Download II. Method

Heart Rate Variability in healthy people compared with patients
with Congestive Heart Failure
Leandro Pecchia, Paolo Melillo, and Marcello Bracale

Abstract—To be written.
I. INTRODUCTION
C
ONGESTIVEHeart
Failure (CHF) is a patho-physiological
state in which an abnormality of cardiac function is
responsible for the failure of the heart to pump blood as
really needed by the body. CHF is a strongly degenerative
syndrome and age related. Actually CHF prevalence
increases rapidly with age, raising from about 3% in 65 year
old patients to more than 11% in patients over 84 [1]. For
this reason it is considered as a typical syndrome of the old
age, with 75 years [2] as mean age of the affected
population. The number of patients with CHF is increasing,
mainly because of the growing number of elderly people [3].
A variety of approaches have been used to quantify the
degree of functional limitation imposed by HF. The most
widely used scale is the NYHA functional classification,[4]
but this system is subject to considerable interobserver
variability and is insensitive to important changes in exercise
capacity. Although the history and physical examination
may provide important clues about the nature of the
underlying cardiac abnormality, identification of the
structural abnormality leading to HF generally requires
invasive or noninvasive imaging of the cardiac chambers or
great vessels.
The diagnostic test, which seems to be most useful in the
evaluation of patients with HF is the comprehensive 2dimensional echocardiogram coupled with Doppler flow
studies to determine whether abnormalities of myocardium,
heart valves, or pericardium are present and which chambers
are involved. The reason is that with this analysis is possible
to assess the Left Ventricular (LV) ejection fraction (EF), is
the structure of the LV is normal or abnormal, if there are
there other structural abnormalities such as valvular,
pericardial, or right ventricular abnormalities that could
Manuscript received July 10, 2009. This work was supported in part by
Regione Campania under Research Project R.H.M. (Remote Health
Monitoring).
L. Pecchia is with the Department of Biomedical, Electronic and
Telecommunication Engineering, University of Naples “Federico II”,
Naples, Italy (corresponding author to provide phone: +39-081-7683790;
fax: +39-081-7683790; e-mail: leandro.pecchia@ unina.it).
P. Melillo is with the Department of Biomedical, Electronic and
Telecommunication Engineering, University of Naples “Federico II”,
Naples, Italy (e-mail: [email protected]).
M. Bracale is with the Department of Biomedical, Electronic and
Telecommunication Engineering, University of Naples “Federico II”,
Naples, Italy (e-mail: [email protected]).
account for the clinical presentation. Noninvasive
hemodynamic data acquired at the time of echocardiography
are an important additional correlate for patients with
preserved or reduced EF.
About electrocardiography, recent guideline [5] evidenced
TABLE I
SELECTED HRV MEASURE
Measure
Description
Unit
SDNN
Standard deviation of all NN intervals.
ms
RMSSD
The square root of the mean of the sum
of the squares of differences between
adjacent NN intervals
ms
AVNN
Average of all NN intervals
ms
pNN50
Percentage of differences between
adjacent NN intervals that are > 50 ms
This is one member of the larger pNNx
family
%
TOTAL
POWER
Total spectral power of all NN intervals
up to 0.04 Hz.
ms2
VLF
Total spectral power of all NN intervals
between 0 and 0.04 Hz
ms2
LF
Total spectral power of all NN intervals
between 0.04 and 0.15 Hz
ms2
HF
Total spectral power of all NN intervals
between 0.15 and 0.4 Hz
ms2
LF/HF
Ratio of low to high frequency power
that a 12-lead electrocardiogram may demonstrate evidence
of prior MI, LV hypertrophy, cardiac conduction
abnormality or a cardiac arrhythmia. However, because of
low sensitivity and specificity, ECG should not form the
primary basis for determining the specific cardiac
abnormality responsible for the development of HF.
In this study…
II. METHOD
To determine values for HRV measure in normal middleaged persons and in patients who suffered form chronic heart
failure, we designed a retrospective analysis of two RR
interval databases. We calculate statistical measures from 5minute RR interval data using standard methods [6].
Moreover short-term frequency domain measures are
estimated by using Lomb periodogram [7].
All the
calculated measure are reported in table I. We made a
comparison between healthy subjects versus patients
suffering from Congestive Heart Failure with different New
York Heart Association (NYHA) class status.
The data for the normal control group were obtained from
24-hour Holter monitor recordings of 54 healthy subject (30
men and 24 women, aged 29-76 years, mean 61 y, standard
deviation 11). These recordings were from the Normal Sinus
Rhythm
RR
Interval
Database
(http://www.physionet.org/physiobank/database/nsr2db/) [8].
The data for the CHF group were obtained from 24-hour
Holter monitor recordings of 29 CHF subject (8 men and 2
women, gender is not known for the remaining subjects, aged
34-79 years, mean 55, standard deviation 11), 4 with NYHA
function class I status, 8 with class II status and 17 with class
III status. These recordings were from the Congestive Heart
Failure
RR
Interval
Database
(http://www.physionet.org/physiobank/database/chf2db/) [8].
All the original ECG recordings were digitized at 128
samples per second, and the beat annotations were obtained
by automated analysis with manual review and correction
[8].
For each selected measure, we calculated probability
distribution by distinguishing subject in normal; NYHA I,
NYHA II; NYHA III.
Fig. 2. Probability distribution of RMSSD for normal subject,
NYHA I NYHA II, NYHA III CHF patients.
III. RESULTS
We observed significantly higher value of SDNN,
RMSSD and TOTAL POWER for healthy subject than for
patient with CHF (fig1,2,3). Also VLF, LF and HF seem to
be depressed in CHF patient (fig4, 5, 6). Further, the mean
and the range of frequency domain measure were compared
with those previously reported [1].
Fig. 3. Probability distribution of TOTAL POWER for normal
subject, NYHA I NYHA II, NYHA III CHF patients.
Fig. 4. Probability distribution of VLF for normal subject, NYHA I
NYHA II, NYHA III CHF patients.
Fig. 1. Probability distribution of SDNN for normal subject, NYHA I
NYHA II, NYHA III CHF patients.
The other parameters, such as AVNN (fig. 7), did not
provide a good separation for the normal versus CHF group.
We also compared data from CHF patient by
distinguishing according to their NYHA class. Each selected
measure failed to distinguish these three subgroup (NYHA
class).
Engineering of University Federico II of Naples..
REFERENCES
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[2]
Fig. 5. Probability distribution of LF for normal subject, NYHA I,
NYHA II, NYHA III CHF patients.
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Fig. 6. Probability distribution of HF for normal subject, NYHA I,
NYHA II, NYHA III CHF patients.
[7]
[8]
[9]
Fig. 7. Probability distribution of AVNN for normal subject, NYHA
I NYHA II, NYHA III CHF patients.
IV. CONCLUSION
To be written
ACKNOWLEDGMENT
All the authors thanks the whole group of Biomedical
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