Real-Time Automatic Seizure Detection Using Ordinary Kriging Method in an Edge-IoMT Computing Paradigm
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ORIGINAL RESEARCH
Real‑Time Automatic Seizure Detection Using Ordinary Kriging Method in an Edge‑IoMT Computing Paradigm Ibrahim L. Olokodana1 · Saraju P. Mohanty1 · Elias Kougianos2 · Oluwaseyi O. Olokodana3 Received: 23 May 2020 / Accepted: 26 July 2020 © Springer Nature Singapore Pte Ltd 2020
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. Keywords Smart home · Smart healthcare · Internet-of-Medical-Things (IoMT) · Seizure detection · Epilepsy · Edge computing · Kriging method · EEG
Introduction This article is part of the topical collection “Technologies and Components for Smart Cities” guest edited by Himanshu Thapliyal, Saraju P. Mohanty, Srinivas Katkoori and Kailash Chandra Ray. * Saraju P. Mohanty [email protected] Ibrahim L. Olokodana [email protected] Elias Kougianos [email protected] Oluwaseyi O. Olokodana [email protected] 1
Department of Computer Science and Engineering, University of North Texas, Denton, USA
2
Department of Electrical Engineering, University of North Texas, Denton, USA
3
Department of Biomedical Engineering, University of North Texas, Denton, USA
Seizures are unpremeditated involuntary activities that often result in loss of consciousness and also cause the subject to be out of control. They are the outcomes of abnormal responses by firing neurons in the central nervous system due to the malfunctioning of the brain’s circuitry. About 10% of the world’s population will have at least one seizure experience during their lifetime [8]. A seizure is referred to as epilepsy when it is recurrent and unprovoked [42]. Epilepsy is among the top five neurological disorders and it affects roughly 70 m
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