Linear and nonlinear analysis of normal and CAD-affected heart rate signals
Introduction
Coronary arteries supply nutrients and oxygen to heart muscles. Coronary Artery Disease (CAD) is a pathological condition where the diameter of the arteries decreases either due to the formation of cholesterol plaque on its inner wall [1] or due to the contraction of the whole wall for other reasons, such as tobacco smoking [75] and environmental pollution [2]. The condition is often ominously silent, but progressive in nature. If it is not treated appropriately, it will eventually lead to ischemia (i.e., interruptions of blood supply) and then infarctions (i.e., the complete loss of blood supply). Usually one of the reasons for Sudden Cardiac Death (SCD) is CAD [3]. Hence, early detection of CAD is essential to prevent SCD.
One of the most commonly used techniques for CAD detection is the Exercise Stress Test (EST). EST increases the workload of the heart and records exaggerated electrophysiological information. For this test to be accurate, a target Heart Rate (HR) has to be attained. Not all CAD patients can reach this rate. Furthermore there is considerable risk for the patient, because such a stress test can trigger Ventricular Tachycardia (VT) or cardiac arrest [4].
Electrocardiogram (ECG) could be a useful physiological measurement tool to detect the presence of CAD. However, visual interpretation of the ECG signals is not so effective as 50–70% of CAD patients do not show any notable difference in their ECGs [5]. However, the minute variations in the ECG signals have to be identified in order to diagnose specific type of heart disease. Due to the presence of noise and baseline wander, it is tedious to detect the minute variations by evaluating the morphological features of ECG signals. Hence, in this study, we extracted the HR from the ECG signals and used them for analysis. The study of heart rate variability (HRV) is a better technique to diagnose CAD risk levels. HR is a nonlinear, non-stationary signal which indicates the subtle variations of the underlying ECG signal [6]. The HRV evaluates the changes in the consecutive heart rates and it assesses the health of the autonomic nervous system (ANS) non-invasively. The HRV analysis conveys information about homeostasis of the body [7]. Standard methods to analyze the HRV were proposed in various domains [8].
Various cardiac and non-cardiac diseases have been diagnosed using HR signals [6], [9], [10], [11], [12]. They have analyzed the HR signals using various linear and non-linear techniques [6]. Huikuri et al. (1994) have analyzed the CAD subjects using HRV signals and showed that, the circadian rhythm decreases in CAD subjects. Hayano et al. [13] have shown a correlation between CAD severity and a reduction low-frequency power reduction decrease in high frequency power were shown in CAD subjects Lavoie et al. [14], Nikolopoulos et al. [15] and features of time and frequency domain were found to be lower for CAD subjects Bigger et al. [16]. The statistical measures changes with time and hence time domain analysis is not effective and effectiveness of frequency domain analysis decreases with reduction in the signal to noise ratio [17].
Nonlinear techniques are more in tune with the nature of physiological signals and systems, therefore, they outperform time and frequency domain methods. Hence, they are widely used in many biological and medical applications [18], [19]. Owis et al. [20] performed ECG-based arrhythmia detection and classification based on nonlinear modeling. Sun et al. [21], Acharya et al. [22] and Chua et al. [23] used nonlinear techniques to analyze cardiac signals for the development of cardiac arrhythmia detection algorithms. Schumacher [24] elaborated the effectiveness of linear and nonlinear techniques in analyzing HR signals. The onset of various cardiovascular diseases like, Ventricular Tachycardia (VT) and Congestive Cardiac Failure (CCF) can be predicted using non-linear analysis of HR signals [25]. Chua et al. [26] introduced a method to extract features like bispectral entropy from HR signals by employing Higher Order Spectra (HOS) techniques. In their study, HOS features from HR signals were used to differentiate between a normal heart beat and seven arrhythmia classes. CAD results in reduced Baroreflex Sensitivity (BRS) and reduced vagal activity which can be understood by HRV analysis. BRS is an indicator of increased risk of SCD in myocardial infarction patients. Arica et al. [27] used HR and systolic pressure signals to assess BRS.
The main aim of this paper is to present time, frequency and non-linear features for normal and CAD-affected HR signals. For this analysis, we extracted and analyzed features in the time domain, frequency domain, and also studied features derived using nonlinear methods. Furthermore, we have proposed various ranges for these features and presented unique nonlinear plots for the normal and CAD classes. Our results show that CAD subjects have less variability in their heart rate signal when compared to normal subjects. This reduced variability can be used as a single measure to diagnose CAD from ECG signals which were obtained under normal conditions. We predict that the consequent use of HRV measures will reduce the need to conduct stress ECG measurements, and therefore, expose patients to less risk.
Section snippets
Data used
ECG signals from 10 CAD patients and an equal number of healthy volunteers were recorded using the BIOPAC™ equipment http://www.biopac.com/ [74]. The sampling frequency of ECG signal was 500 Hz. The average age of both normal and CAD subjects was 55 years (age varied from 40 to 70 years). The CAD patients used for this study, were taken from Iqraa Hospital, Calicut, Kerala, India. Subjects having normal blood pressure, glucose level and ECG were considered in the normal category. For the CAD
Methods used
This section discusses time, frequency and nonlinear domain techniques which were used for analyzing CAD and normal HR signals.
Results
Results of time domain, frequency domain and nonlinear techniques are presented in this section. Table 1 shows the feature values (mean ± standard deviation (SD)) of the time domain parameters of normal and CAD HR signals. In this work, four time domain features were found to be clinically significant (p < 0.05). They are mean HR, RMSSD, NN50 and pNN50 (listed in Table 1).
The clinically significant features like NN50 and pNN50, have lower values for the CAD subjects with respect to the normal. The
Discussion
Goldberger et al. [56] showed that under normal conditions our heart is not a periodic oscillator. Since then, several nonlinear methods were proposed to quantitatively measure the heart rate variations [43], [56]. Nonlinear parameters like recurrence percentage, fractal dimension, etc. were significantly different for normal and CAD subjects of the ECG signals [57], [58], [59], [60] used correlation dimension and entropy features on heart rate signals to diagnose CAD. Karamanos et al. [61]
Conclusion
CAD is one of the prime reasons for the majority of cardiac deaths worldwide. In this work, we analyzed HR signals which were obtained from ECG data recorded from normal and CAD subjects. In our work, we have made an attempt to analyze both normal and CAD heart rate signals in time, frequency and non-linear domain. Our results show that HR signals are less variable in CAD subjects, compared to the normal subjects. We have proposed unique ranges for features in in various domains. Highly
Acknowledgements
Authors thank Ms Ratna Yanti for running the codes and compiling the results and Thanjuddin Ahmad for providing the data. HRV analysis Software, Biomedical Signal Analysis Group, University of Kuopio, Finland for providing the software.
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