Download II. Method

Heart Rate Variability in healthy people compared with patients
with Congestive Heart Failure
Leandro Pecchia, Paolo Melillo, Mario Sansone and Marcello Bracale

Abstract— In this paper we have investigated the differences
of Heart Rate Variability (HRV) features between normal
subjects and patients suffering from Congestive Heart Failure
(CHF) at several levels of NYHA scale. We analyzed 1914.4
hours of ECG of 83 patients of which 54 normal and 29
suffering from CHF with NYHA I, II, III, extracted by public
databases. Following international guidelines, we computed
time and frequency features of HRV. The measured features
have been statistically analyzed per each NHYA class. Finally,
features correlation matrix for each class has been studied.
The results suggest that differences exist between HRV
features of normal subjects and patients suffering from CHF.
These differences seems to be related to the severity of the
pathology. Finally, we observe that also the correlation matrix
changes according to the severity of NYHA.
Index Terms— congestive heart failure, heart rate
variability, New York Heart Association class
I. INTRODUCTION
C
ONGESTIVE Heart
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 New York Heart Association
Manuscript received July 10, 2009. This work was supported in part by
Regione Campania with the 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. Sansone 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]).
(NYHA) functional classification [4], but this system is
subjected to considerable inter-observer variability and is
insensitive to important changes in exercise capacity.
Although clinical history and physical examination may
provide important clues about the nature of the underlying
cardiac abnormality, precise identification of the structural
abnormality leading to HF generally requires invasive or
noninvasive imaging of the cardiac chambers or great
vessels. Moreover, in the diagnosis of CHF it is important to
assess the Left Ventricular (LV) ejection fraction (EF), if the
structure of the LV is normal or abnormal, if there are other
structural abnormalities such as valvular, pericardial, or right
ventricular abnormalities that could account for the clinical
presentation. Noninvasive hemodynamic data are an
important additional correlate for patients with preserved or
reduced EF.
For this reason, recent guidelines [5], report that 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. About electrocardiography, the same guideline
evidenced 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.
International guidelines on care and follow-up of CHF [6][9], define a minimum data set for the monitoring of the
patient and refer to Heart Rate Variability (HRV) as a
quantitative marker of autonomic nervous system activity
and as a powerful method of analysis to diagnose and
prevent critical events [10]. HRV [11] refers to the variations
of beat-to-beat intervals in heart rate, which are called RR
intervals. Intervals between normal sinus beats are referred
as NN intervals. HRV reflects the actions of sympathetic and
parasympathetic branches of the autonomic nervous system
on the regulation of the sinus node, which is the natural
pacemaker of the heart. The beat-to-beat oscillations in heart
rhythm provide an indirect measure of the physiological
behavior of the heart. For this reason, it is considered as a
good marker of HF destabilizations.
In this paper we have investigated the differences of Heart
Rate Variability (HRV) features between normal subjects
TABLE II
SELECTED HRV MEASURE [11]
and patients suffering from Congestive Heart Failure (CHF)
at several levels of NYHA scale.
Measure
Description
Unit
II. METHOD
We performed a retrospective analysis of two RR interval
databases, to compare values for HRV measure in normal
middle-aged subjects and in patients who suffered from
chronic heart failure, with NYHA I, II and III.
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
TABLE I
NYHA CLASS
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
NYHA
Class
I
II
III
IV
Symptoms
No symptoms and no limitation in ordinary physical
activity, e.g. shortness of breath when walking,
climbing stairs etc.
Mild symptoms (mild shortness of breath and/or
angina) and slight limitation during ordinary activity.
Marked limitation in activity due to symptoms, even
during less-than-ordinary activity, e.g. walking short
distances (20-100 m).
Comfortable only at rest.
Severe limitations. Experiences symptoms even while
at rest. Mostly bedbound patients.
We calculated statistical measures from 5-minute RR interval
data using standard methods [11]. Moreover, we estimated
short-term frequency domain measures using the Lomb
periodogram [12]. We report all the selected features in
table II.
The data for the normal subjects (control group) were
obtained segmenting 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 [13].
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 [13].
We obtained beat annotations by automated analysis, and
performed a three hands blind manual review and correction
[13]. For each selected feature and for each NYHA scale,
histogram distribution has been computed. Finally, we
analyzed how the correlation matrix between features of NN
series changes according to the severity of CHF.
LF/HF
Ratio of low to high frequency power
TABLE III
VALUE OF NN MEASURES
Measure
Unit
SDNN
RMSSD
AVNN
pNN50
TOT. POW.
