Godot is not coming: when we will let innovations enter psychiatry?
Milena B. \v{C}uki\'c

TL;DR
This paper proposes integrating electrophysiological signals, electromagnetic stimulation, and physiological complexity measures to improve early detection and treatment of depression in psychiatry.
Contribution
It introduces novel methods combining biophysical signals, complexity analysis, and machine learning to enhance psychiatric diagnostics and therapeutic strategies.
Findings
Fractal and nonlinear measures detect early depression-related changes.
Electrophysiological signals can improve diagnostic accuracy.
Complexity measures can guide personalized treatment decisions.
Abstract
Current diagnostic practice in psychiatry is not relying on objective biophysical evidence. Recent pandemic emphasized the need to address the rising number of mood disorders (in particular, depression) cases in a more efficient way. We are proposing several already developed practices that can help improve that diagnostic process: detection based on electrophysiological signals (both electroencephalogram and electrocardiogram based) that were shown to be accurate for clinical practice and several modalities of electromagnetic stimulation that were proven to ameliorate symptoms of depression. In this work, we are connecting the two with explanations coming from physiological complexity studies (and our own work) as well as advanced statistical methods like machine learning and the Bayesian inference approach. It is shown that fractal and nonlinear measures can adequately quantify…
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Taxonomy
TopicsFunctional Brain Connectivity Studies · Mental Health Research Topics · Heart Rate Variability and Autonomic Control
