Godot is not coming: when we will let innovations enter psychiatry?

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 previously undetected changes in intrinsic dynamics of physiological systems, providing the basis for early detection of depression. We are also advocating for early screening of cardiovascular risks in depression which is in connection to previously described decomplexification of the autonomous nervous system resulting in symptoms recognized clinically. All that said, additional information about the level of complexity can help clinicians make a better decisions in the therapeutic process, increase the overall effectiveness of the treatment, and finally increase the quality of life of the patient.