Coronary artery disease diagnosis based on exercise electrocardiogram indexes from repolarisation, depolarisation and heart rate variability

The Autonomic Imbalance of Myocardial Ischemia during Exercise Stress Testing: Insight from Short-Term Heart Rate Variability Analysis

Exercise stress testing (EST) has limited power in diagnosing obstructive coronary artery disease (CAD). The heart rate variability (HRV) analysis might increase the sensitivity of CAD detection. This study aimed to evaluate the correlation between short-term HRV and myocardial ischemia during EST, including the acceleration, maximum, and recovery stages of heart rate (HR). The HRV during EST from 19 healthy (RHC) subjects and 35 patients with CAD (25 patients with insignificant CAD (iCAD), and 10 patients with significant CAD (sCAD)) were compared. As a result, all HRV indices decreased at the maximum stage and no significant differences between iCAD and sCAD were found. The low-frequency power of heart rate signal (LF) of the RHC group recovered relatively quickly from the third to the sixth minutes after maximum HR, compared with that of the sCAD group. The relative changes of most HRV indices between maximum HR and recovery stage were lower in the sCAD group than in the RHC group, especially in LF, the standard deviation of all normal to normal intervals (SDNN), and the standard deviation in the long axis direction of the Poincaré plot analysis (SD2) indices (p < 0.05). The recovery slope of LF was significantly smaller in the sCAD group than in the RHC group (p = 0.02). The result suggests that monitoring short-term HRV during EST provides helpful insight into the cardiovascular autonomic imbalance in patients with significant CAD. The relative change of autonomic tone, especially the delayed sympathetic recovery, could be an additional marker for diagnosing myocardial ischemia.

Assessing heart disease using a novel magnetocardiography device

The aim of this paper is to present the use of a portable, unshielded magnetocardiograph (MCG) and identify key characteristics of MCG scans that could be used in future studies to identify parameters that are sensitive to cardiac pathology. We recruited 50 patients with confirmed myocardial infarction (MI) within the past 12 weeks and 46 volunteers with no history of cardiac disease. A set of 38 parameters were extracted from MCG features including both signals from the sensor array and from magnetic images obtained from the device and principal component analysis was used to concentrate the information contained in these parameters into uncorrelated predictors. Linear fits of these parameters were then used to examine the ability of MCG to distinguish between sub-groups of patients. In the fist instance, the primary aim of this study was to ensure that MCG has a basic ability to separate a highly polarised patient group (young controls from post infarction patients) and to identify parameters that could be used in future studies to build a formal diagnostic tool kit. Parameters that parameterised left ventricular ejection fraction (LVEF) were identified and an example is presented to show differential low and high ejection fractions.

Identifying the RNA signatures of coronary artery disease from combined lncRNA and mRNA expression profiles

Coronary artery disease (CAD) is the most common cardiovascular disease. CAD research has greatly progressed during the past decade. mRNA is a traditional and popular pipeline to investigate various disease, including CAD. Compared with mRNA, lncRNA has better stability and thus may serve as a better disease indicator in blood. Investigating potential CAD-related lncRNAs and mRNAs will greatly contribute to the diagnosis and treatment of CAD. In this study, a computational analysis was conducted on patients with CAD by using a comprehensive transcription dataset with combined mRNA and lncRNA expression data. Several machine learning algorithms, including feature selection methods and classification algorithms, were applied to screen for the most CAD-related RNA molecules. Decision rules were also reported to provide a quantitative description about the effect of these RNA molecules on CAD progression. These new findings (CAD-related RNA molecules and rules) can help understand mRNA and lncRNA expression levels in CAD.

Empirical Mode Decomposition as a Novel Approach to Study Heart Rate Variability in Congestive Heart Failure Assessment

Congestive heart failure (CHF) is a cardiovascular disease related to autonomic nervous system (ANS) dysfunction and fragmented patterns. There is a growing demand for assessing CHF accurately. In this work, 24-h RR interval signals (the time elapsed between two successive R waves of the QRS signal on the electrocardiogram) of 98 subjects (54 healthy and 44 CHF subjects) were analyzed. Empirical mode decomposition (EMD) was chosen to decompose RR interval signals into four intrinsic mode functions (IMFs). Then transfer entropy (TE) was employed to study the information transaction among four IMFs. Compared with the normal group, significant decrease in TE (*→1; information transferring from other IMFs to IMF1, p < 0.001) and TE (3→*; information transferring from IMF3 to other IMFs, p < 0.05) was observed. Moreover, the combination of TE (*→1), TE (3→*) and LF/HF reached the highest CHF screening accuracy (85.7%) in IBM SPSS Statistics discriminant analysis, while LF/HF only achieved 79.6%. This novel method and indices could serve as a new way to assessing CHF and studying the interaction of the physiological phenomena. Simulation examples and transfer entropy applications are provided to demonstrate the effectiveness of the proposed EMD decomposition method in assessing CHF.

