Modelling a parasystolic rhythm in a heart-transplant patient

A parasystole from a heart-transplant patient is analysed using a beat-to-beat RR interval time series obtained from an electrocardiogram (ECG). The dysrhythmia, resulting from the co-existence of two pacemakers, the sinus node and an ectopic focus, presents distinctive regular patterns, with transitions from one pattern to another occurring abruptly. It is shown that the parasystolic rhythm can be simulated by a model involving two oscillators firing at fixed rates, under the restriction that neither is allowed to fire during the other's refractory period. It is found that the structure of the generated RR time series is essentially determined by the ratio of the periods of the two oscillators. In the case of a heart-transplant patient with a small heart-rate variability as a result of heart denervation, the model predicts the RR intervals with an error of less than 6% for an 80-beat sequence. From a physiological point of view, the results imply that the interaction between the two pacemakers in the heart is fairly weak, and hence the parasystole observed in the heart-transplant patient can be modelled as pure parasystole.     

Assessment of heart rate variability derived from finger-tip photoplethysmography as compared to electrocardiography

Heart rate variability (HRV) is traditionally derived from RR interval time series of electrocardiography (ECG). Photoplethysmography (PPG) also reflects the cardiac rhythm since the mechanical activity of the heart is coupled to its electrical activity. Thus, theoretically, PPG can be used for determining the interval between successive heartbeats and heart rate variability. However, the PPG wave lags behind the ECG signal by the time required for transmission of pulse wave. In this study, finger-tip PPG and standard lead II ECG were recorded for five minutes from 10 healthy subjects at rest. The results showed a high correlation (median = 0.97) between the ECG-derived RR intervals and PPG-derived peak-to-peak (PP) intervals. PP variability was accurate (0.1 ms) as compared to RR variability. The time domain, frequency domain and Poincaré plot HRV parameters computed using RR interval method and PP interval method showed no significant differences (p < 0.05). The error analysis also showed insignificant differences between the HRV indices obtained by the two methods. Bland-Altman analysis showed high degree of agreement between the two methods for all the parameters of HRV. Thus, HRV can also be reliably estimated from the PPG based PP interval method.     

A new algorithm developed based on a mixture of spectral and nonlinear techniques for the analysis of heart rate variability

In this paper, an algorithm based on a joint use of spectral and nonlinear techniques for heart rate variability (HRV) analysis is proposed. First, the measured RR data are passed into a trimmed moving average (TMA)-based filtering system to generate a lower frequency (LF) time series and a higher frequency (HF) one that approximately reflect the sympathetic and vagal activities, respectively. Since the Lyapunov exponent can be used to characterize the level of chaos in complex physiological systems, the largest Lyapunov exponents corresponding to the complex sympathetic and vagal systems are then estimated from the LF and HF time series, respectively, using an existing algorithm. Numerical results of a postural maneuver experiment indicate that both characteristic exponents or their combinations might serve as a set of innovative and robust indicators for HRV analysis, even under the contamination of sparse impulses due to aberrant beats in the RR data.     

Myocardial infarction complicated by heart block--treatment and long-term prognosis

A number of 597 patients with acute myocardial infarction (AMI) were treated with continuous ECG monitoring of the heart rhythm in a coronary care unit for at least three days. We found 84 patients with heart block, 39 with complete, 29 with at most second degree and 16 with at most first degree heart block. The treatment was primarily conservative; 22 of the 39 patients with complete heart block were given isoproterenol and two received temporary pacemakers. Survival was traced over two years in the whole patient group with myocardial infarctions. Heart block implied a worsened prognosis over the two years, but survival was independent of the degree of heart block. Among those with complete heart block, survival did not differ from that of a comparable patient series from Copenhagen, where all patients were given pacemakers. This does not support indiscriminate artificial pacing of patients with AMI and complete heart block. Our results ought to be controlled in a randomized study.     

