The need to monitor the aging process as a risk factor for disease and mortality beyond chronological age (CA) has led to numerous investigations into the estimation of the biological age (BA) of individuals. However, the accuracy of BA estimation tools is often judged by their ability to approximate CA, questioning their value in capturing the variance in health status and thus correctly estimating BA. Their biological relevance is often assessed in relation to health outcomes or mortality, underexploiting their potential for real-time monitoring of BA. Furthermore, their complexity may limit their clinical translation to large populations. Here, we describe the gene expression-based age monitoring Clock (GamC), a simple biomarker of aging (BOA), and characterize its biological relevance with synchronous cardiovascular (CV) health-related functional data. GamC is calculated from the expression levels of three genes consistently dysregulated with age in blood (ABLIM1, CCR7, and LEF1). GamC shows moderate but reliable association with CA in three independent cohorts, supported by transcriptome-wide changes. It demonstrates specialized biological meaning, as it specifically describes current physical activity levels, but poorly correlates with autonomic nervous system function, both age-related factors associated with CV health. Finally, it expresses BA monitoring capacity by modestly responding to an effective exercise-based intervention in centenarians. In conclusion, GamC is proposed as a simple and affordable candidate BOA for reporting the individuals' current CV health-related BA, thus promoting its broad translation and application into measures aimed at promoting healthy aging in relation to CV health, the leading cause of death worldwide.

Accurate monitoring of heart rate (HR) during various activities is crucial for assessing cardiovascular health and optimizing performance. In this study, we evaluated the accuracy of a wristband PPG device for estimating HR across different activity conditions. Seventeen subjects participated, each equipped with a wristband PPG device and an ECG as a reference, while performing office tasks, walking, jogging, and running. We compared the estimated HR provided by the PPG device to the HR estimated from the synchronously recorded ECG, calculating the relative errors ($E_{r}$) for each activity phase. During office tasks, 100% of the recordings exhibited $E_{r}$ below 10% and 5%. However, during walking, this percentage decreased to 69% and 63%, and further dropped to 50% and $\mathbf{3 8 \%}$ for jogging, respectively. In this work the HR provided by the wristband PPG device was validated. However, given that the device provides the raw PPG and accelerometer signals, further development of signal processing algorithms may improve HR estimation.

Aims Obstructive sleep apnoea (OSA) imposes significant stress on the cardiovascular system and the heart. While long-term cardiac effects are understood, the immediate impact of hypoxaemia on the heart's electrophysiology lacks understanding. Our study aims to explore desaturation severity on cardiovascular repolarisation. Methods We retrospectively analysed ECGs from full diagnostic polysomnographies from 492 patients with suspected OSA. The analyses were conducted before, during and after 9137 nocturnal apnoea- or hypopnoea-related desaturations. The mean and sd of T-wave amplitude change from the baseline level to the level during and after desaturations (ΔTamp_mean and ΔTamp_SD) were calculated. To investigate the modulatory effects of desaturation severity, the data were divided into subgroups based on the desaturation duration (Tdes; 10 s≤Tdes<20 s, 20 s≤Tdes<30 s, 30 s≤Tdes<45 s and Tdes≥45 s) and magnitude of blood oxygen saturation drop (change in peripheral oxygen saturation (ΔSpO2); 3%≤ΔSpO2<4.5%, 4.5%≤ΔSpO2<6%, 6%≤ΔSpO2<7.5% and ΔSpO2≥7.5%) for men and women. Results Desaturations caused significant (p<0.01) changes in ΔTamp_mean during and after desaturations. In men, the median ΔTamp_mean during and after deep (ΔSpO2≥7.5%) desaturations were 21 µV and 24 µV, respectively. In women, the median ΔTamp_mean in deep desaturations was 15 µV during and 21 µV after desaturations. Similarly, the ΔTamp_SD increased during and after deep desaturations. In regression analysis, the desaturation depth was an independent predictor for ventricular repolarisation instability. Conclusion We found an association between the severity of nocturnal desaturations and cardiac repolarisation instability. These findings hold particular importance, as repolarisation instability has been linked with cardiovascular morbidity and could potentially serve as a trigger for arrhythmias and sudden cardiac death.

