PhysioZoo: A Novel Open Access Platform for Heart Rate Variability Analysis of Mammalian Electrocardiographic Data

The effect of group size on sleep in a neotropical bat, Artibeus jamaicensis

Sleep is associated with many costs, but is also important to survival, with a lack of sleep impairing cognitive function and increasing mortality. Sleeping in groups could alleviate sleep-associated costs, or could introduce new costs if social sleeping disrupts sleep. Working with the Jamaican fruit bat (Artibeus jamaicensis), we aimed to: (1) describe sleep architecture, (2) assess how sleeping in groups affects sleep, and (3) quantify total sleep time and identify rapid eye movement (REM) sleep using behavioral indicators that complement physiological evidence of sleep. Twenty-five adult bats were captured in Panama and recorded sleeping in an artificial roost enclosure. Three bats were fitted with an electromyograph and accelerometer and video recorded sleeping alone in controlled laboratory settings. The remaining 22 bats were assigned to differing social configurations (alone, dyad, triad, and tetrad) and video recorded sleeping in an outdoor flight cage. We found that sleep was highly variable among individuals (ranging from 2 h 53 min to 9 h 39 min over a 12-h period). Although we did not detect statistically significant effects and our sample size was limited, preliminary trends suggest that male bats may sleep longer than females, and individuals sleeping in groups may sleep longer than individuals sleeping alone. We also found a high correspondence between total sleep time quantified visually and quantified using actigraphy (with a 2-min immobility threshold) and identified physiological correlates of behaviorally-defined REM. These results serve as a starting point for future work on the ecology and evolution of sleep in bats and other wild mammals.

An artificial intelligence-enabled Holter algorithm to identify patients with ventricular tachycardia by analysing their electrocardiogram during sinus rhythm

Aims

Ventricular tachycardia (VT) is a dangerous cardiac arrhythmia that can lead to sudden cardiac death. Early detection and management of VT is thus of high clinical importance. We hypothesize that it is possible to identify patients with VT during sinus rhythm by leveraging a continuous 24 h Holter electrocardiogram and artificial intelligence.

Methods and results

We analysed a retrospective Holter data set from the Rambam Health Care Campus, Haifa, Israel, which included 1773 Holter recordings from 1570 non-VT patients and 52 recordings from 49 VT patients. Morphological and heart rate variability features were engineered from the raw electrocardiogram signal and fed, together with demographical features, to a data-driven model for the task of classifying a patient as either VT or non-VT. The model obtained an area under the receiving operative curve of 0.76 ± 0.07. Feature importance suggested that the proportion of premature ventricular beats and beat-to-beat interval variability was discriminative of VT, while demographic features were not.

Conclusion

This original study demonstrates the feasibility of VT identification from sinus rhythm in Holter.

Prediction of early postoperative pain using sleep quality and heart rate variability

PURPOSE

Accurate predictions of postoperative pain intensity are necessary for customizing analgesia plans. Insomnia is a risk factor for severe postoperative pain. Moreover, heart rate variability (HRV) can provide information on the sympathetic-parasympathetic balance in response to noxious stimuli. We developed a prediction model that uses the insomnia severity index (ISI), HRV, and other demographic factors to predict the odds of higher postoperative pain.

METHODS

We recruited gynecological surgery patients classified as American Society of Anesthesiologists class 1-3. An ISI questionnaire was completed 1 day before surgery. HRV was calculated offline using intraoperative electrocardiogram data. Pain severity at the postanesthesia care unit (PACU) was assessed with the 0-10 numerical rating scale (NRS). The primary outcome was the model's predictive ability for moderate-to-severe postoperative pain. The secondary outcome was the relationship between individual risk factors and opioid consumption in the PACU.

RESULTS

Our study enrolled 169 women. Higher ISI scores (p = 0.001), higher parasympathetic activity (rMSSD, pNN50, HF; p < 0.001, p < 0.001, p < 0.001), loss of fractal dynamics (SD2, alpha 1; p = 0.012, p = 0.039) in HRV analysis before the end of surgery were associated with higher NRS scores, while laparoscopic surgery (p = 0.031) was associated with lower NRS scores. We constructed a multiple logistic model (area under the curve = 0.852) to predict higher NRS scores at PACU arrival. The five selected predictors were age (OR: 0.94; p = 0.020), ISI score (OR: 1.14; p = 0.002), surgery type (laparoscopic or open; OR: 0.12; p < 0.001), total power (OR: 2.02; p < 0.001), and alpha 1 (OR: 0.03; p < 0.001).

CONCLUSION

We employed a multiple logistic regression model to determine the likelihood of moderate-to-severe postoperative pain upon arrival at the PACU. Physicians could personalize analgesic regimens based on a deeper comprehension of the factors that contribute to postoperative pain.

Self-regulating arousal via pupil-based biofeedback

The brain's arousal state is controlled by several neuromodulatory nuclei known to substantially influence cognition and mental well-being. Here we investigate whether human participants can gain volitional control of their arousal state using a pupil-based biofeedback approach. Our approach inverts a mechanism suggested by previous literature that links activity of the locus coeruleus, one of the key regulators of central arousal and pupil dynamics. We show that pupil-based biofeedback enables participants to acquire volitional control of pupil size. Applying pupil self-regulation systematically modulates activity of the locus coeruleus and other brainstem structures involved in arousal control. Furthermore, it modulates cardiovascular measures such as heart rate, and behavioural and psychophysiological responses during an oddball task. We provide evidence that pupil-based biofeedback makes the brain's arousal system accessible to volitional control, a finding that has tremendous potential for translation to behavioural and clinical applications across various domains, including stress-related and anxiety disorders.

[Artificial intelligence in wearable electrocardiogram monitoring]

Electrocardiogram (ECG) monitoring owns important clinical value in diagnosis, prevention and rehabilitation of cardiovascular disease (CVD). With the rapid development of Internet of Things (IoT), big data, cloud computing, artificial intelligence (AI) and other advanced technologies, wearable ECG is playing an increasingly important role. With the aging process of the population, it is more and more urgent to upgrade the diagnostic mode of CVD. Using AI technology to assist the clinical analysis of long-term ECGs, and thus to improve the ability of early detection and prediction of CVD has become an important direction. Intelligent wearable ECG monitoring needs the collaboration between edge and cloud computing. Meanwhile, the clarity of medical scene is conducive for the precise implementation of wearable ECG monitoring. This paper first summarized the progress of AI-related ECG studies and the current technical orientation. Then three cases were depicted to illustrate how the AI in wearable ECG cooperate with the clinic. Finally, we demonstrated the two core issues-the reliability and worth of AI-related ECG technology and prospected the future opportunities and challenges.

Age-dependent contribution of intrinsic mechanisms to sinoatrial node function in humans

Average beat interval (BI) and beat interval variability (BIV) are primarily determined by mutual entrainment between the autonomic-nervous system (ANS) and intrinsic mechanisms that govern sinoatrial node (SAN) cell function. While basal heart rate is not affected by age in humans, age-dependent reductions in intrinsic heart rate have been documented even in so-called healthy individuals. The relative contributions of the ANS and intrinsic mechanisms to age-dependent deterioration of SAN function in humans are not clear. We recorded ECG on patients (n = 16 < 21 years and n = 23 41-78 years) in the basal state and after ANS blockade (propranolol and atropine) in the presence of propofol and dexmedetomidine anesthesia. Average BI and BIV were analyzed. A set of BIV features were tested to designated the "signatures" of the ANS and intrinsic mechanisms and also the anesthesia "signature". In young patients, the intrinsic mechanisms and ANS mainly contributed to long- and short-term BIV, respectively. In adults, both ANS and intrinsic mechanisms contributed to short-term BIV, while the latter also contributed to long-term BIV. Furthermore, anesthesia affected ANS function in young patients and both mechanisms in adult. The work also showed that intrinsic mechanism features can be calculated from BIs, without intervention.

