands , 18.-21.1.2021
ISBN: 978-1-7281-5001-7
eISBN: 978-9-0827-9705-3
Journal or series: European Signal Processing Conference
ISSN: 2219-5491
eISSN: 2076-1465
Publication year: 2020
Publication date: 24/01/2021
Pages range: 1387-1391
Publisher: IEEE
Publication country: United States
Publication language: English
DOI: https://doi.org/10.23919/Eusipco47968.2020.9287640
Publication open access: Not open
Publication channel open access:
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/84277
Abstract
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.
Keywords: EEG; signal analysis; signal processing; machine learning; neural networks (information technology); epilepsy
Free keywords: electroencephalogram (EEG); seizure detection; convolutional neural networks (CNN); deep learning; time-frequency representationti
Contributing organizations
Ministry reporting: Yes
Reporting Year: 2020
JUFO rating: 1