Allwyn Gnanadas A, Department of Bio Medical Engineering, KPR Institute of Engineering and Technology, India
Sathishbabu, Department of Electronics and Communication Engineering, Thanthai Periyar Government Institute of Technology, India
Prince Ashwin Kumar, Honeywell Technologies, India
Online First : 30 December 2020
Publisher Name : IJAICT India Publications, India.
Print ISBN : 978-81-950008-0-7
Online ISBN : 978-81-950008-1-4
Page :453-458
Abstract
Epilepsy is a chronic condition that is characterized by frequent occurrence of seizure. The treatment of epilepsy using anticonvulsants that suppress the rapid neuron spikes in brain is promising; however, a permanent fix is always lacked. The region on brain that is responsible for seizure if identified exactly, the diseased area can be expelled and that could be a permanent fix. Generally, IEEG (Intracranial Electroencephalogram) an invasive procedure is adopted to diagnose the location of seizure, though the results are very reliable and considered as a golden standard, the procedure is a complex and risky one. To overcome these difficulties, fMRI (Functional magnetic resonance imaging) is used to read the internal anatomical and metabolic nature of brain, an algorithm (erKNOTS, enhanced regional K based Numbering Out of Time Slices) that analyse each individual voxel is developed and implemented. The result obtained by the developed algorithm is found to be in agreement with those obtained through IEEG. The findings were further validated with Regional homogeneity and Functional connectivity.
Keywords
Pandemic, Machine Learning (ML), Corona, XG boost, Na誰ve Bayes.
Cite this article
Allwyn Gnanadas A, Sathishbabu and Prince Ashwin Kumar, “Enhanced Regional Clustering Algorithm in Seizure Location Identification”, Innovations in Information and Communication Technology, pp.447-452, December 2020.
Copyright
© 2020 Allwyn Gnanadas A, Sathishbabu and Prince Ashwin Kumar. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