Autonomic impairment of patients in coma with different Glasgow coma score assessed with heart rate variability

PRIMARY OBJECTIVE

The objective of this study is to assess the functional state of the autonomic nervous system in healthy individuals and in individuals in coma using measures of heart rate variability (HRV) and to evaluate its efficiency in predicting mortality.

DESIGN AND METHODS

Retrospective group comparison study of patients in coma classified into two subgroups, according to their Glasgow coma score, with a healthy control group. HRV indices were calculated from 7 min of artefact-free electrocardiograms using the Hilbert-Huang method in the spectral range 0.02-0.6 Hz. A special procedure was applied to avoid confounding factors. Stepwise multiple regression logistic analysis (SMLRA) and ROC analysis evaluated predictions.

RESULTS

Progressive reduction of HRV was confirmed and was associated with deepening of coma and a mortality score model that included three spectral HRV indices of absolute power values of very low, low and very high frequency bands (0.4-0.6 Hz). The SMLRA model showed sensitivity of 95.65%, specificity of 95.83%, positive predictive value of 95.65%, and overall efficiency of 95.74%.

CONCLUSIONS

HRV is a reliable method to assess the integrity of the neural control of the caudal brainstem centres on the hearts of patients in coma and to predict patient mortality.

Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances

Widely developed for clinical screening, electrocardiogram (ECG) recordings capture the cardiac electrical activity from the body surface. ECG analysis can therefore be a crucial first step to help diagnose, understand and predict cardiovascular disorders responsible for 30% of deaths worldwide. Computational techniques, and more specifically machine learning techniques and computational modelling are powerful tools for classification, clustering and simulation, and they have recently been applied to address the analysis of medical data, especially ECG data. This review describes the computational methods in use for ECG analysis, with a focus on machine learning and 3D computer simulations, as well as their accuracy, clinical implications and contributions to medical advances. The first section focuses on heartbeat classification and the techniques developed to extract and classify abnormal from regular beats. The second section focuses on patient diagnosis from whole recordings, applied to different diseases. The third section presents real-time diagnosis and applications to wearable devices. The fourth section highlights the recent field of personalized ECG computer simulations and their interpretation. Finally, the discussion section outlines the challenges of ECG analysis and provides a critical assessment of the methods presented. The computational methods reported in this review are a strong asset for medical discoveries and their translation to the clinical world may lead to promising advances.

Very High Frequency Oscillations of Heart Rate Variability in Healthy Humans and in Patients with Cardiovascular Autonomic Neuropathy

Literature reports on the very high frequency (VHF) range of 0.4-0.9 Hz in heart rate variability (HRV) are scanty. The VHF presence in cardiac transplant patients and other conditions associated with reduced vagal influence on the heart encouraged us to explore this spectral band in healthy subjects and in patients diagnosed with cardiac autonomic neuropathy (CAN), and to assess the potential clinical value of some VHF indices. The study included 80 healthy controls and 48 patients with spinocerebellar ataxia type 2 (SCA2) with CAN. The electrocardiographic recordings of short 5-min duration were submitted to three different spectral analysis methods, including the most generally accepted procedure, and the two novel methods using the Hilbert-Huang transform. We demonstrated the presence of VHF activity in both groups of subjects. However, VHF power spectral density, expressed in relative normalized units, was significantly greater in the SCA2 patients than that in healthy subjects, amounting to 36.1 ± 17.4% vs. 22.9 ± 14.1%, respectively, as also was the instantaneous VHF spectral frequency, 0.58 ± 0.05 vs. 0.64 ± 0.07 Hz, respectively. These findings were related to the severity of CAN. We conclude that VHF activity of HRV is integral to the cardiovascular autonomic control.

The interpretation of very high frequency band of instantaneous pulse rate variability during paced respiration

BACKGROUND

Pulse rate (PR) indicates heart beat rhythm and contains various intrinsic characteristics of peripheral regulation. Pulse rate variability (PRV) is a reliable method to assess autonomic nervous system function quantitatively as an effective alternative to heart rate variability. However, the frequency range of PRV is limited by the temporal resolution of PR based on heart rate and it is further restricted the exploration of optimal autoregulation frequency based on spectral analysis.

METHODS

Recently, a new novel method, called instantaneous PRV (iPRV), was proposed. iPRV breaks the limitation of temporal resolution and extends the frequency band. Moreover, iPRV provides a new frequency band, called very high frequency band (VHF; 0.4-0.9 Hz).

RESULTS

The results showed that the VHF indicated the influences of respiratory maneuvers (paced respiration at 6-cycle and 30-cycle) and the nonstationary condition (head-up tilt; HUT).

