Poincaré plot indexes of heart rate variability detect dynamic autonomic modulation during general anesthesia induction

A comprehensive evaluation of linear and non-linear HRV parameters between paced breathing and stressful mental state

Background

Heart rate variability (HRV) is a crucial metric that provides valuable insight into the balance between relaxation and stress. Previous research has shown that most HRV parameters improve during periods of mental relaxation, while decreasing during tasks involving cognitive workload. Although a comprehensive analysis of both linear and non-linear HRV parameters has been carried out in existing literature, there still exists a need for further research in this area. Additionally, limited knowledge exists regarding how specific interventions may influence the interpretation of these parameters and how the different parameters correlate under different interventions. This study aims to address these gaps by conducting a thorough comparison of different linear and non-linear HRV parameters under mentally relaxed versus stressful states.

Methodology

Participants were randomly and equally divided among two between-subjects groups: relaxed-stress (RS) (N = 22) and stress-relaxed (SR) (N = 22). In the RS group, a paced breathing task was given for 5 min to create relaxation, and was followed by a 5-min time-based mental calculation task to create stress. In the SR group, the order of the stress and relaxed tasks was reversed. There was a washout period of 15 min after the first task in both groups.

Results

Of the 37 HRV parameters, 33 differed significantly between the two interventions. The majority of the parameters exhibited an improving and degrading tendency of HRV parameters in the relaxed and stressed states, respectively. The correlation of the majority of HRV parameters decreases during stress, while prominent time domain and geometric domain parameters stand out in the correlation.

Conclusion

Overall, HRV parameters can be reliably used to assess a person's relaxed and stressed mental states during paced breathing and mental arithmetic task respectively. Furthermore, non-linear HRV parameters provide accurate estimators of the mental state, in addition to the commonly used linear parameters.

Unravelling COVID-19 waves in Rio de Janeiro city: Qualitative insights from nonlinear dynamic analysis

Since the COVID-19 pandemic was first reported in 2019, it has rapidly spread around the world. Many countries implemented several measures to try to control the virus spreading. The healthcare system and consequently the general quality of life population in the cities have all been significantly impacted by the Coronavirus pandemic. The different waves of contagious were responsible for the increase in the number of cases that, unfortunately, many times lead to death. In this paper, we aim to characterize the dynamics of the six waves of cases and deaths caused by COVID-19 in Rio de Janeiro city using techniques such as the Poincaré plot, approximate entropy, second-order difference plot, and central tendency measures. Our results reveal that by examining the structure and patterns of the time series, using a set of non-linear techniques we can gain a better understanding of the role of multiple waves of COVID-19, also, we can identify underlying dynamics of disease spreading and extract meaningful information about the dynamical behavior of epidemiological time series. Such findings can help to closely approximate the dynamics of virus spread and obtain a correlation between the different stages of the disease, allowing us to identify and categorize the stages due to different virus variants that are reflected in the time series.

Heart rate variability, recovery and stress analysis of an elite rally driver and co-driver during a competition period

To ensure both optimal health and performances, monitoring physiological and psychological states is of main importance for athletes. It is well known that monitoring heart rate variability and using validated questionnaires is useful for monitoring both the health and training status of athletes of different sports. Motorsports such as rally require high levels of physical and mental preparation thus information about psychophysiological status of rally athletes is fundamental. The aim of this study was to assess the autonomic regulation, stress, recovery conditions of one driver and one co-driver competing at the Italian National Rally Championship during their competition period. Heart rate variability parameters, acute recovery and stress states were assessed the day before, during the two days of race and the day following the races. Results showed that driver and co-driver had a sharp decrease of mean RR intervals, root mean square of successive differences between normal heartbeats, and standard deviation of the N-N interval during race days, while the stress index showed the inverse trend, and this behaviour was clearly visible in the Poincaré plots and power spectrum density graphs. The acute recovery and stress states questionnaire showed significant differences in recovery and stress scoring for the driver but not for the co-driver, although the trends were similar. This study describes the psychophysiological demands of a rally competition period suggesting that a daily evaluation of heart rate variability, recovery, stress states is useful for monitoring health status in rally athletes and could be implemented to make decision about training and recovery strategies.

Calming Hungarian Grey Cattle in Headlocks Using Processed Nasal Vocalization of a Mother Cow

Sound analysis is an important field of research for improving precision livestock farming systems. If the information carried by livestock sounds is interpreted correctly, it could be used to improve management and welfare assessment in this field. Therefore, we hypothesized that the nasal vocalization of a mother cow could have a calming effect on conspecifics. The nasal vocalization in our study was recorded from a mother cow (not part of the test herd) while it was licking its day-old calf. The raw sound was analyzed, cleaned from noises, and the most representative vocalization was lengthened to two minutes. Thirty cows having calves were randomly selected from eighty Hungarian grey cattle cows. Two test days were selected, one week apart; the weather circumstances in both days were similar. The herd was collected in a paddock, and the test site (a restraining crate with a headlock) was 21 m away from them. The cows from the herd were gently moved to the restraining crate, and, after the installation of the headlock, Polar® heart rate monitors were fixed on the animals. The recording of the RR intervals was carried out for two minutes. On day one of the test, the processed nasal sound was played to every second cow during the heart rate monitoring. When the sound ended, the heart rate monitor was removed. On test day two, the sound and no sound treatments were switched among the participating cows. At the end of the measurement, the headlock was opened, letting the animals out voluntarily, and a flight test was performed along a 5 m distance. The time needed to pass the 5 m length was measured with a stopwatch and divided by the distance. The RR intervals were analyzed with the Kubios HRV Standard (ver. 3.5.0) software. The following data were recorded for the entire measurement: average and maximum heart rate; SD1 and SD2; pNN50; VLF, LF, and HF. The quasi-periodic signal detected in the sound analyses can hardly be heard, even when it is enhanced to the maximum. This can be considered a vibration probably caused by the basis of articulation, such as a vibration of the tongue, for example. The SD2/SD1 ratio (0.97 vs. 1.07 for the animals having no sound and sound played, respectively, p = 0.0110) and the flight speed (0.92 vs. 1.08 s/m for the animals having no sound and sound played, respectively, p = 0.0409) indicate that the sound treatment had a calming effect on the restrained cows. The day of the test did not influence any of the measured parameters; therefore, no effect of the routine was observed. The yes-no sequence of the sound treatment significantly reduced the pNN50 and flight speed values, suggesting a somewhat more positive association with the headlock and the effectiveness of the processed nasal sound. In conclusion, we have demonstrated that, by means of sound analyses, not only information about individuals and the herd can be gathered but that, with proper processing, the sound obtained can be used to improve animal welfare.

Fractal complexity alternations in paroxysmal atrial fibrillation patients with and without recurrence after pulmonary vein isolation

BACKGROUND

Pulmonary vein isolation (PVI) is a cornerstone therapy for paroxysmal atrial fibrillation (PAF). The variations in nonlinear heart rate variability (HRV) between patients with and without recurrences remain unclear. We aimed to characterize the nonlinear HRV before and after PVI in patients with and without recurrence.

METHODS

Twenty-five drug-refractory PAF patients (56.0 ± 9.1 years old, 20 males) who received PVI were enrolled. Holter electrocardiography were performed before, 1-3, and 6-12 months after PVI. After 8.2 ± 2.5 months of follow-ups after PVI, patients were divided into two groups: the recurrence (n = 8) and non-recurrence (n = 17) groups. Linear and nonlinear HRV variables were analyzed, including the Poincaré Plot analysis and the Detrended Fluctuation Analysis (DFA).

RESULTS

The non-recurrence group, but not the recurrence group, had decreased high-frequency component (HF), the root mean square of successive RR interval differences (RMSSD), and the Poincaré Plot index SD1 1-3 months after PVI and increased DFAslope2 6-12 months after PVI. The non-recurrence group's LF/HF ratio and DFAslope1 decreased significantly 1-3 and 6-12 months after PVI, respectively, whereas there was no significant change in the recurrence group after PVI.

