Detecting Abnormal Electroencephalograms Using Deep Convolutional Networks

Electroencephalography (EEG) can be used to detect the abnormal patterns of brain electrical activity present in a broad range of neurological and medication conditions. For example, EEGs of patients with epilepsy often exhibit characteristic "epileptiform" discharges (epileptic spikes or sharp-waves) (Schomer and Da Silva 2012). Lesions, such as strokes or hemorrhages, can result in asymmetry across left and right hemispheres (Agius Anastasi et al. 2017; Jordan 2004; van Putten 2007). Patients with depressed levels of consciousness exhibit generalized slowing of EEG rhythms or burst suppression patterns (Young 2000; Kaplan 2004; Schomer and Da Silva 2012) Metabolic encephalopathy from acute liver failure can cause abnormalities such as triphasic waves (Boulanger et al.

from Physiology via xlomafota13 on Inoreader https://ift.tt/2FsQeEK
via IFTTT