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 editorsWang, Xiaoshuang; Kärkkäinen, Tommi; Cong, Fengyu

Parent publicationMLSP 2022 : IEEE 32nd International Workshop on Machine Learning for Signal Processing

Place and date of conferenceXi'an, China22.-25.8.2022

ISBN978-1-6654-8548-7

eISBN978-1-6654-8547-0

Journal or seriesIEEE International Workshop on Machine Learning for Signal Processing

ISSN2161-0363

eISSN2161-0371

Publication year2022

Publication date17/11/2022

PublisherIEEE

Publication countryUnited States

Publication languageEnglish

DOIhttps://doi.org/10.1109/MLSP55214.2022.9943413

Publication open accessNot 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.


Keywordsepilepsyseizures (medicine)EEGsignal analysissignal processingmachine learningneural networks (information technology)

Free keywordsepilepsy; intracranial electroencephalogram (iEEG); seizure prediction; channel selection; one-dimensional convolutional neural networks (1D-CNN)


Contributing organizations


Ministry reportingYes

Reporting Year2022

JUFO rating1


Last updated on 2024-25-03 at 09:11