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to those of pink noise, indicating a smoother RR sig-
nal, with stronger long-term correlations. Finally, the
group of individuals with hypertension presents RR
signals with moderate smoothness. Arterial hyperten-
sion is associated with reduced variability of cardiac
frequency, specially that associated with parasympa-
thetic activities, what can partially explain the ob-
served results.
4 CONCLUSIONS
A new tool for analysis of HRV was presented. The
presented software implemented detrended fluctua-
tion analysis, and may facilitate the study of patholo-
gies on long duration examinations or during exams
involving variable stress conditions, since DFA does
not make assumptions about signal stationarity. The
output of DFA analysis is a pair of numerical coeffi-
cients, what could make the statistical analysis of such
signals simple and practical. DFA could be combined
with pattern classification methods based on neural
networks, for a potentially powerful diagnosis tool.
The presented software was first validated with a
set of simulated signals, and then applied to real RR
signals, which demonstrated its utility for the analysis
of HRV. The DFA tool was capable of satisfactorily
discriminating the group of healthy subjects from the
group of Chagas disease individuals. The group of in-
dividuals with mild to moderate hypertension showed
some overlap with a portion of the group of healthy
volunteers, but seemed to present higher α
1
values
than that of the healthy subjects, in average.
The presented tool may help popularizing the use
of DFA among the HRV scientific community. The
software is open source, and is available upon request.
ACKNOWLEDGEMENTS
The authors thank Prof. Luiz Fernando Junqueira Jr.
and the faculty, staff and alumni of the Cardiovascular
Laboratory of the University of Bras
´
ılia for provid-
ing the HRV signals used in this work. The authors
also thank Mr. Pandelis Perakakis for motivating this
work, and for useful discussions.
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