Towards advanced application of artificial intelligence (AI) in epileptic seizure management

Abstract

Epilepsy has a significant adverse impact on almost 1% of people's health and well-being globally. Clinical EEG monitoring devices that enable seizure onset detection and prediction are crucial for epilepsy patients to manage their seizure disorders. In the past three decades, many epileptic seizure detecting, and prediction methods have been developed and reported high performance. However, most of them are retrospective and lack continental and multi-dataset generalization, transparency, and reproducibility, making them hard to implement into clinical utility. Besides, the seizure prediction biomarker is yet to be fully answered, and this issue significantly limits clinician trust when using the seizure prediction algorithms. In this thesis, we propose a generalized epileptic seizure detection AI-assisted system that tested on a large scale of the clinical EEG dataset and proved to improve time efficiency while accuracy alongside the human expert. The seizure detection performance is further improved by combining EEG and ECG using a novel multimodal AI system. Secondly, we propose a Bayesian convolutional neural network to facilitate the exploration of potential seizure forecasting biomarkers. Another problem we address is the need for long recording labeled EEG data for seizure prediction. We propose a novel real-time seizure prediction AI system that learns from the on-the-fly weak label generated by the detection model. Ultimately, we focus on developing a low-power, hardware-friendly implementation method using neuromorphic-compatible Spiking Neural Networks (SNNs) for seizure detection. Overall, the work presented in this thesis has tackled several research problems related to advanced AI applications in epileptic seizure detection and prediction and drove these emerging technologies toward building reliable AI systems in real-world clinical settings.