Using beat-to-beat heart signals for age-independent biometric verification

Use of non-stationary physiological signals for biometric verification, reduces the ability to forge. Such signals should be simple to acquire with inexpensive equipment. The beat-to-beat information embedded within the time intervals between consecutive heart beats is a non-stationary physiological signal; its potential for biometric verification has not been studied. This work introduces a biometric verification method termed "CompaRR". Heartbeat was extracted from longitudinal recordings from 30 mice ranging from 6 to 24 months of age (equivalent to ~ 20-75 human years). Fifty heartbeats, which is close to resting human heartbeats in a minute, were sufficient for the verification task, achieving a minimal equal error rate of 0.21. When trained on 6-month-old mice and tested on unseen mice up to 18-months of age (equivalent to ~ 50 human years), no significant change in the verification performance was noted. Finally, when the model was trained on data from drug-treated mice, verification was still possible.

cAMP signaling affects age-associated deterioration of pacemaker beating interval dynamics

Sinoatrial node (SAN) beating interval variability (BIV) and the average beating interval (BI) are regulated by a coupled-clock system, driven by Ca2+-calmodulin activated adenylyl cyclase, cAMP, and downstream PKA signaling. Reduced responsiveness of the BI and BIV to submaximal, [X]50, β-adrenergic receptor (β-AR) stimulation, and phosphodiesterase inhibition (PDEI) have been documented in aged SAN tissue, whereas the maximal responses, [X]max, do not differ by age. To determine whether age-associated dysfunction in cAMP signaling leads to altered responsiveness of BI and BIV, we measured cAMP levels and BI in adult (2-4 months n = 27) and aged (22-26 months n = 25) C57/BL6 mouse SAN tissue in control and in response to β-AR or PDEI at X50 and [X]max. Both cAMP and average BI in adult SAN were reduced at X50, whereas cAMP and BI at Xmax did not differ by age. cAMP levels and average BI were correlated both within and between adult and aged SAN. BIV parameters in long- and short-range terms were correlated with cAMP levels for adult SAN. However, due to reduced cAMP within aged tissues at [X]50, these correlations were diminished in advanced age. Thus, cAMP level generated by the coupled clock mechanisms is tightly linked to average BI. Reduced cAMP level at X50 in aged SAN explains the reduced responsiveness of the BI and BIV to β-AR stimulation and PDEI.

Investigating the effects of beta-blockers on circadian heart rhythm using heart rate variability in ischemic heart disease with preserved ejection fraction

Heart failure is characterized by sympathetic activation and parasympathetic withdrawal leading to an abnormal autonomic modulation. Beta-blockers (BB) inhibit overstimulation of the sympathetic system and are indicated in heart failure patients with reduced ejection fraction. However, the effect of beta-blocker therapy on heart failure with preserved ejection fraction (HFpEF) is unclear. ECGs of 73 patients with HFpEF > 55% were recruited. There were 56 patients in the BB group and 17 patients in the without BB (NBB) group. The HRV analysis was performed for the 24-h period using a window size of 1,4 and 8-h. HRV measures between day and night for both the groups were also compared. Percentage change in the BB group relative to the NBB group was used as a measure of difference. RMSSD (13.27%), pNN50 (2.44%), HF power (44.25%) and LF power (13.53%) showed an increase in the BB group relative to the NBB group during the day and were statistically significant between the two groups for periods associated with high cardiac risk during the morning hours. LF:HF ratio showed a decrease of 3.59% during the day. The relative increase in vagal modulated RMSSD, pNN50 and HF power with a decrease in LF:HF ratio show an improvement in the parasympathetic tone and an overall decreased risk of a cardiac event especially during the morning hours that is characterized by a sympathetic surge.

Generalizable and robust deep learning algorithm for atrial fibrillation diagnosis across geography, ages and sexes

To drive health innovation that meets the needs of all and democratize healthcare, there is a need to assess the generalization performance of deep learning (DL) algorithms across various distribution shifts to ensure that these algorithms are robust. This retrospective study is, to the best of our knowledge, an original attempt to develop and assess the generalization performance of a DL model for AF events detection from long term beat-to-beat intervals across geography, ages and sexes. The new recurrent DL model, denoted ArNet2, is developed on a large retrospective dataset of 2,147 patients totaling 51,386 h obtained from continuous electrocardiogram (ECG). The model's generalization is evaluated on manually annotated test sets from four centers (USA, Israel, Japan and China) totaling 402 patients. The model is further validated on a retrospective dataset of 1,825 consecutives Holter recordings from Israel. The model outperforms benchmark state-of-the-art models and generalized well across geography, ages and sexes. For the task of event detection ArNet2 performance was higher for female than male, higher for young adults (less than 61 years old) than other age groups and across geography. Finally, ArNet2 shows better performance for the test sets from the USA and China. The main finding explaining these variations is an impairment in performance in groups with a higher prevalence of atrial flutter (AFL). Our findings on the relative performance of ArNet2 across groups may have clinical implications on the choice of the preferred AF examination method to use relative to the group of interest.

Nonlinear Dynamics of Heart Rate Variability after Acutely Induced Myocardial Ischemia by Percutaneous Transluminal Coronary Angioplasty

Several heart rate variability (HRV) characteristics of patients with myocardial ischemia are associated with a higher mortality risk. However, the immediate effect of acute ischemia on the HRV nonlinear dynamical behavior is unknown. The objective of this work is to explore the presence of nonlinearity through surrogate data testing and describe the dynamical behavior of HRV in acutely induced ischemia by percutaneous transluminal coronary angioplasty (PTCA) with linear and recurrence quantification analysis (RQA). Short-term electrocardiographic recordings from 68 patients before and after being treated with elective PTCA were selected from a publicly available database. The presence of nonlinear behavior was confirmed by determinism and laminarity in a relevant proportion of HRV time series, in up to 29.4% during baseline conditions and 30.9% after PTCA without statistical difference between these scenarios. After PTCA, the mean value and standard deviation of HRV time series decreased, while determinism and laminarity values increased. Here, the diminishment in overall variability caused by PTCA is not accompanied by a change in nonlinearity detection. Therefore, the presence of nonlinear behavior in HRV time series is not necessarily in agreement with the change of traditional and RQA measures.

Changes in Heart Rate Variability with Induction of Gastric Ulcers in Adult Horses

Gastric ulceration can be induced by athletic training and is a significant welfare concern. The objective of this study was to evaluate the effect of gastric ulcer induction on heart rate variability (HRV) in the horse. We hypothesized that induction of gastric ulcers would decrease HRV and increase low frequency fluctuations, consistent with increased sympathetic tone. A convenience sample of 8 horses in a larger study were enrolled. Horses were randomly assigned to receive water or 2 mg/kg omeprazole orally once daily for 28 days. Gastric ulcers were induced through intermittent feed withholding on days 21 to 28. Gastroscopy was performed and gastric ulcers were graded (0-IV) by three blinded reviewers on days 21 and 28. Continuous electrocardiograms were obtained for one hour at the start and end of ulcer induction. HRV was assessed in 1-hour recordings for time domain variables and 5 minute sections for frequency domain analysis. HRV and ulcer grade across treatments were compared by a mixed effect model, with treatment and time as fixed effects and horse as a random effect. Gastric ulcer grade increased with induction protocol (P < .0001) and decreased with omeprazole treatment (P = .0007). Omeprazole treatment increased R-R intervals (P = .01) and decreased ratio of low frequency/high frequency signal (P = .008) as compared to horses receiving water. This was attributable to decreasing low frequency fluctuations (P = .05). While limited by the small sample size (four horses/treatment), this study suggests that omeprazole treatment decreases heart rate, and LF/HF ratio during ulcer induction, consistent with a decrease in sympathetic tone.

Guidelines for assessment of cardiac electrophysiology and arrhythmias in small animals

Cardiac arrhythmias are a major cause of morbidity and mortality worldwide. Although recent advances in cell-based models, including human-induced pluripotent stem cell-derived cardiomyocytes (iPSC-CM), are contributing to our understanding of electrophysiology and arrhythmia mechanisms, preclinical animal studies of cardiovascular disease remain a mainstay. Over the past several decades, animal models of cardiovascular disease have advanced our understanding of pathological remodeling, arrhythmia mechanisms, and drug effects and have led to major improvements in pacing and defibrillation therapies. There exist a variety of methodological approaches for the assessment of cardiac electrophysiology and a plethora of parameters may be assessed with each approach. This guidelines article will provide an overview of the strengths and limitations of several common techniques used to assess electrophysiology and arrhythmia mechanisms at the whole animal, whole heart, and tissue level with a focus on small animal models. We also define key electrophysiological parameters that should be assessed, along with their physiological underpinnings, and the best methods with which to assess these parameters.

