Heart rate fragmentation gives novel insights into non-autonomic mechanisms governing beat-to-beat control of the heart's rhythm

Lifelong longitudinal assessment of the contribution of multi-fractal fluctuations to heart rate and heart rate variability in aging mice: role of the sinoatrial node and autonomic nervous system

Aging is a major risk factor for sinoatrial node (SAN) dysfunction, which can impair heart rate (HR) control and heart rate variability (HRV). HR and HRV are determined by intrinsic SAN function and its regulation by the autonomic nervous system (ANS). The purpose of this study was to use multi-scale multi-fractal detrended fluctuation analysis (MSMFDFA; a complexity-based approach to analyze multi-fractal dynamics) to longitudinally assess changes in multi-fractal HRV properties and SAN function in ECG time series recorded repeatedly across the full adult lifespan in mice. ECGs were recorded in anesthetized mice in baseline conditions and after autonomic nervous system blockade every three months beginning at 6 months of age until the end of life. MSMFDFA was used to assess HRV and SAN function every three months between 6 and 27 months of age. Intrinsic HR (i.e. HR during ANS blockade) remained relatively stable until 15 months of age, and then progressively declined until study endpoint at 27 months of age. MSMFDFA revealed sudden and rapid changes in multi-fractal properties of the ECG RR interval time series in aging mice. In particular, multi-fractal spectrum width (MFSW, a measure of multi-fractality) was relatively stable between 6 months and 15 months of age and then progressively increased at 27 months of age. These changes in MFSW were evident in baseline conditions and during ANS blockade. Thus, intrinsic SAN function declines progressively during aging and is manifested by age-associated changes in multi-fractal HRV across the lifespan in mice, which can be accurately quantified by MSMFDFA.

Highly comparative time series analysis of oxygen saturation and heart rate to predict respiratory outcomes in extremely preterm infants

Objective.Highly comparative time series analysis (HCTSA) is a novel approach involving massive feature extraction using publicly available code from many disciplines. The Prematurity-Related Ventilatory Control (Pre-Vent) observational multicenter prospective study collected bedside monitor data from>700extremely preterm infants to identify physiologic features that predict respiratory outcomes.Approach. We calculated a subset of 33 HCTSA features on>7 M 10 min windows of oxygen saturation (SPO2) and heart rate (HR) from the Pre-Vent cohort to quantify predictive performance. This subset included representatives previously identified using unsupervised clustering on>3500HCTSA algorithms. We hypothesized that the best HCTSA algorithms would compare favorably to optimal PreVent physiologic predictor IH90_DPE (duration per event of intermittent hypoxemia events below 90%).Main Results.The top HCTSA features were from a cluster of algorithms associated with the autocorrelation of SPO2 time series and identified low frequency patterns of desaturation as high risk. These features had comparable performance to and were highly correlated with IH90_DPE but perhaps measure the physiologic status of an infant in a more robust way that warrants further investigation. The top HR HCTSA features were symbolic transformation measures that had previously been identified as strong predictors of neonatal mortality. HR metrics were only important predictors at early days of life which was likely due to the larger proportion of infants whose outcome was death by any cause. A simple HCTSA model using 3 top features outperformed IH90_DPE at day of life 7 (.778 versus .729) but was essentially equivalent at day of life 28 (.849 versus .850).Significance. These results validated the utility of a representative HCTSA approach but also provides additional evidence supporting IH90_DPE as an optimal predictor of respiratory outcomes.

Highly comparative time series analysis of oxygen saturation and heart rate to predict respiratory outcomes in extremely preterm infants

Objective

Highly comparative time series analysis (HCTSA) is a novel approach involving massive feature extraction using publicly available code from many disciplines. The Prematurity-Related Ventilatory Control (Pre-Vent) observational multicenter prospective study collected bedside monitor data from > 700 extremely preterm infants to identify physiologic features that predict respiratory outcomes. We calculated a subset of 33 HCTSA features on > 7M 10-minute windows of oxygen saturation (SPO2) and heart rate (HR) from the Pre-Vent cohort to quantify predictive performance. This subset included representatives previously identified using unsupervised clustering on > 3500 HCTSA algorithms. Performance of each feature was measured by individual area under the receiver operating curve (AUC) at various days of life and binary respiratory outcomes. These were compared to optimal PreVent physiologic predictor IH90 DPE, the duration per event of intermittent hypoxemia events with threshold of 90%.

