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.

Design: A self-organizing map (SOM) was used to separate the time-series of 84 subjects into groups based on characteristics of the data alone.

Materials And 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.