Self-organizing neural network analyses of cardiac data in depression.
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.
Gaetz
Michael
M
HeartLink Canada, University of British Columbia, Vancouver, Canada.
Iverson
Grant L
GL
Rzempoluck
Edward J
EJ
Remick
Ronald
R
McLean
Peter
P
Linden
Wolfgang
W
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