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."

Determination of Waste Management Workers' Physical and Psychological Load: A Cross-Sectional Study Using Biometric Data

Waste management workers experience high stress and physical strain in their work environment, but very little empirical evidence supports effective health management practices for waste management workers. Hence, this study investigated the effects of worker characteristics and biometric indices on workers' physical and psychological loads during waste-handling operations. A biometric measurement system was installed in an industrial waste management facility in Japan to understand the actual working conditions of 29 workers in the facility. It comprised sensing wear for data collection and biometric sensors to measure heart rate (HR) and physical activity (PA) based on electrocardiogram signals. Multiple regression analysis was performed to evaluate significant relationships between the parameters. Although stress level is indicated by the ratio of low frequency (LF) to high frequency (HF) or high LF power in HR, the results showed that compared with workers who did not handle waste, those who did had lower PA and body surface temperature, higher stress, and lower HR variability parameters associated with higher psychological load. There were no significant differences in HR, heart rate interval (RRI), and workload. The psychological load of workers dealing directly with waste was high, regardless of their PA, whereas others had a low psychological load even with high PA. These findings suggest the need to promote sustainable work relationships and a quantitative understanding of harsh working conditions to improve work quality and reduce health hazards.

A two-step pre-processing tool to remove Gaussian and ectopic noise for heart rate variability analysis

Artifacts in the Electrocardiogram (ECG) degrade the quality of the recorded signal and are not conducive to heart rate variability (HRV) analysis. The two types of noise most often found in ECG recordings are technical and physiological artifacts. Current preprocessing methods primarily attend to ectopic beats but do not consider technical issues that affect the ECG. A secondary aim of this study was to investigate the effect of increasing increments of artifacts on 24 of the most used HRV measures. A two-step preprocessing approach for denoising HRV is introduced which targets each type of noise separately. First, the technical artifacts in the ECG are eliminated by applying complete ensemble empirical mode decomposition with adaptive noise. The second step removes physiological artifacts from the HRV signal using a combination filter of single dependent rank order mean and an adaptive filtering algorithm. The performance of the two-step pre-processing tool showed a high correlation coefficient of 0.846 and RMSE value of 7.69 × 10^-5 for 6% of added ectopic beats and 6 dB Gaussian noise. All HRV measures studied except HF peak and LF peak are significantly affected by both types of noise. Frequency measures of Total power, HF power, and LF power and fragmentation measures; PAS, PIP, and PSS are the most sensitive to both types of noise.

IL-1β is not critical to chronic heart dysfunction in mice with Chagas disease

Long after Trypanosoma cruzi infection, 40% of individuals develop a progressive chronic chagasic cardiomyopathy (CCC), with systolic dysfunction and arrhythmias. Since we previously showed IL-1β mediates the development of systolic dysfunction and cardiac arrhythmias in diabetes mellitus and cardiorenal syndrome, and IL-1β remains elevated in Chagas disease patients, here we tested the role of IL-1β in CCC using a mouse model. Mice deficient in IL-1R expression (Il-1r^−/−) survived acute T. cruzi infection with greater parasitemia than controls but did not lose weight as wild-type (WT) did. At the chronic stage, WT presented prolonged ventricular repolarization intervals (QJ), while Il-1r^−/− presented intervals like noninfected controls. Infected Il-1r^−/− and WT did not differ in stroke volume (SV), the incidence of cardiac arrhythmias on electrocardiography (EKG), whole heart action potential duration (APD), or the incidence of triggered activity after S1–S2 protocol, which is a measure of susceptibility to cardiac arrhythmias. We also treated chronically infected WT mice with an IL-1R antagonist, anakinra. Treatment shortened the QJ interval but did not improve the SV or the incidence of cardiac arrhythmias on EKG. Anakinra failed to reduce triggered activity following the electrical extra-stimulation protocol. In conclusion, the absence of functional IL-1β/IL-1R signaling did not prevent or reverse the decrease of SV or the incidence of cardiac arrhythmias induced by chronic T. cruzi infection, implying this is not a critical mechanism in generating or maintaining CCC. Since similar cardiac abnormalities were previously credited to IL-1β signaling, ruling out this mechanism is important to discourage further attempts of IL-1β blockade as a therapeutical measure.

Evaluation of non-linear heart rate variability using multi-scale multi-fractal detrended fluctuation analysis in mice: Roles of the autonomic nervous system and sinoatrial node

Nonlinear analyses of heart rate variability (HRV) can be used to quantify the unpredictability, fractal properties and complexity of heart rate. Fractality and its analysis provides valuable information about cardiovascular health. Multi-Scale Multi-Fractal Detrended Fluctuation Analysis (MSMFDFA) is a complexity-based algorithm that can be used to quantify the multi-fractal dynamics of the HRV time series through investigating characteristic exponents at different time scales. This method is applicable to short time series and it is robust to noise and nonstationarity. We have used MSMFDFA, which enables assessment of HRV in the frequency ranges encompassing the very-low frequency and ultra-low frequency bands, to jointly assess multi-scale and multi-fractal dynamics of HRV signals obtained from telemetric ECG recordings in wildtype mice at baseline and after autonomic nervous system (ANS) blockade, from electrograms recorded from isolated atrial preparations and from spontaneous action potential recordings in isolated sinoatrial node myocytes. Data demonstrate that the fractal profile of the intrinsic heart rate is significantly different from the baseline heart rate in vivo, and it is also altered after ANS blockade at specific scales and fractal order domains. For beating rate in isolated atrial preparations and intrinsic heart rate in vivo, the average fractal structure of the HRV increased and multi-fractality strength decreased. These data demonstrate that fractal properties of the HRV depend on both ANS activity and intrinsic sinoatrial node function and that assessing multi-fractality at different time scales is an effective approach for HRV assessment.

Effects of auditory sleep modulation approaches on brain oscillatory and cardiovascular dynamics

Slow waves, the hallmark feature of deep nonrapid eye movement sleep, do potentially drive restorative effects of sleep on brain and body functions. Sleep modulation techniques to elucidate the functional role of slow waves thus have gained large interest. Auditory slow wave stimulation is a promising tool; however, directly comparing auditory stimulation approaches within a night and analyzing induced dynamic brain and cardiovascular effects are yet missing. Here, we tested various auditory stimulation approaches in a windowed, 10 s ON (stimulations) followed by 10 s OFF (no stimulations), within-night stimulation design and compared them to a SHAM control condition. We report the results of three studies and a total of 51 included nights and found a large and global increase in slow-wave activity (SWA) in the stimulation window compared to SHAM. Furthermore, slow-wave dynamics were most pronouncedly increased at the start of the stimulation and declined across the stimulation window. Beyond the changes in brain oscillations, we observed, for some conditions, a significant increase in the mean interval between two heartbeats within a stimulation window, indicating a slowing of the heart rate, and increased heart rate variability derived parasympathetic activity. Those cardiovascular changes were positively correlated with the change in SWA, and thus, our findings provide insight into the potential of auditory slow wave enhancement to modulate cardiovascular restorative conditions during sleep. However, future studies need to investigate whether the potentially increased restorative capacity through slow-wave enhancements translates into a more rested cardiovascular system on a subsequent day.

Heart failure in mice induces a dysfunction of the sinus node associated with reduced CaMKII signaling

Dysfunction of the sinoatrial node (SAN), the natural heart pacemaker, is common in heart failure (HF) patients. SAN spontaneous activity relies on various ion currents in the plasma membrane (voltage clock), but intracellular Ca2+ ([Ca2+]i) release via ryanodine receptor 2 (RYR2; Ca2+ clock) plays an important synergetic role. Whereas remodeling of voltage-clock components has been revealed in HF, less is known about possible alterations to the Ca2+ clock. Here, we analyzed [Ca2+]i handling in SAN from a mouse HF model after transverse aortic constriction (TAC) and compared it with sham-operated animals. ECG data from awake animals showed slower heart rate in HF mice upon autonomic nervous system blockade, indicating intrinsic sinus node dysfunction. Confocal microscopy analyses of SAN cells within whole tissue showed slower and less frequent [Ca2+]i transients in HF. This correlated with fewer and smaller spontaneous Ca2+ sparks in HF SAN cells, which associated with lower RYR2 protein expression level and reduced phosphorylation at the CaMKII site. Moreover, PLB phosphorylation at the CaMKII site was also decreased in HF, which could lead to reduced sarco/endoplasmic reticulum Ca2+-ATPase (SERCA) function and lower sarcoplasmic reticulum Ca2+ content, further depressing the Ca2+ clock. The inhibition of CaMKII with KN93 slowed [Ca2+]i transient rate in both groups, but this effect was smaller in HF SAN, consistent with less CaMKII activation. In conclusion, our data uncover that the mechanism of intrinsic pacemaker dysfunction in HF involves reduced CaMKII activation.

