Frequency Domain Analysis of Heart Rate Variability (HRV) Among the Resident Population of North Eastern Hilly Regions of West Bengal

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1 Indian Journal of Biomechanics: Special Issue (NCBM 7-8 March 9) Frequency Domain Analysis of Heart Rate Variability (HRV) Among the Resident Population of North Eastern Hilly Regions of West Bengal Ankur Ganguly 1, D. N. Tibarewala 2, S. Dasgupta 1 1 Siliguri Institute of Technology, PO: Sukna, Siliguri, W.B,India Schools of Bioscience and Engineering, Jadavpur University, Kolkata, W.B, India. Abstract Background Heart Rate Variability (HRV) is already an established and one of the most promising quantitative markers of autonomic activity. This study establishes the Frequency Domain Analysis of short time recordings of HRV among the resident population of the North Eastern Hilly regions of West Bengal. Key words: Heart Rate Variability, VLF, HF, LF, PSD. 1. Introduction Frequency domain method of analysis is one of the key factors of analyzing for the HRV analysis. Literature survey shows numerous studies relating to versatile spectral methods [13] for the analysis of the tachogram since the last four decades. Analyzing by Power spectral density (PSD) provides the basic information of how power (i.e. variance) distributes as a function of frequency. Independent of the method employed, only an estimate of the true PSD of the signals can be obtained by proper mathematical algorithms. PSD calculation is classified as non-parametric and parametric. It is advantageous considering the factors as, the simplicity of the algorithm employed (FFT in most of the cases), the high processing speed, smoother spectral components which can be identified separately for preselected frequency bands, easy post-processing of the spectrum with an automatic calculation of low and high frequency power components with an easy identification of the central frequency of each component, and accurate estimation of PSD even on a small number of samples on which the signal is supposed to maintain stability. 1.1 Spectral components Three main spectral components are distinguished in a spectrum calculated from short term recordings of 2 to 5 min [1, 2, 3, ], viz. I. Very low frequency (VLF), II. Low frequency (LF), and III. High frequency (HF) components The physiological explanation of the VLF component is defined to a lesser extent and the existence of a specific physiological process attributable to these heart period changes might even be questioned. VLF assessed from short-term recordings (e.g. 5 min) is although a tedious measure is considered here for analysis. 2. Materials and Methods HRV was measured from 5 minutes continuous recording (Short Term HRV) of Heart Rate using SuuntoT6 Heart Rate Monitor and using HRV Analysis Software, version 1.1, on 3 subjects ( Male and 1 - Female), selected randomly (age 17 to 81). The Standard Deviation of age of the set calculated as Three sets of recordings were taken from each subject with time interval of zero, three and five minutes. All data s were recorded in wake state in activity and as well as rest schedule. A data collection form was prepared for 17

2 recording and validation (Signature or LTI taken in almost all cases) of the collected data in three sections as i. General Survey, ii. Life Style survey, Work and Activity Schedule and iii. Medical Survey The domains of analysis selected in the study are: a. Parametric- AR Spectrum: Frequency band (VLF, LF, HF), Ratio of LF/HF, Peaks of the frequencies, Power (ms 2 ), Power (%), Power(nu) b. Non parametric- FFT Spectrum : Frequency band (VLF, LF, HF), Ratio of LF/HF, Peaks of the frequencies, Power (ms 2 ), Power (%), Power(nu) and Welch s Periodogram are selected 3. Evaluation and Experiments 3.1 Measuring Part Recordings of heart rate were taken by using Suunto T6 Heart Rate Monitor in three time intervals as described above and transferred using USB data cable to a computer for analysis. Software, Suunto Training Manager is used for the data collection from the HR monitor. Of the various options of available data such as HR, EPOCH, Respiration Rate, VE, VO2, Energy, Speed, R-R Interval; the data of R-R interval is taken. Fig.1. shows the graph as available from the Suunto Training Manager Software of HR (bpm) and corresponding R-R interval variation plot in m-s. 3.2 Feature Extraction The data of the various recordings are stored as ASCII values in one of the components of the software. The R-R interval datas of the 5 minutes recording were extracted and stored as.txt format in a file for future reference. Fig.2 shows the Plot of RRI and corresponding selected RRI time series for analysis as fed to HRV software 1.1. The stored.txt format values are now fed to HRV Analysis Software, version 1.1 for evaluation and analysis. The software gives us Time domain, Frequency Domain and Poincare Plot analysis of the fed data. We have used frequency domain results for this study. 4. Result and Discussion Frequency domain plots are obtained in parametric and non parametric forms and corresponding datas are noted for evaluation in a larger scale of 3 subjects for three sets of recordings. Fig3. shows the AR spectrum as described. The limiting conditions for the data were as shown in Table1.1 Of the three datas noted at three time interval the third data taken at an interval of 18 minutes from the start of the recording shown stabilization and thereby the third data of every subject is chosen for the study. The AR Spectrum characterization of the datas shows the following characteristics with average value of the HF/LF ratio as and Max at and Min at. FFT characterization is also carried out of the data ranges. The characterization is a random one and the sex and characteristics of the subjects is not reflected in this study and is kept for future analysis. The above characterization can be used for by the researchers in the field of medicine and biomedical engineering and also by the defense for their reference. The research extends to various analysis procedures but remains restricted in this particular study. 18

