DETECTING CONGESTIVE HEART
FAILURE USING HEART RATE
SEQUENTIAL TREND ANALYSIS PLOT
SRINIVAS KUNTAMALLA
Department of Physics, National Institute of Technology Warangal, India -506004
ksvchary@gmail.com
L. RAM GOPAL REDDY
Department of Physics, National Institute of Technology Warangal, India -506004
lrgreddy@gmail.com
Abstract :
Heart rate variability analysis is gaining acceptance as a potential non-invasive means of autonomic nervous system assessment in research as well as clinical domains. In this study, a nonlinear analysis method is developed to detect congestive heart failure. The data obtained from an online and widely used public database (i.e., MIT/BIH physionet database), is used for testing the performance of the method. The method developed is based on the sequential trend analysis plot of heart rate variability and correlates well with the characteristic autonomic nervous system regulations in congestive heart failure. The proposed method can be used for screening as well as diagnosing the heart failure patients. The algorithm is computationally simple and can be implemented in a real time processing hardware. This method classifies 31 out of 32 subjects and has the highest discrimination power in terms of sensitivity, specificity and accuracy.
Keywords
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Heart rate variability; Congestive heart failure; Sequential trend analysis; Sympathetic activity.1. Introduction
Heart rate variability (HRV) analysis is fast emerging as a noninvasive research and clinical tool for assessing cardiac and autonomic nervous system function. HRV analysis is based on the fact that a variable heart rate is the normal physiological state. The spontaneous depolarization rate of sinus node is called intrinsic heart rate (HR0). The HR0 is measured in the absence of sympathetic and parasympathetic inputs and is approximately 100 beats /min in healthy subjects. Actual heart rate is determined by the sympathetic and parasympathetic effects on HR0. Parasympathetic activation slows the heart rate by release of acetylcholine from efferent vagal nerve endings where as sympathetic activation accelerates the heart rate by circulating epinephrine or neural release of norepinephrine. In a stable physiological state, the sympathetic and parasympathetic inputs are generally antagonistic in nature and have a tonic level of activity that determines the actual heart rate at that state. The heart rate consistently responds in an expected direction based on the physiological state i.e., sympathetic and parasympathetic stimulation or blockade. The R-R interval, reciprocal of heart rate is directly related to vagal nerve activity and the variation in the intervals between consecutive heart beats is described as heart rate variability. Therefore, HRV is a measure of the balance between sympathetic and parasympathetic activities [Lahiri, M.K. et al. (2008)]. Although HRV has been the subject of many clinical studies investigating a wide spectrum of cardiological and non-cardiological diseases and clinical conditions, a general consensus of the practical use of HRV in medicine has been reached only in two clinical scenarios: depressed HRV can be used as a predictor of risk after acute myocardial infarction and as an early warning sign of diabetic neuropathy [Task Force (1996)]. Depressed HRV has also been observed in patients suffering from dilated cardiomyopathy and congestive heart failure (CHF) [Ponikowsky, P. et al. (1997)]. Heart failure is characterized by increased sympathetic activity, which decreases the heart rate variability.
proposing several time and frequency parameters basedon short-term (5-min) and long-term (24-h) HRV data [Task Force (1996)]. The HRV can be analyzed using several methods which are broadly classified as time domain and frequency domain methods. Time domain measures are simple statistical operations on R-R intervals, such as standard deviation of normal R-R intervals (SDNN), root mean square of successive R-R interval differences (RMSSD) and the percentage change of normal R-R intervals that differ by > 50 ms (PNN50) etc. Frequency domain analysis includes FFT or AR based power spectral density measures which provide information on how variance distributes as a function of frequency. Three main spectral components are distinguished in a spectrum calculated from short-term recordings in bothabsolute and normalized units: very low frequency (VLF) (≤ 0.04 Hz), Low frequency (LF) (0.04 - 0.15 Hz), and high frequency (HF) (0.15 – 0.4 Hz) components. Heart rate dynamics are nonlinear in nature so, a nonlinear analysis method is more appropriate means to get accurate information about the heart rate variability. Poincare plot, Approximate Entropy, Sample Entropy, Detrended Fluctuation Analysis, Correlation Dimension and Sequential Trend Analysis are some of the nonlinear analysis methods of HRV.
In this work, sequential trend analysis is employed to discriminate the CHF patients from the healthy subjects. Sequential trend analysis is a little explored method for HRV analysis. Heart failure is a condition in which the heart muscle weakens and loses the ability to pump the blood efficiently throughout the body. CHF often is not recognized and have no symptoms until more advanced stage. There are several clinical tests to assess decreased functioning of heart, such as Echocardiogram, chest X-ray etc. Majority of the people do not go for these tests until symptoms are seen. In this background a simple screening tool is needed to detect congestive heart failure in the early stage.
