One Dimensional Convolutional Neural Networks for Seizure Onset Detection Using Long-term Scalp and Intracranial EEG
Abstract
Epileptic seizure detection using scalp electroencephalogram (sEEG) and intracranial electroencephalogram (iEEG) has attracted widespread attention in recent two decades. The accurate and rapid detection of seizures not only reflects the efficiency of the algorithm, but also greatly reduces the burden of manual detection during long-term electroencephalogram (EEG) recording. In this work, a stacked one-dimensional convolutional neural network (1D-CNN) model combined with a random selection and data augmentation (RS-DA) strategy is proposed for seizure onset detection. Firstly, we segmented the long-term EEG signals using 2-sec sliding windows. Then, the 2-sec interictal and ictal segments were classified by the stacked 1D-CNN model. During model training, a RS-DA strategy was applied to solve the problem of sample imbalance, and the patient-specific model was trained with event-based K-fold (K is the number of seizures per patient) cross validation for detecting all seizures of each patient. Finally, we evaluated the performances of the proposed approach in the two levels: the segment-based level and the event-based level. The proposed method was tested on two long-term EEG datasets: the CHB-MIT sEEG dataset and the SWEC-ETHZ iEEG dataset. For the CHB-MIT sEEG dataset, we achieved 88.14 sensitivity, 99.62 specificity and 99.54 accuracy in the segment-based level. From the perspective of the event-based level, 99.31 sensitivity, 0.2/h false detection rate (FDR) and mean 8.1-sec latency were achieved. For the SWEC-ETHZ iEEG dataset, in the segment-based level, 90.09 sensitivity, 99.81 specificity and 99.73 accuracy were obtained. In the event-based level, 97.52 sensitivity, 0.07/h FDR and mean 13.2-sec latency were attained. From these results, we can see that our method can effectively use both sEEG and iEEG data to detect epileptic seizures, and this may provide a reference for the clinical application of seizure onset detection.
Main Authors
Format
Articles
Research article
Published
2021
Series
Subjects
Publication in research information system
Publisher
Elsevier
The permanent address of the publication
https://urn.fi/URN:NBN:fi:jyu-202108184574Use this for linking
Review status
Peer reviewed
ISSN
0925-2312
DOI
https://doi.org/10.1016/j.neucom.2021.06.048
Language
English
Published in
Neurocomputing
Citation
- Wang, X., Wang, X., Liu, W., Chang, Z., Kärkkäinen, T., & Cong, F. (2021). One Dimensional Convolutional Neural Networks for Seizure Onset Detection Using Long-term Scalp and Intracranial EEG. Neurocomputing, 459, 212-222. https://doi.org/10.1016/j.neucom.2021.06.048
Additional information about funding
This work was supported by National Natural Science Foundation of China (Grant No. 91748105), National Foundation in China (No. JCKY2019110B009 & 2020-JCJQ-JJ-252), the scholarship from China Scholarship Council (No. 201806060166) and the Fundamental Research Funds for the Central Universities [DUT20LAB303 & DUT20LAB308] in Dalian University of Technology in China.
Copyright© 2021 the Authors