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

Device-evaluated autonomic nervous function for predicting ventricular arrhythmias and all-cause mortality in patients who underwent cardiac resynchronization therapy-defibrillator

Introduction: Autonomic nervous system (ANS) function quantified by heart rate variability (HRV) was associated with long-term prognosis, but it was rarely used in the evaluation of patients with heart failure, especially those with cardiac resynchronization therapy-defibrillator (CRT-D) implantation. This study aimed to describe the changes in ANS function among patients who underwent CRT-D with remote home monitoring function, and explore predictive value of HRV for ventricular tachyarrhythmias (VTAs) and all-cause mortality. Method: Patients who underwent CRT-D implantation were included. Device-measured all-day HR, night-time HR, and HRV (measured by the standard deviation of the atrial-atrial sensed intervals) were used to quantify ANS function. Multivariate Cox proportional hazards models were fitted to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) of VTAs or all-cause mortality in relation to ANS function at baseline and 6 months post-implantation. The cutoff value was determined using restrictive cubic splines. Multivariable logistic regression was further established to determine factors influencing postoperative HRV. Results: A total of 170 patients treated with CRT-D were eligible for analysis. During a median follow-up period of 50.8 months, 61 patients died and 69 patients experienced at least one spontaneous episode of VTAs. At 6 months after CRT implantation, 114 patients showed improvement in HRV, increasing from 66.4 ± 19.4 ms to 76.7 ± 21.2 ms. The postoperative HRV was associated with both all-cause mortality (HRs: 0.983; 95% CI: 0.968 to 0.998, p = 0.012) and VTAs (HRs: 0.973; 95% CI: 0.954 to 0.993, p = 0.008), and the relative risk would significantly increase when the postoperative HRV lower than 75 ms. After adjusting for basic ANS function and possible influencing factors, patients without diabetes (p = 0.018) and with higher daily physical activity (p = 0.041) could maintain higher postoperative HRV after CRT implantation. Conclusion: More than two-thirds of heart failure patients showed improvement in ANS function following CRT treatment. However, patients with diabetes and low daily physical activity levels have difficulty maintaining a higher postoperative HRV, which is associated with a worse clinical outcome.

Occurrence of atrial fibrillation in pacemaker patients and its association with sleep apnea and heart rate variability

AIMS

Sleep apnea (SA) is a risk factor for atrial fibrillation (AF) occurrence. Sympathovagal imbalance is a mechanism that predisposes to the development of AF and that occurs in SA. Some pacemakers can detect SA events and continuously measure a time domain measure of heart rate variability (HRV), i.e. the standard deviation of 5-min median atrial-atrial sensed intervals (SDANN). We evaluated the association between the occurrence of AF and device-detected SA and SDANN in patients who received pacemakers.

METHODS

We enrolled 150 consecutive patients undergoing implantation of a dual-chamber pacemaker, capable of SA and SDANN estimation. The SA was defined as severe if the Respiratory Disturbance Index was ≥30 episodes/h for at least one night during the first week after implantation.

RESULTS

Sixteen patients in permanent AF were excluded from our analysis. During follow-up, AF (cumulative device-detected AF duration > 6 h/day) occurred in 24(18%) patients out of the remaining 134 patients. Severe SA was detected in 84 patients. SDANN values were available in 74 patients and the median value was 76 ms [25°-75°percentile:58-77]. The risk of AF was higher in patients with severe SA (log-rank test; p = .033). The presence of either or both conditions (severe SA and SDANN < 76 ms) was associated with shorter time to AF event (p = .042) and was an independent predictor of AF (hazard ratio: 2.37; 95%CI:1.08 to 5.21; p = .033).

CONCLUSION

In pacemaker patients, device-diagnosed severe SA and reduced SDANN are associated with a higher risk of AF.