CONCLUSIONS

Significantly reduced vagal tone 1-3 months after PVI, increased long-term fractal complexity 6-12 months after PVI, and decreased sympathetic tone as well as short-term fractal complexity 1-3 and 6-12 months after PVI led to a better AF-free survival after PVI. These findings suggest that neuromodulation and heart rate dynamics play crucial roles in AF recurrence following PVI.

The effect of paired associative stimulation with a high-intensity cortical component and a high-frequency peripheral component on heart rate and heart rate variability in healthy subjects

Objective

A novel protocol for paired associative stimulation (PAS), called high PAS, consists of high-intensity transcranial magnetic stimulation (TMS) and high-frequency peripheral nerve stimulation (PNS). High PAS was developed for spinal cord injury rehabilitation and targets plastic changes in stimulated pathways in the corticospinal tract, which improves motor function. As therapy interventions can last many weeks, it is important to fully understand the effects of high PAS, including its effect on the cardiovascular system. Heart rate variability (HRV) has been used to measure changes in both sympathetic and parasympathetic systems.

Methods

We used short-term HRV measurements to evaluate the effects of one 20-min session of high PAS on 17 healthy individuals. HRV was recorded for 5 min before (PRE), during (STIM), immediately after (POST), 30 min after (POST30), and 60 min after (POST60) the stimulation. Five participants repeated the HRV setup with sham stimulation.

Results

A significant decrease in low-frequency (LF) power (n.u.) (p = 0.002), low-frequency to high-frequency (HF) ratio (p = 0.017), in Poincaré plot [the standard deviation of RR intervals perpendicular to (SD1) and along (SD2) the line of identity SD2/SD1 ratio p < 0.001], and an increase in HF power (n.u.) (p = 0.002) were observed between PRE and STIM conditions; these changes were fully reversible immediately after stimulation. PRE to POST by 3% (p = 0.015) and continued to decline until POST60 by 5% (p = 0.011). LF power (ms2) (p = 0.017) and SD2 (p = 0.015) decreased from PRE to STIM and increased from PRE to POST (p = 0.025 and p = 0.017, respectively). The results from sham PAS exhibited a trend similar to active high-PAS stimulation.

Conclusions

High PAS does not have sustained effects during 60-min follow-up on cardiovascular functions, as measured by HRV. None of the short-term results indicates activation of the sympathetic nervous system in healthy individuals. Observed changes in HRV indicate higher parasympathetic activity during stimulation, which is reversible, and is plausibly explained by the fact that the participants spend 20 min without moving, talking, or using phones while being stimulated.

Development of an Interactive Dashboard to Analyse Physiological Signals in the Neonatal Intensive Care Unit

Premature babies and those born with a medical condition are cared for within the neonatal intensive care unit (NICU) in hospitals. Monitoring physiological signals and subsequent analysis and interpretation can reveal acute and chronic conditions for these neonates. Several advanced algorithms using physiological signals have been built into existing monitoring systems to allow clinicians to analyse signals in real time and anticipate patient deterioration. However, limited enhancements have been made to interactively visualise and adapt them to neonatal monitoring systems. To bridge this gap, we describe the development of a user-friendly and interactive dashboard for neonatal vital signs analysis written in the Python programming language where the analysis can be performed without prior computing knowledge. To ensure practicality, the dashboard was designed in consultation with a neonatologist to visualise electrocardiogram, heart rate, respiratory rate and oxygen saturation data in a time-series format. The resulting dashboard included interactive visualisations, advanced electrocardiogram analysis and statistical analysis which can be used to extract important information on patients' conditions.Clinical Relevance- This will support the care of preterm infants by allowing clinicians to visualise and interpret physiological data in greater granularity, aiding in patient monitoring and detection of adverse conditions. The detection of adverse conditions could allow timely and potentially life-saving interventions for conditions such as sepsis and brain injury.

Physiological Signal Analysis and Stress Classification from VR Simulations Using Decision Tree Methods

Stress is induced in response to any mental, physical or emotional change associated with our daily experiences. While short term stress can be quite beneficial, prolonged stress is detrimental to the heart, muscle tissues and immune system. In order to be proactive against these symptoms, it is important to assess the impact of stress due to various activities, which is initially determined through the change in the sympathetic (SNS) and parasympathetic (PNS) nervous systems. After acquiring physiological data wirelessly through captive electrocardiogram (ECG), galvanic skin response (GSR) and respiration (RESP) sensors, 21 time, frequency, nonlinear, GSR and respiration features were manually extracted from 15 subjects ensuing a baseline phase, virtual reality (VR) roller coaster simulation, color Stroop task and VR Bubble Bloom game. This paper presents a comprehensive physiological analysis of stress from an experiment involving a VR video game Bubble Bloom to manage stress levels. A personalized classification and regression tree (CART) model was developed using a novel Gini index algorithm in order to effectively classify binary classes of stress. A novel K-means feature was derived from 11 other features and used as an input in the Decision Tree (DT) algorithm, strong learners Ensemble Gradient Boosting (EGB) and Extreme Gradient Boosting (XGBoost (XGB)) embedded in a pipeline to classify 5 classes of stress. Results obtained indicate that heart rate (HR), approximate entropy (ApEN), low frequency and high frequency ratio (LF/HF), low frequency (LF), standard deviation (SD1), GSR and RESP all reduced and high frequency (HF) increased following the VR Bubble Bloom game phase. The personalized CART model was able to classify binary stress with 87.75% accuracy. It proved to be more effective than other related studies. EGB was able to classify binary stress with 100% accuracy, which outperformed every other related study. XGBoost and DT were able to classify five classes of stress with 72.22% using the novel K-means feature. This feature produced less error and better model performance in comparison to using all the features. Results substantiate that our proposed methods were more effective for stress classification than most related studies.

A New ECG Data Processing Approach to Developing an Accurate Driving Fatigue Detection Framework with Heart Rate Variability Analysis and Ensemble Learning

More than 1.3 million people are killed in traffic accidents annually. Road traffic accidents are mostly caused by human error. Therefore, an accurate driving fatigue detection system is required for drivers. Most driving fatigue detection studies concentrated on improving feature engineering and classification methods. We propose a novel driving fatigue detection framework concentrating on the development of the preprocessing, feature extraction, and classification stages to improve the classification accuracy of fatigue states. The proposed driving fatigue detection framework measures fatigue using a two-electrode ECG. The resampling method and heart rate variability analysis were used to extract features from the ECG data, and an ensemble learning model was utilized to classify fatigue states. To achieve the best model performance, 40 possible scenarios were applied: a combination of 5 resampling scenarios, 2 feature extraction scenarios, and 4 classification model scenarios. It was discovered that the combination of a resampling method with a window duration of 300 s and an overlap of 270 s, 54 extracted features, and AdaBoost yielded an optimum accuracy of 98.82% for the training dataset and 81.82% for the testing dataset. Furthermore, the preprocessing resampling method had the greatest impact on the model’s performance; it is a new approach presented in this study.

ECG-Based Semi-Supervised Anomaly Detection for Early Detection and Monitoring of Epileptic Seizures

Epilepsy is one of the most common brain diseases, characterized by frequent recurrent seizures or "ictal" states. A patient experiences uncontrollable muscular contractions, inducing loss of mobility and balance, which may result in injury or even death during these ictal states. Extensive investigation is vital to establish a systematic approach for predicting and informing patients about oncoming seizures ahead of time. Most methodologies developed are focused on the detection of abnormalities using mostly electroencephalogram (EEG) recordings. In this regard, research has indicated that certain pre-ictal alterations in the Autonomic Nervous System (ANS) can be detected in patient electrocardiogram (ECG) signals. The latter could potentially provide the basis for a robust seizure prediction approach. The recently proposed ECG-based seizure warning systems utilize machine learning models to classify a patient's condition. Such approaches require the incorporation of large, diverse, and thoroughly annotated ECG datasets, limiting their application potential. In this work, we investigate anomaly detection models in a patient-specific context with low supervision requirements. Specifically, we consider One-Class SVM (OCSVM), Minimum Covariance Determinant (MCD) Estimator, and Local Outlier Factor (LOF) models to quantify the novelty or abnormality of pre-ictal short-term (2-3 min) Heart Rate Variability (HRV) features of patients, trained on a reference interval considered to contain stable heart rate as the only form of supervision. Our models are evaluated against labels that were either hand-picked or automatically generated (weak labels) by a two-phase clustering procedure for samples of the "Post-Ictal Heart Rate Oscillations in Partial Epilepsy" (PIHROPE) dataset recorded by the Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, achieving detection in 9 out of 10 cases, with average AUCs of over 93% across all models and warning times ranging from 6 to 30 min prior to seizure. The proposed anomaly detection and monitoring approach can potentially pave the way for early detection and warning of seizure incidents based on body sensor inputs.