Main Results

The top HCTSA features were from a cluster of algorithms associated with the autocorrelation of SPO2 time series and identified low frequency patterns of desaturation as high risk. These features had comparable performance to and were highly correlated with IH90_DPE but perhaps measure the physiologic status of an infant in a more robust way that warrants further investigation. The top HR HCTSA features were symbolic transformation measures that had previously been identified as strong predictors of neonatal mortality. HR metrics were only important predictors at early days of life which was likely due to the larger proportion of infants whose outcome was death by any cause. A simple HCTSA model using 3 top features outperformed IH90_DPE at day of life 7 (.778 versus .729) but was essentially equivalent at day of life 28 (.849 versus .850). These results validated the utility of a representative HCTSA approach but also provides additional evidence supporting IH90_DPE as an optimal predictor of respiratory outcomes.

The connection between heart rate variability (HRV), neurological health, and cognition: A literature review

The heart and brain have bi-directional influences on each other, including autonomic regulation and hemodynamic connections. Heart rate variability (HRV) measures variation in beat-to-beat intervals. New findings about disorganized sinus rhythm (erratic rhythm, quantified as heart rate fragmentation, HRF) are discussed and suggest overestimation of autonomic activities in HRV changes, especially during aging or cardiovascular events. When excluding HRF, HRV is regulated via the central autonomic network (CAN). HRV acts as a proxy of autonomic activity and is associated with executive functions, decision-making, and emotional regulation in our health and wellbeing. Abnormal changes of HRV (e.g., decreased vagal functioning) are observed in various neurological conditions including mild cognitive impairments, dementia, mild traumatic brain injury, migraine, COVID-19, stroke, epilepsy, and psychological conditions (e.g., anxiety, stress, and schizophrenia). Efforts are needed to improve the dynamic and intriguing heart-brain interactions.

Method to quantify the impact of sleep on cardiac neuroautonomic functionality: application to the prediction of cardiovascular events in the Multi-Ethnic Study of Atherosclerosis

We introduce the concept of cardiac neuroautonomic renewability and a method for its quantification. This concept refers to the involuntary nervous system's capacity to improve cardiac control in response to restorative interventions, such as sleep. We used the change in heart rate fragmentation (ΔHRF), before sleep onset compared with after sleep termination, to quantify the restorative effects of sleep. We hypothesized that the ability to improve cardiac neuroautonomic functionality would diminish with age and be associated with lower risk of major adverse cardiovascular events (MACE). We analyzed the ECG channel of polysomnographic recordings from an ancillary investigation of the Multi-Ethnic Study of Atherosclerosis (MESA). In a cohort of 659 participants (mean ± SD age, 69.7 ± 8.8; 42% male), HRF was significantly (P < 0.001) lower after sleep (before: 74 ± 12%, after: 67 ± 13%). Furthermore, the magnitude of the decrease significantly (P < 0.001) diminished with cross-sectional age. In addition, a larger reduction in HRF following sleep (i.e., higher ΔHRF) was associated with lower risk of MACE, independent of traditional cardiovascular risk factors and current measures of sleep quality. Specifically, over a mean follow-up period of 6.4 ± 1.6 yr, in which 60 participants had their first MACE, a one-SD (12%) increase in ΔHRF was associated with a 36% (95% CI: 12%-53%) decrease in the risk of MACE. The results demonstrate the restorative impact of sleep on heart rate control. As such they support the concept of cardiac neuroautonomic renewability and the utility of ΔHRF for its quantification.

Emergence of heartbeat frailty in advanced age I: perspectives from life-long EKG recordings in adult mice

The combined influences of sinoatrial nodal (SAN) pacemaker cell automaticity and its response to autonomic input determine the heart's beating interval variability and mean beating rate. To determine the intrinsic SAN and autonomic signatures buried within EKG RR interval time series change in advanced age, we measured RR interval variability before and during double autonomic blockade at 3-month intervals from 6 months of age until the end of life in long-lived (those that achieved the total cohort median life span of 24 months and beyond) C57/BL6 mice. Prior to 21 months of age, time-dependent changes in intrinsic RR interval variability and mean RR interval were relatively minor. Between 21 and 30 months of age, however, marked changes emerged in intrinsic SAN RR interval variability signatures, pointing to a reduction in the kinetics of pacemaker clock mechanisms, leading to reduced synchronization of molecular functions within and among SAN cells. This loss of high-frequency signal processing within intrinsic SAN signatures resulted in a marked increase in the mean intrinsic RR interval. The impact of autonomic signatures on RR interval variability were net sympathetic and partially compensated for the reduced kinetics of the intrinsic SAN RR interval variability signatures, and partially, but not completely, shifted the EKG RR time series intervals to a more youthful pattern. Cross-sectional analyses of other subsets of C57/BL6 ages indicated that at or beyond the median life span of our longitudinal cohort, noncardiac, constitutional, whole-body frailty was increased, energetic efficiency was reduced, and the respiratory exchange ratio increased. We interpret the progressive reduction in kinetics in intrinsic SAN RR interval variability signatures in this context of whole-body frailty beyond 21 months of age to be a manifestation of "heartbeat frailty."