Obstructive sleep apnea (OSA) is associated with the progression of cardiovascular diseases, arrhythmias, and sudden cardiac death (SCD). However, the acute impacts of OSA and its consequences on heart function are not yet fully elucidated. We hypothesized that desaturation events acutely destabilize ventricular repolarization, and the presence of accompanying arousals magnifies this destabilization. Ventricular repolarization lability measures, comprising heart rate corrected QT (QTc), short-time-variability of QT (STVQT), and QT variability index (QTVI), were calculated before, during, and after 20,955 desaturations from lead II electrocardiography signals of 492 patients with suspected OSA (52% men). Variations in repolarization parameters were assessed during and after desaturations, both with and without accompanying arousals, and groupwise comparisons were performed based on desaturation duration and depth. Regression analyses were used to investigate the influence of confounding factors, comorbidities, and medications. The standard deviation (SD) of QT, mean QTc, SDQTc, and STVQT increased significantly (P < 0.01), whereas QTVI decreased (P < 0.01) during and after desaturations. The changes in SDQT, mean QTc, SDQTc, and QTVI were significantly amplified (P < 0.01) in the presence of accompanying arousals. Desaturation depth was an independent predictor of increased SDQTc (beta = 0.405, P < 0.01), STVQT (beta = 0.151, P < 0.01), and QTVI (beta = 0.009, P < 0.01) during desaturation. Desaturations cause acute changes in ventricular repolarization, with deeper desaturations and accompanying arousals independently contributing to increased ventricular repolarization lability. This may partially explain the increased risk of arrhythmias and SCD in patients with OSA, especially when the OSA phenotype includes high hypoxic load and fragmented sleep.

Heart rate variability (HRV) has been studied for decades in clinical environments. Currently, the exponential growth of wearable devices in health monitoring is leading to new challenges that need to be solved. These devices have relatively poor signal quality and are affected by numerous motion artifacts, with data loss being the main stumbling block for their use in HRV analysis. In the present paper, it is shown how data loss affects HRV metrics in the time domain and frequency domain and Poincaré plots. A gap-filling method is proposed and compared to other existing approaches to alleviate these effects, both with simulated (16 subjects) and real (20 subjects) missing data. Two different data loss scenarios have been simulated: (i) scattered missing beats, related to a low signal to noise ratio; and (ii) bursts of missing beats, with the most common due to motion artifacts. In addition, a real database of photoplethysmography-derived pulse detection series provided by Apple Watch during a protocol including relax and stress stages is analyzed. The best correction method and maximum acceptable missing beats are given. Results suggest that correction without gap filling is the best option for the standard deviation of the normal-to-normal intervals (SDNN), root mean square of successive differences (RMSSD) and Poincaré plot metrics in datasets with bursts of missing beats predominance (p<0.05), whereas they benefit from gap-filling approaches in the case of scattered missing beats (p<0.05). Gap-filling approaches are also the best for frequency-domain metrics (p<0.05). The findings of this work are useful for the design of robust HRV applications depending on missing data tolerance and the desired HRV metrics.

The ideal moment to withdraw respiratory supply of patients under Mechanical Ventilation at Intensive Care Units (ICU), is not easy to be determined for clinicians. Although the Spontaneous Breathing Trial (SBT) provides a measure of the patients’ readiness, there is still around 15–20% of predictive failure rate. This work is a proof of concept focused on adding new value to the prediction of the weaning outcome. Heart Rate Variability (HRV) and Cardiopulmonary Coupling (CPC) methods are evaluated as new complementary estimates to assess weaning readiness. The CPC is related to how the mechanisms regulating respiration and cardiac pumping are working simultaneously, and it is defined from HRV in combination with respiratory information. Three different techniques are used to estimate the CPC, including Time-Frequency Coherence, Dynamic Mutual Information and Orthogonal Subspace Projections. The cohort study includes 22 patients in pressure support ventilation, ready to undergo the SBT, analysed in the 24 h previous to the SBT. Of these, 13 had a successful weaning and 9 failed the SBT or needed reintubation –being both considered as failed weaning. Results illustrate that traditional variables such as heart rate, respiratory frequency, and the parameters derived from HRV do not differ in patients with successful or failed weaning. Results revealed that HRV parameters can vary considerably depending on the time at which they are measured. This fact could be attributed to circadian rhythms, having a strong influence on HRV values. On the contrary, significant statistical differences are found in the proposed CPC parameters when comparing the values of the two groups, and throughout the whole recordings. In addition, differences are greater at night, probably because patients with failed weaning might be experiencing more respiratory episodes, e.g. apneas during the night, which is directly related to a reduced respiratory sinus arrhythmia. Therefore, results suggest that the traditional measures could be used in combination with the proposed CPC biomarkers to improve weaning readiness.

