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The Journal of Supercomputing
Published in: The Journal of Supercomputing 14/2023

24-04-2023

An overview of machine learning methods in enabling IoMT-based epileptic seizure detection

Authors: Alaa Lateef Noor Al-hajjar, Ali Kadhum M. Al-Qurabat

Published in: The Journal of Supercomputing | Issue 14/2023

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Abstract

The healthcare industry is rapidly automating, in large part because of the Internet of Things (IoT). The sector of the IoT devoted to medical research is sometimes called the Internet of Medical Things (IoMT). Data collecting and processing are the fundamental components of all IoMT applications. Machine learning (ML) algorithms must be included into IoMT immediately due to the vast quantity of data involved in healthcare and the value that precise forecasts have. In today’s world, together, IoMT, cloud services, and ML techniques have become effective tools for solving many problems in the healthcare sector, such as epileptic seizure monitoring and detection. One of the biggest hazards to people’s lives is epilepsy, a lethal neurological condition that has become a global issue. To prevent the deaths of thousands of epileptic patients each year, there is a critical necessity for an effective method for detecting epileptic seizures at their earliest stage. Numerous medical procedures, including epileptic monitoring, diagnosis, and other procedures, may be carried out remotely with the use of IoMT, which will reduce healthcare expenses and improve services. This article seeks to act as both a collection and a review of the different cutting-edge ML applications for epilepsy detection that are presently being combined with IoMT.
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Metadata
Title
An overview of machine learning methods in enabling IoMT-based epileptic seizure detection
Authors
Alaa Lateef Noor Al-hajjar
Ali Kadhum M. Al-Qurabat
Publication date
24-04-2023
Publisher
Springer US
Published in
The Journal of Supercomputing / Issue 14/2023
Print ISSN: 0920-8542
Electronic ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-023-05299-9

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