JOURNAL OF INFORMATION, KNOWLEDGE AND RESEARCH IN
BIOMEDICAL ENGINEERING
DIGITAL SIGNAL PROCESSING APPROACH TO EXTRACT HEART RATE VARIABILITY FROM QRS WAVE DERIVED FROM PATIENT BODY
PROF. MEHUL K.SHAH
Assistant Professor, IC Deptt.,Govt. Engg. College, Rajkot.
ABSTRACT: The analysis of HRV (Heart Rate Variability) has become a non-invasive tool for assessing the activities of ANS (Autonomic Nervous System). This paper suggests the new estimation technique of determining the Heart Rate Variability with the use of correlation (Auto-correlation and cross-correlation of the sequence) obtained from the patients ECG. The ECG waveform comprises of VLF (0.0033-0.04 Hz), LF(0.04-0.15Hz) and HF(0.15-0.5 Hz) components in the QRS wave. In R wave detection method which requires the sampling frequency Fs≥100 Hz. This method suggested may be used to extract the instantaneous information like Normal to Normal (NN) intervals, intervals between consecutive pulses. The correlation of pulses in practical applications can be used to identify periodicities in the ECG waveforms. Due to the presence of noise picked up, the problem becomes worse. Even with the SNR of 5 dB, correlation can help to determine periodicity of ECG.
ISSN: 0975 – 6752 | NOV 10 TO OCT 11| Volume 1, Issue 2Page 1
JOURNAL OF INFORMATION, KNOWLEDGE AND RESEARCH IN
BIOMEDICAL ENGINEERING
1. INTRODUCTION :
The human body is like a mini power generator. ECG Electrocardiography deals with the electrical activity of heart. With the help of placing limb electrodes on patient’s body, these electrical activities can be picked up. Electrocardiogram is a record of origin and propagation of the electric potential through cardiac muscles[6].
In ones life, the legs and feets are under tremendous pressure of the body’s weight for approximately about two thirds of time. The feet are at the farthest distance from the heart. Consequently, the blood which is pumped from the heart to feet and circulated back to the heart will have an increase in of blood difficulty in its ability to circulate. This may lead to various ailments in the legs and feet. The reflexology will allow the repeated movement of muscles to produce a very prominent overall pressuring action on the walls of the blood vessels in the lower extremities. These blood vessels will have more strength to contract and expand and will enhance circulation of blood back to the heart. The heart will in turn have a greater supply of blood to nourish the body. Hence, there will be more variation in the heart rate and becomes more chaotic.
Last two decades have witnessed the recognition of significant relationship between the autonomic nervous system and cardiovascular mortality[6], including sudden cardiac death. HRV represents one of the most promising such marker. The apparently easy derivation of this measure has popularized its use. For the diagnosis, cardiologists are provided with simple tool in most modern instruments. The phenomena of HRV are oscillations
between consecutive instantaneous heart beats. “Heart Rate Variability” has become the most accepted term to describe variation of both instantaneous heart rate and RR interval[8]. Other terms used to describe the HRV are cycle length variability, heart period variability, RR variability and RR interval Tachogram[13]. These parameter more emphasize on the fact that it is the interval between consecutive beats that is being analyzed rather than the heart rate per se.
2. HEART RATE VARIABILITY
HRV is a measurement of the interaction between sympathetic and parasympathetic activity in autonomic functioning. The main twoapproaches used for HRV are time domain which includes standard deviation of normal to normal intervals (SDNN)[13]; and frequency domain analysis of Power Spectrum Density (PSD). The PSD technique provides low frequency (sympathetic activity) and high frequency (parasympathetic activity) and sympathetic / parasympathetic balance values (total power) [12]. Spectral analysis is the most popular linear technique used in the high frequency band which reflects respiratory sinus arrhythmia[2] and thus cardiac vagal activity. Low frequency power is related to baroreceptor control and mediated by both vagal and sympathetic systems[10]. Very low frequency power is related to thermo-regulatory and vascular mechanisms, and rennin-angio tension system[1] [2].
3. METHODS FOR MEASUREMENT OF HRV
TIME DOMAIN METHOD The variation in the heart rate may be calculated by number of methods. Time domain method is the simplest of them. In time domain analysis, either the heart rate at any point in time or the intervals between consecutive QRS pulses is detected and the so called NN intervals. Simple time domain variables can be calculated i.e. instantaneous heart rate, mean NN interval, mean heart rate, difference between the longest and the shortest NN interval, difference between the night and day heart beats etc. These differences can be described as either differences in heart rate or cycle length.
FREQUENCY DOMAIN METHOD The spectral methods for analysis of the ECG have been used more widely. PSD method provides basic information of how power (variance) distributes as a function of frequency. Using proper mathematical algorithm, the estimation about PSD can be derived.
