A4 Article in conference proceedings
Seizure Prediction Using EEG Channel Selection Method (2022)
Wang, X., Kärkkäinen, T., & Cong, F. (2022). Seizure Prediction Using EEG Channel Selection Method. In MLSP 2022 : IEEE 32nd International Workshop on Machine Learning for Signal Processing. IEEE. IEEE International Workshop on Machine Learning for Signal Processing. https://doi.org/10.1109/MLSP55214.2022.9943413
JYU authors or editors
Publication details
All authors or editors: Wang, Xiaoshuang; Kärkkäinen, Tommi; Cong, Fengyu
Parent publication: MLSP 2022 : IEEE 32nd International Workshop on Machine Learning for Signal Processing
Place and date of conference: Xi'an, China, 22.-25.8.2022
ISBN: 978-1-6654-8548-7
eISBN: 978-1-6654-8547-0
Journal or series: IEEE International Workshop on Machine Learning for Signal Processing
ISSN: 2161-0363
eISSN: 2161-0371
Publication year: 2022
Publication date: 17/11/2022
Publisher: IEEE
Publication country: United States
Publication language: English
DOI: https://doi.org/10.1109/MLSP55214.2022.9943413
Publication open access: Not open
Publication channel open access:
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/84278
Abstract
Seizure prediction using intracranial electroencephalogram (iEEG) is still challenging because of complicated signals in spatial and time domains. Feature selection in the spatial domain (i.e., channel selection) has been largely ignored in this field. Hence, in this paper, a novel approach of iEEG channel selection strategy combined with one-dimensional convolutional neural networks (1D-CNN) was presented for seizure prediction. First, 15-sec and 30-sec iEEG segments with an increasing number of channels (from one channel to all channels) were sequentially fed into 1D-CNN models for training and testing. Then, the channel case with the best classification rate was selected for each participant. We tested our method on the Freiburg iEEG dataset. A sensitivity of 89.03-90.84%, specificity of 98.99-99.73%, and accuracy of 98.07-98.99% were achieved at the segment-based level. At the event-based level, we attained a sensitivity of 98.48-98.85% and a false prediction rate (FPR) of 0-0.02/h.
Keywords: epilepsy; seizures (medicine); EEG; signal analysis; signal processing; machine learning; neural networks (information technology)
Free keywords: epilepsy; intracranial electroencephalogram (iEEG); seizure prediction; channel selection; one-dimensional convolutional neural networks (1D-CNN)
Contributing organizations
Ministry reporting: Yes
Reporting Year: 2022
JUFO rating: 1