Channel Increment Strategy-Based 1D Convolutional Neural Networks for Seizure Prediction Using Intracranial EEG
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
Date
2023Discipline
TekniikkaHuman and Machine based Intelligence in LearningTietotekniikkaSecure Communications Engineering and Signal ProcessingEngineeringHuman and Machine based Intelligence in LearningMathematical Information TechnologySecure Communications Engineering and Signal ProcessingCopyright
© Authors, 2023
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.
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Institute of Electrical and Electronics Engineers (IEEE)ISSN Search the Publication Forum
1534-4320Keywords
Publication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/160172752
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This work was supported by the National Natural Science Foundation of China (Grant No. 91748105), the National Foundation in China (No. JCKY2019110B009 & 2020-JCJQ-JJ-252), the scholarship from China Scholarship Council (No. 201806060166), the Science and Technology Planning Project of Liaoning Province (No. 2021JH1/10400049), and the Fundamental Research Funds for the Central Universities [DUT20LAB303 & DUT20LAB308] in Dalian University of Technology, China.![showless](/themes/JYX2//images/showless.png)
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