Analyzing the 24-hour blood pressure and heart-rate variability with self-organizing feature maps

Tambouratzis, G; Papakonstantinou, G; Stamatelopoulos, S; Zakopoulos, N; Moulopoulos, S

dc.contributor.author	Tambouratzis, G	en
dc.contributor.author	Papakonstantinou, G	en
dc.contributor.author	Stamatelopoulos, S	en
dc.contributor.author	Zakopoulos, N	en
dc.contributor.author	Moulopoulos, S	en
dc.date.accessioned	2014-03-01T01:17:33Z
dc.date.available	2014-03-01T01:17:33Z
dc.date.issued	2002	en
dc.identifier.issn	0884-8173	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/14554
dc.subject	Blood Pressure	en
dc.subject	Heart Rate Variability	en
dc.subject	Self Organized Feature Map	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.other	Cardiology	en
dc.subject.other	Computer aided diagnosis	en
dc.subject.other	Data reduction	en
dc.subject.other	Hemodynamics	en
dc.subject.other	Medical applications	en
dc.subject.other	Statistical methods	en
dc.subject.other	Blood pressure variability	en
dc.subject.other	Heart rate variability	en
dc.subject.other	Pulse rate measurements	en
dc.subject.other	Self organizing maps	en
dc.title	Analyzing the 24-hour blood pressure and heart-rate variability with self-organizing feature maps	en
heal.type	journalArticle	en
heal.identifier.primary	10.1002/int.1003	en
heal.identifier.secondary	http://dx.doi.org/10.1002/int.1003	en
heal.language	English	en
heal.publicationDate	2002	en
heal.abstract	In this article, the self-organizing map (SOM) is employed to analyze data describing the 24-hour blood pressure and heart-rate variability of human subjects. The number of observations varies widely over different subjects, and therefore a direct statistical analysis of the data is not feasible without extensive pre-processing and interpolation for normalization purposes. The SOM network operates directly on the data set, without any pre-processing, determines several important data set characteristics, and allows their visualization on a two-dimensional plot. The SOM results are very similar to those obtained using classic statistical methods, indicating the effectiveness of the SOM method in accurately extracting the main characteristics from the data set and displaying them in a readily understandable manner. In this article, the relation is studied between the representation of each subject on the SOM, and his blood pressure and pulse-rate measurements. Finally, some indications are included regarding how the SOM can be used by the medical community to assist in diagnosis tasks. (C) 2002 John Wiley Sons, Inc.	en
heal.publisher	JOHN WILEY & SONS INC	en
heal.journalName	International Journal of Intelligent Systems	en
dc.identifier.doi	10.1002/int.1003	en
dc.identifier.isi	ISI:000173198200004	en
dc.identifier.volume	17	en
dc.identifier.issue	1	en
dc.identifier.spage	63	en
dc.identifier.epage	76	en