Heart transplant, social support, and psychiatric sequelae: a 10-year follow-up clinical case review

The authors document a case of a 65-year-old heart transplant recipient at 10-year follow-up, with particular reference to his psychiatric recovery. This case illustrates the importance of social support as both an acute intervention and for long-term maintenance in the heart-transplant patient with psychiatric and multiple medical conditions. It was found that the influence of social support on transplant recovery may be affected by critical periods, including initial postoperative stabilization and convalescence, and then again with longer-term changes in social roles. Enhanced collaboration between cardiac transplant teams and mental health professionals is warranted.     

心率变异性检测分析系统的研制

心率变异性检查作为一种无创性的技术，不仅适于自主神经系统的临床诊断，而且可以用于心血管疾病的检测［１］。木系统先从心电信号中计算出Ｒ－Ｒ间期，而后在时域中进行直方图分析，在频域中进行功率谱密度分析，或采用混沌理论来分析。在时域和频域中，用一些参数来评估心率变异的大小。频域中的谱峰还提供了神经系统均衡性的信息。本系统所反映的自主神经系统的平衡关系对心血管疾病的研究有重要意义。     

A comparison of two Hilbert spectral analyses of heart rate variability

The present paper compares the performance of two Hilbert spectral analyses when applied to a synthetic RR series from a nonstationary integral pulse frequency modulation model and to real RR series from a dataset of normal sinus arrhythmia. The Hilbert–Huang transformation based on empirical mode decomposition is compared to the presently introduced Hilbert–Olhede–Walden transformation based on stationary wavelet packet decomposition. The comparison gives consistent results pointing to a superior performance of the Hilbert–Olhede–Walden transformation showing 33–163 times smaller deviations when estimating the instantaneous frequency traces of the synthetic RR series. Artificial fluctuations caused by mode mixing in the Hilbert–Huang spectrum are seen in both the synthetic and real RR series. It can be concluded that the instantaneous frequencies and amplitudes estimated by the Hilbert–Huang transformation should be interpreted with caution when investigating heart rate variability.     

Sampling frequency of the RR interval time series for spectral analysis of heart rate variability

Spectral analysis of heart rate variability (HRV) is an accepted method for assessment of cardiac autonomic function and its relationship to numerous disorders and diseases. Various non-parametric methods for HRV estimation have been developed and extensive literature on their respective properties is available. The RR interval time series can be seen as a series of non-uniformly spaced samples. To analyse the power spectra of this series using the discrete Fourier transform (DFT), we need to interpolate the series for obtaining uniformly spaced intervals. The selection of sampling period plays a critical role in obtaining the power spectra in terms of computational efficiency and accuracy. In this paper, we shall analyse the RR interval time series from selected subjects for different sampling frequencies to compare the error introduced in selected frequency-domain measures of HRV at a constant frequency resolution for a specific duration of electrocardiogram (ECG) data. It should be pointed out that, although many other error causes are possible in the frequency-domain measures, our attention will be confined only to the performance comparison due to the different sampling frequencies. While the choice of RR interval sampling frequency (f(s)) is arbitrary, the sampling rate of RR interval series must be selected with due consideration to mean and minimum RR interval; f(s = )4 Hz was proposed for a majority of cases. This is an appropriate sampling rate for the study of autonomic regulation, since it enables us to compute reliable spectral estimates between dc and 1 Hz, which represents the frequency band within which the autonomic nervous system has significant response. Furthermore, resampled RR intervals are evenly spaced in time and are synchronized with the samples of the other physiologic signals, enabling cross-spectral estimates with these signals.     

