  A stroke is defined as a neurologic deficit arising from an interruption in
blood supply to the brain. According to the World Health Organization, over 15
million people suffer from strokes annually, of which almost 70% die or are
permanently disabled. Effective treatment must be administered within one hour
to prevent irreversible brain damage. Unfortunately, the current gold standards
for diagnosis, CT and MRI, are time-consuming, expensive, and immobile.
Electroencephalograms reveal biomarkers of strokes while being inexpensive and
available for remote use, but no system exists that utilizes them for this
purpose. To address this issue, we created StrokeSight, a novel, open-source
web application that automatically provides a full diagnosis and visualization
of ischemic and hemorrhagic strokes in under 50 seconds using 60-second
electroencephalograms. We first calculated the averaged power spectral
densities for 132, 60-second electroencephalogram readings, which we then used
to train three deep neural networks that respectively predict a stroke type
(control/ischemic/hemorrhagic), location (left/right hemisphere), and severity
(small/large) with accuracies of 97.5%, 94.4%, and 100%. StrokeSight also
implements a novel process to visualize spectral abnormalities caused by
strokes. Azimuthal equidistant projection and multivariate spline interpolation
are used to reshape 3D electrodes onto a head-shaped 2D plane and then a
contour map of each frequency band power is created, allowing neurologists to
quickly and accurately interpret electroencephalogram data. StrokeSight could
act as a revolutionary solution for stroke care that drastically improves the
speed, cost efficiency, and accessibility of stroke diagnosis while allowing
for personalized treatment and interpretation.
