Economic evaluations of nonpharmacological treatments for drug-resistant epilepsy: A systematic review

Early prediction of drug-resistant epilepsy using clinical and EEG features based on convolutional neural network

OBJECTIVE

Machine learning utilization in electroencephalogram (EEG) analysis and epilepsy care is fast evolving. Thus, we aim to develop and validate two one-dimensional convolutional neural network (CNN) algorithms for predicting drug-resistant epilepsy (DRE) in patients with newly-diagnosed epilepsy based on EEG and clinical features.

METHODS

We included a total of 1010 EEG signal epochs and 15 clinical features from 101 patients with epilepsy. Each patient had 10 epochs of EEG signal data, with each signal recorded for 90 s. The ratio of development set and validation set was 80:20, and ten-fold cross validation was performed. First, a CNN algorithm was used to extract EEG features automatically. Then, Two one-dimensional CNNs were crafted.. Accuracy, specificity, precision, sensitivity, F1-score, kappa statistics, mean square error (MSE) and area under the curve (AUC) were calculated to evaluate the classifiers performance.

RESULTS

The clinical-EEG model showed good performance and clinical practical value, with the accuracy, specificity, precision, sensitivity, F1-score, kappa statistics, best MSE and AUC in test set were 0.99, 0.72, 0.82, 0.96, 0.89, 0.83, 32.00, 0.81, respectively, and the accuracy in validation set was 0.84. In the EEG model, the accuracy, specificity, precision, sensitivity, F1-score, kappa statistics, best MSE and AUC in test set were 0.99, 0.59, 0.82, 0.90, 0.86, 0.72, 181.76, 0.76, respectively, and the accuracy in validation set was 0.81.

CONCLUSION

We constructed a clinical-EEG model showed good potential for predicting DRE in patients with newly-diagnosed epilepsy, which could help identify patients at high risk of developing DRE at earlier stages.