Oxford Hill Foundation Scholarship
Automated symptoms assessment in mental health
Mental disorders introduce a significant burden on society. According to theWorld Health Organization Global Burden of Disease report, unipolar depressive disorders will be the leading cause of disabilities by 2030 (from 3rd place in 2004). Diagnostics of mental disorders heavily depend on clinical judgment. Numerous mental health scales have been suggested to bring objectivity into the diagnostic process. But there is evidence, that reliance on such scales may also lead to mistaken conclusions, so more objective diagnostic criteria may be necessary. An automated assessment of mental health may provide more timely interventions and decrease the severity of significant events. Psychophysiology, since the classical work of Lacey in 1959, is a heavily debated approach to introduce objectivity into the diagnostic and psychotherapeutic process. Such measures as heart rate (HR), electrodermal activity (EDA), skin temperature, muscle potentials, as well as clinical indexes based on these measures and features derived from them, may provide valuable information to assess a patient’s mental state. While there is some skepticism among practicing clinicians about the possibility of automating the diagnostic process, psychophysiological measures are extensively used by researchers to analyze mental conditions, including aggression, psychopathy, conduct problems, schizophrenia and others. However, most existing research activities are focused on small subsets of psychophysiological variables, collected in controlled laboratory settings, while there is evidence, that social environment is also an important factor for psychophysiological response. Therefore, in order to create a complete picture of patient’s state, longitudinal measurements may be required, capturing data from the patient in normal living conditions and using multiple psychophysiological measures.