2014 11th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)
SDNN Index of Heart Rate Variability as an Indicator of Change in Rats Exposed to Fine Particles: Study of the Impact of Air Pollution in Mexico City G. Vega-Martínez, C. Toledo-Peral, C. AlvaradoSerrano, L. Leija-Salas Depto. de Ingeniería Eléctrica – Sección Bioelectrónica Centro de Investigación y de Estudios Avanzados del IPN México, D.F. México
[email protected],
[email protected] Abstract—This paper comprises the analysis of rats electrocardiogram (EKG) recorded within a toxicology study. Acquired signals are processed to assess changes in heart response to air pollution in Mexico City, by means of Heart Rate Variability (HRV) using the SDNN index. EKG records were acquired along 8 days from 12 rats, divided into control group, supplied with filtered air (FA), and exposed group, who was provided a concentration of fine particles (FP) in the polluted air of Mexico City. After 8 recordings, FP group showed a significant decrease (p=0.028) in SDNN index values, while FA group showed no significant change (p=0.752) in SDNN index values. Keywords— Heart Rate Variability, rat EKG, SDNN, R-wave location.
I.
INTRODUCTION
Studies in different areas have proven the impact of a variety of toxics in the human body. The air breathed in big cities is highly contaminated; it is a complex mix of gas, liquids and diverse particles. The heart is one of the organs that manifest the most sensibility to this exposure. There is evidence about the relation kept between cardiovascular disease and polluted air [1]. Discussion about its impact is ongoing due to the different concentrations of particles in each place where studies have been conducted. One of the techniques used to assess possible cardiovascular damage is Heart Rate Variability (HRV) that is obtained calculating the time between detected R–waves in an electrocardiogram (EKG). Big cities dynamics is variable along the years; this issue has been addressed trying to find a solution. In 1998, mobile systems that were able to quantify air quality [2] had been proposed and a few years later [3] a study including 21 subjects from a community in Boston reported that particle and ozone exposition results in a decrease in HRV. Another study, in this same city [4], reports higher *Research supported by CONACYT Fondo SALUD No. 201590, and CONACYT Project No. 167778.
O. G. Aztati-Aguilar, A. de Vizcaya-Ruiz Departamento de Toxicología Centro de Investigación y de Estudios Avanzados del IPN México, D.F. México
[email protected].
sensibility, and higher damage, to people that already suffer from ischemic heart disease, hypertension and diabetes. These studies are expensive; but even more importantly, it is very difficult to measure in a controlled manner the concentrations of diverse particles in the air. This is why animal models became so useful. In reference [5], anatomy and physiology of the heart of small rodents is described, as well as its similarities with larger mammals, man included. This work stablishes the basic care and needs that has to be considered when performing controlled studies. It establishes the conditions in which EKG has to be obtained, from these animal models, in order to be a useful tool for toxicology studies. HRV values and parameters for its analysis in rats have been validated [6], which present a reliable method to be used. In Mexico City, air quality is an important public health issue and different actions have been taken to diminish the amount of pollution particles. Diverse studies about the impact of particles in the air have been performed, one of which [7] present results consistent with those obtained in other cities: a decrease in HRV. The aim of this project is to develop a tool for the Toxicology Department that allows HRV indices calculations of rat EKG. To be used among a set of parameters obtained from the same animal model, as blood chemistry and blood pressure. The complete set of tests will bring more information about the effects of these fine suspended particles in the air of Mexico City over its population. II.
MATERIALS AND METHODS
A. Animal Model In this experiment, 12 Sprague-Dawley strain rats were kept in cages in an environment with controlled temperature and feeding conditions, and a 12 hr. night/day cycle. The rats were divided in 2 groups; the first was kept in a controlled filtered air (FA) environment acting as the control group. The second group was exposed to a concentrated sample of fine particles (FP) present in polluted air in Mexico City.