CONCLUSIONS

VHF is as a potential indication of autoregulation in higher frequency range and with peripheral regulation. It helps for the frequency exploration of cardiovascular autoregulation.

Frequency range extension of spectral analysis of pulse rate variability based on Hilbert-Huang transform

Heart rate variability (HRV) is a well-accepted indicator for neural regulatory mechanisms in cardiovascular circulation. Its spectrum analysis provides the powerful means of observing the modulation between sympathetic and parasympathetic nervous system. The timescale of HRV is limited by discrete beat-to-beat time intervals; therefore, the exploration region of frequency band of HRV spectrum is relatively narrow. It had been proved that pulse rate variability (PRV) is a surrogate measurement of HRV in most of the circumstances. Moreover, arterial pulse wave contains small oscillations resulting from complex regulation of cardiac pumping function and vascular tone at higher frequency range. This study proposed a novel instantaneous PRV (iPRV) measurement based on Hilbert-Huang transform. Fifteen healthy subjects participated in this study and received continuous blood pressure wave recording in supine and passive head-up tilt. The result showed that the very-high-frequency band (0.4-0.9 Hz) varied during head-up tilt and had strong correlation (r = 0.77) with high-frequency band and medium correlation (r = 0.643) with baroreflex sensitivity. The very-high-frequency band of iPRV helps for the exploration of non-stationary autoregulation and provides the non-stationary spectral evaluation of HRV without distortion or information loss.

Respiration effect on wavelet-based ECG T-wave end delineation strategies

The main purpose of this paper is to study the influence of the mechanical effect of respiration over the T-wave end delineation. We compared the performance of an automatic delineation system based on the wavelet transform (WT), considering single lead (SL), global delineation locations obtained from SL annotations (SLR), and multilead (ML) approaches. The linear relation between the variations on T-wave end locations obtained with each of the methods and the mechanical effect of respiration was quantified using spectral coherence and ARARX modeling both in simulated signals and in real data. We also explored the evolution of the vectorcardiographic spatial loop using the projection on the main direction of the WT in the region close to the T-wave end ( T(e)) and its relation with respiration. The dispersion of the additional T-wave end location error due to respiration was reduced by 15% using SLR with respect to SL, while ML allows for a reduction of around 40%. The percentage of that error correlated with respiration was in average 99% using SL while 82% and 72% using SLR and ML, respectively. Thus, results suggest that ML is the most adequate strategy for T-wave delineation, allowing the reduction of the instability of T-wave end location caused by respiration.

Respiration effect on single and multi lead ECG delineation strategies

The main purpose of this work is to study the influence of the mechanical effect of the respiration over T wave end delineation. The performance of automatic delineation systems based in Wavelet Transform (WT) was compared, considering single lead (SL), post processing selection rules (SLR) and multi lead (ML) approaches. The T wave locations obtained over real and simulated ECG signals were analyzed together with the respective respiratory signal (ECG-derived or simulated). The linear relation between the variations on obtained marks and the mechanical effect of the respiration was measured using spectral coherence. With respect to the ML strategy we also explored the evolution of the vectorcardiographic spatial loop using the direction of maximum projection of the WT in the region close to the T wave end (T(e)). The relation between this direction and the respiration is also explored. The marks obtained from the SLR and ML delineation strategies show advantage over the SL strategy based marks. The coherence around the respiratory frequency between the respiratory signal and the error in T end marks was found to be higher using SLR (a minimum of 0.92) than using ML (a maximum of 0.80). According to obtained results, the multi lead delineation presents a lower sensibility to the mechanical effect of the respiration for the T wave end delineation, particularly the obtained with ML.

A multilead scheme based on periodic component analysis for T-wave alternans analysis in the ECG

T-wave alternans (TWA) is a cardiac phenomenon that appears in the electrocardiogram (ECG) and is associated with the mechanisms leading to sudden cardiac death (SCD). In this study, we propose the use of a multilead TWA analysis scheme that combines the Laplacian likelihood ratio (LLR) method and periodic component analysis (piCA), an eigenvalue decomposition technique whose aim is to extract the most periodic sources of the signal. The proposed scheme is evaluated in different scenarios--from synthetic signals to stress test ECGs--and is compared to other reported schemes based on the LLR method. Results demonstrate that the piCA-based scheme provides a superior ability to detect TWA than previously reported schemes, and has the potential to improve the prognostic value of testing for TWA.

