New Technology for Intelligent Analysis of Physiological Data for the Development of Medical Applications in Psychiatry

In this work, we propose a new technology for the intelligent analysis of complex physiological signals generated by different areas of the human cerebral cortex, with the aim of subsequently developing medical applications in psychiatry. The search for diagnostic criteria for schizophrenia and schizophrenia spectrum diseases is carried out within the framework of Flicker-Noise Spectroscopy from electroencephalograms of two groups of subjects: healthy subjects and children/adolescents with varying degrees of manifestation of mental disorders. In the course of studying the dynamic and spectral characteristics, as well as the effects of synchronization of electroencephalograms, we established classification parameters that can be used to implement machine learning algorithms in automated systems for the analysis and diagnosis of mental disorders. Moreover, these parameters consider the high degree of individuality inherent in biomedical and physiological signals generated by living organisms. The proposed solution will be of interest to specialists developing machine learning methods, applied artificial intelligence technologies in medical applications, as well as biophysics and physics of complex systems, in terms of digital processing of biomedical and physiological signals.