RT Journal Article
T1 Classification of Depression Through Resting-State Electroencephalogram as a Novel Practice in Psychiatry: Review
A1 Čukić, Milena
A1 López, Victoria
A1 Pavón Mestras, Juan Luis
AB Machine learning applications in health care have increased considerably in the recent past, and this review focuses on an important application in psychiatry related to the detection of depression. Since the advent of computational psychiatry, research based on functional magnetic resonance imaging has yielded remarkable results, but these tools tend to be too expensive for everyday clinical use.This review shows the need for larger data sets and more systematic procedures to improve the use of the solution for clinical diagnostics. Therefore, regulation of the pipeline and standard requirements for methodology used should become mandatory to increase the reliability and accuracy of the complete methodology for it to be translated to modern psychiatry.
PB JMIR Publications
SN 1438-8871
YR 2020
FD 2020
LK https://hdl.handle.net/20.500.14352/95553
UL https://hdl.handle.net/20.500.14352/95553
LA eng
NO Čukić M, López V, Pavón J Classification of Depression Through Resting-State Electroencephalogram as a Novel Practice in Psychiatry: Review J Med Internet Res 2020;22(11):e19548 doi: 10.2196/19548
DS Docta Complutense
RD 6 abr 2025