2. Methods 2.1. Data Group
The data used in this study are obtained from the normal and BIDMC congestive heart failure databases of MIT/BIH database [Goldberger, A.L. et al. (2000)]. These databases include beat annotation files for long-term (~24 h) ECG recordings and were obtained by automated analysis with manual review and correction. The MIT/BIH normal database includes 18 long term ECG recordings of subjects (5 men, 13 women) in the age group of 20-50 years, referred to the Arrhythmia Laboratory at Bostan’s Beth Issrael Hospital. These subjects were found to have no significant arrhythmias. These recordings were digitized at 128 samples per second. The BIDMC congestive heart failure data base includes long-term ECG recordings from 15 subjects (11men, aged 22-71 and 4 women, aged 54 to 63) with severe congestive heart failure (NYHA class 3-4). This group of subjects was a part of larger study group receiving conventional medical therapy prior to receiving the he oral inotropic agent, mirinone. The individual recordings are each 20 hours in duration and sampled at 250 samples per second. Autonomic nervous system regulations act on the time scales of seconds, so short term analysis is preferred in this work. A 5-minute record segment is randomly taken from each long term record for the analysis. Chf06 record from BIDMC congestive heart failure database is excluded from the study because of lot of ectopic beats.
Fig. 1 R-R Interval tachogram of CHF patient (top) and normal subject (bottom).
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2.2. Sequential Trend Analysis
Sequential trend analysis (STA) of HRV is a nonlinear scatter plot technique, in which the differences among successive R-R intervals (t) are plotted with tn on x-axis and t n+1 on y-axis (Fig. 2).
Where tn = tn – tn-1 tn+1 = tn+1 – tn and tn is the nth R-R interval
The STA plot can be envisioned as values distributed over an area defined by four quadrants. Each quadrant indicates the direction of two consecutive changes in the interval length. Therefore, this plot demonstrate 1) the ratio of short to long term changes in the heart rate 2) the trends of variation in heart rate. The t values may be positive or negative so the points in the STA plot are scattered around the point (0,0) i.e., origin. This plot has an advantage that it clearly segregates the moments that increase the RR interval and those which decreases it. Thus it separates the vagal activity from sympathetic activity and displays them separately in +/+ and -/- quadrants respectively. Whereas the other two quadrants -/+ and +/- show points of decrease in the R-R interval length followed by an increase and vice versa. The number of points in +/+ and -/- quadrant gives a measure of parasympathetic and sympathetic activity [Srinivas, K. et al. (2007), Carvalho et al. (2002)]. Further, STA plots showed a peculiar pattern for CHF patients resembling an ellipse shape, where as it is in circular shape for a normal subject (Fig. 3).
In this study, the STA plot is quantified by parameters which describe the sympathetic and parasympathetic tonic levels. Let us consider if there is no autonomous nervous system activity on the heart rate then there is no variation in the heart rate and STA plot show all the points at the origin i.e., at (0,0) position. Therefore, the point (0,0) can be treated as a point of no activity. The distance from this point to any point in the four quadrants represents the tonic levels of the respective activities. Therefore, the mean of the distances in each quadrant is taken as a measure of STA plot and is given by the equation
where ‘n’is the total number of points in each quadrant.
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Fig. 2 Schematic diagram of a STA plot
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Fig. 3 STA plot of a) CHF patient b) Normal subject
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The mean distances for four quadrants are calculated for each subject. They are designated as d1, d2, d3 and d4 for +/+, -/+, -/- and +/- quadrants respectively. Here the points on the axes are also counted into their respective quadrants depending on their directions (increase or decrease). From Fig. 3 the STA plot is like ellipse in case of CHF patient and it is like a circle for a normal subject, which implies that (d2+d4) > (d1+d3) for CHF patients and (d2+d4) = (d1+d3) for normal subjects. The values of (d2+d4) and (d1+d3) of all CHF and normal subjects are ploted seperately. The d1, d3 values of STA plot represents the parasympathetic, sympathetic tonic levels. Therefore we propose d1 as a measure of parasympathetic nervous system activity (PNS measure) and d3 as a measure of sympathetic nervous system activity (SNS measure). The mean distance from origin comprising of +/+ and -/- quadrants is a measure of total autonommic nervous system activity (ANS measure).The d2, d4 values of STA plot represents the change in R-R Interval while switching from parasympathetic to sympathetic activity and vice versa.
The parameters extracted from the STA plot are tested for null hypothesis using a significance test (test). T-test is the most commonly used method to evaluate the differences in means between the two groups. The significance level for rejection of null hypothesis is set to 0.001 in this study. The P value < 0.001 is considered to be statistically significant.
3. Results
From Fig. 4 and Fig. 5 it is clear that for all the CHF patients the STA plot is in elliptical in shape and for all the normal subjects it is in circular shape (P values 0.00083 and 0.37 respectively). Fig. 6 shows SNS measure and PNS measure values for CHF and normal subjects. The figure shows a clear discrimination between the two groups (P value =1.601e-09 for SNS, P value = 5.7e-10 for PNS). The SNS measure and PNS measure values for congestive heart failure patients are less than 20 with a average value of 10.82(±3.44) for SNS and 10.26(±2.73) for PNS (P value = 0.64). The SNS measure and PNS measure values for normal subjects are greater than 20 with an average value of 32.48(±8.96) for SNS and 35.20(±10.11) for PNS (P value = 0.39). The total ANS activity is plotted in Fig. 7. The ANS measure for congestive heart failure patients is less than 20 with a average value of 10.48(±2.90) for normal subjects is greater than 20 with a mean value of 33.87(±9.69) (P value = 1.046e-09).
