Volume 20 No 8 (2022)
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Development of Machine Learning Based Epileptic Seizureprediction using Web of Things(WoT)
Dr.Kazi Kutubuddin Sayyad Liyakat , Dr. K. P. Paradeshi , Dr J A Shaikh , Dr K K Pandyaji , Dr. D. B. Kadam
Abstract
A significant chronic neurological illness called epilepsy and identified by examining Brain signals that
Brain Neurons’ produce. In order to generate messages and communicate with bodily organs, neurons
are intricately coupled to one another. Electrocorticography (ECoG) and Electroencephalogram (EEG)
media are frequently used to detect these brain impulses. These signals generate a large amount of data
and are complicated, noisy, non-stationary andnon-linear. As a result, it is tough to identify seizures and
learn about knowledge relating to the brain. Without sacrificing performance, machine learning
classifiers can classify EEG data, detect seizures, and highlight pertinent meaningful patterns. As a result,
numerous researchers have created a variety of seizure detection methods combining statistical
characteristics and machine learning classifiers.The biggest difficulties lie in choosing the right classifiers
and characteristics. The purpose of this work is to demonstrate a machine learning methodology for
using WoT to detect epileptic seizures
Keywords
Epilepsy; EEG; Brain; seizer; PyEEG;
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