Machine learning to support triage of children at risk for epileptic seizures in the pediatric intensive care unit

OBJECTIVE

Epileptic seizures are relatively common in critically-ill children admitted to the pediatric intensive care unit (PICU) and thus serve as an important target for identification and treatment. Most of these seizures have no discernible clinical manifestation but still have a significant impact on morbidity and mortality. Children that are deemed at risk for seizures within the PICU are monitored using continuous-electroencephalogram (cEEG). cEEG monitoring cost is considerable and as the number of available machines is always limited, clinicians need to resort to triaging patients according to perceived risk in order to allocate resources. This research aims to develop a computer aided tool to improve seizures risk assessment in critically-ill children, using an ubiquitously recorded signal in the PICU, namely the electrocardiogram (ECG).

APPROACH

A novel data-driven model was developed at a patient-level approach, based on features extracted from the first hour of ECG recording and the clinical data of the patient.

MAIN RESULTS

The most predictive features were the age of the patient, the brain injury as coma etiology and the QRS area. For patients without any prior clinical data, using one hour of ECG recording, the classification performance of the random forest classifier reached an area under the receiver operating characteristic curve (AUROC) score of 0.84. When combining ECG features with the patients clinical history, the AUROC reached 0.87.

SIGNIFICANCE

Taking a real clinical scenario, we estimated that our clinical decision support triage tool can improve the positive predictive value by more than 59% over the clinical standard.

Changes in cAMP signaling are associated with age-related downregulation of spontaneously beating atrial tissue energetic indices

The prevalence of atria-related diseases increases exponentially with age and is associated with ATP supply-to-demand imbalances. Because evidence suggests that cAMP regulates ATP supply-to-demand, we explored aged-associated alterations in atrial ATP supply-to-demand balance and its correlation with cAMP levels. Right atrial tissues driven by spontaneous sinoatrial node impulses were isolated from aged (22-26 months) and adult (3-4 months) C57/BL6 mice. ATP demand increased by addition of isoproterenol or 3-Isobutyl-1-methylxanthine (IBMX) and decreased by application of carbachol. Each drug was administrated at the dose that led to a maximal change in beating rate (X_max) and to 50% of that maximal change in adult tissue (X₅₀). cAMP, NADH, NAD + NADH, and ATP levels were measured in the same tissue. The tight correlation between cAMP levels and the beating rate (i.e., the ATP demand) demonstrated in adult atria was altered in aged atria. cAMP levels were lower in aged compared to adult atrial tissue exposed to X₅₀ of ISO or IBMX, but this difference narrowed at X_max. Neither ATP nor NADH levels correlated with ATP demand in either adult or aged atria. Baseline NADH levels were lower in aged as compared to adult atria, but were restored by drug perturbations that increased cAMP levels. Reduction in Ca²⁺-activated adenylyl cyclase-induced decreased cAMP and prolongation of the spontaneous beat interval of adult atrial tissue to their baseline levels in aged tissue, brought energetics indices to baseline levels in aged tissue. Thus, cAMP regulates right atrial ATP supply-to-demand matching and can restore age-associated ATP supply-to-demand imbalance.

Active force generation contributes to the complexity of spontaneous activity and to the response to stretch of murine cardiomyocyte cultures

Abstract

Cardiomyocyte cultures exhibit spontaneous electrical and contractile activity, as in a natural cardiac pacemaker. In such preparations, beat rate variability exhibits features similar to those of heart rate variability in vivo. Mechanical deformations and forces feed back on the electrical properties of cardiomyocytes, but it is not fully elucidated how this mechano‐electrical interplay affects beating variability in such preparations. Using stretchable microelectrode arrays, we assessed the effects of the myosin inhibitor blebbistatin and the non‐selective stretch‐activated channel blocker streptomycin on beating variability and on the response of neonatal or fetal murine ventricular cell cultures against deformation. Spontaneous electrical activity was recorded without stretch and upon predefined deformation protocols (5% uniaxial and 2% equibiaxial strain, applied repeatedly for 1 min every 3 min). Without stretch, spontaneous activity originated from the edge of the preparations, and its site of origin switched frequently in a complex manner across the cultures. Blebbistatin did not change mean beat rate, but it decreased the spatial complexity of spontaneous activity. In contrast, streptomycin did not exert any manifest effects. During the deformation protocols, beat rate increased transiently upon stretch but, paradoxically, also upon release. Blebbistatin attenuated the response to stretch, whereas this response was not affected by streptomycin. Therefore, our data support the notion that in a spontaneously firing network of cardiomyocytes, active force generation, rather than stretch‐activated channels, is involved mechanistically in the complexity of the spatiotemporal patterns of spontaneous activity and in the stretch‐induced acceleration of beating.

Key points

Monolayer cultures of cardiac cells exhibit spontaneous electrical and contractile activity, as in a natural cardiac pacemaker. Beating variability in these preparations recapitulates the power‐law behaviour of heart rate variability in vivo. However, the effects of mechano‐electrical feedback on beating variability are not yet fully understood.

Using stretchable microelectrode arrays, we examined the effects of the contraction uncoupler blebbistatin and the non‐specific stretch‐activated channel blocker streptomycin on beating variability and on stretch‐induced changes of beat rate.

Without stretch, blebbistatin decreased the spatial complexity of beating variability, whereas streptomycin had no effects.

Both stretch and release increased beat rate transiently; blebbistatin attenuated the increase of beat rate upon stretch, whereas streptomycin had no effects.

Active force generation contributes to the complexity of spatiotemporal patterns of beating variability and to the increase of beat rate upon mechanical deformation. Our study contributes to the understanding of how mechano‐electrical feedback influences heart rate variability.

Quantitative measurements of zebrafish heartrate and heart rate variability: A survey between 1990-2020

Zebrafish is an essential model organism for studying cardiovascular diseases, given its advantages of fast proliferation and high gene homology with humans. Zebrafish embryos/larvae are valuable experimental models used in toxicology studies to analyze drug toxicity, including hepatoxicity, nephrotoxicity and cardiotoxicity, as well as for drug discovery and drug safety screening in the preclinical stage. Heart rate (HR) serves as a functional endpoint in studies of cardiotoxicity, while heart rate variability (HRV) serves as an indicator of cardiac arrhythmia. Cardiotoxicity is a major cause of early and late termination of drug trials, so a more comprehensive understanding of zebrafish HR and HRV is important. This review summarized HR and HRV in a specific range of applications and fields, focusing on zebrafish heartbeat detection procedures, signal analysis technology and well-established commercial software, such as LabVIEW, Rvlpulse, and ZebraLab. We also compared HR detection algorithms and electrocardiography (ECG)-based methods of heart signal extraction. The relationship between HR and HRV was also systematically analyzed; HR was shown to have an inverse correlation with HRV. Applications to drug testing are also highlighted in this review. Furthermore, HR and HRV were shown to be regulated by the automatic nervous system; their connections with ECG measurements are also summarized herein.

Estimating Entropy Production from Waiting Time Distributions

Living systems operate far from thermal equilibrium by converting the chemical potential of ATP into mechanical work to achieve growth, replication, or locomotion. Given time series observations of intra-, inter-, or multicellular processes, a key challenge is to detect nonequilibrium behavior and quantify the rate of free energy consumption. Obtaining reliable bounds on energy consumption and entropy production directly from experimental data remains difficult in practice, as many degrees of freedom typically are hidden to the observer, so that the accessible coarse-grained dynamics may not obviously violate detailed balance. Here, we introduce a novel method for bounding the entropy production of physical and living systems which uses only the waiting time statistics of hidden Markov processes and, hence, can be directly applied to experimental data. By determining a universal limiting curve, we infer entropy production bounds from experimental data for gene regulatory networks, mammalian behavioral dynamics, and numerous other biological processes. Further considering the asymptotic limit of increasingly precise biological timers, we estimate the necessary entropic cost of heartbeat regulation in humans, dogs, and mice.