Implementation and verification of an enhanced algorithm for the automatic computation of RR-interval series derived from 24 h 12-lead ECGs

An important tool in early diagnosis of cardiac dysfunctions is the analysis of electrocardiograms (ECGs) obtained from ambulatory long-term recordings. Heart rate variability (HRV) analysis became a significant tool for assessing the cardiac health. The usefulness of HRV assessment for the prediction of cardiovascular events in end-stage renal disease patients was previously reported. The aim of this work is to verify an enhanced algorithm to obtain an RR-interval time series in a fully automated manner. The multi-lead corrected R-peaks of each ECG lead are used for RR-series computation and the algorithm is verified by a comparison with manually reviewed reference RR-time series. Twenty-four hour 12-lead ECG recordings of 339 end-stage renal disease patients from the ISAR (rISk strAtification in end-stage Renal disease) study were used. Seven universal indicators were calculated to allow for a generalization of the comparison results. The median score of the indicator of synchronization, i.e. intraclass correlation coefficient, was 96.4% and the median of the root mean square error of the difference time series was 7.5 ms. The negligible error and high synchronization rate indicate high similarity and verified the agreement between the fully automated RR-interval series calculated with the AIT Multi-Lead ECGsolver and the reference time series. As a future perspective, HRV parameters calculated on this RR-time series can be evaluated in longitudinal studies to ensure clinical benefit.

The degree of heart rate asymmetry is crucial for the validity of the deceleration and acceleration capacity indices of heart rate: A model-based study

The deceleration capacity (DC) and acceleration capacity (AC) of heart rate are a pair of indices used for evaluating the autonomic nervous system (ANS). We assessed the role of heart rate asymmetry (HRA) in defining the relative performance of DC and AC using a mathematical model, which is able to generate a realistic RR interval (RRI) time series with controlled ANS states. The simulation produced a set of RRI series with random sympathetic and vagal activities. The multi-scale DCs and ACs were computed from the RRI series, and the correlation of DC and AC with the ANS functions was analyzed to evaluate the performance of the indices. In the model, the HRA level was modified by changing the inspiration/expiration (I/E) ratio to examine the influence of HRA on the performances of DC and AC. The results show that on the conventional scales (T=1, s=2), an HRA level above 50% results in a stronger association of DC with the ANS, compared with AC. On higher scales (T=4, s=6), there was no HRA and DC showed a similar performance to AC for all I/E ratios. The data suggest that the HRA level determines which of DC or AC is the optimal index for expressing ANS functions. Future clinical applications of DC and AC should be accompanied by an HRA analysis to provide a better index for assessing ANS.

Automatic filtering of outliers in RR intervals before analysis of heart rate variability in Holter recordings: a comparison with carefully edited data

BACKGROUND

Undetected arrhythmic beats seriously affect the power spectrum of the heart rate variability (HRV). Therefore, the series of RR intervals are normally carefully edited before HRV is analysed, but this is a time consuming procedure when 24-hours recordings are analysed. Alternatively, different methods can be used for automatic removal of arrhythmic beats and artefacts. This study compared common frequency domain indices of HRV when determined from manually edited and automatically filtered RR intervals.

METHODS AND RESULTS

Twenty-four hours Holter recordings were available from 140 healthy subjects of age 1-75 years. An experienced technician carefully edited all recordings. Automatic filtering was performed using a recursive procedure where RR intervals were removed if they differed from the mean of the surrounding RR intervals with more than a predetermined limit (ranging from 10% to 50%). The filtering algorithm was evaluated by replacing 1% of the beats with synthesised ectopic beats. Power spectral analysis was performed before and after filtering of both the original edited data and the noisy data set. The results from the analysis using the noisy data were used to define an age-based filtering threshold. The age-based filtration was evaluated with completely unedited data, generated by removing all annotations from the series of RR intervals, and then comparing the resulting HRV indices with those obtained using edited data. The results showed equivalent results after age-based filtration of both the edited and unedited data sets, where the differences in HRV indices obtained by different preprocessing methods were small compared to the mean values within each age group.

CONCLUSIONS

The study showed that it might not be necessary to perform the time-consuming careful editing of all detected heartbeats before HRV is analysed in Holter recordings.In most subjects, it is sufficient to perform the regular editing needed for valid arrhythmia analyses, and then remove undetected ectopic beats and artefacts by age-based filtration of the series of RR intervals, particularly in subjects older than 30 years.

Nonlinear additive autoregressive model-based analysis of short-term heart rate variability

In this contribution we test the hypothesis that nonlinear additive autoregressive model-based data analysis improves the diagnostic ability based on short-term heart rate variability. For this purpose, a nonlinear regression approach, namely, the maximal correlation method is applied to the data of 37 patients with dilated cardiomyopathy as well as of 37 age- and sex-matched healthy subjects. We find that this approach is a powerful tool in discriminating both groups and promising for further model-based analyses.

