Study of Relationship between Heart Rate Variability ...

2011 IEEE Colloquium on Humanities, Science and Engineering Research (CHUSER 2011), Dec 5-6 2011, Penang

Study of Relationship between Heart Rate Variability and Autonomic Function using Cold pressor Test for Malaysian Population *Noor Aimie-Salleh and **MB Malarvili *Department of Biomedical Instrumentation and Signal Processing, Universiti Teknologi Malaysia, Johor Bahru, Johor [email protected]. **Department of Biomedical Instrumentation and Signal Processing, Universiti Teknologi Malaysia, Johor Bahru, Johor [email protected].

suggested as a promising tool for assessment of autonomic function.

Abstract—This paper investigates HRV response of healthy subject during cold pressor test using AR spectral analysis with Burg estimation. Cold pressor test is one of the autonomic function tests performed by immersing the hand into cold water. In this study, ECG was recorded from 26 young adults; 9 males and 17 females with age ranging from 20-40. Autoregressive estimated by Burg is used to compute the power spectral density of the R-R interval. From the ongoing results, 22 subjects show an increase of LFnu and a decrease of HFnu during cold pressor while four subjects show opposite response. For LF/HF ratio, nine subjects show values greater than one which indicates a parasympathetic predominance during the CP while 17 from them indicate less than one, reflecting sympathetic predominance.

A. Heart Rate Variability Heart rate variability (HRV) is a well established noninvasive method that is being used to investigate the autonomic function of human body. HRV is defined as the variation over time of the period between consecutive heartbeats. HRV reflects the ability of the heart to detect and respond to unpredictably changing conditions. It also serves as a powerful and dynamic tool in examining the interaction between the sympathetic and parasympathetic nervous systems [10-11]. The sympathetic and parasympathetic autonomic activities modulate the heart rate interval which is known as R-R interval in the electrocardiogram (ECG), at separate frequencies. In the modulation frequency of the heart rate, sympathetic activity falls within low frequency range (0.04– 0.15 Hz) while parasympathetic activity is associated with the higher frequency range (0.15–0.4 Hz) [12]. The difference in frequency ranges allows HRV analysis to split the sympathetic and parasympathetic contributions in the observed condition [13]. Furthermore, HRV measurement is one of the easiest methods and has the capability in producing a good quality result from signal processing techniques [13-14]. Spectral analysis has been widely used as a processing technique of HRV. Spectral analysis involves decomposition of the HRV into its various frequency components and computed as power spectrum. Fast Fourier Transform (FFT) which is a non-parametric analysis is the most common method used by researchers to investigate the HRV during autonomic function. A better analysis technique based on parametric which is autoregressive (AR) analysis have also been applied to transform signals into the frequency domain [14]. This paper presents a preliminary study on investigating the response of heart rate variability during cold pressor test for normal healthy subjects. Spectral

Keywords-component; Spectral analysis, Heart rate variability, Autoregressive, Cold pressor, Autonomic function

I.

INTRODUCTION

Autonomic function test (AFT) is a physiological activity or stimuli applied to patients to get their physiological response upon internal or external changes. Common AFT methods in use include the valsalva maneuver [1], head up tilt table [2], deep breathing [3], cold pressor [4-5] and various active postural changes [6]. Cold pressor test (CPT) has been used by many researchers as it might give a significant indication of the autonomic function and hence may give vital information to the clinicians or researchers [7-8]. Compared with other AFT, this test is also easy to apply without the risk of subject movement and therefore may reduce the noise. In Malaysia, studies currently undertaken in the area of autonomic functioning in connection with AFT are very few and still in its early stages [9]. Therefore, more research is needed to introduce advance diagnosis method in assessing the autonomic function. In addition, AFT used now only utilizes basic and simple measurements such as HR, blood pressure and oxygen saturated (SPO2). These basic measurements are not sufficient in giving precise results. Therefore, measurement of heart rate variability (HRV) is

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analysis is used to study the HRV indices and for this study Autoregressive analysis with Burg estimation is selected. II.

R-R Interval

MATERIALS AND METHODS

A. Experimental setup Electrocardiograph (ECG) was recorded from 26 young adults; 9 males and 17 females with age ranging from 20-40. The signals were sampled with a frequency of 600 Hz and recorded from lead II using Clevelab ECG System. First, the subjects were asked to lie in supine position for at least 20 minutes. Then, baseline ECG measurements started for 10 minutes. Subsequently, the subject immersed his/her left hand [15] to the wrist level into a 0-5°C water bath for a period of 3 minutes, followed by removal of the hand from the water bath, dried and continuation of recording for another 5 minutes [5] (Fig. 1). The subjects were instructed to not consume heavy meals for at least 3 hours before testing, and were asked to refrain from ingesting beverages containing caffeine. Subjects were studied between 8.00 a.m. and 12.00 p.m.

