title: Objective identification and analysis of physiological and behavioral signs of schizophrenia.
abstract: BACKGROUND
  A patient's physical activity is often used by psychiatrists to contribute to the diagnostic process for mental disorders. Typically, it is based mostly on self-reports or observations, and hardly ever upon actigraphy. Other signals related to physiology are rarely used, despite the fact that the autonomic nervous system is often affected by mental disorders.
  
  
  OBJECTIVE
  This study attempted to fuse physiological and physical activity data and discover features that are predictive for schizophrenia.
  
  
  METHODS
  Continuous simultaneous heart rate (HR) and physical activity recordings were made on 16 individuals with schizophrenia and 19 healthy controls. Statistical characteristics of the recorded data were analyzed, as well as non-linear rest-activity measures and disorganization measures.
  
  
  RESULTS
  Four most predictive features for schizophrenia were identified, namely, the standard deviation and mode of locomotor activity, dynamics of Multiscale Entropy change over scales of HR signal and the mean HR. A classifier trained on these features provided a cross-validation accuracy of 95.3% (AUC = 0.99) for differentiating between schizophrenia patients and controls, compared to 78.5 and 85.5% accuracy (AUC = 0.85 and AUC = 0.90) using only the HR or locomotor activity features.
  
  
  CONCLUSIONS
  Physiological and physical activity signals provide complimentary information for assessment of mental health.
authors.affiliation: a Department of Engineering Science , Institute of Biomedical Engineering, University of Oxford , Oxford , UK .
authors.forename: Maxim
authors.initials: M
authors.lastname: Osipov
authors.affiliation: b Proteus Digital Health, Inc. , Redwood City , CA , USA , and.
authors.forename: Yashar
authors.initials: Y
authors.lastname: Behzadi
authors.affiliation: c Hofstra North Shore-LIJ School of Medicine, Hofstra University , Hempstead , NY , USA.
authors.forename: John M
authors.initials: JM
authors.lastname: Kane
authors.affiliation: c Hofstra North Shore-LIJ School of Medicine, Hofstra University , Hempstead , NY , USA.
authors.forename: Georgios
authors.initials: G
authors.lastname: Petrides
authors.affiliation: a Department of Engineering Science , Institute of Biomedical Engineering, University of Oxford , Oxford , UK .
authors.forename: Gari D
authors.initials: GD
authors.lastname: Clifford
mesh_terms: D056044:Actigraphy / Q000379:methods*; D000328:Adult; D015716:Electrocardiography, Ambulatory / Q000379:methods*; D005260:Female; D006339:Heart Rate; D006801:Humans; D008297:Male; D008875:Middle Aged; D009043:Motor Activity; D012559:Schizophrenia / Q000175:diagnosis / Q000503:physiopathology*; D060388:Support Vector Machine
keywords: Activity; heart rate; multiscale entropy; schizophrenia; transfer entropy