Combination of Channel Reordering Strategy and Dual CNN-LSTM for Epileptic Seizure Prediction Using Three iEEG Datasets

• Published in: IEEE journal of biomedical and health informatics Vol. 28; no. 11; pp. 6557 - 6567
• Main Authors: Wang, Xiaoshuang, Gao, Ziheng, Zhang, Meiyan, Wang, Ying, Yang, Lin, Lin, Jianwen, Karkkainen, Tommi, Cong, Fengyu
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-11-2024
• Subjects: Accuracy; Channel reordering strategy; CNN-LSTM; Electrodes; Epilepsy; Feature extraction; iEEG; Long short term memory; seizure prediction; Sensitivity; Support vector machines
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2024.3438829

Objective: Intracranial electroencephalogram (iEEG) signals are generally recorded using multiple channels, and channel selection is therefore a significant means in studying iEEG-based seizure prediction. For n channels, <inline-formula><tex-math notation="LaTeX">2^{\text{n}}{-1}</tex-math></inline-formula> channel cases can be generated for selection. However, by this means, an increase in n can cause an exponential increase in computational consumption, which may result in a failure of channel selection when n is too large. Hence, it is necessary to explore reasonable channel selection strategies under the premise of controlling computational consumption and ensuring high classification accuracy. Given this, we propose a novel method of channel reordering strategy combined with dual CNN-LSTM for effectively predicting seizures. Method: First, for each patient with n channels, interictal and preictal iEEG samples from each single channel are input into the CNN-LSTM model for classification. Then, the F1-score of each single channel is calculated, and the channels are reordered in descending order according to the size of F1-scores ( channel reordering strategy ). Next, iEEG signals with an increasing number of channels are successively fed into the CNN-LSTM model for classification again. Finally, according to the classification results from n channel cases, the channel case with the highest classification rate is selected. Results: Our method is evaluated on the three iEEG datasets: the Freiburg, the SWEC-ETHZ and the American Epilepsy Society Seizure Prediction Challenge (AES-SPC). At the event-based level, the sensitivities of 100%, 100% and 90.5%, and the false prediction rates (FPRs) of 0.10/h, 0/h and 0.47/h, are achieved for the three datasets, respectively. Moreover, compared to an unspecific random predictor, our method also shows a better performance for all patients and dogs from the three datasets. At the segment-based level, the sensitivities-specificities-accuracies-AUCs of 88.1%-94.0%-93.5%-0.9101, 99.1%-99.7%-99.6%-0.9935, and 69.2%-79.9%-78.2%-0.7373, are attained for the three datasets, respectively. Conclusion: Our method can effectively predict seizures and address the challenge of an excessive number of channels during channel selection.