Key result
A self-organizing neural network analyzing cardiac time-series data correctly classified subjects with and without depression with an overall accuracy ranging from 54% to 70.2%.
Why the study?
Does a self-organizing neural network analysis of cardiac time-series data accurately classify subjects with and without depression?
Observational (n=84)
Does a self-organizing neural network analysis of cardiac time-series data accurately classify subjects with and without depression?
Unsupervised self-organizing neural networks can differentiate between subjects with and without depression using specific features of cardiac time-series data.
Accuracy too low for clinical use; leaves open whether cardiac time-series aid unsupervised depression classification.
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.
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Gaetz et al. (2004) conducted an observational in Depression (n=84). Self-organizing neural network (SOM) analysis of cardiac time-series data was evaluated on Correct classification of depression versus no depression. A self-organizing neural network analyzing cardiac time-series data correctly classified subjects with and without depression with an overall accuracy ranging from 54% to 70.2%.
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