Linear and nonlinear analysis of normal and CAD-affected heart rate signals

Coronary Artery Disease (CAD) is one of the dangerous cardiac disease, often may lead to sudden cardiac death. It is difficult to diagnose CAD by manual inspection of electrocardiogram (ECG) signals. To automate this detection task, in this study, we extracted the Heart Rate (HR) from the ECG signals and used them as base signal for further analysis. We then analyzed the HR signals of both normal and CAD subjects using (i) time domain, (ii) frequency domain and (iii) nonlinear techniques. The following are the nonlinear methods that were used in this work: Poincare plots, Recurrence Quantification Analysis (RQA) parameters, Shannon entropy, Approximate Entropy (ApEn), Sample Entropy (SampEn), Higher Order Spectra (HOS) methods, Detrended Fluctuation Analysis (DFA), Empirical Mode Decomposition (EMD), Cumulants, and Correlation Dimension. As a result of the analysis, we present unique recurrence, Poincare and HOS plots for normal and CAD subjects. We have also observed significant variations in the range of these features with respect to normal and CAD classes, and have presented the same in this paper. We found that the RQA parameters were higher for CAD subjects indicating more rhythm. Since the activity of CAD subjects is less, similar signal patterns repeat more frequently compared to the normal subjects. The entropy based parameters, ApEn and SampEn, are lower for CAD subjects indicating lower entropy (less activity due to impairment) for CAD. Almost all HOS parameters showed higher values for the CAD group, indicating the presence of higher frequency content in the CAD signals. Thus, our study provides a deep insight into how such nonlinear features could be exploited to effectively and reliably detect the presence of CAD

Prediction of the HRV signal during treadmill running

Abstract. Our heart rate is varying every time, and the autonomic nervous system maintains this complex control mechanism. Analysis of heart rate variability (HRV) is a useful tool for autonomic nervous system assessment. It can be a useful marker for different cardiac arrhythmias and heart diseases, and its’ clinical relevance is increasing day by day. HRV analysis has an important impact on exercise physiology since it can be a useful marker for stress and recovery. HRV during exercise differs a lot from the normal condition as body movement, exercise intensity, and other factors modulate the HRV. Few recent studies show the effect of running cadence and pedaling frequency on the HRV during treadmill exercise and cycling exercise, respectively. Our research is based on incremental treadmill exercise, and we tried to figure out which part of HRV can be explained by running cadence. We tried to create a polynomial model for HRV, which can predict the future HRV by training the model with appropriate training data and later validate the model with the HRV signal from different running intervals. We observed a significant reduction in the model performance with the increment of running speed. The reduction in model performances validates that the HRV signal is affected most when the running intensity is maximum. We tried to correlate our model residuals with the actual acceleration signal, but due to some complexity, we couldn’t achieve what we have hypothesized