An Empirical Methodology for the Definition of Frequency Bands for Spectral Analysis of Heart Rate Variability in Animals: Application to Sprague-Dawley Rats M.A. García-González1, M. Fernández-Chimeno1, R.M. Escorihuela2, Ll. Capdevila3 and J. RamosCastro1 1
Group of Biomedical and Electronic Instrumentation, Department of Electronic Engineering, Technical University of Catalonia (UPC), Barcelona, Spain 2 Department of Psychiatry and Forensic Medicine, Institute of Neurosciences, Autonomous University of Barcelona, Barcelona, Spain. 3 Sport Psychology Laboratory, Autonomous University of Barcelona, Barcelona, Spain
Abstract— A new methodology for definition of frequency bands for spectral analysis of Heart Rate Variability (HRV) in animals is presented. There are differences between species and strains in the cardiovascular properties that hamper the definition of guidelines for standardizing the collection and analysis of HRV in animal research. We propose an empirical method to define the limits of the low frequency (LF) and high frequency bands (HF) that can be used to any animal species without pharmacological intervention. The method is applied to RR series in unrestrained Sprague-Dawley rats. Keywords— Heart Rate Variability, Spectral Analysis, Sprague-Dawley rats, Band Limits.
I. INTRODUCTION The Heart Rate Variability (HRV) is the beat-to-beat fluctuation of the heart period (RR) (or rate), that is the variation over time of the period of consecutive heartbeats. HRV is a reliable reflection of the many physiological factors modulating the normal rhythm of the heart [1]. The determinants of HRV are among others [2]: Pacemaker activity, reflex arcs (respiration, Bainbridge, carotid sinus) and central nervous system connections to autonomous nervous system, genetics, circadian oscillations in plasma hormones and thermoregulation, myocardial phenotype, other receptors located in the sinusal node including angiotensin II, dopamine, and adenosine receptors. HRV is a useful signal for understanding the status of the ANS. The normal variability in HR is due to autonomic neural regulation of the heart and the circulatory system [3]. The balancing action of the sympathetic nervous system (SNS) and parasympathetic nervous system (PNS) branches of the ANS controls the HR. Sympathetic stimulation as response to stress, exercise or a heart disease for example, results in an increasing of heart rate (HR). Parasympathetic activity, primarily resulting from the function of internal organs, trauma, allergic reactions and the inhalation of irritants decreases the HR. The degree of variability in the HR provides information about the functioning of the nervous con-
trol on the HR and the heart’s ability to respond. HRV analysis in humans is applied to several areas. As mentioned above, is used to study the function of the autonomic nervous system, it is used also in the studies of blood pressure , myocardial infarction, disorders in the central and peripheral nervous system, cardiac arrhythmia, diabetes and renal failure [1]. It is also known that HRV changes with gender and age, with the intake of drugs and alcohol, and with cigarette smoking. Also sleep stages have some influence on HRV. The first clinical study that involves heart rate variability was those of Hon and Lee in 1965 [4]. During the last 1960s, the 1970s and the early 1980s there was an increasing number of studies that uses HRV analysis for different purposes: the study of physiological rhythms imbedded in the beat-to-beat heart rate signal, studies of diabetes, studies of risk of post-infarction mortality and others. Simultaneously several signal processing techniques were applied to the study of HRV, contributing to better understand the role of HRV the influencing factors. Although all of this intensive research there was no attempt of standardization of HRV measurement procedures and signal analysis methods until 1996, when the European Society of Cardiology and the North American Society of Pacing and Electrophysiology published the results of Task Force of Heart Rate Variability [5], regarding standards of measurement, physiological interpretation and clinical use. There are also many investigations of HRV applied to animal models. In particular, environmental health researchers have begun to use this physiological measure as an outcome in epidemiological or animal toxicological studies. Despite the growing popularity of HRV measures in animal research, there are as yet no similar guidelines for standardizing the collection and analyses of HRV parameters. We need also to take into account that, animal studies may investigate a variety of different species (and strains within species) with cardiovascular properties that can differ significantly from those of humans, as well as among themselves, so the definition of HRV parameters and the
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analysis methodology could not be sometimes directly transferred to the animal studies. One particular analysis that has need of standardization is the spectral analysis of HRV. In humans and for short-term recordings, the frequency bands of interest are standardized in [5] as three bands: a very low frequency band (VLF) for frequencies lower than 0.04 Hz, a low frequency band (LF) for frequencies between 0.04 Hz and 0.15 Hz and a high frequency band (HF) for frequencies between 0.15 Hz and 0.40 Hz. Due to the lack of knowledge of the causes that generate the oscillations in the VLF band, the more used spectral indices are the power inside the LF band (PLF), the power inside the HF band (PHF) and the ratio between these two powers (LF/HF). Due to the severe differences in mean heart rate among humans and other animal species, it is clear that a definition for frequency bands is needed for each species. The aim of this paper is to describe an empirical methodology to define the limits of the LF and HF bands with application to animal species and, in particular, to SpragueDawley rats. The method relies on the previous observation of the power spectra of RR time series in unrestrained animals during several days and in the characterization of the spectra with indices that don’t need any band definition.