978-1-4799-6230-3/14/$31.00 ©2014 IEEE
124
2014 11th International Conference on Electrical Engineering, Computing Science and Automaatic Control (CCE) Exposure was performed 4 days a week ((Monday through Thursday) for a 5-hour period, from 8:00 a.m. to 1:00 p.m., each animal thoracic region was carefully shaved, prior to EKG recording. These measures were acquirred on Thursday (immediately after exposure), Friday (24 hrss after exposure) and Sunday (72 hrs after exposure). In ordder to obtain the highest amplitude of R-waves in EKG rrecords, Lead II configuration was used. EKG records weree obtained from unrestrained rats inside metallic trays, to avooid stress derived from manipulation. B. Data Acquisition To acquire the EKG signal, an ADS12922R TI acquisition board was used. It has two 24 bit-channels, oone for EKG and another intended to record respiration (this channel was not used). Manufacturer provides the ADS1x9xxECG-FE Demo Software; that connects the computer and tthe board via an USB-mini USB cable. Rat’s heart rate goes from 350 Hz to 500 Hz when stressed; choosing 1 KHz sampling rate complies the Nyquist theorem. For a 1 KHz sampling rrate, the software allowed to record events of 65,500 samples, this is 1 minute 5.5 seconds. EKG event was recorded 6 times per rat. In fig. 1, it is observable the likeness between the illustration of a rat/mouse EKG [5], and the measured results.
Fig. 1. Comparison between a) Rat/Mouse EKG G illustration [5], and b) Measured Rat EKG
Fig. 2. Interference commonly preseents in rat EKG records of this experiment. Drifting baseline is observed; also, a electrical line interference is present in the last portion of the record d, from minute 0.07 to 0.085.
The experiment gathered 96 EK KG recordings from 12 rats along 8 days, each of which wass processed to reduce the influence of interference, as describeed above. The next step is to identify R-waaves. The base algorithm is reported in [9], using the continuouss wavelet transform (CWT) with splines, fig. 3.
Fig. 3. R-wave identification and locatio on is performed using an algorithm based on CW WT.
C. Conditioning, R–wave Algorithm and Tachhogram All signal processing of EKG records that will be described was done using Matlab®. In any biological siggnal recording, as EKG, interference is present. Fig. 2 shows cable movement interference cases present in the records of this experiment; drifting baseline can be observed, as well as tthe electrical line noise (60 Hz) that is present in the last portion of the record. To diminish artefact influence from bbaseline drifting, algorithm presented in [8] is used as a starting point. Interference caused by the electrical line is redduced using zero– phase filtering, which helps preserve featuress in the resulting signal in the exact same time where they presented in the unfiltered one.
Then the tachogram is generated, which is the record of the time between every heart beat. D. Heart Rate Variability Analysis An RR interval is defined as the t length in time between each R-wave detected. Its graph hical representation is the tachogram. Existing fluctuations in RR intervals are associated with respiratory conditions and med diated by the activity of the sympathetic and parasympathetic nervous n systems [10]. It is generally accepted that a decreased HRV is associated with an increased risk of developing cardiov vascular disease. In reference [10], authors set th he parameters that must be followed to implement an analysis of HRV, the domain in
125
2014 11th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE) which measures are done (spectral, temporal, geometric and non-linear), the units used for data display, as well as recommendations on how to report graphic information in the frequency domain. This paper also provides a physiological relationship between HRV and certain pathologies. However, the parameters outlined in [10] set a standard, in agreement of a group of specialists, focused on human studies and there is no other reference that applies to calculate these parameters in other animals. Back to the importance of using animal models, some studies [6] have proposed adjustments to HRV studies so they can used for certain species. The aim of this paper is to present a tool that performs HRV indices calculations; data that later can become more useful information to complement other studies for the area of toxicology. A graphical user interface was developed, displaying the tachogram of