Patient-specific approach using data fusion and adversarial training for epileptic seizure prediction

Abstract

Epilepsy is the second common neurological disorder after headache, accurate and reliable prediction of seizures is of great clinical value. Most epileptic seizure prediction methods consider only the EEG signal or extract and classify the features of EEG and ECG signals separately, the improvement of prediction performance from multimodal data is not fully considered. In addition, epilepsy data are time-varying, with differences between each episode in a patient, making it difficult for traditional curve-fitting models to achieve high accuracy and reliability. In order to improve the accuracy and reliability of the prediction system, we propose a novel personalized approach based on data fusion and domain adversarial training to predict epileptic seizures using leave-one-out cross-validation, which achieves an average accuracy, sensitivity and specificity of 99.70, 99.76, and 99.61%, respectively, with an average error alarm rate (FAR) of 0.001. Finally, the advantage of this approach is demonstrated by comparison with recent relevant literature. This method will be incorporated into clinical practice to provide personalized reference information for epileptic seizure prediction.

Feature separation and adversarial training for the patient-independent detection of epileptic seizures

“…The experiment used accuracy (ACC), sensitivity (SN), and specificity (SP) to quantify the performance of the algorithm ( Yang et al, 2023 ).…”

“…The experiment used accuracy (ACC), sensitivity (SN), and specificity (SP) to quantify the performance of the algorithm (Yang et al, 2023). Where, TP (True Positive): The sample which is positive is judged to be positive, TN (True Negative): The sample which is negative is judged to be negative, FP (False Positive): The sample which is negative is judged to be positive, FN (False Negative): The sample which is positive is judged to be negative.…”