title: Self-organizing neural network analyses of cardiac data in depression.
abstract: OBJECTIVE
  To determine if an unsupervised self-organizing neural network could create a clinically meaningful distinction of 'depression' versus 'no depression' based on cardiac time-series data.
  
  
  METHODS
  A self-organizing map (SOM) was used to separate the time-series of 84 subjects into groups based on characteristics of the data alone.
  
  
  METHODS
  Analyses included natural log transformations and two types of filtering to enhance characteristics of the data as well as classifications of unprocessed data. A Pearson chi(2) analysis was performed to determine if the SOM groups bore any relation to the binary clinical groups.
  
  
  RESULTS
  Overall correct SOM classifications ranged from 54 to 70.2% with two classifications being clinically meaningful.
  
  
  CONCLUSIONS
  SOM classifications of cardiac time-series data with enhanced ultradian variations and cardiac data recorded around the interval when a person was in bed were useful in differentiating clinically meaningful subgroups with and without depression.
authors.affiliation: HeartLink Canada, University of British Columbia, Vancouver, Canada.
authors.forename: Michael
authors.initials: M
authors.lastname: Gaetz
authors.forename: Grant L
authors.initials: GL
authors.lastname: Iverson
authors.forename: Edward J
authors.initials: EJ
authors.lastname: Rzempoluck
authors.forename: Ronald
authors.initials: R
authors.lastname: Remick
authors.forename: Peter
authors.initials: P
authors.lastname: McLean
authors.forename: Wolfgang
authors.initials: W
authors.lastname: Linden
mesh_terms: D000328:Adult; D000465:Algorithms; D016009:Chi-Square Distribution; D002940:Circadian Rhythm / Q000502:physiology; D003198:Computer Simulation; D003863:Depression / Q000175:diagnosis* / Q000503:physiopathology*; D005260:Female; D006321:Heart / Q000503:physiopathology; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D013997:Time Factors