Correlation among Poincare plot and traditional heart rate variability indices in adults with different risk levels of metabolic syndrome: a cross-sectional approach from Southern India

OBJECTIVES

Heart rate variability (HRV) is an important marker of cardiac autonomic modulation. Metabolic syndrome (MetS) can alter cardiac autonomic modulation, raising the risk of cardiovascular disease (CVD). Poincaré plot analysis (PPA) is a robust scatter plot-based depiction of HRV and carries similar information to the traditional HRV measures. However, no prior studies have examined the relationship between PPA and traditional HRV measures among different risk levels of MetS. We evaluated the association between the Poincare plot and traditional heart rate variability indices among adults with different risk levels of MetS.

METHODS

We measured anthropometric data and collected fasting blood samples to diagnose MetS. The MetS risk was assessed in 223 participants based on the number of MetS components and was classified as control (n=64), pre-MetS (n=49), MetS (n=56), and severe MetS (n=54). We calculated the Poincaré plot (PP) and traditional HRV measures from a 5 min HRV recording.

RESULTS

Besides the traditional HRV measures, we found that various HRV indices of PPA showed significant differences among the groups. The severe MetS group had significantly lower S (total HRV), SD1 (short-term HRV), SD2 (long-term HRV), and higher SD2/SD1. The values of S, SD1, SD2, and SD2/SD1 were significantly correlated with most traditional HRV measures.

CONCLUSIONS

We found gradual changes in HRV patterns as lower parasympathetic and higher sympathetic activity alongside the rising number of MetS components. The HRV indices of PPA integrating the benefits of traditional HRV indices distinguish successfully between different risk levels of MetS and control subjects.

Heart Rate Variability in Children with Moderate and Severe Traumatic Brain Injury: A Prospective Observational Study

The aim of this study was to assess the feasibility of continuous monitoring of heart rate variability (HRV) in children with traumatic brain injury (TBI) hospitalized in a pediatric intensive care unit (PICU) and collect preliminary data on the association between HRV, neurological outcome, and complications. This is a prospective observational cohort study in a tertiary academic PICU. Children admitted to the PICU ≤24 hours after moderate or severe TBI were included in the study. Children suspected of being brain dead at PICU entry or with a pacemaker were excluded. Children underwent continuous monitoring of electrocardiographic (ECG) waveforms over 7 days post-TBI. HRV analysis was performed retrospectively, using a standardized, validated HRV analysis software (CIMVA). The occurrence of medical complications (“event”: intracranial hypertension, cerebral hypoperfusion, seizure, and cardiac arrest) was prospectively documented. Outcome of children 6 months post-TBI was assessed using the Glasgow Outcome Scale – Extended Pediatric (GOS-E Peds). Fifteen patients were included over a 20-month period. Thirteen patients had ECG recordings available and 4 had >20% of missing ECG data. When ECG was available, HRV calculation was feasible (average 88%; range 70–97%). Significant decrease in overall HRV coefficient of variation and Poincaré SD2 (p < 0.05) at 6 hours post–PICU admission was associated with an unfavorable outcome (defined as GOS-E Peds ≥ 3, or a deterioration of ≥2 points over baseline score). Several HRV metrics exhibited significant and nonsignificant variation in HRV during event. This study demonstrates that it is feasible to monitor HRV in the PICU provided ECG data are available; however, missing ECG data are not uncommon. These preliminary data suggest that altered HRV is associated with unfavorable neurological outcome and in-hospital medical complications. Larger prospective studies are needed to confirm these findings and to explore if HRV offers reliable and clinically useful prediction data that may help clinical decision making.

Diet-induced obesity leads to sleep fragmentation independently of the severity of sleep-disordered breathing

Obesity is associated with sleep-disordered breathing (SDB) and unrefreshing sleep. Residual daytime sleepiness and sleep impairments often persist after SDB treatment in patients with obesity, which suggests an independent effect of obesity on breathing and sleep. However, examining the relationship between sleep architecture and SDB in patients with obesity is complex and can be confounded by multiple factors. The main goal of this study was to examine the relationship between obesity-related changes in sleep architecture and SDB. Sleep recordings were performed in 15 lean C57BL/6J and 17 diet-induced obesity (DIO) mice of the same genetic background. Arousals from sleep and apneas were manually scored. Respiratory arousals were classified as events associated with ≥30% drops in minute ventilation (VE) from baseline. We applied Poincaré analysis of VE during sleep to estimate breathing variability. Obesity augmented the frequency of arousals by 45% and this increase was independent of apneas. Respiratory arousals comprised only 15% of the arousals in both groups of mice. Breathing variability during non-rapid-eye-movment (NREM) sleep was significantly higher in DIO mice, but it was not associated with arousal frequency. Our results suggest that obesity induces sleep fragmentation independently of SDB severity.NEW & NOTEWORTHY Our diet-induced obesity (DIO) model reproduces sleep features of human obesity, including sleep fragmentation, increased apnea frequency, and larger breathing variability. DIO induces sleep fragmentation independently of apnea severity. Sleep fragmentation in DIO mice is mainly attributed to non-respiratory arousals. Increased breathing variability during sleep did not account for the higher arousal frequency in DIO. Our results provide a rationale to examine sleep in patients with obesity even when they are adequately treated for sleep-disordered breathing.

A Biophysical Model for Cardiovascular Effects of Acupuncture-Underlying Mechanisms Based on First Principles

According to recent translations by medical professionals of the foundational texts of Chinese Medicine, the acupuncture channel system can be reconciled with the neurovasculature. From there, the underlying mechanisms of the effects of acupuncture can be drawn from established physiology and known physical laws. A large body of research has been carried out using cardiovascular markers to measure the effects of acupuncture. Three of these parameters are re-viewed and explored anew in detail. The focus is on changes in microcirculation, blood pressure, and heart rate variability. The physiological mechanisms accounting for the observed changes are proposed to be ascending vasodilatation, resetting of the baroreceptor reflex, and re-organization of heart beating patterns around intrinsically assigned attractor sets.

Calcium Chloride Toxicology for Food Safety Assessment Using Zebrafish (Danio rerio) Embryos

The salt calcium chloride (CaCl₂) is widely used in industry as a food additive; levels for human consumption are regulated by international or governmental agencies. Generally, the food industry relies on toxicity studies conducted in mammals such as mice, rats, and rabbits for determining food safety. However, testing in mammals is time-consuming and expensive. Zebrafish have been used in a range of toxicological analyses and offer advantages with regard to sensitivity, time, and cost. However, information in not available with regard to whether the sensitivity of zebrafish to CaCl₂ is comparable to the concentrations of CaCl₂ used as food additives. The aim of this study was to compare the CaCl₂ tolerance of zebrafish embryos and larvae with concentrations currently approved as food additives. Acute toxicity, embryotoxicity, cardiotoxicity, and neurotoxicity assays were used to determine the threshold toxic concentration of CaCl₂ in zebrafish embryos and larvae. The data showed that doses above 0.4% had toxic effects on development and on the activity of the cardiac and neuronal systems. Furthermore, all embryos exposed to 0.8 and 1.6% of CaCl₂ died after 24 hpf. These findings are consistent with the limits of CaCl₂ concentrations approved by Codex Alimentarius. Therefore, zebrafish embryos could be suitable for screening food additives.

