by Huang, Yiing-Shiuan, Iakubovskii, Pavel, Lim, Li Zhen, Tyndall, Don
Published in Oral surgery, oral medicine, oral pathology and oral radiology (01.02.2023)

by Gluch, Greg, Goldwasser, Shafi
Year of Publication 10.07.2025

by Karwowski, Jan, Okulewicz, Michał, Legierski, Jarosław
Published in Engineering Applications of Neural Networks (2013)

by The research aims to differentiate EEG of normal subjects from epileptic EEGs Additionally the developed algorithm attempts to identify the types of seizures Our work is to help the neurologists make an efficient, quick automated screening, diagnosis Pandya addsIn their study the team re ports a novel algorithm that can sift through EEG data, electrical signal patterns After initial training the algorithm was able to detect whether a human subject could have epilepsy or not based on these patterns in their respective analyses with a high degree of accuracy the researchers sayTo develop, train the algorithm the researchers first examined EEG data from 88 human subjects acqui red at AIIMS Rishikesh Each subject underwent a 45minute EEG test divided into two parts An initial 10minute test when the subject was awake which included p hotic stimulation, hyperventilation followed by a 35minute sleep period when the subject was asked to sleepNext the team analysed this data, classified different wave patterns into sharp signals spikes, slow waves Spikes are patte rns where a signal rises, falls within a very short duration of time around 70 milliseconds while sharps are those with rises, falls spread over a slightly longer duration around 250 milliseconds an d slow waves have a much longer duration around 400 milliseconds IISc said
Published in The Times of India (Online) (02.10.2022)