Fig. 4 Plot showing d2+d4 > d1+d3 for all CHF patients 0
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The performance of the proposed method is evaluated by three main metrics: sensitivity, specificity, and accuracy as defined below
Sensitivity% = TP *100 / (TP+FN) Specificity% = TN *100 / (TN+FP) Accuracy% = (TN+TP) *100 / T
Where TP, TN, FN, FP are true positives, true negatives, false negatives, false positives and T is the total number of data under test. Sensitivity represents the ability of a classifier to detect the positive cases, e.g. CHF. Specificity indicates the ability of a classifier to detect negative cases, e.g. normal subjects. Accuracy represents the overall performance of a classifier. It indicates the percentage of correctly classified positives and negative cases from the total cases [Rangayyan, R.M. (2001)]. The sensitivity of the proposed method is 100%, specificity is 94.44% and accuracy is 96.68%.
4. Discussion and Conclusion
In this study, using HRV data obtained from an online, publicly available and widely used database, the performance of a method developed to classify CHF patients from normal subjects is investigated. Several attempts have been made for discriminating CHF patients from normal subjects. Abdulnasir Hossen and Bader Al-Ghunaimi [2008] proposed a screening method for CHF patients with an efficiency of 90%. This method has a limitation that it could be used only on long length data (24 h records). Acharya, U.R et.al [2008] used power spectral densities of the RR intervals derived from ARMA model and ANN to classify the CHF patients with a sensitivity of 83.3%. Isler Yalcm and Mehmet Kuntalp [2007] combined classical HRV indices with wavelet entropy measure to improve the performance in diagnosing congestive heart failure and obtained a sensitivity of 96.43% by using a KNN classifier. This study uses 30 distinct short term HRV measures and a genetic algorithm to for feature selection and is more complex to implement. In another method, a success rate of 86.7% is achieved by using a nonlinear detrended fluctuation analysis technique along with K-means clustering
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Fig. 6 STA plot measures for all the subjects a) SNS measure b) PNS measure
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analysis [Alis, C. et al. (2009)]. Acquiring a 24 h record for screening purpose is not desirable. Our method only uses short term records of 5 minute duration and the algorithm is very simple to implement so that it can be used for screening large populations in a less time. This method is computationally simple and can be implemented in a real time processing hardware. Some studies are suggesting photoplethysmogram (PPG) instead of ECG to derive HRV data [Lu Sheng et al. (2008), Srinivas, K. et al. (2007)]. Although ECG provide much more insight into the problems of the heart, the PPG based HRV can be used as a preliminary screening tool for large populations with increased risk of heart failure due to smoking, alcohol consumption, diabetes and genetic heredity. This is because PPG method is simple to use and takes much less time as it does not require any electrodes and even semi skilled workers can handle it.
5. References
[1]. Abdulnasir Hossen and Bader Al-Ghumaini (2008). Identification of patients with congestive heart failure by recognition of sub-bands spectral patterns, World Aca. Sci. Eng. Tech, 44, pp 21-24
[2]. Acharya, U.R. et al. (2008). Autonomic identification of cardiac healthnusing modeling techniques: a comparative study, Information Sciences, 178, 23, pp 4571-4582
[3]. Alis, C et al. (2009). Lifelink ; 3G based mobile telemedicine system, Telemedicine and e-Health, 15, 3, pp 241-247
[4]. Carvalho et al. (2002). Development of a Matlab software for analysis of heart rate variability, ICSP proceedings, 2, pp 1488-1491 [5]. Goldberger, A.L. , L.A.N. Amaral et al. (2000). Physiobank, physiotoolkit, and physionet: components of a new research resource for
complex physiologic signals, Circulation , 101, 23, pp. e215–e220
[6]. Isler, Y., M. Kuntalp (2007). Combing classical HRV indices with wavelet entropy measures improves to performance in diagnosing congestive heart failure, Computers in Biology and medicine, 37, pp. 1502-1510
[7]. Lahiri, M. K. et al. (2008). Assessment of Autonomic function in cardiovascular disease, J Am Coll Cardiol, 51, pp 1725-1733 [8]. Lu Sheng et al. (2008). Can photoplethysmography variability serve as an alternative approach to obtain heart rate variability
information?, J Clin Monit Comput, 22, 23-29
[9]. Ponikowski, P. Anker, S. D., Chau, T.P. et al. (1997). Depressed heart rate variability as an independent predictore of death in chronic congestive heart failure secondary to ischemic or idiopathic dialeted cardiomyopathy, Am. J cardiol, 79, pp. 1645-1650
[10].Rangayyan, R.M. (2001). Biomedical Signal Analysis: Acase study approach, IEEE Press
[11].Srinivas, K., Ram Gopal Reddy, L. Srinivas, R. (2007). Estimation of heart rate variability from peripheral pulse wave using PPG sensor, IFMBE proc., 15, pp 325-328