each EKG recording and reporting HRV indices in time and frequency domain. The first stage of the developed algorithm is to remove artifacts from the RR series calculated; here two cases may arise: failure to detect a QRS complex or the detection of an extra RR interval. These alterations manifest in short RR intervals followed by long periods of compensation; another way to find these is as spikes in the tachogram. These transients affect the indices calculated in time and frequency domains. To handle and overcome these artifacts a modification of what [6] recommends is proposed, where each RR interval is analyzed and the one that changes more than a 20 % of the previous interval is replaced by the average value of the pre 5 beats and post 5 beats from its location. Parameters calculated in time domain and their metrics are: RR(ms) – mean all NN intervals, SDNN(ms) – standard deviation of all NN intervals, SDSD(ms) – standard deviation of differences between adjacent NN intervals, RMSSD(ms) – square root of the mean of the sum of the squares of differences between adjacent NN intervals (equivalent to SDSD), pNN5(%) – percentage of successive RR interval differences greater than 5 ms [6], pNN10(%) – percentage of successive RR interval differences greater than 10 ms and, finally, a histogram. In frequency domain, the tachogram is resampled at a frequency of 10 Hz and subsequently measured areas/energy under the PSD curve within the frequency bands defined by Low Frequency (LF) and High Frequency (HF). This returns areas/energies as [ms2], (%) percentage, also returns the LF/HF ratio. LF range is set from 0.195 to 0.74 Hz and HF from 0.78 to 2.5 Hz [6]. Then, to calculate the PSD, Lomb’s estimation method is used [11]; it decreases the negative effects that may occur due to the calculation of tachogram resampling. Fig. 4 shows the developed graphic user interface, in the top of the window the tachogram is displayed and in the lower portion the HRV indices, time domain indices and histogram in the left and PSD plot and spectral indices in the right.
Fig. 4. Graphic user interface of Heart Rate Variability Analyzer. It displays the final tachogram, value and plot of HRV indices for both time and frequency domain.
III.
RESULTS
According to [10] one of the simplest variables to calculate is SDNN index and it can reflect the behavior components related to variability during record period. SDNN index is reported [12] along with RMSSD as indicators of changes in HRV in a study of rats exposed to a specific particle. A test that is proposed for tachograms obtained in this work is the calculation and analysis of SDNN index. Each of the 96 records was processed in the analyzer and the HRV indices stored on tables for further analysis. In Fig. 5, the declining value of SDNN index was observed in the experiment.
Fig. 5. The value of SDNN index decreases from the first experiment to the last
Since it matters for us to see changes in HRV indices over time, a longitudinal study is performed, comparing the FP group with itself between experiments, with the goal of finding changes through the experiments, as exposure time added up as days went by. So the statistical significance (p) was calculated using a nonparametric test, as the indices obtained do not have a normal distribution a Wilcoxon test was performed.
126
2014 11th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE) TABLE I.
STATISTICAL SIGNIFICANCE BETWEEN THE 8 DAYS OF EXPERIMENT.
Exp day
1
2
3
4
5
6
7
8
FA Group
1
0.753
0.753
0.600
0.713
0.753
0.833
0.752
FP Group
1
0.345
0.916
0.462
0.109
0.172
0.115
0.028
The statistical test is performed in the SPSS Statistics (IBM Corp. IBM SPSS Statistics for Windows. Armonk, NY: IBM Corp). Experiment day 1 is used as the reference to compare
the rest of the days. In table I, the significance p obtained between Day 1 versus the rest, for SDNN index, is displayed for both groups: FP and FA. When plotting the values in Table I, shown in Fig. 6, the statistical significance value (p) remained unchanged for the FA group; in return, for the FP group exposed to contaminants, the tendency for it is to become more and more significant.
Fig. 6. Comparison between p-values for FA and FP groups. A tendency of diminished p-value for SDNN index is observed.
So after 3 weeks of exposure, FP group showed a statistically significant change of SDNN index values (p=0.028), from Experiment 1 through 8. When, the FA group shows no significant change (p=0.752). IV.
