Čukić, MilenaLópez, VictoriaPavón Mestras, Juan Luis2024-01-252024-01-252020Čukić 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: 10.2196/195481438-887110.2196/19548https://hdl.handle.net/20.500.14352/95553Machine 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.engAttribution-NonCommercial-ShareAlike 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-sa/4.0/journal articlehttps://doi.org/10.2196/1954833141088open accessComputational psychiatryPhysiological complexityMachine learningResting-state EEGComputational neuroscienceInformática (Informática)1203.17 Informática