Moving average and standard deviation thresholding (MAST): a novel algorithm for accurate R-wave detection in the murine electrocardiogram

Advances in implantable radio-telemetry or diverse biologging devices capable of acquiring high-resolution ambulatory electrocardiogram (ECG) or heart rate recordings facilitate comparative physiological investigations by enabling detailed analysis of cardiopulmonary phenotypes and responses in vivo. Two priorities guiding the meaningful adoption of such technologies are: (1) automation, to streamline and standardize large dataset analysis, and (2) flexibility in quality-control. The latter is especially relevant when considering the tendency of some fully automated software solutions to significantly underestimate heart rate when raw signals contain high-amplitude noise. We present herein moving average and standard deviation thresholding (MAST), a novel, open-access algorithm developed to perform automated, accurate, and noise-robust single-channel R-wave detection from ECG obtained in chronically instrumented mice. MAST additionally and automatically excludes and annotates segments where R-wave detection is not possible due to artefact levels exceeding signal levels. Customizable settings (e.g. window width of moving average) allow for MAST to be scaled for use in non-murine species. Two expert reviewers compared MAST's performance (true/false positive and false negative detections) with that of a commercial ECG analysis program. Both approaches were applied blindly to the same random selection of 270 3-min ECG recordings from a dataset containing varying amounts of signal artefact. MAST exhibited roughly one quarter the error rate of the commercial software and accurately detected R-waves with greater consistency and virtually no false positives (sensitivity, Se: 98.48% ± 4.32% vs. 94.59% ± 17.52%, positive predictivity, +P: 99.99% ± 0.06% vs. 99.57% ± 3.91%, P < 0.001 and P = 0.0274 respectively, Wilcoxon signed rank; values are mean ± SD). Our novel, open-access approach for automated single-channel R-wave detection enables investigators to study murine heart rate indices with greater accuracy and less effort. It also provides a foundational code for translation to other mammals, ectothermic vertebrates, and birds.

Psychological Effects of Heart Rate and Physical Vibration on the Operation of Construction Machines: Experimental Study

BACKGROUND

A construction method has emerged in which a camera is installed around a construction machine, and the operator remotely controls the machine while synchronizing the vibration of the machine with the images seen from the operator's seat using virtual reality (VR) technology. Indices related to changes in heart rate (HR) and physical vibration, such as heart rate variability (HRV) and multiscale entropy (MSE), can then be measured among the operators. As these indices are quantitative measures of autonomic regulation in the cardiovascular system, they can provide a useful means of assessing operational stress.

OBJECTIVE

In this study, we aimed to evaluate changes in HR and body vibration of machine operators and investigate appropriate methods of machine operation while considering the psychological load.

METHODS

We enrolled 9 remote operators (18-50 years old) in the experiment, which involved 42 measurements. A construction machine was driven on a test course simulating a construction site, and three patterns of operation-riding operation, remote operation using monitor images, and VR operation combining monitor images and machine vibration-were compared. The heartbeat, body vibration, and driving time of the participants were measured using sensing wear made of a woven film-like conductive material and a three-axis acceleration measurement device (WHS-2). We used HRV analysis in the time and frequency domains, MSE analysis as a measure of the complexity of heart rate changes, and the ISO (International Standards Organization) 2631 vibration index. Multiple regression analysis was conducted to model the relationship among the low frequency (LF)/high frequency (HF) HRV, MSE, vibration index, and driving time of construction equipment. Efficiency in driving time was investigated with a focus on stress reduction.

RESULTS

Multiple comparisons conducted via the Bonferroni test and Kruskal-Wallis test showed statistically significant differences (P=.05) in HRV-LF/HF, the vibration index, weighted acceleration, motion sickness dose value (MSDVz), and the driving time among the three operation patterns. The riding operation was found to reduce the driving time of the machine, but the operation stress was the highest in this case; operation based on the monitor image was found to have the lowest operation stress but the longest operation time. Multiple regression analysis showed that the explanatory variables (LH/HF), RR interval, and vibration index (MSDVz by vertical oscillation at 0.5-5 Hz) had a negative effect on the driving time (adjusted coefficient of determination R2=0.449).

CONCLUSIONS

A new method was developed to calculate the appropriate operating time by considering operational stress and suppressing the physical vibration within an acceptable range. By focusing on the relationship between psychological load and physical vibration, which has not been explored in previous studies, the relationship of these variables with the driving time of construction machines was clarified.

Opening the Schrödinger Box: Short- and Long-Range Mammalian Heart Rate Variability

Background

The interactions between the autonomic nervous system (ANS), intrinsic systems (e.g., endocrine), and internal pacemaker mechanisms govern short (milliseconds–seconds)- and long (seconds–minutes)-range heart rate variability (HRV). However, there is a debate regarding the identity of the mechanism underlying HRV on each time scale. We aim to design a general method that accurately differentiates between the relative contribution of the ANS and pacemaker mechanisms to HRV in various mammals, without the need for drug perturbations or organ isolation. Additionally, we aim to explore the universality of the relative contribution of the ANS and pacemaker system of different mammals.

Methods

This work explored short- and long-range HRVs using published ECG data from dogs, rabbits, and mice. To isolate the effects of ANS on HRV, ECG segments recorded before and after ANS-blockade were compared.

Results

Differentiation of the ANS from extrinsic and intrinsic pacemaker mechanisms was successfully achieved. In dogs, the internal pacemaker mechanisms were the main contributors to long-range and the ANS to short-range HRV. In rabbits and mice, the ANS and the internal pacemaker mechanisms affected both time scales, and anesthesia changed the relative contribution of the pacemaker mechanism to short- and long-range HRVs. In mice, the extrinsic mechanisms affected long-range HRV, while their effect was negligible in rabbits.

Conclusion

We offer a novel approach to determine the relative contributions of ANS and extrinsic and intrinsic pacemaker mechanisms to HRV and highlight the importance of selecting mammalian research models with HRV mechanisms representative of the target species of interest.

Estimating Left Ventricle Ejection Fraction Levels Using Circadian Heart Rate Variability Features and Support Vector Regression Models

OBJECTIVES

The purpose of this study was to set an optimal fit of the estimated LVEF at hourly intervals from 24-hour ECG recordings and compare it with the fit based on two gold-standard guidelines.

METHODS

Support vector regression (SVR) models were applied to estimate LVEF from ECG derived heart rate variability (HRV) data in one-hour intervals from 24-hour ECG recordings of patients with either preserved, mid-range, or reduced LVEF, obtained from the Intercity Digital ECG Alliance (IDEAL) study. A step-wise feature selection approach was used to ensure the best possible estimations of LVEF levels.

RESULTS

The experimental results have shown that the lowest Root Mean Square Error (RMSE) between the original and estimated LVEF levels was during 3-4 am, 5-6 am and 6-7 pm.

CONCLUSION

The observations suggest these hours as possible times for intervention and optimal treatment outcomes. In addition, LVEF classifications following the ACCF/AHA guidelines leads to a more accurate assessment of mid-range LVEF.

SIGNIFICANCE

This study paves the way to explore the use of HRV features in the prediction of LVEF percentages as an indicator of disease progression, which may lead to an automated classification process for CAD patients.

Beating Rate Variability of Isolated Mammal Sinoatrial Node Tissue: Insight Into Its Contribution to Heart Rate Variability

Background

Because of the complexity of the interaction between the internal pacemaker mechanisms, cell interconnected signals, and interaction with other body systems, study of the role of individual systems must be performed under in vivo and in situ conditions. The in situ approach is valuable when exploring the mechanisms that govern the beating rate and rhythm of the sinoatrial node (SAN), the heart's primary pacemaker. SAN beating rate changes on a beat-to-beat basis. However, to date, there are no standard methods and tools for beating rate variability (BRV) analysis from electrograms (EGMs) collected from different mammals, and there is no centralized public database with such recordings.

Methods

We used EGM recordings obtained from control SAN tissues of rabbits (n = 9) and mice (n = 30) and from mouse SAN tissues (n = 6) that were exposed to drug intervention. The data were harnessed to develop a beat detector to derive the beat-to-beat interval time series from EGM recordings. We adapted BRV measures from heart rate variability and reported their range for rabbit and mouse.

