In:
Neuropsychobiology, S. Karger AG, Vol. 49, No. 1 ( 2004), p. 30-37
Abstract:
〈 i 〉 Objective: 〈 /i 〉 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. 〈 i 〉 Design: 〈 /i 〉 A self-organizing map (SOM) was used to separate the time-series of 84 subjects into groups based on characteristics of the data alone. 〈 i 〉 Materials and Methods: 〈 /i 〉 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 χ 〈 sup 〉 2 〈 /sup 〉 analysis was performed to determine if the SOM groups bore any relation to the binary clinical groups. 〈 i 〉 Results: 〈 /i 〉 Overall correct SOM classifications ranged from 54 to 70.2% with two classifications being clinically meaningful. 〈 i 〉 Conclusions: 〈 /i 〉 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.
Type of Medium:
Online Resource
ISSN:
0302-282X
,
1423-0224
Language:
English
Publisher:
S. Karger AG
Publication Date:
2004
detail.hit.zdb_id:
1483094-2
SSG:
5,2
SSG:
15,3
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