The association between chronotype and sleep quality among female home care workers performing shift work

There is a scarcity of evidence on the association between shift work, sleeping parameters, heart rate variability (HRV), and chronotype, i.e., morningness and eveningness. The aims of this study were to 1) compare participants with different chronotypes (morning (M), evening (E), or neither (N)) in terms of their total sleep time, sleep efficiency, and HRV parameters, taking their age into account, and 2) examine whether self-reported work-related stress, the length of the working career and years performing shift work affect this association. The participants of the study were home care workers working in two shifts in one municipality in Eastern Finland (N = 395). Of these, 52 females (mean age 42.78 y, SD 12.92 y) completed the study questionnaire and participated in physiological measurements. Several sleep-related parameters were assessed (total sleep time, sleep efficiency, number of awakenings, and length of awakening) and indices of autonomic nervous system based on HRV were calculated. The participants worked in two shifts: a morning shift (7:00-15:00 h) and an evening shift (14:00-21:30 h). All these parameters were assessed during the night before the first work shift (N1), the night before the second work shift (N2), the night before the final work shift (N3), and the night before the first day off work (N4). According to the results, 21.2% of the participants were M-types, 17.3% were E-types, and 61.5% were N-types. On average, the participants had been in working life for 18.8 years and performing shift work for 13.7 years. On night N3, E-types had a significantly shorter total sleep time and spent less time in bed compared to M- and N-types. The total sleep time of M-type and N-type participants was on average 66 minutes and 82 minutes longer, respectively, when compared to E-types on night N3. There were no statistically significant differences in actigraphy-based sleep quality parameters between M-, N-, and E-types on nights N1, N2, and N4. Our results together indicate that M- and N-type individuals may have better sleep quality than E-types, which was also reflected in HRV parameters. Further research with longitudinal study design and workplace interventions is needed to determine how the chronotype can be optimally and individually utilized to improve the health and well-being of morning-type and evening-type people. This is particularly important for both younger and older workers entering the workforce to support healthier and longer working lives.

Does the ventricle limit cardiac contraction rate in the anoxic turtle (Trachemys scripta)? II. In vivo and in vitro assessment of the prevalence of cardiac arrhythmia and atrioventricular block

Previous studies have reported evidence of atrio-ventricular (AV) block in the oxygen-limited Trachemys scripta heart. However, if cardiac arrhythmia occurs in live turtles during prolonged anoxia exposure remains unknown. Here, we compare the effects of prolonged anoxic submergence and subsequent reoxygenation on cardiac electrical activity through in vivo electrocardiogram (ECG) recordings of 21 °C- and 5 °C-acclimated turtles to assess the prevalence of cardiac arrhythmia. Additionally, to elucidate the influence of extracellular conditions on the prominence of cardiac arrhythmia, we exposed spontaneously contracting T. scripta right atrium and electrically coupled ventricle strip preparations to extracellular conditions that sequentially and additively approximated the shift from the normoxic to anoxic extracellular condition of warm- and cold-acclimated turtles. Cardiac arrhythmia was prominent in 21 °C anoxic turtles. Arrhythmia was qualitatively evidenced by groupings of contractions in pairs and trios and quantified by an increased coefficient of variation of the RR interval. Similarly, exposure to combined anoxia, acidosis, and hyperkalemia induced arrhythmia in vitro that was not counteracted by hypercalcemia or combined hypercalcemia and heightened adrenergic stimulation. By comparison, cold acclimation primed the turtle heart to be resilient to cardiac arrhythmia. Although cardiac irregularities were present intermittently, no change in the variation of the RR interval occurred in vivo with prolonged anoxia exposure at 5 °C. Moreover, the in vitro studies at 5 °C highlighted the importance of adrenergic stimulation in counteracting AV block. Finally, at both acclimation temperatures, cardiac arrhythmia and irregularities ceased upon reoxygenation, indicating that the T. scripta heart recovers from anoxia-induced disruptions to cardiac excitation.

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Cardiac arrhythmia was prominent in 21 °C anoxic turtles.

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Cold acclimation primes the turtle heart to be resilient to the cardiac arrhythmia induced by prolonged anoxic submergence.

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Adrenergic stimulation counteracts atrioventricular block at 5 °C.

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The turtle heart recovers from anoxia-induced disruptions to cardiac electrical activity.

Altered Heart Rate Variability Early in ICU Admission Differentiates Critically Ill Coronavirus Disease 2019 and All-Cause Sepsis Patients

IMPORTANCE

Altered heart rate variability has been associated with autonomic dysfunction in a number of disease profiles, in this work we elucidate differences in the biomarker among patients with all-cause sepsis and coronavirus disease 2019.

OBJECTIVES

To measure heart rate variability metrics in critically ill coronavirus disease 2019 patients with comparison to all-cause critically ill sepsis patients.

DESIGN SETTING AND PARTICIPANTS

Retrospective analysis of coronavirus disease 2019 patients admitted to an ICU for at least 24 hours at any of Emory Healthcare ICUs between March 2020 and April 2020 up to 5 days of ICU stay. The comparison group was a cohort of all-cause sepsis patients prior to coronavirus disease 2019 pandemic.

MAIN OUTCOMES AND MEASURES

Continuous waveforms were captured from the patient monitor. The electrocardiogram was then analyzed for each patient over a 300 seconds observational window that was shifted by 30 seconds in each iteration from admission till discharge. A total of 23 heart rate variability metrics were extracted in each iteration. We use the Kruskal-Wallis and Steel-Dwass tests (p < 0.05) for statistical analysis and interpretations of heart rate variability multiple measures.

RESULTS

A total of 141 critically ill coronavirus disease 2019 patients met inclusion criteria, who were compared with 208 patients with all-cause sepsis. Three nonlinear markers, including the ratio of standard deviation derived from the Poincaré plot, sample entropy, and approximate entropy and four linear features, including mode of beat-to-beat interval, acceleration capacity, deceleration capacity, and the proportion of consecutive RR intervals that differ by more than 50 ms, were all statistically significant (p < 0.05) between the coronavirus disease 2019 and all-cause sepsis cohorts. The three nonlinear features and acceleration capacity, deceleration capacity, and beat-to-beat interval (mode) were statistically significant (p < 0.05) when comparing pairwise analysis among the combinations of survivors and nonsurvivors between the coronavirus disease 2019 and sepsis cohorts. Temporal analysis of the main markers showed low variability across the 5 days of analysis compared with sepsis patients.

CONCLUSIONS AND RELEVANCE

In this descriptive statistical study, heart rate variability measures were found to be statistically different across critically ill patients infected with severe acute respiratory syndrome coronavirus 2 and distinct from bacterial sepsis.

Cardiac Autonomic Modulation and Anti-Thyroid Peroxidase (TPO) Antibodies in Subclinical Hypothyroidism: Does a Correlation Exist?

Background: Heart rate variability (HRV) reflects the balance between the sympathetic and parasympathetic divisions of the autonomic nervous system. Anti-thyroid antibodies like anti-TPO and anti-Thyroglobulin have long been associated with thyroid dysfunction and abnormal thyroid profile testing. Subclinical hypothyroidism (SCHypo) is characterized by elevated thyroid-stimulating hormone (TSH) with normal thyroid hormones. We hypothesize that autonomic function may be deranged in anti-TPO positive sub-clinical hypothyroid cases, even before the onset of overt hypothyroidism.

Objectives: To investigate the association between anti-Thyroid Peroxidase antibodies (anti-TPOAb) positive SCHypo and sympathovagal imbalance (SVI), if any.

Methodology: The study was conducted on the age and body mass index (BMI) matched subclinical hypothyroid patients (n=52) and healthy controls (n=20). The cardiac autonomic activity was assessed by short-term HRV in the time (SDNN, RMSSD, pNN50) and frequency domains (LFms², HFms², LFnu, HFnu, TP, and LF/HF ratio). Nonlinear geometric measures (SD1, SD2, SD1/SD2, TINN, HRV triangular index) were also evaluated. Biochemical evaluation of serum thyroid profile and anti-TPOAb was done in all the subjects.

Results: Decreased HRV was observed in the anti-TPOAb positive group when compared to the antibody-negative and control groups. Significant positive correlation of anti-TPOAb with TSH, LFnu, LF/HF and negative correlation with SDNN, RMSSD, pNN50, SD1, SD1/SD2, HFnu, and TP of HRV was observed.