Multilead analysis of T-wave alternans in the ECG using principal component analysis

T-wave alternans (TWA) is a cardiac phenomenon associated with the mechanisms leading to sudden cardiac death. Several methods exist to automatically detect and estimate TWA in the ECG on a single-lead basis, and their main drawback is their poor sensitivity to low-amplitude TWA. In this paper, we propose a multilead analysis scheme to improve the detection and estimation of TWA. It combines principal component analysis with a single-lead method based on the generalized likelihood ratio test. The proposed scheme is evaluated and compared to a single-lead scheme by means of a simulation study, in which different types of simulated and physiological noise are considered under realistic conditions. Simulation results show that the multilead scheme can detect TWA with an SNR 30 dB lower and allows the estimation of TWA with an SNR 25 dB lower than the single-lead scheme. The two analysis schemes are also applied to stress test ECG records. Results show that the multilead scheme provides a higher detection power and that TWA detections obtained with this scheme are significantly different in healthy volunteers and ischemic patients, whereas they are not with the single-lead scheme.

Design and evaluation of a wireless decision-support system for heart rate variability study in haemodialysis follow-up procedures

In this paper a new wireless decision-support system for haemodialysis patients using heart rate variability (HRV) is presented. The telemedicine system provides connectivity to three participant sites: the general practitioner or nurse at the point of care in the dialysis unit, the remote information and processing server and the cardiologist. At the clinical point of care, the nurse acquires the electrocardiogram (ECG) by using a tailored mobile telecardiology system as well as other relevant physiological information during the clinical procedure, and sends it to the information server. The received information is stored in a secure file server, linked to the patient database and the ECG signal is automatically analyzed by using advanced signal processing tools in the processing server, where a complete clinical results report is generated. The cardiologist can then be linked by means of a web browser to the information server to analyze these results for further clinical diagnosis support. The system has been applied to study HRV in patients undergoing haemodialysis. The clinical report consisted of trends for time- and frequency-domain HRV indexes and other supplementary information automatically calculated, which show the response of the electrical activity of the heart to the dialysis process and that can be helpful for the follow-up of these patients. The telecardiology framework has been successfully evaluated both by the patients and the hospital personnel showing a high compliance with the system. The design and implementation of the telecardiology system have followed the most recent advances in web technologies, biomedical information and storage standards and signal processing techniques. The presented system can be used as a telemedicine tool for clinical diagnosis support and could also be used in other clinical settings.

Heart rate variability derived from exercise ECG in the detection of coronary artery disease

The diagnostic performance of heart rate variability (HRV) analysis from exercise ECG in the detection of coronary artery disease (CAD) is unknown. Bicycle exercise ECG recordings from The Finnish Cardiovascular Study (FINCAVAS) of angiography-proofed CAD patients (n = 112) and a patient group with a low likelihood of CAD (n = 114) were analyzed. HRV parameters (SDNN, RMSSD, Poincaré SD1 and SD2) were calculated from 1 min segments before exercise, during exercise and after exercise. All the parameters were in addition calculated from heart rate (HR)-corrected RR-interval segments. The ST-segment depressions in each stage were also determined. The diagnostic performance of the parameters was evaluated with the area under the receiver operating characteristic (ROC) curve method. The uncorrected HRV parameters showed the best diagnostic performance in the recovery segments but the correlation with HR was also high (SDNN: 0.758/-0.64, RMSSD: 0.747/-0.60; area under the ROC/correlation coefficient). The HR correction decreased the correlation and the diagnostic performance in recovery segments (SDNN: 0.515/-0.12, RMSSD: 0.609/0.20). The diagnostic performance of ST-level at its best was higher than any of HRV parameters (ST-level: 0.795/0.36). According to the results, the HR correction decreased the diagnostic performance of the recovery phase. The HRV parameters calculated from 1 min segments of exercise test ECG were not as capable as traditional ST-segment analysis. In conclusion, the HRV analysis from exercise or recovery phase seems to be inadequate in the detection of CAD.

A robust method for ECG-based estimation of the respiratory frequency during stress testing

A robust method is presented for electrocardiogram (ECG)-based estimation of the respiratory frequency during stress testing. Such ECGs contain highly nonstationary noise and exhibit changes in QRS morphology which, when combined with the dynamic nature of the respiratory frequency, make most existing methods break down. The present method exploits the oscillatory pattern of the rotation angles of the heart's electrical axis as induced by respiration. The series of rotation angles, obtained from least-squares loop alignment, is subject to power spectral analysis and estimation of the respiratory frequency. Robust techniques are introduced to handle the nonstationary properties of exercise ECGs. The method is evaluated by means of both simulated signals, and ECG/airflow signals recorded from 14 volunteers and 20 patients during stress testing. The resulting respiratory frequency estimation error is, for simulated signals, equal to 0.5% +/- 0.2%, mean +/- SD (0.002 +/- 0.001 Hz), whereas the error between respiratory frequencies of the ECG-derived method and the airflow signals is 5.9% +/- 4% (0.022 +/- 0.016Hz). The results suggest that the method is highly suitable for analysis of noisy ECG signals recorded during stress testing.