Real-Time Automatic Seizure Detection Using Ordinary Kriging Method in an Edge-IoMT Computing Paradigm

Abstract

Epilepsy is one of the leading neurological diseases in the world, affecting approximately 70 million of the world’s population and often results in early mortality if not properly managed. The primary purpose of seizure detection is to reduce threat to life in the event of a seizure crisis. Previous efforts in the literature concentrate mostly on performance based on accuracy and other similar metrics. However, there is a short time lapse between the onset of a seizure attack and a potential injury that could claim the life of the patient. Therefore, there is the need for a more time-sensitive seizure detection model. We hereby propose a real-time seizure detection model in an edge computing paradigm using the ordinary kriging method, relying on the premise that the brain can be modeled as a three-dimensional spatial object, similar to a geographical panorama where kriging excels. Fractal dimensional features were extracted from patients’ electroencephalogram (EEG) signals and then classified using the proposed ordinary kriging model. The proposed model achieves a training accuracy of 99.4% and a perfect sensitivity, specificity, precision and testing accuracy. Hardware implementation in an edge computing environment results in a mean detection latency of 0.85 s. To the best of the authors’ knowledge, this is the first work that uses the kriging method for early detection of seizure.