treadmill sessions were administered daily from Monday to Friday and the duration was 30 min. The weight of the rats just before starting the treadmill sessions was 451 g ± 29 g. Each measurement session consisted of the acquisition of the ECG during a whole weekend with a sampling frequency of 5 kHz. The home cage was placed on a platform receiver (RPC-1) from Friday (4:00 p.m.) until Monday (2:00 p.m.). For each rat, the acquisition session was repeated six times with a periodicity of three weeks. So, for each animal a total of twelve days were acquired. The first measurement was done three weeks after surgery when the animals were housed isolated, the second measurement was done when the animals were housed in pairs and before starting the treadmill exercise. The measurements 3-6 were done after treadmill training started. Once the ECG was acquired the QRS complexes where detected offline with a QRS detector developed in MATLAB and tailored to the characteristics of the ECG of these rats. The QRS detector is based on the PanThompkins algorithm [6] with and adaptation to this animal species. The enhancement of the QRS complex is performed by band pass filtering of the ECG signal in the 50 Hz to 100 Hz range. The obtained RR time series were saved for further processing. A total of 12849 one-minute long RR time series without artifacts or outliers where automatically recognized from the total of 86400 available one-minute long RR time series. The temporal location of the selected time series is uniformly distributed with the time of day and don’t has relationship with the mean heart rate.
II. MATERIALS AND METHODS A. Collection of RR time series in Sprague-Dawley rats Subjects were five male Sprague-Dawley rats obtained from different litters from the breeding centre of the Autonomous University of Barcelona. They had free access to food and water and were always maintained in standard temperature conditions (21ºC±2) and on 12-12 h light-dark schedule (lights on at: 8:00 a.m.). The experimental protocol was approved by the Ethics Committee of the Universitat Autònoma de Barcelona, following the ‘Principles of laboratory animal care’ and was carried out in accordance the European Communities Council Directive (86/609/EEC). A telemetry system (Data Sciences International, St. Paul, MN, USA) was used. Surgical implantation of transmitters (TA11CA-F40) was performed in 2-monthold rats under isofluorane anesthesia. After implantation, the rats were allowed to recover and housed individually for 1 month, after which a control companion rat (same age) was housed in each home cage. Seven weeks after surgery the treadmill exercise sessions started. Briefly, the treadmill speed was increased from 0 to 7.2 m/min over days in the first week and, from 5.4 to 10.8 m/min in the second week, the treadmill speed was maintained at 10.8 m/min in the third week and increased to 12 m/min in the weeks 4-6. The
B. Computation of the power spectrum of RR time series and spectral indices without band definition The power spectrum of each one-minute long RR time series has been computed after resampling with cubic splines at 30 Hz. Because this work is focused in the LF and HF bands, we have removed the slow frequency components with a smoothness priors detrending procedure [7] with O=3000. Figure 1 shows an example of the detrending procedure in an actual RR time series. After detrending, the power spectrum has been computed using the FFT and using a Hann window. To characterize without band definitions the power spectrum, we have computed the accumulated power spectrum as: n
¦P
(i )
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APRR ( n )
RR
i 1
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An Empirical Methodology for the Definition of Frequency Bands for Spectral Analysis of Heart Rate Variability in Animals
where PRR(i) is the power spectrum estimation at frequency f(i) and f(N) is the Nyquist frequency (15 Hz in our case).
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central frequencies between 0.4 Hz to 1 Hz is quite improbable to find a low bandwidth indicating that the probability of presence of a dominant oscillator in this range is very low. Because the shape of the dots in the graph has no horizontal symmetry (the dots have more spread in the high frequencies) we have fitted a parabola in the graph of the squared root of CF against BW 50% . The maximum of this parabola occurs at 0.85 Hz. Then, the limit between the LF and HF bands is 0.85 Hz according with the results and the fitting procedure.
(b) 12 Power spectral density (ms2/Hz)
Fig. 1 An example of the smoothness priors detrending procedure. (a) Original time series (solid line) and estimated trend (dotted line). (b) RR time series after the trend is removed
The following indices have been defined: i) Central frequency (CF): The frequency f(n) when APRR(n)=0.5. ii) Minimum frequency at M% of power (Fmin M%): The frequency f(k) at which APRR(k)=0.5-M/200. As an example, Fmin 95% reports the frequency when APRR crosses the 0.025 threshold. iii) Maximum frequency at M% of power (Fmax M%): The frequency f(j) at which APRR(j)=0.5+M/200. iv) Bandwidth at M% of power (BW M%): the difference between the maximum and minimum frequency at M% of power. The figure 2 shows an example of the computation of these indices in an actual spectrum. If the obtained power spectrum is concentrated in a very narrow band (a dominant oscillator), CF will provide the location of this oscillator and BW 50% will be small. In the case of two oscillators with similar powers, the CF will be the mean of the frequencies of the oscillators and BW 50% will depend on the separation of the oscillators. In the case of a power spectrum with no dominant oscillator, the BW 50% will be high. Then, monitoring the values of the CF and BW 50% will be valuable in order to test the presence of oscillators in the power spectrum and as a way to define the limit between the LF and HF bands. Moreover, Fmin 95% can be used in order to obtain the lower limit of the LF band and Fmax 95% can be used to define the upper limit of the HF band. So, the CF, BW 50%, Fmin 95% and Fmax 95% have been computed in the 12849 available power spectra.