Conclusion: Anti-TPOAb positive SCHypo group exhibited modifications in HRV characterized by decreased parasympathetic modulation, as compared to controls. The findings were also suggestive of increased risk of autonomic dysfunction in TPOAb-positive patients, as compared to antibody negative. An increase in anti-TPO antibodies was significantly correlated with TSH and SVI in SCHypo patients.

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.

Flexible Wearable Sensors for Cardiovascular Health Monitoring

Cardiovascular diseases account for the highest mortality globally, but recent advances in wearable technologies may potentially change how these illnesses are diagnosed and managed. In particular, continuous monitoring of cardiovascular vital signs for early intervention is highly desired. To this end, flexible wearable sensors that can be comfortably worn over long durations are gaining significant attention. In this review, advanced flexible wearable sensors for monitoring cardiovascular vital signals are outlined and discussed. Specifically, the functional materials, configurations, mechanisms, and recent advances of these flexible sensors for heart rate, blood pressure, blood oxygen saturation, and blood glucose monitoring are highlighted. Different mechanisms in bioelectric, mechano-electric, optoelectric, and ultrasonic wearable sensors are presented to monitor cardiovascular vital signs from different body locations. Present challenges, possible strategies, and future directions of these wearable sensors are also discussed. With rapid development, these flexible wearable sensors will potentially be applicable for both medical diagnosis and daily healthcare use in tackling cardiovascular diseases.

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

Electrocardiographic signals (ECG) and heart rate viability measurements (HRV) provide information in a range of specialist fields, extending to musical perception. The ECG signal records heart electrical activity, while HRV reflects the state or condition of the autonomic nervous system. HRV has been studied as a marker of diverse psychological and physical diseases including coronary heart disease, myocardial infarction, and stroke. HRV has also been used to observe the effects of medicines, the impact of exercise and the analysis of emotional responses and evaluation of effects of various quantifiable elements of sound and music on the human body. Variations in blood pressure, levels of stress or anxiety, subjective sensations and even changes in emotions constitute multiple aspects that may well-react or respond to musical stimuli. Although both ECG and HRV continue to feature extensively in research in health and perception, methodologies vary substantially. This makes it difficult to compare studies, with researchers making recommendations to improve experiment planning and the analysis and reporting of data. The present work provides a methodological framework to examine the effect of sound on ECG and HRV with the aim of associating musical structures and noise to the signals by means of artificial intelligence (AI); it first presents a way to select experimental study subjects in light of the research aims and then offers possibilities for selecting and producing suitable sound stimuli; once sounds have been selected, a guide is proposed for optimal experimental design. Finally, a framework is introduced for analysis of data and signals, based on both conventional as well as data-driven AI tools. AI is able to study big data at a single stroke, can be applied to different types of data, and is capable of generalisation and so is considered the main tool in the analysis.

Differential cardiotoxic electrocardiographic response to doxorubicin treatment in conscious versus anesthetized mice

INTRODUCTION

Doxorubicin (DOX), an anticancer drug used in chemotherapy, causes significant cardiotoxicity. This study aimed to investigate the effects of DOX on mouse cardiac electrophysiology, in conscious versus anesthetized state.

METHODS

Male and female C57BL/6 mice were injected with saline, 20 or 30 mg/kg DOX. ECGs were recorded 5 days post-injection in conscious and isoflurane anesthetized states. ECGs were analyzed using a custom MATLAB software to determine P, PR, QRS, QTc, and RR intervals as well as heart rate variability (HRV).

RESULTS

ECGs from the same mouse demonstrated P wave and QTc shortening as well as PR and RR interval prolongation in anesthetized versus conscious saline-treated mice. ECG response to DOX was also modulated by anesthesia. DOX treatment induced significant ECG modulation in female mice alone. While DOX20 treatment caused decrease in P and QRS durations, DOX30 treatment-induced QTc and RR interval prolongation in anesthetized but not in conscious female mice. These data suggest significant sex differences and anesthesia-induced differences in ECG response to DOX. HRV measured in time and frequency domains, a metric of arrhythmia susceptibility, was increased in DOX20-treated mice compared to saline.

CONCLUSIONS

This study for the first time identifies that the ECG response to DOX is modulated by anesthesia. Furthermore, this response demonstrated stark sex differences. These findings could have significant implications in clinical diagnosis of DOX cardiotoxicity.

Heart Rate Variability in Psychology: A Review of HRV Indices and an Analysis Tutorial

The use of heart rate variability (HRV) in research has been greatly popularized over the past decades due to the ease and affordability of HRV collection, coupled with its clinical relevance and significant relationships with psychophysiological constructs and psychopathological disorders. Despite the wide use of electrocardiograms (ECG) in research and advancements in sensor technology, the analytical approach and steps applied to obtain HRV measures can be seen as complex. Thus, this poses a challenge to users who may not have the adequate background knowledge to obtain the HRV indices reliably. To maximize the impact of HRV-related research and its reproducibility, parallel advances in users' understanding of the indices and the standardization of analysis pipelines in its utility will be crucial. This paper addresses this gap and aims to provide an overview of the most up-to-date and commonly used HRV indices, as well as common research areas in which these indices have proven to be very useful, particularly in psychology. In addition, we also provide a step-by-step guide on how to perform HRV analysis using an integrative neurophysiological toolkit, NeuroKit2.

Heart rate variability and haemodynamic factors associated with hypotension during spinal anaesthesia for caesarean delivery: A case-control study

BACKGROUND

Hypotension frequently occurs during spinal anaesthesia for caesarean delivery, with potential adverse effects.

OBJECTIVE

To investigate heart rate variability and haemodynamic factors associated with spinal anaesthesia-induced hypotension.

DESIGN

Secondary case-control analysis of a randomised study.

SETTING

Single obstetric centre.

PATIENTS

Data were obtained from 230 healthy term singleton parturients who underwent elective caesarean delivery under spinal anaesthesia.

INTERVENTION

With parturients at rest, continuous haemodynamic measurements were recorded using a Nexfin cardiac monitor. Baseline pre-operative values were defined as the average of five minutes of continuous measurements. After initiation of standardised spinal anaesthesia, vasopressors were administered to maintain SBP within 10% of pre-operative values. Hypotension was defined as any 10 seconds average SBP less than 80% of pre-operative values from initiation of spinal anaesthesia to foetal delivery. Parturients were classified into cases (hypotensive) or controls (normotensive), and both univariate and multivariable logistic regression models were used to identify independent factors associated with hypotension.

MAIN OUTCOME MEASURES

Pre-operative standard deviation of the interbeat interval (SDNN), root mean square of successive interbeat difference, low-frequency to high-frequency ratio, SD1, SD2, approximate entropy, sample entropy, mean arterial pressure, SBP, stroke volume variation and systemic vascular resistance index were recorded, as were sensory block height, intravenous fluid volume and vasopressor use between spinal anaesthesia and foetal delivery.

RESULTS

Of 230 parturients, 113 (49.1%) experienced hypotension. Pre-operative lower SDNN [odds ratio (OR) 0.87, 95% confidence interval (CI) 0.78 to 0.97], higher SD2 (OR 25.06, 95% CI 2.41 to 261.06), and lower SBP (OR 0.98, 95% CI 0.97 to 1.00) were independently associated with hypotension. Between spinal anaesthesia to foetal delivery, lower sensory block height (OR 0.76, 95% CI 0.65 to 0.90) and higher intravenous fluid volume (OR 0.98, 95% CI 0.96 to 0.99 per 15 ml change) were associated with a lower incidence of hypotension. Area under the receiver operating characteristic curve was 0.701.

CONCLUSION

Pre-operative higher SD2, lower SDNN and lower SBP were associated with hypotension during spinal anaesthesia for caesarean delivery.

TRIAL REGISTRATION

NCT02277730.