3 5. Conclusion The finding reflects that all values available from the subjects irrespective of their age group, sex etc. shows either low vales in LF & HF characteristics or a very high value in the LF/HF ratio. This can be significant finding in this region which shows a misbalance of the sympathetic and parasympathetic nervous system. A rigorous research in this region is being carried out in the region taking higher number of samples and particular age domains and sex domains. References 1. B. Surawicz and T. K. Knilans, Normal electrocardiogram: origin and description in Chou selectrocardiography in clinical practice. Adult and pediatric edited by B. Surawicz, T. K. Knilans (W. B. Saunders Company. New York 1) pp D. J. Ewing, J. M. M. Neilson, P. Travis, "New method for assessing cardiac parasympathetic activity using 24 hour electrocardiograms," British Heart Journal Vol. 52, pp. 3962, D. L. Eckberg, J. Physiol. 548, 339 (3). 4. G. Parati, J. P. Saul and P. Castiglioni, J. Hypertens 22, 1259 (4). 5. Hirsh JA,Bishop B. Respiratiory sinus arrhythmia in humans.am J Physology 1981: H. V. Huikuri, K. M. Kessler, E. Terracall, A. Castellanos, M. Linnaluoto, R. J. Myerburg. "Reproducibility and circadian rhythm of heart rate variability in healthy subjects," American Journal of Biomechanics 7. Heart rate variability. Standards o measurement, physiological interpretation, and clinical use.task Force of the Working Groups on Arrhythmias and Computers in Cardiology of the ESC and the North American Society of Pacing and Electrophysiology (NASPE), European Heart Journal 93, (1996). 8. Jose AD, Collison D. The normal range and determinants of the intrinsic heart rate in man. Cardiovasc Res 197;4: Kleiger RE, Miller PJ, Rigger JT, Moss N. MPRG. Decreased heart rate variability and its association with increased mortality after acute myocardial infarction. Am J Cardiol1987, 59: Lti Fei, Malik M. Heart rate variability. Methodology and physiologic basis. In:Cardiac Pacing and Electrophysiology. A bridge to the 21st century. Kluwer AcademicPublishers,1994: Malik M, Farrell T, Camm AJ. Heart rate variability in relation to prognosis after Myocardial infarction: Selection of optimal processing techniques. Eur Heart Journal 1989,1: Malik M, Camm AJ. Components of heart rate variability - what they really mean and what we really measure. Am J Cardiol 1993;72: P. F. Carleton, Physiology of the cardiovascular system, in: Pathophysiology. Clinical aspects of disease process edited by S. A. Price (Mosby Year Book, St. Louis 1992) pp R. E. Kleiger, T. J. Bigger, L. Rolnitzky, J. Cook, R. Steinman, "Relationship between time and frequency domain measures of heart rate variability," Journal of the American College of Cardiology, Vol. 17, No. 2, p. 191A, R. E. Kleiger, J. P. Miller, J. T. Bigger, A. J. Moss, "Decreased heart rate variability and its association with increased mortality after acute myocardial infarction," American Journal of Cardiology, Vol. 59, pp ,

4 16. R. Hainsworth, Physiology of the cardiac autonomic system in: Clinical guide to cardiac autonomic tests edited by M. Malik (Kluwer Academic Publishers, London 1998) pp R. Mrowka, P. B. Persson, H. Theres and A. Patzak, Am. J. Physiol. Regul. Integr. Comp. Physiol. 279, R1171 (). 18. Sayers BM. Analysis of Heart Rate Variability. Ergonomics 1973:16: W. Wiedmeier, Cardiac and circulatory physiology in: Essential of human physiology. Integrated Medical Curriculum edited by T. M. Nosek (1999). Table1.1. Boundary Conditions Frequency Band VLF.-.4 Hz LF.4.15 Hz Interpolation RR series Interpol Rate (Hz) 2 Detrending RR series HF.15.4 Hz Trend - Smooth Priors Points Frequency Domain in 124 Model - Eye Welch s Window width 124 Periodogram One Window overlap 512 Start (s)-126 AR Model Order 16 End (s) VLF Characteristics 7 % power Fig.1.HR and RRI plot from Suunto Training Manager Software Fig.5. VLF Characteristics of the subjects in AR spectrum with an average of of %Power LF Characteristics 1 1 Power in n.u 8 Fig.2. Plot of RRI and corresponding selected RRI time series for analysis as fed to HRV software 1.1. Fig.6. LF Characteristics of the subjects in AR spectrum with an average of of power in n.u

5 HF Characteristics P ower in n.u AR Fig.3. AR Spectrum (Parametric) of a subject of the third recording taken at rest in supine Fig.7. HF Characteristics of the subjects in spectrum with an average of of power in n.u Fig.4.FFT Spectrum of the same data (Welch s Periododgram- non parametric) 21