Abstract
Coronary artery disease (CAD) is the most common cause of death globally. To detect CAD noninvasively at an early stage before clinical symptoms occur is still nowadays challenging. Analysis of the variation of heartbeat interval (RRI) opens a new avenue for evaluating the functional change of cardiovascular system which is accepted to occur at the subclinical stage of CAD. In addition, systolic time interval (STI) and diastolic time interval (DTI) also show potential. There may be coupling in these electromechanical time series due to their physiological connection. However, to the best of our knowledge no publication has systematically investigated how can the coupling be measured and how it changes in CAD patients. In this study, we enrolled 39 CAD patients and 36 healthy subjects and for each subject the electrocardiogram (ECG) and photoplethysmography (PPG) signals were recorded simultaneously for 5 min. The RRI series, STI series, and DTI series were constructed, respectively. We used linear cross correlation (CC), coherence function (CF), as well as nonlinear mutual information (MI), cross conditional entropy (XCE), cross sample entropy (XSampEn), and cross fuzzy entropy (XFuzzyEn) to analyse the bivariate RRI-DTI coupling, RRI-STI coupling, and STI-DTI coupling, respectively. Our results suggest that the linear CC and CF generally have no significant difference between the two groups for all three types of bivariate coupling. The MI only shows weak change in RRI-DTI coupling. By comparison, the three entropy-based coupling measurements show significantly decreased coupling in CAD patients except XSampEn for RRI-DTI coupling (less significant) and XCE for STI-DTI and RRI-STI coupling (not significant). Additionally, the XFuzzyEn performs best as it was still significant if we further applied the Bonferroni correction in our statistical analysis. Our study indicates that the intrinsic electromechanical coupling is most probably nonlinear and can better be measured by nonlinear entropy-based measurements especially the XFuzzyEn. Besides, CAD patients are accompanied by a loss of electromechanical coupling. Our results suggest that cardiac electromechanical coupling may potentially serve as a noninvasive diagnostic tool for CAD.
Highlights
mutual information (MI) decrease whereas cross conditional entropy (XCE), cross sample entropy (XSampEn), and XFuzzyEn increase in coronary artery disease (CAD) patients compared with healthy healthy subjects, all of which indicate a reduced r6">6].to Systole heartbeat interval (RRI)-diastole time interval (DTI) coupling
While diastolic murmurs were studied by Zhao and Ma [46], they proposed a technique based on the empirical mode decomposition-teager energy operator for feature extraction and back propagation neural network as the classifier for CAD diagnosis
Many linear and nonlinear parameters are extracted from heart rate signals and used as diagnostic features to predict the subjects with CAD [3,4,5,6], the features were fed into classifiers for automated diagnosis of CAD subjects [4,5,6]
Summary
In order to construct these time-series, certain feature points were extracted firstly method [42]. STI series wasofconstructed by detected intervalwith between systolic foot first- and second-order differential counterparts of PPG [14,15,43]. The raw RRI series was and dicrotic notch of waveform in the same cardiac cycle, whereas. The raw STI series was constructed by interval between the dicrotic notch and the systolic foot of the following cardiac cycle. 2. Anomalous intervals to signal quality in RRI series were detected removed. The due corresponding ectopic beats or poor signal quality in RRI series were detected and removed [44]. Note here none of the corresponding intervals in STI and DTI were removed in order to consolidate their lengths. The embedding dimension m and time delay τ were set at 2 and 1, respectively, for XCE, XSampEn, and XFuzzyEn
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