Novel application of Poincaré analysis to detect and quantify exercise oscillatory ventilation

Objective.Exercise oscillatory ventilation (EOV) is frequently observed in individuals with cardiac disease. Assessment of EOV relies on pattern recognition and this subjectivity and lack of quantification limits the widespread clinical use of EOV as a prognostic marker. Poincaré analysis quantifies the short (SD1) and long-term (SD2) variability of a signal and may provide an alternative means to identify and quantify unstable exercise breathing patterns. This study aimed to determine if Poincaré analysis can distinguish between the breathing patterns of healthy control subjects and individuals being assessed for heart transplantation with and without EOV.Approach.Thirty-nine subjects performed a cardiopulmonary exercise test as part of heart transplant assessment and were subjectively classified into two groups according to the presence of EOV: non-EOV (n = 19) and EOV (n = 20). The control group (n = 24) consisted of healthy adults. Poincaré analysis (SD1 and SD2) was performed for minute ventilation (V̇_E) and tidal volume (VT) normalized to forced vital capacity (V̇_EnandV̇_Tn), and breathing frequency (BF) for breath-by-breath data over the 10-15 ml · min^-1 · kg^-1V̇_O2range.Main results.Poincaré analysis showed similar exercise ventilatory responses between the non-EOV and control group. BF was found to discriminate between subjects with stable and unstable ventilation. BF SD1 was significantly higher in the EOV group compared to the non-EOV (7.9 versus 4.6,p < 0.01) and control (7.9 versus 4.2,p < 0.01) groups. The EOV group had significantly greater BF SD2 compared to the non-EOV (5.7 versus 3.5,p < 0.01) and control (5.7 versus 3.5,p < 0.01) groups.Significance.We demonstrated that this novel application of Poincaré analysis can objectively distinguish and quantify unstable from stable breathing patterns during exercise. In subjects being assessed for heart transplantation the presence of EOV is associated with greater BF variability. Poincaré analysis provides an objective measure to identify and quantify EOV.Summary at a glance.As EOV may indicate abnormal ventilatory control, there is a need for an objective measure to identify and quantify unstable from stable ventilation during exercise. We developed a method of quantifying BF variation by the application of Poincaré analysis and demonstrated higher than normal variability of BF in subjects being assessed for heart transplantation who demonstrated EOV.

Life events are associated with elevated heart rate and reduced heart complexity to acute psychological stress

The current study examined whether the exposure to life events and reported impact of life events are associated with altered cardiac reactivity to an acute psychological stressor. Participants (N = 69) completed the Life Experience Survey (LES) and Positive and Negative Affect Schedule (PANAS) and undertook a standardized social-evaluative stress task. Cardiac activity was measured via heart rate and non-linear heart rate variability (HRV) indices Sample Entropy, SD1, SD2 and SD1/SD2 ratio. Heart rate and non-linear HRV were measured before, during and after stress exposure. Findings suggest higher heart rate reactivity in individuals reporting higher number and impact of negative and total life events. Decreases in Sample Entropy were evident for number as well as impact of life events. No associations were found for SD1, SD2 and SD1/SD2 ratio. Findings suggest that life-events are associated with elevated heart rate and diminished heart rate complexity in response to acute stress.

Heart rate variability-derived features based on deep neural network for distinguishing different anaesthesia states

Background

Estimating the depth of anaesthesia (DoA) is critical in modern anaesthetic practice. Multiple DoA monitors based on electroencephalograms (EEGs) have been widely used for DoA monitoring; however, these monitors may be inaccurate under certain conditions. In this work, we hypothesize that heart rate variability (HRV)-derived features based on a deep neural network can distinguish different anaesthesia states, providing a secondary tool for DoA assessment.

Methods

A novel method of distinguishing different anaesthesia states was developed based on four HRV-derived features in the time and frequency domain combined with a deep neural network. Four features were extracted from an electrocardiogram, including the HRV high-frequency power, low-frequency power, high-to-low-frequency power ratio, and sample entropy. Next, these features were used as inputs for the deep neural network, which utilized the expert assessment of consciousness level as the reference output. Finally, the deep neural network was compared with the logistic regression, support vector machine, and decision tree models. The datasets of 23 anaesthesia patients were used to assess the proposed method.

Results

The accuracies of the four models, in distinguishing the anaesthesia states, were 86.2% (logistic regression), 87.5% (support vector machine), 87.2% (decision tree), and 90.1% (deep neural network). The accuracy of deep neural network was higher than those of the logistic regression (p < 0.05), support vector machine (p < 0.05), and decision tree (p < 0.05) approaches. Our method outperformed the logistic regression, support vector machine, and decision tree methods.

Conclusions

The incorporation of four HRV-derived features in the time and frequency domain and a deep neural network could accurately distinguish between different anaesthesia states; however, this study is a pilot feasibility study. The proposed method—with other evaluation methods, such as EEG—is expected to assist anaesthesiologists in the accurate evaluation of the DoA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12871-021-01285-x.

Cardiophysiological responses of the air-breathing Alaska blackfish to cold acclimation and chronic hypoxic submergence at 5°C

The Alaska blackfish (Dallia pectoralis) remains active at cold temperatures when experiencing aquatic hypoxia without air access. To discern the cardiophysiological adjustments that permit this behaviour, we quantified the effect of acclimation from 15°C to 5°C in normoxia (15N and 5N fish), as well as chronic hypoxic submergence (6-8 weeks; ∼6.3-8.4 kPa; no air access) at 5°C (5H fish), on in vivo and spontaneous heart rate (fH), electrocardiogram, ventricular action potential (AP) shape and duration (APD), the background inward rectifier (IK1) and rapid delayed rectifier (IKr) K+ currents and ventricular gene expression of proteins involved in excitation-contraction coupling. In vivo fH was ∼50% slower in 5N than in 15N fish, but 5H fish did not display hypoxic bradycardia. Atypically, cold acclimation in normoxia did not induce shortening of APD or alter resting membrane potential. Rather, QT interval and APD were ∼2.6-fold longer in 5N than in 15N fish because outward IK1 and IKr were not upregulated in 5N fish. By contrast, chronic hypoxic submergence elicited a shortening of QT interval and APD, driven by an upregulation of IKr The altered electrophysiology of 5H fish was accompanied by increased gene expression of kcnh6 (3.5-fold; Kv11.2 of IKr), kcnj12 (7.4-fold; Kir2.2 of IK1) and kcnj14 (2.9-fold; Kir2.4 of IK1). 5H fish also exhibited a unique gene expression pattern that suggests modification of ventricular Ca2+ cycling. Overall, the findings reveal that Alaska blackfish exposed to chronic hypoxic submergence prioritize the continuation of cardiac performance to support an active lifestyle over reducing cardiac ATP demand.

Using Resting State Heart Rate Variability and Skin Conductance Response to Detect Depression in Adults

Depression is the leading cause of disability worldwide, yet rates of missed- and mis-diagnoses are alarmingly high. The introduction of objective biomarkers, to aid diagnosis, informed by depression's physiological pathology may alleviate some of the burden on strained mental health services. Three minutes of eyes-closed resting state heart rate and skin conductance response (SCR) data were acquired from 27 participants (16 healthy controls, 11 with major depressive disorder (MDD)). Various classifiers were trained on state-of-the-art and novel features. We are aware of no previous studies analysing the utility of multimodal vs. individual modalities for classification. We found no improvement using multimodal classifiers over using heart rate variability (HRV) alone, which achieved 81% test accuracy. The best multimodal and SCR only classifiers were only slightly less accurate at 78%. Despite not improving depression detection, SCR features did show stronger correlation with suicidal ideation than HRV. SD1/SD2² is a novel HRV feature proposed in this paper, similar to the commonly used ratio SD1/SD2 but with more marked separation between classes, having the largest Rank Biserial Correlation of all examined features (p-value = 0.002, RBC = -0.73). We recommend further studies in this area.

Cardiac surgical outcome prediction by blood pressure variability indices Poincaré plot and coefficient of variation: a retrospective study

Background

Recent literature suggests a significant association between blood pressure variability (BPV) and postoperative outcomes after cardiac surgery. However, its outcome prediction ability remains unclear. Current prediction models use static preoperative patient factors. We explored the ability of Poincaré plots and coefficient of variation (CV) by measuring intraoperative BPV in predicting adverse outcomes.