VLF
LF
HF
LF/HF
ms
ms
ms
%
ms2
ms2
ms2
ms2
-
Normal
(mean±SD)
47.4±24.8
24.8±14.6
6.2±10.1
803±156
3089±4474
2130±3721
671±965
287±494
3.7±2.9
NYHAI
(mean±SD)
50.4±29.6
25.5±14.2
7.5±12.3
797±157
4160±6740
2810±5179
806±1301
544±770
2.1±1.6
NYHAII
(mean±SD)
24.3±17.6
14.5±8.0
1.6±4.9
639±87
917±1719
670±1290
158±355
89±214
2.2±2.0
NYHAIII
(mean±SD)
25.8±16.9
17.0±8.1
2.3±4.6
701±103
968±1599
716±1360
149±244
103±139
1.5±1.4
III. RESULTS
Table III shows the mean value and standard deviation of
the variables analyzed, organized per NHYA classes. Figures
from 1 to 7 show histogram distribution of some of the
selected features, per each NYHA class.
Fig. 1. Histogram distribution of AVNN for normal subjects, NYHA
I, NYHA II, NYHA III CHF patients.
Fig. 2. Histogram distribution of SDNN for normal subjects, NYHA I,
NYHA II, NYHA III CHF patients.
Fig. 5. Histogram distribution of VLF for normal subjects, NYHA I,
NYHA II, NYHA III CHF patients
Fig. 3. Histogram distribution Histogram distribution of RMSSD for
normal subjects, NYHA I, NYHA II, NYHA III CHF patients.
Fig. 6. Histogram distribution of LF for normal subjects, NYHA I, NYHA
II, NYHA III CHF patients.
Fig. 4. Histogram distribution of TOTAL POWER for normal subjects,
NYHA I, NYHA II, NYHA III CHF patients.
Fig. 7. Histogram distribution of HF for normal subjects, NYHA I, NYHA
II, NYHA III CHF patients.
Tables form IV to VII report correlation between the
selected feature per each NYHA class. Correlation values
higher than 0.7 have been empathized in bold.
The mean and the range of frequency domain features are
in agreement with those previously reported [11].
The other parameters, such as AVNN (fig. 7), did not
appear to be different between normal and CHF.
Finally, observation of tables IV, V, VI, VII suggests that
the correlations between some features of NN series increase
according to the severity of CHF.
From the shown results it is possible conclude that there is
a variation in HRV features, according to NYHA
classification.
IV. DISCUSSION
From the table III it is possible to observe that SDNN,
RMSSD and TOTAL POWER has higher values in healthy
subjects than in CHF patients. Moreover, VLF, LF and HF
seem to be depressed in CHF patient (fig 4, 5, 6).
TABLE IV
CORRELATION BETWEEN NN MEASURES IN NORMAL SUBJECTS
AVNN
ACKNOWLEDGMENT
AVNN
SDNN
RMSSD
pNN50
VLF
LF
HF
1
0.30
0.50
0.47
0.16
0.35
0.40
1
0.51
0.48
0.84
0.61
0.53
1
0.96
0.23
0.53
0.85
1
0.22
0.51
0.83
1
0.45
0.32
1
0.61
SDNN
RMSSD
pNN50
VLF
LF
HF
1
TABLE V
CORRELATION BETWEEN NN MEASURES IN NYHA I
AVNN
SDNN
AVNN
SDNN
RMSSD
1
0.41
0.75
0.67
1
0.63
1
RMSSD
pNN50 VLF
[2]
HF
0.27
0.39
0.55
0.51
0.88
0.78
0.79
0.95
0.42
0.65
0.82
[3]
1
0.32
0.55
0.74
[4]
1
0.70
0.65
1
0.86
VLF
LF
HF
SDNN
1
TABLE VI
CORRELATION BETWEEN NN MEASURES IN NYHA II
RMSS
AVNN SDNN
pNN50 VLF
LF
D
1
0.46
0.45
0.42
0.34 0.34
1
RMSSD
0.62
0.88
0.77
0.69
0.89
0.57
0.74
0.86
1
0.52
0.74
0.93
1
0.70
0.63
1
0.84
LF
HF
AVNN
SDNN
RMSSD
pNN50
VLF
LF
HF
0.42
1
VLF
[5]
HF
0.73
pNN50
REFERENCES
[1]
LF
pNN50
AVNN
The study has been partially financed from the Project
Remote Health Monitoring (RHM), sponsored by Regione
Campania. All the authors thanks the whole group of
Biomedical Engineering of University Federico II of Naples,
especially the secretary Gabriella Boscaino and Technician
of the laboratory Mr. Cosmo Furno Palumpo.
