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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 [1] [2] Fig. 5. Probability distribution of LF for normal subject, NYHA I, NYHA II, NYHA III CHF patients. [3] [4] [5] [6] 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 A. Mosterd , A.W. Hoes , M.C. de Bruyne , J.W. Deckers , D.T. Linker , A. Hofman , and D.E. Grobbee, “ Prevalence of heart failure and left ventricular dysfunction in the general population; The Rotterdam Study” Eur Heart J 20: 447-455. Kenneth Dickstein , Alain Cohen-Solal , Gerasimos Filippatos , John J.V. McMurray , Piotr Ponikowski , Philip Alexander Poole-Wilson , Anna Strömberg , Dirk J. van Veldhuisen , Dan Atar , Arno W. Hoes , Andre Keren , Alexandre Mebazaa , Markku Nieminen , Silvia Giuliana Priori , and Karl Swedberg, “ 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. Developed in collaboration with the Heart Failure Association of the ESC (HFA) and endorsed by the European Society of Intensive Care Medicine (ESICM)” , Eur J Heart Fail 10: 933-989. 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. Mariell Jessup, William T. Abraham, Donald E. Casey, Arthur M. Feldman, Gary S. Francis, Theodore G. Ganiats, Marvin A. Konstam, Donna M. Mancini, Peter S. Rahko, Marc A. Silver, Lynne Warner Stevenson, and Clyde W. Yancy, “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 published March 26, 2009, doi:10.1161/CIRCULATIONAHA.109.192064 Marek Malik , J. Thomas Bigger , A. John Camm , Robert E. Kleiger , Alberto Malliani , Arthur J. Moss , and Peter J. Schwartz “Heart rate variability: Standards of measurement, physiological interpretation, and clinical use”, Eur Heart J 17: 354-381. Clifford, G.D., Tarassenko L., “Quantifying errors in spectral estimates of HRV due to beat replacement and resampling”, IEEE Transactions on Biomedical Engineering, 2005, 52 (4):630 - 638 Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE., “PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals”. Circulation 101(23):e215-e220 [Circulation Electronic Pages; http://circ.ahajournals.org/cgi/content/full/101/23/e215]; 2000 (June 13).