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