Parametric and non-parametric are the two methods generally classified for the calculation of PSD[5] [9]. In most instances, both methods provide comparable results. The advantages of non-parametric method are simple FFT (Fast Fourier Transform) algorithm and high processing speed. Parametric method has advantages of smoother spectral components that be distinguished independent of pre selected frequency bands and easy post processing of the spectrum with an automatic calculation of low and high frequency power components with easy identification of the central frequency of each component. With later method, the accurate estimation even with lower samples can be obtained. The basic disadvantages with parametric methods are the need of verification of the suitability of the chosen model and of its complexities due to order of the model.
4. CORRELATION - A DSP APPROACH TO THE HRV
The problem of correlation is similar to the convolution except in case of correlation the sole objective is to calculate the similarities or the differences between two sequences. In this paper emphasis is laid down on the heart rate variability which is nothing but the differences between consecutive pulse train dynamics from the heart. The ECG waveform derived from the heart are sampled and quantized with sampler and a quantizer. The sequence thus obtained may be used for comparison with the upcoming sequences of derived ECG pulses. The comparison of the same sequence with the delayed or advanced version of the same signal is known as auto-correlation whereas with different sequence is known as cross-correlation.
In notations auto-correlation of sequence x(n) with itself is referred to as rxx (auto correlation) and with y(n) is rxy (cross correlation)[4]. If the present ECG sequence is compared with either the previous or the next, differences and similarities can be calculated which is nothing but the HRV. The correlation term is defined as[4].
Where the index l is the time shift (lag) parameter and the subscript xy on the cross correlation sequence rxy(l) indicate sequence being correlated. The xcorr(x,y) function in MATLAB is used to compute the cross correlation of two sequences x(n) and y(n). To compute auto correlation of sequence xcorr(x) is used.
5. CONCLUSION
As conventionally the use of correlation is to find out the periodicities of the signals. Due to the white noise or signal pick up problems, the ECG signals of very low amplitude is corrupted and looses its periodicities. The author has tested this using MATLAB. Random Gaussian noise signal is generated and mixed with a periodic signal and using correlation properties, periodicity has been extracted. But the actual testing on ECG signal is yet to be performed as ECG signal is not readily available.
6. REFERENCES
[1] GD Clifford, Azuaje F, and McSharry PE, Advanced methods and tools for ECG analysis, Artech Publishing House, London, 2006. pp-384
[2] Task force of European Society of Cardiology and North American Society of Pacing Electrophysiology. Heart rate variability: standards of measurement, physiological interpretation and clinical use. Circulation 1996;93: pp 1043-1065.
[3] Rangraj M. Rangayyan, Biomedical Signal Pres analysis, IEEE press, Wiley interscience, 2002-594p.
[4] John G. Proakis and Dimitris G. Manolkis, “Digital Signal Processing, Principles, Algorithms and Application”, Prentice Hall of India, 2004.
[5] AN Kalinichenko, MI Nilicheva, SV Khaseva, OD Yurieva, and OV Mamontov, Signal stationary assessment for the Heart Rate Variability Spectral Analysis, 2008; pp 965-968
[6] Natalia Trayanova, In the spotlight: Cardiovascular Engineering, IEEE
[7] Lown B, Verrier RL. Neural and ventricular fibrillation. N Engl J Med, 1976;294:1165-1170
[8] Levy MN, Schwartz PJ, eds, Vagal Control of heart: Experimental Basis and Clinical Implications, Armonk, NY, Futura,1994.
[9] Sayers BM, Analysis of heart rate variability. Ergonomics, 1973;16: pp17-32.
[10] Akselrod S, Gordon D, Ubel FA, Shannon DC, Cohen RJ., Power Spectrum analysis of heart rate fluctuation: a quantitative probe of beat to beat cardiovascular control. Science, 1981;213:220-222.
[11] Pomenraz M, Macaulay RJB, Caudill MA, Kurtz I, Adam D, Gordon D, Kilborn KM, Berger AC, Shannon DC, Cohen RJ, Benson M, Assessment of autonomic functions in humans by heart rate spectral analysis. Am J Physiol, 1985;248:H151-H153.
[12] Luczak H, Lauring WJ. An analysis of heart rate variability. Ergonomics. 1973;16:85-97.
[13]Scherer P, Ohler JP, Hirche H, Hopp H-W. Definitions of beat to beat parameter of heart rate variability. PACE pacing Clin Electophysiol. 1993;16:939.
[14] Berger RD, Akselrod S, Gordon D, Cohen RJ. An efficient algorithm for spectral analysis of heart rate variability. IEEE Transaction Biomed Engg., 1986;33;900-904.
[15] Malik M, Camm AJ. Components of heart rate variability: what they really mean and what we really measure. AM J Cardiol, 1993;72:821-822.
ISSN: 0975 – 6752 | NOV 10 TO OCT 11| Volume 1, Issue 2Page 1
JOURNAL OF INFORMATION, KNOWLEDGE AND RESEARCH IN
BIOMEDICAL ENGINEERING
ISSN: 0975 – 6752 | NOV 10 TO OCT 11| Volume 1, Issue 2Page 1