Heart rate variability as a biomarker in patients with Chronic Chagas Cardiomyopathy with or without concomitant digestive involvement and its relationship with the Rassi score

Background

Dysautonomia plays an ancillary role in the pathogenesis of Chronic Chagas Cardiomyopathy (CCC), but is the key factor causing digestive organic involvement. We investigated the ability of heart rate variability (HRV) for death risk stratification in CCC and compared alterations of HRV in patients with isolated CCC and in those with the mixed form (CCC + digestive involvement). Thirty-one patients with CCC were classified into three risk groups (low, intermediate and high) according to their Rassi score. A single-lead ECG was recorded for a period of 10–20 min, RR series were generated and 31 HRV indices were calculated. The HRV was compared among the three risk groups and regarding the associated digestive involvement. Four machine learning models were created to predict the risk class of patients.

Results

Phase entropy is decreased and the percentage of inflection points is increased in patients from the high-, compared to the low-risk group. Fourteen patients had the mixed form, showing decreased triangular interpolation of the RR histogram and absolute power at the low-frequency band. The best predictive risk model was obtained by the support vector machine algorithm (overall F1-score of 0.61).

Conclusions

The mixed form of Chagas' disease showed a decrease in the slow HRV components. The worst prognosis in CCC is associated with increased heart rate fragmentation. The combination of HRV indices enhanced the accuracy of risk stratification. In patients with the mixed form of Chagas disease, a higher degree of sympathetic autonomic denervation may be associated with parasympathetic impairment.

Cardiovascular waveforms - can we extract more from routine signals?

Cardiovascular waveforms such as blood pressure, ECG and photoplethysmography (PPG), are routinely acquired by specialised monitoring devices. Such devices include bedside monitors, wearables and radiotelemetry which sample at very high fidelity, yet most of this numerical data is disregarded and focus tends to reside on single point averages such as the maxima, minima, amplitude, rate and intervals. Whilst, these measures are undoubtedly of value, we may be missing important information by simplifying the complex waveform signal in this way. This Special Collection showcases recent advances in the appraisal of routine signals. Ultimately, such approaches and technologies may assist in improving the accuracy and sensitivity of detecting physiological change. This, in turn, may assist with identifying efficacy or safety signals for investigational new drugs or aidpatient diagnosis and management, supporting scientific and clinical decision making.

Heart rate fragmentation, a novel approach in heart rate variability analysis, is altered in rats 4 and 12 weeks after myocardial infarction

An approach recently proposed to assess ultra-rapid patterns of heart rate variability, namely heart rate fragmentation (HRF), is increased in aging and coronary disease. We aimed to evaluate and to correlate HRF with cardiac functional parameters in a rat model of myocardial infarction (MI). Wistar rats were submitted to MI (n = 18) or sham operation (n = 20), and after 4 or 12 weeks, their arterial pressure was recorded. Subsequently, cardiac function was evaluated by echocardiography. From pulse interval series, HRF patterns with zero, one, two, or three inflection points were estimated, as well as the total percentage of inflection points (PIP). Cardiac function was reduced in MI rats. Ejection fraction was smaller 4 (28 ± 3 vs 68 ± 2%, p < 0.0001) and 12 weeks after MI (38 ± 3 vs 70 ± 3%, p < 0.0001). Fractional shortening was also smaller 4 (13 ± 2 vs 41 ± 2%, p < 0.0001) and 12 weeks after MI (20 ± 2 vs 41 ± 3%, p < 0.0001). PIP was increased in MI rats 4 (74 ± 2 vs 69 ± 1%, p = 0.03) and 12 weeks after surgery (70 ± 2 vs 63 ± 1%, p = 0.02). We found a significant negative correlation between cardiac functional parameters and HRF at both 4 and 12 weeks after MI. These findings reveal that MI increases HRF, reinforcing the importance of this approach to explore pathophysiological conditions. Evaluation of heart rate fragmentation (HRF) in a rat model of myocardial infarction (MI). Wistar rats were submitted to MI (n = 18) or sham operation (n = 20), and after 4 or 12 weeks, their arterial pressure was recorded. Cardiac function was evaluated by echocardiography. From pulse interval series, HRF patterns with zero (W0), one (W1), two (W3), or three (W3) inflection points were estimated, as well as the total percentage of inflection points (PIP). Cardiac function was reduced, while PIP was increased in all MI rats. Fluent patterns (W0 and W1) decreased in MI rats after 12 weeks. Altogether, the findings reveal that MI increases HRF, reinforcing the potential of this approach to explore pathophysiological conditions.