Methods

In this retrospective, observational, cohort study, 3687 adult patients (> 18 years) undergoing cardiac surgery requiring cardio-pulmonary bypass from 2008 to 2014 were included. Blood pressure variability was computed by Poincare plots and CV. Standard descriptors (SD) SD1, SD2 were measured with Poincare plots by ellipse fitting technique. The outcomes analyzed were the 30-day mortality and postoperative renal failure. Logistic regression models adjusted for preoperative and surgical factors were constructed to evaluate the association between BPV parameters and outcomes. C-statistics were used to analyse the predictive ability.

Results

Analysis found that, 99 (2.7%) patients died within 30 days and 105 (2.8%) patients suffered from in-hospital renal failure. Logistic regression models including BPV parameters (standard descriptors from Poincare plots and CV) performed poorly in predicting postoperative 30-day mortality and renal failure [Concordance(C)-Statistic around 0.5]. They did not add any significant value to the standard STS risk score [C-statistic: STS alone 0.7, STS + BPV parmeters 0.7].

Conclusions

In conclusion, BP variability computed from Poincare plots and CV were not predictive of mortality and renal failure in cardiac surgical patients. Patient comorbid conditions and other preoperative factors are still the gold standard for outcome prediction. Future directions include analysis of dynamic parameters such as complexity of physiological signals in identifying high risk patients and tailoring management accordingly.

Heart rate variability is reduced in normal pregnancy irrespective of trimester: A cross-sectional study from Gujarat, India

Background:

Pregnancy is associated with profound cardiovascular adaptation with altered cardiac autonomic balance. It can be studied by heart rate variability (HRV) which indicates beat to beat RR interval variation on ECG.

Objective:

We studied 5 min HRV in normal pregnant females divided by trimesters, compared to matched control.

Methodology:

We recruited 89 normal pregnant females and 30 age matched controls. Five minutes resting HRV was measured by Variowin HR, software-based instrument, by standard protocols to yield time-domain, frequency domain, and Poincare plot parameters. They were further compared between groups for difference.

Results:

Case groups (three based on trimesters) and control group were comparable. There was reduced HRV in case than control group, with statistical significance for all, more for frequency domain than time-domain or Poincare plot parameters. There was no pattern of HRV trend across three trimesters, but mostly second trimester was associated with major decline. Primipara revealed significantly reduced HRV than multipara, but anemia or working status was not significantly associated with HRV in case group.

Conclusion:

There is global HRV reduction in normal pregnancy across all trimesters, associated with primiparity. This indicates pregnancy as a significant risk with reference to altered cardiac balance and use of HRV as a good tool to assess the same.

Non-invasive real-time autonomic function characterization during surgery via continuous Poincaré quantification of heart rate variability

Heart rate variability (HRV) provides an excellent proxy for monitoring of autonomic function, but the clinical utility of such characterization has not been investigated. In a clinical setting, the baseline autonomic function can reflect ability to adapt to stressors such as anesthesia. No monitoring tool has yet been developed that is able to track changes in HRV in real time. This study is a proof-of-concept for a non-invasive, real-time monitoring model for autonomic function via continuous Poincaré quantification of HRV dynamics. Anonymized heart rate data of 18 healthy individuals (18-45 years) undergoing minor procedures and 18 healthy controls (21-35 years) were analyzed. Patients underwent propofol and fentanyl anesthesia, and controls were at rest. Continuous heart rate monitoring was carried out from before aesthetic induction to the end of the surgical procedure. HRV components (sympathetic and parasympathetic) were extracted and analyzed using Poincaré quantification, and a real-time assessment tool was developed. In the patient group, a significant decrease in the sympathetic and parasympathetic components of HRV was observed following anesthesia (SD1: p = 0.019; SD2: p = 0.00027). No corresponding change in HRV was observed in controls. HRV parameters were modelled into a real-time graph. Using the monitoring technique developed, autonomic changes could be successfully visualized in real-time. This could provide the basis for a novel, fast and non-invasive method of autonomic assessment that can be delivered at the point of care.

The Application of the Extended Poincaré Plot in the Analysis of Physiological Variabilities

The Poincaré plot is a geometrical technique used to visualize and quantify the correlation between two consecutive data points in a time-series. Since the dynamics of fluctuations in physiological rhythms exhibit long-term correlation and memory, this study aimed to extend the Poincaré plot by calculating the correlation between sequential data points in a time-series, rather than between two consecutive points. By incorporating this so-called lag, we hope to integrate a temporal aspect into quantifying the correlation, to depict whether a physiological system holds prolonged association between events separated by time. In doing so, it attempts to instantaneously characterize the intrinsic behavior of a complex system. We tested this hypothesis on three different physiological time-series: heart rate variability in patients with liver cirrhosis, respiratory rhythm in asthma and body temperature fluctuation in patients with cirrhosis, to evaluate the potential application of the extended Poincaré method in clinical practice. When studying the cardiac inter-beat intervals, the extended Poincaré plot revealed a stronger autocorrelation for patients with decompensated liver cirrhosis compared to less severe cases using Pearson's correlation coefficient. In addition, long-term variability (known as SD2 in the extended Poincaré plot) appeared as an independent prognostic variable. This holds significance by acting as a non-invasive tool to evaluate patients with chronic liver disease and potentially facilitate transplant selection as an adjuvant to traditional criteria. For asthmatics, employing the extended Poincaré plot allowed for a non-invasive tool to differentially diagnose various classifications of respiratory disease. In the respiratory inter-breath interval analysis, the receiver operating characteristic (ROC) curve provided evidence that the extension of the Poincaré plot holds a greater advantage in the classification of asthmatic patients, over the traditional Poincaré plot. Lastly, the analysis of body temperature from patients using the extended Poincaré plot helped identify inpatients from outpatients with cirrhosis. Through these analyses, the extended Poincaré plot provided unique and additional information which could potentially make a difference in clinical practice. Conclusively, the potential use of our work lies in its possible application of predicting mortality for the organ allocation procedure in patients with cirrhosis and non-invasively distinguish between atopic and non-atopic asthma.

Early Heart Rate Variability and Electroencephalographic Abnormalities in Acutely Brain-Injured Children Who Progress to Brain Death

OBJECTIVES

Heart rate variability is controlled by the autonomic nervous system. After brain death, this autonomic control stops, and heart rate variability is significantly decreased. However, it is unknown if early changes in heart rate variability are predictive of progression to brain death. We hypothesized that in brain-injured children, lower heart rate variability is an early indicator of autonomic system failure, and it predicts progression to brain death. We additionally explored the association between heart rate variability and markers of brain dysfunction such as electroencephalogram and neurologic examination between brain-injured children who progressed to brain death and those who survived.

DESIGN

Retrospective case-control study.

SETTING

PICU, single institution.

PATIENTS

Children up to 18 years with a Glasgow Coma Scale score of less than 8 admitted between August of 2016 and December of 2017, who had electrocardiographic data available for heart rate variability analysis, were included.

EXCLUSION CRITERIA

patients who died of causes other than brain death. Twenty-three patients met inclusion criteria: six progressed to brain death (cases), and 17 survived (controls). Five-minute electrocardiogram segments were used to estimate heart rate variability in the time domain (SD of normal-normal intervals, root mean square successive differences), frequency domain (low frequency, high frequency, low frequency/high frequency ratio), Poincaré plots, and approximate entropy.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Patients who progressed to brain death exhibited significantly lower heart rate variability in the time domain, frequency domain, and Poincaré plots (p < 0.01). The odds of death increased with decreasing low frequency (odds ratio, 4.0; 95% CI, 1.2-13.6) and high frequency (odds ratio, 2.5; 95% CI, 1.2-5.4) heart rate variability power (p < 0.03). Heart rate variability was significantly lower in those with discontinuous or attenuated/featureless electroencephalogram versus those with slow/disorganized background (p < 0.03).

CONCLUSIONS

These results support the concept of autonomic system failure as an early indicator of impending brain death in brain-injured children. Furthermore, decreased heart rate variability is associated with markers of CNS dysfunction such as electroencephalogram abnormalities.