[6]
[7]
[8]
1
TABLE VII
CORRELATION BETWEEN NN MEASURES IN NYHA III
RMSS
AVNN SDNN
pNN50
VLF
LF HF
D
1
0,43
0,38
0,24
0,31 0,33 0,34
1
0,59
0,44
0,88
0,76 0,64
1
0,89
0,33
0,53 0,78
1
0,24
0,42 0,73
1
0,62 0,44
1
[9]
[10]
[11]
[12]
0,72
1
[13]
Only a few studies[14]-[15] have been focused on using
HRV measures for diagnosis purpose in CHF and these
studies proposed a binary classifier for identify normal and
CHF patient without considering NYHA class. We suppose
that the variation in HRV feature according to NYHA classes
may drive the research of a hierarchic classifier in order to
distinguish not only normal versus CHF patient but also mild
versus severe CHF.
[14]
[15]
A. Mosterd, A. W. Hoes, M. C. de Bruyne et al., “Prevalence of heart
failure and left ventricular dysfunction in the general population; The
Rotterdam Study,” Eur Heart J, vol. 20, no. 6, pp. 447-455, March 2,
1999.
K. Dickstein, A. Cohen-Solal, G. Filippatos et al., “ESC Guidelines
for the diagnosis and treatment of acute and chronic heart failure
2008: The Task Force for the Diagnosis and Treatment of Acute and
Chronic Heart Failure 2008 of the European Society of Cardiology.”
Eur J Heart Fail, vol. 10, no. 10, pp. 933-989, October 1, 2008.
American Heart Association, 2000 Heart and Stroke Statistical
Update. Dallas TX: American Heart Association; 2000.
The Criteria Committee of the New York Heart Association.
Nomenclature and Criteria for Diagnosis of Diseases of the Heart and
Great Vessels. 9th ed. Boston, Mass: Little, Brown & Co; 1994:253256.
M. Jessup W. T. Abraham, D. E. Casey et al., “2009 Focused Update:
ACCF/AHA Guidelines for the Diagnosis and Management of Heart
Failure in Adults: A Report of the American College of Cardiology
Foundation/American Heart Association Task Force on Practice
Guidelines,” Circulation, vol. 119, no. 14, pp. 1977-2016, April 14,
2009, 2009
A. J. Camm, M. Malik, J. T. Bigger et al., “Heart rate variability.
Standards of measurement, physiological interpretation, and clinical
use,” European Heart Journal, vol. 17, no. 3, pp. 354-381, Mar, 1996
J. G. Cleland, E. Erdmann, R. Ferrari et al., “Guidelines for the
Diagnosis of Heart-Failure,” European Heart Journal, vol. 16, no. 6,
pp. 741-751, Jun, 1995..
Hunt, “ACC/AHA 2005 guideline update for the diagnosis and
management of chronic heart failure in the adult: A report of the
American College of Cardiology/American Heart Association task
force on practice guidelines” Journal of the American College of
Cardiology, vol. 47, no. 7, pp. 1503-1505, Apr 4, 2006
W. J. Remme, K. Swedberg, and E. S. Cardiology, “Guidelines for the
diagnosis and treatment of chronic heart failure,” European Heart
Journal, vol. 22, no. 17, pp. 1527-1560, Sep, 2001.
M. W. Rich, V. Beckham, C. Wittenberg et al., “A multidisciplinary
intervention to prevent the readmission of elderly patients with
congestive heart failure,” N Engl J Med, vol. 333, no. 18, pp. 1190-5,
Nov 2, 1995.
A. J. Camm, M. Malik, J. T. Bigger et al., “Heart rate variability.
Standards of measurement, physiological interpretation, and clinical
use,” European Heart Journal, vol. 17, no. 3, pp. 354-381, Mar,
1996.
G. D. Clifford, and L. Tarassenko, “Quantifying errors in spectral
estimates of HRV due to beat, replacement and resampling,” Ieee
Transactions on Biomedical Engineering, vol. 52, no. 4, pp. 630-638,
Apr, 2005.–
A. L. Goldberger, L. A. N. Amaral, L. Glass et al., “PhysioBank,
PhysioToolkit, and PhysioNet - Components of a new research
resource for complex physiologic signals,” Circulation, vol. 101, no.
23, pp. E215-E220, Jun 13, 2000;
M.H. Asyali, Discrimination power of long-term heart rate variability
measures, in: Proceedings of the 25th Annual International
Conference of the IEEE Engineering in Medicine and Biology
Society, Cancun, September 17–21, 2003.
Y. Isler, and M. Kuntalp, “Combining classical HRV indices with
wavelet entropy measures improves to performance in diagnosing
congestive heart failure,” Computers in Biology and Medicine, vol.
37, no. 10, pp. 1502-1510, Oct, 2007.