Undersensing of demand pacemakers in acute myocardial infarction

Summary Trasient asynchronous pacing due to abnormal sensing function is reported in two patients with inserted demand pacemakers during the early phase of acute myocardial infarction.The hazards of the pacemaker induced parasystole with R on T phenomenon in conditions of enhanced electrical instability could be successfully overcome applying overdrive suppression of the inserted pacing system by external chest wall stimulation     

Assessment of heart rate variability derived from finger-tip photoplethysmography as compared to electrocardiography

Heart rate variability (HRV) is traditionally derived from RR interval time series of electrocardiography (ECG). Photoplethysmography (PPG) also reflects the cardiac rhythm since the mechanical activity of the heart is coupled to its electrical activity. Thus, theoretically, PPG can be used for determining the interval between successive heartbeats and heart rate variability. However, the PPG wave lags behind the ECG signal by the time required for transmission of pulse wave. In this study, finger-tip PPG and standard lead II ECG were recorded for five minutes from 10 healthy subjects at rest. The results showed a high correlation (median = 0.97) between the ECG-derived RR intervals and PPG-derived peak-to-peak (PP) intervals. PP variability was accurate (0.1 ms) as compared to RR variability. The time domain, frequency domain and Poincaré plot HRV parameters computed using RR interval method and PP interval method showed no significant differences (p < 0.05). The error analysis also showed insignificant differences between the HRV indices obtained by the two methods. Bland-Altman analysis showed high degree of agreement between the two methods for all the parameters of HRV. Thus, HRV can also be reliably estimated from the PPG based PP interval method.     

Sampling frequency of the RR interval time series for spectral analysis of heart rate variability

Spectral analysis of heart rate variability (HRV) is an accepted method for assessment of cardiac autonomic function and its relationship to numerous disorders and diseases. Various non-parametric methods for HRV estimation have been developed and extensive literature on their respective properties is available. The RR interval time series can be seen as a series of non-uniformly spaced samples. To analyse the power spectra of this series using the discrete Fourier transform (DFT), we need to interpolate the series for obtaining uniformly spaced intervals. The selection of sampling period plays a critical role in obtaining the power spectra in terms of computational efficiency and accuracy. In this paper, we shall analyse the RR interval time series from selected subjects for different sampling frequencies to compare the error introduced in selected frequency-domain measures of HRV at a constant frequency resolution for a specific duration of electrocardiogram (ECG) data. It should be pointed out that, although many other error causes are possible in the frequency-domain measures, our attention will be confined only to the performance comparison due to the different sampling frequencies. While the choice of RR interval sampling frequency (f_s) is arbitrary, the sampling rate of RR interval series must be selected with due consideration to mean and minimum RR interval; f_s?=?4 Hz was proposed for a majority of cases. This is an appropriate sampling rate for the study of autonomic regulation, since it enables us to compute reliable spectral estimates between dc and 1 Hz, which represents the frequency band within which the autonomic nervous system has significant response. Furthermore, resampled RR intervals are evenly spaced in time and are synchronized with the samples of the other physiologic signals, enabling cross-spectral estimates with these signals.     

Automatic measurement of long-term heart rate variability by implanted single-chamber devices

