A1 Journal article (refereed)
Channel Increment Strategy-Based 1D Convolutional Neural Networks for Seizure Prediction Using Intracranial EEG (2023)


Wang, X., Zhang, C., Kärkkäinen, T., Chang, Z., & Cong, F. (2023). Channel Increment Strategy-Based 1D Convolutional Neural Networks for Seizure Prediction Using Intracranial EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, 316-325. https://doi.org/10.1109/TNSRE.2022.3222095


JYU authors or editors


Publication details

All authors or editorsWang, Xiaoshuang; Zhang, Chi; Kärkkäinen, Tommi; Chang, Zheng; Cong, Fengyu

Journal or seriesIEEE Transactions on Neural Systems and Rehabilitation Engineering

ISSN1534-4320

eISSN1558-0210

Publication year2023

Publication date14/11/2022

Volume31

Pages range316-325

PublisherInstitute of Electrical and Electronics Engineers (IEEE)

Publication countryUnited States

Publication languageEnglish

DOIhttps://doi.org/10.1109/TNSRE.2022.3222095

Publication open accessOpenly available

Publication channel open accessOpen Access channel

Publication is parallel published (JYX)https://jyx.jyu.fi/handle/123456789/85316


Abstract

The application of intracranial electroencephalogram (iEEG) to predict seizures remains challenging. Although channel selection has been utilized in seizure prediction and detection studies, most of them focus on the combination with conventional machine learning methods. Thus, channel selection combined with deep learning methods can be further analyzed in the field of seizure prediction. Given this, in this work, a novel iEEG-based deep learning method of One-Dimensional Convolutional Neural Networks (1D-CNN) combined with channel increment strategy was proposed for the effective seizure prediction. First, we used 4-sec sliding windows without overlap to segment iEEG signals. Then, 4-sec iEEG segments with an increasing number of channels (channel increment strategy, from one channel to all channels) were sequentially fed into the constructed 1D-CNN model. Next, the patient-specific model was trained for classification. Finally, according to the classification results in different channel cases, the channel case with the best classification rate was selected for each patient. Our method was tested on the Freiburg iEEG database, and the system performances were evaluated at two levels (segment- and event-based levels). Two model training strategies (Strategy-1 and Strategy-2) based on the K-fold cross validation (K-CV) were discussed in our work. (1) For the Strategy-1, a basic K-CV, a sensitivity of 90.18%, specificity of 94.81%, and accuracy of 94.42% were achieved at the segment-based level. At the event-based level, an event-based sensitivity of 100%, and false prediction rate (FPR) of 0.12/h were attained. (2) For the Strategy-2, the difference from the Strategy-1 is that a trained model selection step is added during model training. We obtained a sensitivity, specificity, and accuracy of 86.23%, 96.00% and 95.13% respectively at the segment-based level. At the event-based level, we achieved an event-based sensitivity of 98.65% with 0.08/h FPR. Our method also showed a better performance in seizure prediction compared to many previous studies and the random predictor using the same database. This may have reference value for the future clinical application of seizure prediction.


KeywordsepilepsyforecastsEEGsignal analysissignal processingmachine learningdeep learningneural networks (information technology)


Contributing organizations


Ministry reportingYes

Reporting Year2023

Preliminary JUFO rating2


Last updated on 2024-22-04 at 15:08