Results

The beat detector algorithm performed with 99% accuracy, sensitivity, and positive predictive value on the test (mouse) and validation (rabbit and mouse) sets. Differences in the frequency band cutoff were found between BRV of SAN tissue vs. heart rate variability (HRV) of in vivo recordings. A significant reduction in power spectrum density existed in the high frequency band, and a relative increase was seen in the low and very low frequency bands. In isolated SAN, the larger animal had a slower beating rate but with lower BRV, which contrasted the phenomena reported for in vivo analysis. Thus, the non-linear inverse relationship between the average HR and HRV is not maintained under in situ conditions. The beat detector, BRV measures, and databases were contributed to the open-source PhysioZoo software (available at: https://physiozoo.com/).

Conclusion

Our approach will enable standardization and reproducibility of BRV analysis in mammals. Different trends were found between beating rate and BRV or HRV in isolated SAN tissue vs. recordings collected under in vivo conditions, respectively, implying a complex interaction between the SAN and the autonomic nervous system in determining HRV in vivo.

Digital oximetry biomarkers for assessing respiratory function: standards of measurement, physiological interpretation, and clinical use

Pulse oximetry is routinely used to non-invasively monitor oxygen saturation levels. A low oxygen level in the blood means low oxygen in the tissues, which can ultimately lead to organ failure. Yet, contrary to heart rate variability measures, a field which has seen the development of stable standards and advanced toolboxes and software, no such standards and open tools exist for continuous oxygen saturation time series variability analysis. The primary objective of this research was to identify, implement and validate key digital oximetry biomarkers (OBMs) for the purpose of creating a standard and associated reference toolbox for continuous oximetry time series analysis. We review the sleep medicine literature to identify clinically relevant OBMs. We implement these biomarkers and demonstrate their clinical value within the context of obstructive sleep apnea (OSA) diagnosis on a total of n = 3806 individual polysomnography recordings totaling 26,686 h of continuous data. A total of 44 digital oximetry biomarkers were implemented. Reference ranges for each biomarker are provided for individuals with mild, moderate, and severe OSA and for non-OSA recordings. Linear regression analysis between biomarkers and the apnea hypopnea index (AHI) showed a high correlation, which reached [Formula: see text]. The resulting python OBM toolbox, denoted "pobm", was contributed to the open software PhysioZoo ( physiozoo.org ). Studying the variability of the continuous oxygen saturation time series using pbom may provide information on the underlying physiological control systems and enhance our understanding of the manifestations and etiology of diseases, with emphasis on respiratory diseases.

PASS: A Multimodal Database of Physical Activity and Stress for Mobile Passive Body/ Brain-Computer Interface Research

With the burgeoning of wearable devices and passive body/brain-computer interfaces (B/BCIs), automated stress monitoring in everyday settings has gained significant attention recently, with applications ranging from serious games to clinical monitoring. With mobile users, however, challenges arise due to other overlapping (and potentially confounding) physiological responses (e.g., due to physical activity) that may mask the effects of stress, as well as movement artifacts that can be introduced in the measured signals. For example, the classical increase in heart rate can no longer be attributed solely to stress and could be caused by the activity itself. This makes the development of mobile passive B/BCIs challenging. In this paper, we introduce PASS, a multimodal database of Physical Activity and StresS collected from 48 participants. Participants performed tasks of varying stress levels at three different activity levels and provided quantitative ratings of their perceived stress and fatigue levels. To manipulate stress, two video games (i.e., a calm exploration game and a survival game) were used. Peripheral physical activity (electrocardiography, electrodermal activity, breathing, skin temperature) as well as cerebral activity (electroencephalography) were measured throughout the experiment. A complete description of the experimental protocol is provided and preliminary analyses are performed to investigate the physiological reactions to stress in the presence of physical activity. The PASS database, including raw data and subjective ratings has been made available to the research community at http://musaelab.ca/pass-database/. It is hoped that this database will help advance mobile passive B/BCIs for use in everyday settings.

Alcohol consumption combined with dietary low-carbohydrate/high-protein intake increased the left ventricular systolic dysfunction risk and lethal ventricular arrhythmia susceptibility in apolipoprotein E/low-density lipoprotein receptor double-knockout mice

Alcohol abuse is positively associated with cardiovascular disease. Dietary low-carbohydrate/high-protein (LCHP) intake confers a greater mortality risk. Here, the impact of ethanol consumption in combination with dietary LCHP intake on left ventricular (LV) systolic function and lethal ventricular arrhythmia susceptibility were investigated in apolipoprotein E/low-density lipoprotein receptor double-knockout (AL) mice. The underlying mechanisms, cardiac sympathovagal balance, beta-adrenergic receptor (ADRB) levels, and gap junction channel protein connexin 43 (Cx43) expression, were examined. Male AL mice fed an LCHP diet with or without ethanol were bred for 16 weeks. Age-matched male AL and wild-type mice received standard chow diet and served as controls. The following were used to assess LV systolic function, lethal ventricular arrhythmia susceptibility, cardiac sympathovagal balance, Cx43 expression, and ADRB levels: The results demonstrated that ethanol consumption in combination with dietary LCHP intake worsened LCHP-induced LV systolic dysfunction in AL mice and enhanced their susceptibility in the ventricular arrhythmia-evoked test. There were concomitant increases in LV weight, LF/HF ratio shown by HRV, TH, ADRB1, ADRB2, and Cx43 expressions by LV fluorescence immunohistochemistry, and LV Cx43 messenger ribonucleic acid expression by PCR. In AL mice, alcohol consumption combined with dietary LCHP intake may thus promote a shift in cardiac sympathovagal balance toward sympathetic predominance, the increases in beta-adrenergic receptors (ADRB1 and ADRB2), and then affect the gap junction channel protein Cx43, which in turn could contribute to increased risks of LV systolic dysfunction and susceptibility to lethal ventricular arrhythmia.

WAUC: A Multi-Modal Database for Mental Workload Assessment Under Physical Activity

Assessment of mental workload is crucial for applications that require sustained attention and where conditions such as mental fatigue and drowsiness must be avoided. Previous work that attempted to devise objective methods to model mental workload were mainly based on neurological or physiological data collected when the participants performed tasks that did not involve physical activity. While such models may be useful for scenarios that involve static operators, they may not apply in real-world situations where operators are performing tasks under varying levels of physical activity, such as those faced by first responders, firefighters, and police officers. Here, we describe WAUC, a multimodal database of mental Workload Assessment Under physical aCtivity. The study involved 48 participants who performed the NASA Revised Multi-Attribute Task Battery II under three different activity level conditions. Physical activity was manipulated by changing the speed of a stationary bike or a treadmill. During data collection, six neural and physiological modalities were recorded, namely: electroencephalography, electrocardiography, breathing rate, skin temperature, galvanic skin response, and blood volume pulse, in addition to 3-axis accelerometry. Moreover, participants were asked to answer the NASA Task Load Index questionnaire after each experimental section, as well as rate their physical fatigue level on the Borg fatigue scale. In order to bring our experimental setup closer to real-world situations, all signals were monitored using wearable, off-the-shelf devices. In this paper, we describe the adopted experimental protocol, as well as validate the subjective, neural, and physiological data collected. The WAUC database, including the raw data and features, subjective ratings, and scripts to reproduce the experiments reported herein will be made available at: http://musaelab.ca/resources/.

PyBioS: A freeware computer software for analysis of cardiovascular signals

BACKGROUND AND OBJECTIVE

Several software applications have been proposed in the past years as computational tools for assessing biomedical signals. Many of them are focused on heart rate variability series only, with their strengths and limitations depending on the necessity of the user and the scope of the application. Here, we introduce new software, named PyBioS, intended for the analysis of cardiovascular signals, even though any type of biomedical signal can be used. PyBioS has some functionalities that differentiate it from the other software.

METHODS

PyBioS was developed in Python language with an intuitive, user-friendly graphical user interface. The basic steps for using PyBioS comprise the opening or creation (simulation) of signals, their visualization, preprocessing and analysis. Currently, PyBioS has 8 preprocessing tools and 15 analysis methods, the later providing more than 50 metrics for analysis of the signals' dynamics.