Heart rate variability (HRV) measurement is an established technology for the assessment of cardiac autonomic status. Recently 24 h HRV has been shown to correlate with disease severity in heart failure. This potentially makes continuous 24h HRV measurement suitable for monitoring of heart-failure patients. Day-to-day 24 h measurement of HRV is, in principle, feasible when implemented using implanted devices (pacemakers and defibrillators)_ ued in patients who are predominantly in the sinus rhythm. However, a number of such devices used in heart-failure patients are single-chamber devices, in which the distinction between sinus rhythm beats and ectopic beats is problematic. The study investigates whether a reasonably accurate 24h HRV measurement can be achieved by automatic algorithms, suitable for implementation using implanted devices, without the need for identification of ectopic beats. A set of 5321 nominal 24 h Holter recordings of cardiac patients are used. Each of the recordings contains at least one ectopic beat; approximately 30% of the recordings have more than 1% of ectopic beats. Conventional 24h measures of HRV, that is the SDNN, HRV index, and SDANN indices, are obtained from each recording after elimination of the ectopic beats and are approximated by HRV measures computed by the same formulas without exclusion of the ectopic beats. The SDANN values are also approximated by the standard deviation of 5 min medians of all RR intervals (SDMRR measure). The errors introduced by including the ectopic beats in the HRV computation were evaluated using the Bland-Altman statistics and by Cohen's kappa statistics investigating the precision of identifying patients with depressed and preserved 24 h HRV. The SDNN measure is very sensitive to the quality of the RR interval sequence and cannot be reasonably used without the distinction between sinus rhythm and ectopic beats. The HRV index measure is marginally more acceptable when used without ectopic elimination. The SDANN is rather insensitive, and its replacement by SDMRR values leads to relative errors in the region of 2–5% that are almost independent of the number of ectopic beats included. Even in recordings with a substantial proportion of ectopic beats, a practically acceptable (κ>0.9) identification of depressed and preserved SDANN values is possible without ectopic elimination. Thus, continuous monitoring of 24h HRV is technically feasible within implanted devices, provided the SDANN measure is monitored and either computed from the sequence of all RR intervals or, potentially preferably, replaced by the SDMRR measure.     

Assessment of Granger Causality by Nonlinear Model Identification: Application to Short-term Cardiovascular Variability

A method for assessing Granger causal relationships in bivariate time series, based on nonlinear autoregressive (NAR) and nonlinear autoregressive exogenous (NARX) models is presented. The method evaluates bilateral interactions between two time series by quantifying the predictability improvement (PI) of the output time series when the dynamics associated with the input time series are included, i.e., moving from NAR to NARX prediction. The NARX model identification was performed by the optimal parameter search (OPS) algorithm, and its results were compared to the least-squares method to determine the most appropriate method to be used for experimental data. The statistical significance of the PI was assessed using a surrogate data technique. The proposed method was tested with simulation examples involving short realizations of linear stochastic processes and nonlinear deterministic signals in which either unidirectional or bidirectional coupling and varying strengths of interactions were imposed. It was found that the OPS-based NARX model was accurate and sensitive in detecting imposed Granger causality conditions. In addition, the OPS-based NARX model was more accurate than the least squares method. Application to the systolic blood pressure and heart rate variability signals demonstrated the feasibility of the method. In particular, we found a bilateral causal relationship between the two signals as evidenced by the significant reduction in the PI values with the NARX model prediction compared to the NAR model prediction, which was also confirmed by the surrogate data analysis. Furthermore, we found significant reduction in the complexity of the dynamics of the two causal pathways of the two signals as the body position was changed from the supine to upright. The proposed is a general method, thus, it can be applied to a wide variety of physiological signals to better understand causality and coupling that may be different between normal and diseased conditions.     

A new algorithm developed based on a mixture of spectral and nonlinear techniques for the analysis of heart rate variability

In this paper, an algorithm based on a joint use of spectral and nonlinear techniques for heart rate variability (HRV) analysis is proposed. First, the measured RR data are passed into a trimmed moving average (TMA)-based filtering system to generate a lower frequency (LF) time series and a higher frequency (HF) one that approximately reflect the sympathetic and vagal activities, respectively. Since the Lyapunov exponent can be used to characterize the level of chaos in complex physiological systems, the largest Lyapunov exponents corresponding to the complex sympathetic and vagal systems are then estimated from the LF and HF time series, respectively, using an existing algorithm. Numerical results of a postural maneuver experiment indicate that both characteristic exponents or their combinations might serve as a set of innovative and robust indicators for HRV analysis, even under the contamination of sparse impulses due to aberrant beats in the RR data.     