Stress test electrocardiogram (ECG) analysis is widely used for coronary artery disease (CAD) diagnosis despite its limited accuracy. Alterations in autonomic modulation of cardiac electrical activity have been reported in CAD patients during acute ischemia. We hypothesized that those alterations could be reflected in changes in ventricular repolarization dynamics during stress testing that could be measured through QT interval variability (QTV). However, QTV is largely dependent on RR interval variability (RRV), which might hinder intrinsic ventricular repolarization dynamics. In this study, we investigated whether different markers accounting for low-frequency (LF) oscillations of QTV unrelated to RRV during stress testing could be used to separate patients with and without CAD. Power spectral density of QTV unrelated to RRV was obtained based on time-frequency coherence estimation. Instantaneous LF power of QTV and QTV unrelated to RRV were obtained. LF power of QTV unrelated to RRV normalized by LF power of QTV was also studied. Stress test ECG of 100 patients were analysed. Patients referred to coronary angiography were classified into non-CAD or CAD group. LF oscillations in QTV did not show significant differences between CAD and non-CAD groups. However, LF oscillations in QTV unrelated to RRV were significantly higher in the CAD group as compared with the non-CAD group when measured during the first phases of exercise and last phases of recovery. ROC analysis of these indices revealed area under the curve values ranging from 61 to 73%. Binomial logistic regression analysis revealed LF power of QTV unrelated to RRV, both during the first phase of exercise and last phase of recovery, as independent predictors of CAD. In conclusion, this study highlights the importance of removing the influence of RRV when measuring QTV during stress testing for CAD identification and supports the added value of LF oscillations of QTV unrelated to RRV to diagnose CAD from the first minutes of exercise. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.

Periodic repolarization dynamics (PRD) is a novel electrocardiographic marker of cardiac repolarization instability with powerful risk stratification capacity for total mortality and sudden cardiac death. Here, we use a time-frequency analysis approach to continuously quantify PRD at rest and during exercise, assess its dependence on heart rate variability (HRV) and characterize the effects of age (young adults/middle-aged adults/older adults), body mass index (non-overweight/overweight) and cardiorespiratory fitness level (fit/unfit). Sixty-six male volunteers performed an exercise test. RR and dT variabilities (RRV, dTV), as well as the fraction of dT variability unrelated to RR variability, were computed based on time-frequency representations. The instantaneous LF power of dT (PdTV), representing the same concept as PRD, and of its RRV-unrelated component (PdTVuRRV) were quantified. dT angle was found to mostly oscillate in the LF band. Overall, 50–70% of PdTV was linearly unrelated to RRV. The onset of exercise caused a sudden increase in PdTV and PdTVuRRV, which returned to pre-exercise levels during recovery. Clustering analysis identified a group of overweight and unfit individuals with significantly higher PdTV and PdTVuRRV values at rest than the rest of the population. Our findings shed new light on the temporal profile of PRD during exercise, its relationship to HRV and the differences in PRD between subjects according to phenotypic characteristics.

This work aims to validate the Polar H7 heart rate (HR) sensor for heart rate variability (HRV) analysis at rest and during various exercise intensities in a cohort of male volunteers with different age, body composition and fitness level. Cluster analysis was carried out to evaluate how these phenotypic characteristics influenced HR and HRV measurements. For this purpose, sixty-seven volunteers performed a test consisting of the following consecutive segments: sitting rest, three submaximal exercise intensities in cycle-ergometer and sitting recovery. The agreement between HRV indices derived from Polar H7 and a simultaneous electrocardiogram (ECG) was assessed using concordance correlation coefficient (CCC). The percentage of subjects not reaching excellent agreement (CCC > 0.90) was higher for high-frequency power (PHF) than for low-frequency power (PLF) of HRV and increased with exercise intensity. A cluster of unfit and not young volunteers with high trunk fat percentage showed the highest error in HRV indices. This study indicates that Polar H7 and ECG were interchangeable at rest. During exercise, HR and PLF showed excellent agreement between devices. However, during the highest exercise intensity, CCC for PHF was lower than 0.90 in as many as 60% of the volunteers. During recovery, HR but not HRV measurements were accurate. As a conclusion, phenotypic differences between subjects can represent one of the causes for disagreement between HR sensors and ECG devices, which should be considered specifically when using Polar H7 and, generally, in the validation of any HR sensor for HRV analysis.

The purpose of this work is to study the feasibility of estimating the first and second ventilatory thresholds (VT1 and VT2, respectively) by using electrocardiogram (ECG)-derived respiratory rate during exercise testing. The ECGs of 25 healthy volunteers during cycle ergometer exercise test with increasing workload were analyzed. Time-varying respiratory rate was estimated from an ECG-derived respiration signal obtained from QRS slopes' range method. VT1 and VT2 were estimated as the points of maximum change in respiratory rate slope using polynomial spline smoothing. Reference VT1 and VT2 were determined from the ventilatory equivalents of $O_{2}$ and $CO_{2}$. Estimation errors (in watts) of -13.96 (54.84) W for VT1 and -8.06 (39.63) Wfor VT2 (median (interquartile range)) were obtained, suggesting that ventilatory thresholds can be estimated from solely the ECG signal.