CONCLUSIONS
The analysis of EKG in rats is a helpful tool for studies of pollution effects for areas such as toxicology. HRV indices are a set of parameters that give information about the heart response in time and frequency. This information will allow us to know more about the autonomous nervous system. Within HRV indices, SDNN is widely used in literature, since it is clearly affected by exposure to contaminants in polluted air. Here, we presented a tool that extracts information from EKG signals by means of a statistical analysis of HRV indices. SDNN showed a significative decrease along the experiments in the group exposed to FP (p=0.028); while the control group that was supplied with FA showed no significant variation (p=0.752).
The obtention of a tachogram is of great value for general HRV studies purposes. A simple and fast location of R–waves gives thought of many other applications in the HRV area, since it simplifies the first step of the process. Further analyses of HRV frequency indices response is yet to be analyzed with the intention of finding new information from the frequency response of the heart. Now that this tool is built and validated, additional analyses of rat EKG can be performed; in order to process other indices and assess new information that is not yet available using current techniques. For example, it will be interesting to assess the involvement of the sympathetic and parasympathetic nervous systems, and how much each system contributes with the complete response of the heart.
REFERENCES [1]
R.D. Brook, et al., “Air pollution and cardiovascular disease: a statement for healthcare professionals from the expert panel on population and prevention science of the American Heart Association” Circulation, Vol. 109, pp. 2655–2671, 2004. [2] A. Boscolo, C. Mangiavacchi, "Pollution dynamic monitoring system for city air quality control," Instrumentation and Measurement Technology Conference, IMTC 1998, IEEE Conference Proceedings, Vol. 1, pp. 202-205, May 1998. [3] D.R. Gold, et al., “Ambient pollution and heart rate variability” Circulation, Vol. 101, pp. 1267–1273, 2000. [4] S.K. Park, M.S. O'Neill, PS Vokonas, D Sparrow, J Schwartz, “Effects of air pollution on heart rate variability: The VA normative aging study” Environ Health Perspect, Vol. 113, pp. 304–309. [5] A.K. Farraj, M.S. Hazari, and W.E. Cascio, “The utility of the small rodent electrocardiogram in toxicology”, Toxicol. Sci., Vol. 121, No. 1, pp. 11-30, 2011. [6] AE Aubert, D Ramaekers, F Beckers, “The analysis of heart rate variability in unrestrained rats. Validation of methods and results”, Comput Methods Programs Biomed., Vol. 60, No. 3, pp. 197–213, 1999. [7] K.N.Shields, J.M.Cavallari, M.J.Hunt, M. Lazo, M. Molina, L. Molina, F.Holguin, “Traffic-related air pollution exposures and changes in heart rate variability in Mexico City: A panel study”, Environ. Health., Vol. 12, No. 7, 2013. [8] G. Vega-Martinez, C. Alvarado-Serrano, L. Leija-Salas, "ECG baseline drift removal using discrete wavelet transform", 8th International Conference on Electrical Engineering Computing Science and Automatic Control, CCE 2011, pp.1-5, October 2011. [9] C. Alvarado, J. Arregui, J. Ramos, R. Pallas-Areny, "Automatic detection of ECG ventricular activity waves using continuous spline wavelet transform", 2nd International Conference on Electrical and Electronics Engineering, CCE 2005, pp. 189-192, September 2005. [10] A.J. Camm, M. Malik, J.T. Bigger, “Heart rate variability: standards of measurement, physiological interpretation and clinical use”, European Heart Journal, Vol. 17, pp. 354-381, 1996. [11] P Laguna, GB Moody and RG Mark, “Power spectral density of unevenly sampled data by least-square analysis: performance and application to heart rate signals” IEEE Transactions on Biomedical Engineering, Vol. 45, No. 6, June 1998. [12] AC Rohr, A Kamal, M Morishita, B Mukherjee, GJ Keeler, JR Harkema, JG Wagner, “Altered heart rate variability in spontaneously hypertensive rats is associated with specific particulate matter components in Detroit, Michigan”, Environ. Health Perspect Vol. 11, No. 4, pp. 474–480, 2011.
127