RESULTS

The possibility to create simulated signals and save the preprocessed signals is a strength of PyBioS. Besides, the software allows batch processing of files, making the analysis of a large amount of data easy and fast. Finally, PyBioS has plenty of analysis methods implemented, with the focus on nonlinear and complexity analysis of signals and time series.

CONCLUSIONS

Although PyBioS is not intended to overcome all the necessities from users, it has useful functionalities that may be helpful in many situations. Moreover, PyBioS is continuously under improvement and several simulated signals, tools and analysis methods are still to be implemented. Also, a new module is being implemented on it to provide machine learning algorithms for classification and regression of data extracted from the biomedical signals.

Remote health diagnosis and monitoring in the time of COVID-19

Coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is rapidly spreading across the globe. The clinical spectrum of SARS-CoV-2 pneumonia requires early detection and monitoring, within a clinical environment for critical cases and remotely for mild cases, with a large spectrum of symptoms. The fear of contamination in clinical environments has led to a dramatic reduction in on-site referrals for routine care. There has also been a perceived need to continuously monitor non-severe COVID-19 patients, either from their quarantine site at home, or dedicated quarantine locations (e.g. hotels). In particular, facilitating contact tracing with proximity and location tracing apps was adopted in many countries very rapidly. Thus, the pandemic has driven incentives to innovate and enhance or create new routes for providing healthcare services at distance. In particular, this has created a dramatic impetus to find innovative ways to remotely and effectively monitor patient health status. In this paper, we present a review of remote health monitoring initiatives taken in 20 states during the time of the pandemic. We emphasize in the discussion particular aspects that are common ground for the reviewed states, in particular the future impact of the pandemic on remote health monitoring and consideration on data privacy.

Investigating Circadian Heart Rate Variability in Coronary Artery Disease Patients with Various Degrees of Left Ventricle Ejection Fraction

Early and noninvasive identification of heart failure progression is an important adjunct to successful and timely intervention. Severity of heart failure (HF) was assessed by Left Ventricular Ejection Fraction (LVEF). In this paper, we explore the circadian (24-hour) heart rate variability (HRV) features from ''normal" (EF >50%), "at-risk" (EF <40%), and "border-line" (40% ≤ EF ≤ 50%) patient data to determine whether HRV features can predict the stage of heart failure. All coronary artery disease (CAD) 24-hour circadian heart rate data were fitted by a cosinor analysis algorithm. Hourly HRV features from time- and frequency-domains were then extracted from all 24-hour patient data. A one-way ANOVA test was performed followed by a Tukey post-hoc multiple comparison test to investigate the differences among the three groups. The results showed a statistically significant difference between the three groups when using the normalized high frequency (HF Norm), low frequency peak (LF Peak), and the normalized very-low frequency (VLF Norm) for the 05:00-06:00 and 18:00-19:00 time periods. These results highlight a possible link between the circadian variation of sympathetic and parasympathetic nervous system activity and LVEF for CAD patients. The results could be useful in differentiating the various degrees of LVEF by using only noninvasive HRV features derived over a 24-hour period.Clinical relevance- The proposed method could be clinically useful to estimate the extent of LVEF associated with the severity of heart failure by recording the circadian variation of the heart rate in CAD patients. However, further clinical trials on a larger cohort of patients and controls are required.

Prediction of Stress and Mental Workload during Police Academy Training Using Ultra-Short-Term Heart Rate Variability and Breathing Analysis

Heart rate variability (HRV) has been studied in the context of human behavior analysis and many features have been extracted from the inter-beat interval (RR) time series and tested as correlates of constructs such as mental workload, stress and anxiety. Such constructs are crucial in assessing quality-of-life of individuals, as well as their overall performance when doing critical tasks. Most studies, however, have been conducted in controlled laboratory environments with artificially-induced psychological responses. While this assures that high quality data are collected, the amount of data is limited and the transferability of the findings to more ecologically-appropriate settings remains unknown. Additionally, it is desirable for such mental state monitoring systems to have high temporal resolution, thus allowing for quick feedback and adaptive decision making. In this article, we explore the use of features computed from time windows much shorter than typically reported in the literature. More specifically, we evaluate the potential of HRV and breathing features computed over so-called ultra-short-term segments (i.e., < 5 minutes) for stress and mental workload prediction. Experiments with 27 police academy trainees show that short time windows as low as 60 seconds can provide useful insights, in particular for mental workload assessment. Moreover, the fusion of HRV and breathing features showed to be an important aspect for reliable behavioural assessment in highly ecological settings.

Spontaneous Right Ventricular Pseudoaneurysms and Increased Arrhythmogenicity in a Mouse Model of Marfan Syndrome

Patients with Marfan syndrome (MFS), a connective tissue disorder caused by pathogenic variants in the gene encoding the extracellular matrix protein fibrillin-1, have an increased prevalence of primary cardiomyopathy, arrhythmias, and sudden cardiac death. We have performed an in-depth in vivo and ex vivo study of the cardiac phenotype of Fbn1mgR/mgR mice, an established mouse model of MFS with a severely reduced expression of fibrillin-1. Using ultrasound measurements, we confirmed the presence of aortic dilatation and observed cardiac diastolic dysfunction in male Fbn1mgR/mgR mice. Upon post-mortem examination, we discovered that the mutant mice consistently presented myocardial lesions at the level of the right ventricular free wall, which we characterized as spontaneous pseudoaneurysms. Histological investigation demonstrated a decrease in myocardial compaction in the MFS mouse model. Furthermore, continuous 24 h electrocardiographic analysis showed a decreased heart rate variability and an increased prevalence of extrasystolic arrhythmic events in Fbn1mgR/mgR mice compared to wild-type littermates. Taken together, in this paper we document a previously unreported cardiac phenotype in the Fbn1mgR/mgR MFS mouse model and provide a detailed characterization of the cardiac dysfunction and rhythm disorders which are caused by fibrillin-1 deficiency. These findings highlight the wide spectrum of cardiac manifestations of MFS, which might have implications for patient care.

Ambulatory Electrocardiographic Monitoring and Ectopic Beat Detection in Conscious Mice

Ambulatory electrocardiography (AECG) is a primary diagnostic tool in patients with potential arrhythmic disorders. To study the pathophysiological mechanisms of arrhythmic disorders, mouse models are widely implemented. The use of a technique similar to AECG for mice is thus of great relevance. We have optimized a protocol which allows qualitative, long-term ECG data recording in conscious, freely moving mice. Automated algorithms were developed to efficiently process the large amount of data and calculate the average heart rate (HR), the mean peak-to-peak interval and heart rate variability (HRV) based on peak detection. Ectopic beats are automatically detected based on aberrant peak intervals. As we have incorporated a multiple lead configuration in our ECG set-up, the nature and origin of the suggested ectopic beats can be analyzed in detail. The protocol and analysis tools presented here are promising tools for studies which require detailed, long-term ECG characterization in mouse models with potential arrhythmic disorders.

Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review

BACKGROUND

The electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare. Deep learning methods have achieved promising results on predictive healthcare tasks using ECG signals.

OBJECTIVE

This paper presents a systematic review of deep learning methods for ECG data from both modeling and application perspectives.

METHODS

We extracted papers that applied deep learning (deep neural network) models to ECG data that were published between January 1st of 2010 and February 29th of 2020 from Google Scholar, PubMed, and the Digital Bibliography & Library Project. We then analyzed each article according to three factors: tasks, models, and data. Finally, we discuss open challenges and unsolved problems in this area.

RESULTS

The total number of papers extracted was 191. Among these papers, 108 were published after 2019. Different deep learning architectures have been used in various ECG analytics tasks, such as disease detection/classification, annotation/localization, sleep staging, biometric human identification, and denoising.

CONCLUSION

The number of works on deep learning for ECG data has grown explosively in recent years. Such works have achieved accuracy comparable to that of traditional feature-based approaches and ensembles of multiple approaches can achieve even better results. Specifically, we found that a hybrid architecture of a convolutional neural network and recurrent neural network ensemble using expert features yields the best results. However, there are some new challenges and problems related to interpretability, scalability, and efficiency that must be addressed. Furthermore, it is also worth investigating new applications from the perspectives of datasets and methods.

SIGNIFICANCE

This paper summarizes existing deep learning research using ECG data from multiple perspectives and highlights existing challenges and problems to identify potential future research directions.