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Fig. 2 An example of computation of spectral indices that don’t require band definition (a) Power spectrum of the time series (b) Accumulated power spectrum and obtained spectral indices computed for the 50% of the power
The 0.1% percentile of Fmin 95% is 0.067 Hz so the lower limit of the LF band has been chosen rounding towards zero so we define the LF band in Wistar rats between 0.06 Hz and 0.85 Hz. The 99.9% percentile of Fmin 95% is 3.76 Hz so we have defined the HF band between 0.85 Hz and 4 Hz.
III. RESULTS Figure 3 shows the plot of CF against BW 50%. As can be seen, the results are grouped in a particular shape. For
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We have compared in our RR time series the results of our proposed bands (LFp: 0.06 Hz – 0.85 Hz, HFp: 0.85 Hz – 4.0 Hz) with those proposed in [8] (LFc: 0.06 Hz – 0.60 Hz, HFc: 0.60 Hz – 2.4 Hz). Table 1 shows the mean values of the indices as well as the standard deviations and the standard deviation of the absolute difference between indices. Comparing the mean values, PLFc is slightly lower than PLFp because the LF band ends in this case in 0.60 Hz. The same reasoning applies to the power on the HF band and consequently, the LF/HF is slightly higher in our proposal of bands. Moreover, we have checked that the power in HF band doesn’t significantly change if we use an upper limit of 2.4 Hz. So, the standard deviation of the absolute differences in indices on table 1 must be attributed to the definition of the limit between LF and HF bands.
though the limit between bands can vary with different criteria, we think that CF, BW 50%, Fmin 95% and Fmax 95% accompanied with a suitable detrending procedure in order to remove the VLF band can provide a good guidance in order to perform the selection of the limits without any pharmacological intervention. IV. CONCLUSIONS A new method to identify the LF and HF bands in RR series independent of the animal species and strands has been proposed. We have compared the new defined LF and HF bands with proposed bands in the literature for SpragueDawley rats.
ACKNOWLEDGMENT
2.5 Bandwidth at 50% of the total power (Hz)
f= 0.85 Hz
This work was supported by MEC and FEDER projects:PSI200806417-C03 and DEP2006-56125-C03
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REFERENCES
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1. 2.
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1 1.5 Central Frequency (Hz)
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Fig. 3 Results of central frequency against the bandwidth at 50% of the total power. The dotted line is the fitting of a parabola when the x axis is the squared root of the central frequency in order to obtain a shape with better symmetry
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Table 1 Comparison of results for our definition of bands and that
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proposed in [8]. See text for further details Index
Mean
Standard Standard deviation deviation of the absolute differences
PLFp (ms2)
2.25
2.23
PLFc (ms2)
2.10
2.13
PHFp (ms2) 1.36
1.02
2
PHFc (ms ) 1.38
1.05
LF/HFp
2.15
2.09
LF/HFc
2.01
1.99
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Rajendra Acharya U, Paul Joseph K et al. (2006) Heart rate variability: a review. Med Bio Eng Comput 44: 1031–1051 Swynghedauw B, Jasson S et al (1997) Myocardial determinants in regulation of the heart rate. J Mol Med 75: 860–866 Saul JP (1990) Beat-to-beat variations of heart rate reflect modulation of cardiac autonomic outflow. News Physiol Sci 5: 32–37 Hon EH, Lee ST (1965) Electronic evaluations of the fetal heart rate patterns preceding fetal death, further observations. Am J Obstet Gynec 87: 814–826 Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology (1996) Heart rate variability, standards of measurement, physiological interpretation and clinical use. Eur Heart J 17: 354–381 Pan J and Tompkins WJ (1985) A real-time QRS detection algorithm. IEEE Trans Biomed Eng 32: 230–236 Tarvainen MP, Ranta-aho PO and Karjalainen PA (2002) An advanced detrending method with application to HRV analysis. IEEE Trans Biomed Eng 49: 172–175 Kuo TBJ, Lai CJ et al (2003) Sleep-related sympathovagal imbalance in SHR. Am J Physiol Heart Circ Physiol 286: H1170–H1176
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These results exemplify the need for a set of standard procedures that recognize the start and end of spectral bands (especially, the limit between the LF and HF bands). Al-
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Miguel Ángel García González Department of Electronic Engineering, UPC Jordi Girona 1-3 Barcelona Spain
[email protected]