Automatic measurement of long-term heart rate variability by implanted single-chamber devices

Heart rate variability (HRV) measurement is an established technology for the assessment of cardiac autonomic status. Recently 24 h HRV has been shown to correlate with disease severity in heart failure. This potentially makes continuous 24 h HRV measurement suitable for monitoring of heart-failure patients. Day-to-day 24 h measurement of HRV is, in principle, feasible when implemented using implanted devices (pacemakers and defibrillators) used in patients who are predominantly in the sinus rhythm. However, a number of such devices used in heart-failure patients are single-chamber devices, in which the distinction between sinus rhythm beats and ectopic beats is problematic. The study investigates whether a reasonably accurate 24 h HRV measurement can be achieved by automatic algorithms, suitable for implementation using implanted devices, without the need for identification of ectopic beats. A set of 5321 nominal 24 h Holter recordings of cardiac patients are used. Each of the recordings contains at least one ectopic beat; approximately 30% of the recordings have more than 1% of ectopic beats. Conventional 24 h measures of HRV, that is the SDNN, HRV index, and SDANN indices, are obtained from each recording after elimination of the ectopic beats and are approximated by HRV measures computed by the same formulas without exclusion of the ectopic beats. The SDANN values are also approximated by the standard deviation of 5 min medians of all RR intervals (SDMRR measure). The errors introduced by including the ectopic beats in the HRV computation were evaluated using the Bland-Altman statistics and by Cohen's kappa statistics investigating the precision of identifying patients with depressed and preserved 24 h HRV. The SDNN measure is very sensitive to the quality of the RR interval sequence and cannot be reasonably used without distinction between sinus rhythm and ectopic beats. The HRV index measure is marginally more acceptable when used without ectopic elimination. The SDANN is rather insensitive, and its replacement by SDMRR values leads to relative errors in the region of 2-5% that are almost independent of the number of ectopic beats included. Even in recordings with a substantial proportion of ectopic beats, a practically acceptable (kappa > 0.9) identification of depressed and preserved SDANN values is possible without ectopic elimination. Thus, continuous monitoring of 24 h HRV is technically feasible within implanted devices, provided the SDANN measure is monitored and either computed from the sequence of all RR intervals or, potentially preferably, replaced by the SDMRR measure.     

ECG artifacts and heart period variability: Don't miss a beat!

The impact of artifacts on estimates of heart period variability were evaluated by modeling the effects of missed R-waves and spurious R-wave detections in actual and simulated heart period series. Results revealed that even a single artifact, occurring within a 128-s interbeat interval series, can impart substantial spurious variance into all commonly analyzed frequency bands, including that associated with respiratory sinus arrhythmia. In fact, the spurious variance introduced by a single artifact may be greater than that associated with true basal heart period variability and can far exceed typical effect sizes in psychophysiological studies. The effects of artifacts are not related to a specific analytical method and are apparent in both frequency and time domain analyses. Results emphasize the importance of artifact detection and resolution for studies of heart period variability.     

Sinus rhythm heart rate estimation in high noise environments by application of a priori RR interval statistics

Most existing heart beat detection algorithms serially process peaks, which can be either noise or true beats. Serial processing can result in inaccurate detections in the context of high noise. The proposed method relies on the relative regularity of sinus rhythm RR interval changes to select the best sequences of peaks in a 5–10?s long segment of cardiac data. The best sequences with a current data segment are subjected to a trending analysis, to determine whether their associated RR intervals fit within a pattern of prior best segments. The RR regularity scores and the results of the trending analysis are combined into a single sequence score and the final sequence for a segment is chosen from the best sequences based on this overall score. The current heart rate estimate is updated with the final sequence’s RR interval by an adaptive filter that weights the overall score. Twenty-four hour RR interval records for 54 normal individuals were parsed into 10-s segments and corrupted with spurious ‘noise’ peaks, which resulted in a revised RR interval series that included a number of false RR intervals. The algorithm was run on these corrupted RR interval series. The percentages of mean heart rate values within 5 beats min^?1 of the true value were 95%, 88% and 77% for 10, 20 and 30 added noise spikes, respectively. The percentages of mean heart rate values within 10 beats min^?1 of the true value were 98%, 96% and 91% for 10, 20 and 30 added noise spikes, respectively. Accuracy was higher for data segments characterized by relatively low RR interval variability. The proposed algorithm shows promise for estimating average heart rate for sinus rhythm in high noise environments.     