Beat-to-Beat Patterning of Sinus Rhythm Reveals Non-linear Rhythm in the Dog Compared to the Human

The human and dog have sinus arrhythmia; however, the beat-to-beat interval changes were hypothesized to be different. Geometric analyses (R–R interval tachograms, dynamic Poincaré plots) to examine rate changes on a beat-to-beat basis were analyzed along with time and frequency domain heart rate variability from 40 human and 130 canine 24-h electrocardiographic recordings. Humans had bell-shaped beat-interval distributions, narrow interval bands across time with continuous interval change and linear changes in rate. In contrast, dogs had skewed non-singular beat distributions, wide interval bands {despite faster average heart rate of dogs [mean (range); 81 (64–119)] bpm compared to humans [74.5 (59–103) p = 0.005]} with regions displaying a paucity of intervals (zone of avoidance) and linear plus non-linear rate changes. In the dog, dynamic Poincaré plots showed linear rate changes as intervals prolonged until a point of divergence from the line of identity at a mean interval of 598.5 (95% CI: 583.5–613.5) ms (bifurcation interval). The dog had bimodal beat distribution during sleep with slower rates and greater variability than during active hours that showed singular interval distributions, higher rates and less variability. During sleep, Poincaré plots of the dog had clustered or branched patterns of intervals. A slower rate supported greater parasympathetic modulation with a branched compared to the clustered distribution. Treatment with atropine eliminated the non-linear patterns, while hydromorphone shifted the bifurcated branching and beat clustering to longer intervals. These results demonstrate the unique non-linear nature of beat-to-beat variability in the dog compared to humans with increases in interval duration (decrease heart rate). These results provoke the possibility that changes are linear with a dominant sympathetic modulation and non-linear with a dominant parasympathetic modulation. The abrupt bifurcation, zone of avoidance and beat-to-beat patterning are concordant with other studies demonstrating the development of exit block from the sinus node with parasympathetic modulation influencing not only the oscillation of the pacing cells, but conduction to the atria. Studies are required to associate the in vivo sinus node beat patterns identified in this study to the mapping of sinus impulse origin and exit from the sinus node.

Acclimation to a thermoneutral environment abolishes age-associated alterations in heart rate and heart rate variability in conscious, unrestrained mice

Mice are among the most widely used translational models of cardiovascular aging and offer a method to quickly assess lifespan changes in a controlled environment. The standard laboratory temperature (20-22 °C), however, imposes a cold stress on mice that causes an increase in sympathetic nervous system-mediated activation of brown adipose tissue (BAT) to maintain a core body temperature of 36-37 °C. Thus, while physiologic data obtained recapitulate human physiology to a certain degree, interpretations of previous research in mice may have been contaminated by a cold stress, due to housing mice below their thermoneutral zone (30 °C). The purpose of this investigation was to examine how chronic sympathetic stimulation evoked by acclimation to 20 °C might obscure interpretation of changes in autonomic modulation of heart rate (HR) and heart rate variability (HRV) that accompany advancing age. HR and HRV before and after administration of a dual-autonomic blockade were measured via in-vivo ECG in young (3 months) and aged (30 months) male C57BL/6 telemetry-implanted mice following temperature acclimation for 3 days at 30 °C or 20 °C. Mean basal and intrinsic HR of both young and aged mice became markedly reduced at 30 °C compared to 20 °C. In both age groups, HRV parameters in time, frequency, and non-linear domains displayed increased variability at 30 °C compared to 20 °C under basal conditions. Importantly, age-associated declines in HRV observed at 20 °C were ameliorated when mice were studied at their thermoneutral ambient temperature of 30 °C. Thus, an accurate understanding of autonomic modulation of cardiovascular functions in mice of advanced age requires that they are housed in a metabolically neutral environment.

Resting-state fMRI data of awake dogs (Canis familiaris) via group-level independent component analysis reveal multiple, spatially distributed resting-state networks

Resting-state networks are spatially distributed, functionally connected brain regions. Studying these networks gives us information about the large-scale functional organization of the brain and alternations in these networks are considered to play a role in a wide range of neurological conditions and aging. To describe resting-state networks in dogs, we measured 22 awake, unrestrained individuals of both sexes and carried out group-level spatial independent component analysis to explore whole-brain connectivity patterns. In this exploratory study, using resting-state functional magnetic resonance imaging (rs-fMRI), we found several such networks: a network involving prefrontal, anterior cingulate, posterior cingulate and hippocampal regions; sensorimotor (SMN), auditory (AUD), frontal (FRO), cerebellar (CER) and striatal networks. The network containing posterior cingulate regions, similarly to Primates, but unlike previous studies in dogs, showed antero-posterior connectedness with involvement of hippocampal and lateral temporal regions. The results give insight into the resting-state networks of awake animals from a taxon beyond rodents through a non-invasive method.

Potential Physiological Parameters to Indicate Inner States in Dogs: The Analysis of ECG, and Respiratory Signal During Different Sleep Phases

The sleeping activity of family dogs has been studied increasingly in the past years. Recently, a validated, non-invasive polysomnographic method has been developed for dogs, enabling the parallel recording of several neurophysiological signals on non-anesthetized family dogs, including brain activity (EEG), eye movements (EOG), cardiac (ECG), and respiratory activity (PNG). In this study, we examined the ECG (N = 30) and respiratory signals (N = 19) of dogs during a 3-h sleep period in the afternoon, under laboratory conditions. We calculated four time-domain heart rate variables [mean heart rate (HR), SDNN, RMSSD, and pNN50] from the ECG and the estimated average respiratory frequency from the respiratory signal. We analyzed how these variables are affected by the different sleep-wake phases (wakefulness, drowsiness, NREM, and REM) as well as the dogs' sex, age and weight. We have found that the sleep-wake phase had a significant effect on all measured cardiac parameters. In the wake phase, the mean HR was higher than in all other phases, while SDNN, RMSSD, and pNN50 were lower than in all other sleep phases. In drowsiness, mean HR was higher compared to NREM and REM phases, while SDNN and RMSSD was lower compared to NREM and REM phases. In REM, SDNN, and RMSSD was higher than in NREM. However, the sleep-wake phase had no effect on the estimated average respiratory frequency of dogs. The dogs' sex, age and weight had no effect on any of the investigated variables. This study represents a detailed analysis of the cardiac and respiratory activity of dogs during sleep. Since variations in these physiological signals reflect the dynamics of autonomic functions, a more detailed understanding of their changes may help us to gain a better understanding of the internal/emotional processes of dogs in response to different conditions of external stimuli. As such, our results are important since they are directly comparable to human findings and may also serve as a potential basis for future studies on dogs.

Falk T. Multi-Scale Heart Beat Entropy Measures for Mental Workload Assessment of Ambulant Users

Mental workload assessment is crucial in many real life applications which require constant attention and where imbalance of mental workload resources may cause safety hazards. As such, mental workload and its relationship with heart rate variability (HRV) have been well studied in the literature. However, the majority of the developed models have assumed individuals are not ambulant, thus bypassing the issue of movement-related electrocardiography (ECG) artifacts and changing heart beat dynamics due to physical activity. In this work, multi-scale features for mental workload assessment of ambulatory users is explored. ECG data was sampled from users while they performed different types and levels of physical activity while performing the multi-attribute test battery (MATB-II) task at varying difficulty levels. Proposed features are shown to outperform benchmark ones and further exhibit complementarity when used in combination. Indeed, results show gains over the benchmark HRV measures of 24.41% in accuracy and of 27.97% in F1 score can be achieved even at high activity levels.

Heart rate variability monitoring for emotion and disorders of emotion

BACKGROUND

Emotion is composed of cognitive processing, physiological response and behavioral reaction. Heart rate variability (HRV) refers to the fluctuations between consecutive heartbeat cycles, and is considered as a non-invasive method for evaluating cardiac autonomic function. HRV analysis plays an important role in emotional study and detection.

OBJECTIVE

In this paper, the physiological foundation of HRV is briefly described, and then the relevant literature relating to HRV-based emotion studies for the performance of HRV in different emotions, emotion recognition, the evaluation of emotional disorders, HRV biofeedback, as well as HRV-based emotion analysis and management enhanced by wearable devices, are reviewed.

SIGNIFICANCE

It is suggested that HRV is an effective tool for the measurement and regulation of emotional response, with a broad application prospect.