This study focuses on the application of a non-immersive virtual reality (VR)-based neurocognitive intervention in critically ill patients. Our aim was to assess the feasibility of direct outcome measures to detect the impact of this digital therapy on patients’ cognitive and emotional outcomes. Seventy-two mechanically ventilated adult patients were randomly assigned to the “treatment as usual” (TAU, n = 38) or the “early neurocognitive stimulation” (ENRIC, n = 34) groups. All patients received standard intensive care unit (ICU) care. Patients in the ENRIC group also received adjuvant neurocognitive stimulation during the ICU stay. Outcome measures were a full neuropsychological battery and two mental health questionnaires. A total of 42 patients (21 ENRIC) completed assessment one month after ICU discharge, and 24 (10 ENRIC) one year later. At one-month follow-up, ENRIC patients had better working memory scores (p = 0.009, d = 0.363) and showed up to 50% less non-specific anxiety (11.8% vs. 21.1%) and depression (5.9% vs. 10.5%) than TAU patients. A general linear model of repeated measures reported a main effect of group, but not of time or group–time interaction, on working memory, with ENRIC patients outperforming TAU patients (p = 0.008, ηp2 = 0.282). Our results suggest that non-immersive VR-based neurocognitive stimulation may help improve short-term working memory outcomes in survivors of critical illness. Moreover, this advantage could be maintained in the long term. An efficacy trial in a larger sample of participants is feasible and must be conducted.

Under the hypothesis that sympathetic control of ventricular repolarization may change once the second ventilatory threshold (VT2) has been reached, a novel methodology for non-invasive VT2 estimation based on the analysis of the T wave from the electrocardiogram (ECG) is proposed, and potential underlying physiological mechanisms are suggested. 25 volunteers (33.4 ± 5.2 years) underwent an incremental power cycle ergometer test (25 W/minute). During the test, respiratory gas exchange and multi-lead ECG were acquired. The former was employed to determine VT2, used here as a reference, whereas the latter was used to compute the temporal profiles of an index of ventricular repolarization instability (dT) and its low-frequency (LF) oscillations (LFdT). The sudden increases observed in dT and LFdT profiles above an established heart rate threshold were employed to derive VT2 estimates, referred to as VT2d T and VT2LF d T , respectively. Estimation errors of -4.7 ± 25.2 W were obtained when considering VT2d T . Errors were lower than the one-minute power increment of 25 W in 68% of the subjects and lower than 50 W in 89.5% of them. When using VT2LF d T , estimation error was of 15.3 ± 32.4 W. Most of the subjects shared common characteristic dT and LFdT profiles, which could be reflecting changes in the autonomic control of ventricular repolarization before and after reaching VT2. The analysis of ventricular repolarization dynamics during exercise allows non-invasive ECG-based estimation of VT2, possibly in relation to changes in the autonomic control of ventricular electrical activity when VT2 is reached.

Actualmente, la Prueba de Respiración Espontánea (SBT), indica si un paciente está listo para el destete de la ventilación mecánica. Sin embargo, en torno al 20% de los pacientes “preparados” para ser desintubados, realmente no lo estaban. En este trabajo, se ha estudiado la Capacidad del Barorreflejo durante la hora anterior a la SBT, y se ha visto que existen diferencias significativas entre los pacientes listos para el destete y los que realmente no lo estaban, sugiriendo que la predicción del éxito del destete puede ser mejorada con este nuevo índice.

VoluMetrix has developed a new version of its NIVA, a wrist device that measures pressure variations in the veins together with the photoplethysmogram (PPG) at the same point. Previous studies have shown that the venous pressure signal (NIVA) reflected an increased HF power with respect to the electrocardiogram. This suggests that it may be useful for parasympathetic characterization guided by signal-derived respiration. Performance of NIVA signal is compared to that of PPG in a controlled breathing experiment (8 subjects) with different respiratory rates (6, 12 and 18 bpm), where a downward trend in parasympathetic estimates is expected with increasing respiratory rates. The NIVA signal is able to accurately estimate the respiratory rate (less than 0.03 Hz estimation error) in all the subjects, outperforming the PPG in the same task. In addition, respiratory-guided parasympathetic estimates significantly decreases with increased respiratory rate.

