Generalized epilepsy is a common neurological disorder characterised by sudden abnormal brain activity, called a seizure, leading to uncontrolled physical conditions like staring, jerking, stiffening, or loss of consciousness, resulting in seizure-related accidents. These seizures are managed and treated with drugs. The anti-seizure drugs are developed by preclinical and clinical studies involving analysis of Local Field Potential (LFP) recordings from the brain by identifying biomarkers for the development of epilepsy. The analysis is usually done manually, making it a time-consuming process. Automating the manual process is challenging as the LFP recordings are nuanced and require temporal context for seizure detection and analysis due to the variable morphology of seizures in different subjects. To effectively capture these nuances and model long-term dependencies in LFP signals, the application of Large Language Models (LLMs) is an emerging focus of research and development. Hence, as a contribution to this advancing field of research, we present an explainable Generative AI framework, EpiLiteGPT. We present a pipeline under this framework that detects generalized seizures in a Dravet Syndrome mouse model. The pipeline classifies intracranial EEG (iEEG) segments as normal, artefact, or seizure, and the Seizure Insight Module (SIM) then carries out seizure detection based on these classifications. Furthermore, the pipeline is optimized for hardware resource efficiency, gearing towards potential real-time edge deployment. The pipeline achieves an average seizure event detection sensitivity of 81.5% across 6 subjects (2 Training Mice, 4 Held-Out Mice), while the hardware optimizations reduce the energy consumption by 85%, speed up the pipeline by 2.86x and reduce the LLM memory footprint by 75% from the baseline implementation. Fundamentally, the pipeline reduces time required for seizure detection by 97% as compared to manual analysis, thereby accelerating epilepsy research. Additionally, the proposed framework introduces a novel curriculum learning strategy for training LLMs on iEEG signals and develops a GPT-2-based backbone with potential for the development of personalised seizure detection devices. In doing so, it contributes to the growing body of research on LLMs for seizure detection and their prospective deployment on edge devices.

One-third of patients suffering from chronic epilepsy, which is caused by abnormal brain activity, is drug-resistant. Animal models are widely used to study the mechanisms leading to epilepsy so better drug treatments can be developed for this disease. In such studies, epileptiform activity, assessed by LFP recordings, can be used as a marker for the development and chronification of disease. However, the analysis of LFP recordings is typically done manually, which is time-consuming, subject to observer bias, error-prone, and lacks consistency and efficiency. Therefore, we present a work which developed a new, automated detection and classification method for epileptiform activity, which was tested in the intrahippocampal kainic acid (IHKA) mouse model, a model of human temporal lobe epilepsy. Our method relies on a spike detector using an improved version of the nonlinear energy operator (NEO) in combination with automatic NEO thresholding (ANT). The detected spikes form the basis of epileptiform event detection and classification. The proposed method is implemented in Python as an automated and time-efficient algorithm that can be used in preclinical studies. Epileptiform event detection accuracy was 93.1% and classification accuracy 95.8%. Moreover, the time for analysis of LFP recordings was reduced by 98.8% compared to manual analysis. Additionally, to demonstrate the potential of the algorithm for application in Brain-Machine Interfaces (BMI), we performed a real-time implementation using both an application-specific integrated circuit (ASIC) and a field programmable gate array (FPGA). The FPGA demonstrated the feasibility of real-time implementation, and the ASIC resulted in an area and power efficient chip using the Taiwan semiconductor manufacturing company (TSMC) 45nm library that constitutes a post-layout area of 9114 µm2 and a power usage of 6.11 µW.