Accelerating Epilepsy Diagnosis and Prediction on EEG Signals with GPU-Based Frequency Domain Analysis and Resampling.

Abstract This paper underscores the importance of accurate seizure detection in epilepsy. Current diagnostic methods, such as Video-Electroencephalography, are often costly and time-consuming. However, recent research demonstrates the potential of artificial intelligence for seizure detection using EEG or ECG data. To overcome challenges related to high-frequency data acquisition, the paper proposes employing Fast Fourier Transform for frequency analysis and GPUs for efficient data processing, allowing for reduced sampling frequencies. These advancements enhance data storage, transmission, and research efficiency, thereby facilitating neural network training for epilepsy prediction and real-time execution. Ultimately, these innovations hold significant promise for improving medical care and enhancing the quality of life for epilepsy patients.