BACKGROUND: This study examined the effects of a mountain ultra-marathon (MUM) on the activity of the autonomous nervous system through heart rate variability (HRV) monitoring and determined whether this variable related to final performance. METHODS: Heart rate and HRV were measured in eight male amateur runners (aged 37-60 years). Measurements were recorded before and after the event, in resting conditions, as well as continuously throughout the whole MUM. In addition, percentage (%) of heart rate reserve (HRres) and partial and total times during the race were analyzed. RESULTS: Average heart rate (HRavg) measured at rest was increased after the event (+37%). Standard deviation of successive differences (SDSD) and the square root of the mean squared differences of successive NN intervals (RMSSD) were reduced after the MUM (-56% and -59%, respectively). There was a positive relationship between the frequency-domain index normalized low frequency power (P (LFn)) measured at rest before the event and race time (0.79) while there was a negative relationship between race time and the difference in HRavg before and after the event. In the last half of the event, there was a high correlation (Spearman coefficient of correlation >0.9) between race time and the standard deviation of the NN intervals (SDNN) registered during the race. CONCLUSIONS: Autonomous cardiac regulation can be related to the performance in a mountain ultra-marathon. HRV monitoring could represent a practical tool for the evaluation of the relationship between the autonomous nervous system activity and performance in a mountain ultra-marathon.

Monitoring of cardiac and respiratory activity results crucial in clinical settings. However, specific, expensive and usually wired devices are required, which results in cumbersome configurations. Therefore, there is a large research interest in the development of noninvasive monitoring systems. Some authors have proposed the use of fiber-optic speckle interferometry, enabling for contact-less patient monitoring. In this study, we exploited this technology, and explored the performance of different fiber-optic configurations, in combination with robust signal processing algorithms, for heart and respiratory rates estimation. Several configurations were tested, all of them resulting in very low estimation errors. A qualitative analysis revealed that our system can be suited to track fast changes in respiration, as well as the presence of apneas. Such a system might be useful for noninvasive patient monitoring, specially in overnight recordings (e.g., OSAS or AF) or specific scenarios, such as magnetic resonances.

Centenarians are the paradigm of human extreme longevity and healthy aging, because they have postponed, if not avoided, mayor age-related diseases. The purpose of this study was to investigate potential differences in resting heart rate variability (HRV) between young adults, octogenarians, and centenarians and assess whether HRV variables are predictors of all-cause mortality in centenarians. To this end, three groups of participants: young adults (N = 20; 20.6 ± 2.3 years), octogenarians (N = 18; 84.1 ± 2.6 years), and centenarians (N = 17; 101.9 ± 1.9 years) were monitored for 15 min at rest (seated, without moving or talking) to measure RR intervals, from which HRV was evaluated. Our results showed a clear decrease with age in the main parasympathetic HRV variables, as well as in the standard deviation (SD) of the RR series [SD of normal-to-normal interval (SDNN)] and in low frequency (LF) heart rate (HR) oscillations, although differences between octogenarians and centenarians did not reach statistical significance. In 14 centenarians followed until death, only SDNN showed significant correlation (ρ = 0.536; p = 0.048) with survival prognosis. Additionally, SDNN <19 ms was associated with early mortality (≤1 year) in centenarians (Hazard Ratio = 5.72). In conclusion, HRV indices reflecting parasympathetic outflow as well as SDNN and LF all present an age-related reduction, which could be representative of a natural exhaustion of allostatic systems related to age. Moreover, low SDNN values (<19 ms) could be associated with early mortality in centenarians. HRV seems to play a role in exceptional longevity, which could be accounted for by centenarians' exposome.

Weaning is the process of withdrawing mechanical ventilation at the Intensive Care Units. The problem is that around 20% of weaned patients were not actually ready for discontinuation. Studies suggest that vagal dysfunction is lower in patients successfully weaned. Therefore, the Baroreflex Sensitivity (BRS) and Heart Rate Variability (HRV) are estimated to see if they can provide additional information to improve the prediction of weaning outcomes. 9 successfully weaned patients (S-group) and 6 unsuccessfully weaned (F-group) were monitored in the last hour prior to the Spontaneous Breathing Trial. The BRS is estimated through spectral analysis, to obtain the a parameter in the low and high frequency bands, and through the capacity, C, estimated by the Bivariate Phase Rectified Signal Average (BPRSA) method. The current clinic parameters of weaning readiness do not show statistical differences. However, the capacity to changes of the BRS, C, estimated via BPRSA, exhibits significant differences between the two groups. Negative values of C, and with higher absolute values, were obtained for the S-group. Temporal indices of HRV also show differences, but not significant. These results suggest that BRS should be further explored for predicting weaning outcomes.