5 min

R

R

R

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T P Q S Figure 2. The recorded ECG during the CPT

C. Spectral Analysis of HRV using Autoregressive with Burg Estimation The AR modeling involves selection of an appropriate model order and estimation of model parameters from the available data. Spectral estimation is then carried out using the model parameter. A signal spectrum shows how the power (variance) is distributed as a function of frequency. AR spectral analysis can provide the number, centre frequency, and associated power of oscillatory components in a time series [17]. The power spectral density (PSD) can be estimated from various AR methods including autocorrelation, covariance, modified covariance and burg [18]. The Burg’s estimation method is based on minimizing the forward and backward prediction errors where this method is it resolve closely spaced sinusoids in signals with low noise levels. The advantage of the Burg’s method is it will resolve closely spaced sinusoids in signals with low noise levels. This method also ensures a stable AR model and is computationally efficient [19].The PSD of a pth order autoregressive method is:

5 min

(1)

Figure 1. The recorded ECG during the CPT

B. Preprocessing The analysis begins with preprocessing technique of the ECG signal in order to extract the R-R interval. Ectopic beat is selected visually and removed manually while 50Hz power line interference is removed by using notch filter. QRS complexes are then detected using Pan and Tompkins algorithm. This method is chosen because it has been proven to detect 99.3% of adult QRS using the MIT/BIH database [16]. The QRS complex is illustrated in Fig. 2. The algorithm started by passing through the signal to a digital band-pass filter in order to attenuate noises. The band-pass filter composed of cascaded high-pass and low-pass filters. The process continues with differentiation followed by squaring and moving window integration. Next, the locations of QRS complexes are classified by adaptive thresholds. The R-R interval is then passes through a few more processes which are the removal of outliers, signal resampling and detrending.

where êp denotes the total least square error and it is the sum of the forward and backward prediction errors. While a(l) is the AR coefficient [14]. 1) Model order selection In spectral estimation, the accuracy of the estimated spectrum is critically dependent upon the model order, p that is chosen. One classical method to determine an optimum model order is the Akaike Information Criterion (AIC) [20]. By this method, the optimum model order for the given segment of signal is determined when the error variance becomes minimum. In this study the AR models are computed using AIC and the minimum of error variance as observed from Fig. 3 at model order 32.

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Figure 5. The changes of HFnu during cold pressor test

Figure 3. The minimum of error variance

Fig. 6 demonstrates the LF/HF ratio during CP. It shows that nine from the total subject have the value of LF/HF ratio greater than 1 and 17 from them indicate less than 1.

Ratio

D. Spectral Analysis of HRV The spectral analysis is carried out using AR spectrum analysis by fitting a 32nd-order AR model into the R-R data. Power spectral density (PSD) was then computed by Burg estimation method. The PSD is estimated in three frequency bands of very low frequency (VLF, 0.003-0.04 Hz), low frequency (LF, 0.040-0.15 Hz) and high frequency (HF, 0.15-0.4 Hz). The normalized unit (n.u) of LF and HF are presented as LFnu and HFnu [12]: (2) (3)

Figure 6. The LF/HF ratio during immersing hand in cold water

The present preliminary study analyzes the HRV response from healthy subjects during cold pressor test using AR spectral analyses with Burg estimation. This preliminary study demonstrates the variation of subjects’ response during CPT. An increase of LFnu with decrease of HFnu during CP shows that sympathetic activity is increased during the pressor. Similar results were reported in previous study [5, 8, 21]. This effect is caused by the body reaction to the stress exerted through the cold pressor test. However, four from the total subject produced opposite result. This might due to the inconsistent temperature set up where the cold water temperature might react differently according to the different subject’s hand temperature. The environmental temperature including the weather might also give significant effect to the results. However, a small size database as such is unable to present a strong argument. The LF/HF ratio represents the evaluation of the autonomic balance. From the 26 subjects, 17 of them have the ratio less than 1. This indicates a parasympathetic predominance during the CP, whereas for the remainder, the ratios are above 1 and this reflects a sympathetic predominance [8, 12, 22]. These inconsistent results were also reported previously [4-5] and yet so far no study is able to clearly explain this scenario. This preliminary study involves a small number of subjects and more subjects need to be recruited for validation. In addition, further analysis needs to be carried out in order to explain the different reaction of the subject during the CPT.

The LF/HF ratio is also computed. III.

RESULTS AND DISCUSSIONS

Fig. 4 shows the changes of LFnu during CPT for all subjects. The results indicate that from 26 subjects, 22 show an increase of LFnu during immersing their hand in cold water (CP). The LFnu then decreases after the subjects remove their hand from the cold water. The changes of HFnu during CPT are shown in Fig. 5. Similar with LFnu, the 26 subjects show a similar pattern of changes of HFnu where the value decreases during immersing their hand in cold water. The HFnu then increases after their hand is removed from the cold water. However, the changes vary depending on subject adaptation to the cold pressor. The remaining other subjects which are subject 8, 9, 12 and 14 show the opposite response where the LFnu decreases during the immersing of their hand in cold water.

Figure 4. The changes of LFnu during cold pressor test

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IV.

[9]

CONCLUSION

This study is conducted to investigate the relationship between HRV and AFT for Malaysian population. CPT is chosen specifically to investigate this relationship. The results lead to the conclusion that HRV can be used to see the changes of autonomic function by using CPT. However further analysis is needed to explain the different reaction of subject during the test. The finding will then be implemented as a detection tool for autonomic dysfunction.

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