Enhanced Early Detection of Epileptic Seizures Through Advanced Line Spectral Estimation and XGBoost Machine Learning

This paper proposes a fast epileptic seizure detection method to allow for early clinical intervention. The primary goal is to enhance computational and predictive performance to make the method viable for online implementation. An advanced Line Spectral Estimation (LSE)-based method for EEG analysis was developed with Bayesian inference and Toeplitz structure-based fast inversion with Capon and non-uniform Fourier transforms to reduce computational requirements. XGBoost classifier with parallel boosting was employed to increase prediction performance. The method was tested with patients’ EEG data using multiple embedded Graphic Processing Unit (GPU) platforms and achieved 95.5% accuracy, and 23.48 and 33.46 min average and maximum lead times before a seizure, respectively. The sensitivity and specificity values (92.23% and 93.38%) show the method to be reliable. The integration of LSE and XGBoost can be extended to create an efficient and practical online seizure detection and management tool.