Cardiorespiratory interactions in the Pacific spiny dogfish, Squalus suckleyi

Elasmobranchs are a group of cartilaginous fish with no direct sympathetic innervation of the heart or gills. Fast cardiorespiratory regulation is controlled solely by the parasympathetic branch of the autonomic nervous system. Cardiovascular changes associated with ventilation are commonly present in the form of respiratory sinus arrhythmia (RSA) and as cardiorespiratory synchrony (CRS, in which there is a 1:1 beat to breath ratio). The latter has been hypothesized to maximize oxygen uptake, coupling the pulsatile flows of blood and water in the gills. Given this, we hypothesized that CRS should be more prevalent in situations of low oxygen supply and RSA should be abolished by vagotomy. To test this, we investigated the role of the vagus nerve in mediating cardiorespiratory responses to changing environmental oxygen conditions in the elasmobranch Squalus suckleyi Hypoxia and hyperoxia had little effect on heart rate but did alter breathing frequency and amplitude. Atropine yielded an overall tachycardia in all oxygen conditions and abolished all heart rate variability (HRV), suggesting that HRV solely reflects fluctuating vagal tonus on the heart. Regardless of the presence of atropine, hypoxia still induced an increase in ventilation rate and depth. CRS was only found during progressive hyperoxia post-atropine, when heart rate was uninhibited and ventilation was slowed owing to the increase in oxygen supply, suggesting that in S. suckleyi, CRS is an epiphenomenon and not actively regulated to maximize gas exchange efficiency.

HRV-derived data similarity and distribution index based on ensemble neural network for measuring depth of anaesthesia

Evaluation of depth of anaesthesia (DoA) is critical in clinical surgery. Indices derived from electroencephalogram (EEG) are currently widely used to quantify DoA. However, there are known to be inaccurate under certain conditions; therefore, experienced anaesthesiologists rely on the monitoring of vital signs such as body temperature, pulse rate, respiration rate, and blood pressure to control the procedure. Because of the lack of an ideal approach for quantifying level of consciousness, studies have been conducted to develop improved methods of measuring DoA. In this study, a short-term index known as the similarity and distribution index (SDI) is proposed. The SDI is generated using heart rate variability (HRV) in the time domain and is based on observations of data distribution differences between two consecutive 32 s HRV data segments. A comparison between SDI results and expert assessments of consciousness level revealed that the SDI has strong correlation with anaesthetic depth. To optimise the effect, artificial neural network (ANN) models were constructed to fit the SDI, and ANN blind cross-validation was conducted to overcome random errors and overfitting problems. An ensemble ANN was then employed and was discovered to provide favourable DoA assessment in comparison with commonly used Bispectral Index. This study demonstrated the effectiveness of this method of DoA assessment, and the results imply that it is feasible and meaningful to use the SDI to measure DoA with the additional use of other measurement methods, if appropriate.

Panel performance monitoring by Poincaré plot: A case study on flavoured bottled waters

The application of the Poincaré method is discussed on the field of sensory panel performance monitoring. The Poincaré plot evaluates and visualizes the difference from the consensus for each panelist even for one product and for one attribute. Sensory tests were carried out on 5 commercially available brands of pear-lemon and josta flavoured bottled waters. Several sensory parameters were assessed and a complex dissimilarity index was calculated. Based on this index the attributes and panelists can be ranked highlighting the attribute which was the hardest to evaluate and the most consistent panel member. Our method has been compared to the other methods using the sum of ranking difference method and cross-validated by leave-one-out technique. Regarding both the ranking of the panelists and the ranking of the attributes significant differences were found between the Poincaré method and the traditional techniques since the sum of ranking difference was greater than the critical value of 39 and 79 at 5%, respectively. Only the Poincaré method was able to indicate sweet flavour as the easiest attribute to evaluate. Free R code is available to carry out the calculations and apply this method.

Pattern Analysis of Oxygen Saturation Variability in Healthy Individuals: Entropy of Pulse Oximetry Signals Carries Information about Mean Oxygen Saturation

Pulse oximetry is routinely used for monitoring patients' oxygen saturation levels with little regard to the variability of this physiological variable. There are few published studies on oxygen saturation variability (OSV), with none describing the variability and its pattern in a healthy adult population. The aim of this study was to characterize the pattern of OSV using several parameters; the regularity (sample entropy analysis), the self-similarity [detrended fluctuation analysis (DFA)] and the complexity [multiscale entropy (MSE) analysis]. Secondly, to determine if there were any changes that occur with age. The study population consisted of 36 individuals. The “young” population consisted of 20 individuals [Mean (±1 SD) age = 21.0 (±1.36 years)] and the “old” population consisted of 16 individuals [Mean (±1 SD) age = 50.0 (±10.4 years)]. Through DFA analysis, OSV was shown to exhibit fractal-like patterns. The sample entropy revealed the variability to be more regular than heart rate variability and respiratory rate variability. There was also a significant inverse correlation between mean oxygen saturation and sample entropy in healthy individuals. Additionally, the MSE analysis described a complex fluctuation pattern, which was reduced with age (p < 0.05). These findings suggest partial “uncoupling” of the cardio-respiratory control system that occurs with aging. Overall, this study has characterized OSV using pre-existing tools. We have showed that entropy analysis of pulse oximetry signals carries information about body oxygenation. This may have the potential to be used in clinical practice to detect differences in diseased patient subsets.

Visual and Quantitative Analysis Methods of Respiratory Patterns for Respiratory Gated PET/CT

We integrated visual and quantitative methods for analyzing the stability of respiration using four methods: phase space diagrams, Fourier spectra, Poincaré maps, and Lyapunov exponents. Respiratory patterns of 139 patients were grouped based on the combination of the regularity of amplitude, period, and baseline positions. Visual grading was done by inspecting the shape of diagram and classified into two states: regular and irregular. Quantitation was done by measuring standard deviation of x and v coordinates of Poincaré map (SD_x, SD_v) or the height of the fundamental peak (A₁) in Fourier spectrum or calculating the difference between maximal upward and downward drift. Each group showed characteristic pattern on visual analysis. There was difference of quantitative parameters (SD_x, SD_v, A₁, and MUD-MDD) among four groups (one way ANOVA, p = 0.0001 for MUD-MDD, SD_x, and SD_v, p = 0.0002 for A₁). In ROC analysis, the cutoff values were 0.11 for SD_x (AUC: 0.982, p < 0.0001), 0.062 for SD_v (AUC: 0.847, p < 0.0001), 0.117 for A₁ (AUC: 0.876, p < 0.0001), and 0.349 for MUD-MDD (AUC: 0.948, p < 0.0001). This is the first study to analyze multiple aspects of respiration using various mathematical constructs and provides quantitative indices of respiratory stability and determining quantitative cutoff value for differentiating regular and irregular respiration.

A method of REM-NREM sleep distinction using ECG signal for unobtrusive personal monitoring

Computers are used extensively in sleep labs for polysomnography and for assistance in sleep staging. However, the test is highly inconvenient to the patient and requires availability of specially equipped sleep labs. Alternative approaches that enable unobtrusive in-home sleep staging with ECG or other signals are highly desirable. In this paper we describe a method that can be used for distinction of REM and NREM sleep stages using spectral and non-linear features of ECG derived RR interval series. To test the accuracy of the system, we extracted the RR interval series from sleep studies of 20 young healthy individuals. Time domain, spectral and non-linear features were computed and tested for discriminability. Features showing high degree of discrimination were selected. A polynomial support vector machine was trained with selected features - percent power in HF band, LF/HF, Poincare plot parameters, exponents from Detrended fluctuation analysis, and sampEn of the half of the signals. The hyperplane was used to classify the other half of the data. The results show an accuracy of 76.25% with Cohen's kappa as 0.52 for a two-class model of five minute signal. The results dropped to 72.8% accuracy and k=0.48 for the two class model of one minute signal. The minimal set offers a reasonable trade-off for possible in-home monitoring, at least for some conditions that require only REM-NREM distinction. The method after extensive trials and standardisation, can alleviate the load of special purpose PSG labs and enable the tests to be done on general purpose computers.