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Home > Archives > Volume 20, No 8 (2022) > Article

DOI: 10.14704/nq.2022.20.8.NQ44960

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


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.


Epilepsy; EEG; Brain; seizer; PyEEG

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