Heart failure in COVID-19 patients: Critical care experience

Patients with heart failure (HF) may be at a higher risk of coronavirus disease 2019 (COVID-19) infection and may have a worse outcome due to their comorbid conditions and advanced age. In this narrative review, we aim to study the interaction between COVID-19 and HF from a critical care perspective. We performed a systematic search for studies that reported HF and critical care-related outcomes in COVID-19 patients in the PubMed and Medline databases. From a total of 1050 papers, we identified 26 that satisfied the eligibility criteria for our review. Data such as patient demographics, HF, intensive care unit (ICU) admission, management, and outcome were extracted from these studies and analyzed. We reported outcomes in heart-transplant patients with COVID-19 separately. In hospitalized patients with COVID-19, the prevalence of HF varied between 4% and 21%. The requirement for ICU admission was between 8% and 33%. HF patients with COVID-19 had an overall mortality rate between 20% and 40%. We identified that HF is an independent predictor of mortality in hospitalized COVID-19 patients, and patients with HF were more likely to require ventilation, ICU admission and develop complications. Patients with HF with reduced ejection fraction did worse than those with HF with midrange ejection fraction, and HF with preserved ejection fraction. COVID-19 patients with HF should be identified early and managed aggressively in an attempt to improve outcomes in this cohort of patients.     

Spectral analysis of heart rate variability using the integral pulse frequency modulation model

Spectral analysis of heart rate variability (HRV) is a widely accepted approach for assessment of cardiac autonomic function and its relationship to numerous disorders and diseases. As a rule, the non-parametric methods for HRV spectral analysis are tested using the integral pulse frequency modulation (IPFM) model. However, published results with simulated HRV signals show differences requiring further development of the existing methods. With the aim of improving estimation accuracy, an entirely IPFM-based method for HRV analysis is investigated. According to this method, the spectra are computed by finding the least squares solution of two matrix equations that are derived using the IPFM model and involve irregular samples of a signal representing the HRV. The method is validated with various synthesised signals (in all tests, the relative errors of the power estimates at the modulating frequencies are within 3%, and the relative power of the spurious terms is less than 0.8% only) and is furthermore applied to the spectral analysis of RR interval series obtained from diabetic children. The results, with simulated and real HRV signals, show that the developed method yields very accurate estimations of the spectral region below half the mean heart rate. Moreover, it allows the detection and assessment of certain genuine modulating components beyond the traditional frequency limit of the HRV spectra.     

Influence of autoregressive model parameter uncertainty on spectral estimates of heart rate dynamics

Linear autoregressive (AR) model-based heart rate (HR) spectral analysis has been widely used to study HR dynamics. Owing to system and measurement noise, the parameters of an AR model have intrinsic statistical uncertainty. In this study, we evaluate how this AR parameter uncertainty can translate to uncertainty in HR power spectra. HR time series, obtained from seven subjects in supine standing positions, were fitted to AR models by least squares minimization via singular value decomposition. Spectral uncertainty due to inexact parameter estimation was assessed through a Monte Carlo study in which the AR model parameters were varied randomly according to their Gaussian distributions. Histogram techniques were used to evaluate the distribution of 50,000 AR spectral estimates of each HR time series. These Monte Carlo uncertainties were found to exceed those predicted by previous theoretical approximations. It was determined that the uncertainty of AR HR spectral estimates, particularly the locations and magnitudes of spectral peaks, can often be large. The same Monte Carlo analysis was applied to synthetic AR time series and found levels of spectral uncertainty similar to that of the HR data, thus suggesting that the results of this study are not specific to experimental HR data. Therefore, AR spectra may be unreliable, and one must be careful in assigning pathophysiological origins to specific spectral features of any one spectrum.