Moderate Exercise Induces Different Autonomic Modulations of Sinus and AV Node

BACKGROUND

The routine determination of heart rate variability (HRV) from surface ECGs is based on RR intervals because of the difficulty to precisely locate the P-wave fiducial point on surface ECG recordings. The aim of the study was to assess the changes of RR, PP, and PR intervals at rest and during moderate exercise. The time intervals were determined from atrial and ventricular pacemaker-mediated intracardiac electrograms.

METHODS

Ten patients in sinus rhythm with intrinsic AV node conduction who had received the dual-chamber pacemaker Logos (Biotronik) were included. High-resolution atrial and ventricular intracardiac electrograms were transmitted at rest in supine position and during walking to a portable external recorder. Recording sequences of 150 successive heart cycles were used for HRV analyses after computer-assisted triggering of P and R events. The HRV-index SDNN and power spectral densities for the low (LF; 0.04-0.15 Hz) as well as high (HF; 0.15-0.40 Hz) frequency bands were determined.

RESULTS

SDNN decreased from 26.0 +/- 8.1 ms at rest to 18.3 +/- 4.2 ms during exercise for the PP intervals (P < 0.05) and from 26.8 +/- 8.1 to 18.4 +/- 4.1 ms for the RR intervals (P < 0.05). The LF/HF ratio increased from 2.02 +/- 1.3 to 4.5 +/- 1.5 in the atrium (P < 0.05) and from 2.0 +/- 1.2 to 5.2 +/- 1.9 in the ventricle P < 0.05). Comparing atrial and ventricular HRV at both activity levels, no significant differences were observed for the power of LF and HF spectral components. Regarding the PR intervals SDNN, the total power and the LF/HF ratio did not significantly change during exercise.

CONCLUSIONS

The described technique enabled to record intracardiac electrograms not only at rest, but also during moderate exercise and to use them for HRV evaluation. The changes of PP and RR, but not of the PR intervals, during exercise indicate that autonomic inputs to the sinus node and AV node are independent from each other. The ventricular HRV seems to derive mainly from variations of the sinus node pulse formation.

An Approach to Measure Atrial and Ventricular Heart Rate Variability Using Pacemaker-Mediated Intracardiac Electrograms

Heart rate variability (HRV) measurements are usually performed from ventricular beat-to-beat intervals because of the difficulty to precisely locate the P wave fiducial point in surface ECG recordings. The aim of the study was to describe an approach to determine the atrial and ventricular HRV using pacemaker-mediated intracardiac electrograms. Twelve patients with the dual chamber pacemaker Logos were included. The atrial and ventricular intracardiac electrograms were transmitted with the high resolution telemetry channel of the pacemaker to an external recorder for 20 minutes while the patients were supine. During the measurements the patients were in sinus rhythm with intrinsic AV conduction. After computer assisted triggering of the atrial and ventricular events, the resultant intervals were used to calculate the standard deviation of all NN intervals (SDNN), the square root of the mean squared differences of successive NN intervals (RMSSD), and the percentage of successive interval differences >50 ms (pNN50). The differences between atrial and ventricular HRV-Indexes were assessed for each patient with a cut-off point of 1%. Differences >1% were analyzed in detail. A total of 15,504 heart cycles were analyzed. A manual correction due to false or not triggered atrial or ventricular events was necessary in 0.9%. The overall difference between atrial and ventricular pNN50 was-0.5%+/-2.1%and differences >1% were observed in 4 patients. The NN50 events occurred in the atrial as well as in the related ventricular interval in 84%. NN50 events occurred only in the atrium in 6% and only in the ventricle in 10%. The mean differences between atrial and ventricular SDNN and RMSSD were 0.4+/-2.1 ms and-0.1+/-3.5 ms with intra-individual differences <1%. The present study described a new method and demonstrated its feasibility to determine atrial as well as ventricular HRV from pacemakermediated intracardiac electrograms. The differences for pNN50 indicate that ventricular HRV did not reflect the changes of sinus node activity in all patients.