Normal Heartbeat Research Articles

Expiratory-gated Transcutaneous Auricular Vagus Nerve Stimulation (taVNS) does not Further Augment Heart Rate Variability During Slow Breathing at 0.1 Hz

As cardiac vagal control is a hallmark of good health and self-regulatory capacity, researchers are seeking ways to increase vagally mediated heart rate variability (vmHRV) in an accessible and non-invasive way. Findings with transcutaneous auricular vagus nerve stimulation (taVNS) have been disappointing in this respect, as its effects on vmHRV are inconsistent at best. It has been speculated that combining taVNS with other established ways to increase vmHRV may produce synergistic effects. To test this idea, the present study combined taVNS with slow breathing in a cross-over design. A total of 22 participants took part in two sessions of breathing at 6 breaths/min: once combined with taVNS, and once combined with sham stimulation. Electrical stimulation (100 Hz, 400 µs) was applied during expiration, either to the tragus and cavum conchae (taVNS) or to the earlobe (sham). ECG was recorded during baseline, 20-minutes of stimulation, and the recovery period. Frequentist and Bayesian analyses showed no effect of taVNS (in comparison to sham stimulation) on the root mean square of successive differences between normal heartbeats, mean inter-beat interval, or spectral power of heart rate variability at a breathing frequency of 0.1 Hz. These findings suggest that expiratory-gated taVNS combined with the stimulation parameters examined here does not produce acute effects on vmHRV during slow breathing.

Pharmacological Potentials of Omega 3 Fatty Acid in Reduction of Atrial Fibrillation

Atrial fibrillation (AF) is the most common type of arrhythmia in the general population with a prevalence that reaches one third of patients with arterial hypertension, diabetic and other risk factors. In atrial fibrillation, the regular impulses produced by the sinus node for a normal heart beat are overwhelmed by rapid electrical discharges produced in the atria and adjacent parts of the pulmonary veins. Omega-3 polyunsaturated fatty acids (PUFAs), namely eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) are permanent subjects of interest in relation to the protection of cardiovascular health and the prevention of the incidence of both ventricular and atrial arrhythmias. It has been proposed that Omega-3 polyunsaturated fatty acid possess anti-inflammatory properties that can inhibit arrhythmogenic mechanisms. Potential Targets of Omega-3 PUFAs Relevant to Arrhythmias Prevention are direct inhibition of sarcolemmal ion channels & restoring cardiac connexion channels. Intake of omega-3 fatty acids significantly prevents the occurrence of AF. The purpose of this review is to focus on the novel cellular and molecular effects of omega-3 PUFAs, in the context of the mechanisms and factors involved in the development of atrial fibrillation.
 Keywords: Eicosapentaenoic acid, Docosahexaenoic acid, Atrial Fibrillation, Polyunsaturated fatty acids, Arrhythmias.

Coexisting Behavior and Status Transition of the Hodgkin-Huxley Cardiac Purkinje Fiber Model Under External AC Injection

The membrane current I_m of the Hodgkin-Huxley (HH) cardiac Purkinje fiber (CPF) model is usually calculated as direct current (DC). In this paper, a conventional alternating current (AC) is used, namely I_{AC} = textrm{Asin}(2 pi ft), as the impressed injection. Dynamic characters of the model with different AC parameters and various initial conditions are observed through phase plane orbits, waveforms, bifurcation diagrams, and Lyapunov exponent spectra, which reveal multiple coexisting membrane action potential patterns, corresponding to the coexisting attractors of the same or different periods in the phase diagram. Meanwhile, the model undergoes period, quasi-period and local forward or inverse period-doubling bifurcations with changes in amplitude A or frequency f, which further proves the complex nonlinear property of the AC-injected model. In addition, by changing the external current I_m, the sodium-ion and potassium-ion equilibrium potentials, i.e. E_{Na} and E_{K}, respectively, the regularity of the CPF heartbeat frequency is observed. The state transformations of CPF are found between normal, abnormal and sudden cardiac arrest, and the method adjusting from the dangerous state to the normal heartbeat frequency range is investigated. This study may provide a reference for exploring the evolution of nonlinear dynamics in HH CPF model and protecting the health of life and heart.

Abstract 13: Circadian Misalignment is Associated With Impaired Heart Rate Variability in Adolescents

Introduction: Adequate sleep has been recognized by AHA as part of Life’s Essential 8 in the prevention of cardiovascular disease (CVD). The circadian system, which governs the sleep-cycle, is also involved in cardiac, metabolic and autonomic regulation. Heart rate variability (HRV), a marker of cardiac autonomic modulation (CAM), is known to predict CVD. As a result, circadian misalignment of the sleep-wake cycle, which is highly prevalent in adolescents, may impact CAM and be a potential risk factor for adverse CVD outcomes in early life. Hypothesis: We hypothesize that deviations in the circadian timing of sleep are associated with blunted HRV in adolescents. Methods: We analyzed data from 337 population-based randomly-selected adolescents from the Penn State Child Cohort (median 16 years; 47% female; 21% racial/ethnic minority) who had at least 3-night at-home actigraphy (ACT), in-lab 9-h polysomnography (PSG) and 24-h Holter-monitoring heart rate variability (HRV) data. ACT-sleep duration and sleep midpoint (SM) were calculated as the intra-individual mean total sleep time and midpoint of the sleep period, respectively. ACT-sleep variability and sleep regularity (SR) were calculated as the intra-individual standard deviation of sleep duration and sleep midpoint across nights, respectively. Frequency and time-domain HRV indices, including HR oscillations at high frequencies (HF), standard deviation between normal heart beats or R-R intervals (SDNN), and root mean square of successive differences between normal heartbeats (RMSSD), were primary outcomes. Linear regression models tested SM and SR as predictors of HRV indices, while accounting for sex, race/ethnicity, age, body mass index, ACT-sleep duration, ACT-sleep variability, and PSG-apnea/hypopnea index. Results: While there were no meaningful associations between SM or SR with daytime HRV, there were significant associations between SM with nighttime HRV. A later SM was associated with lower nighttime Log-HF, SDNN and RMSSD, yet higher nighttime LF/HF (all p<0.03). For each standard deviation increase in SM (i.e., 1.5 hours later), there was a decrease of -5.50ms (1.92) in nighttime RMSSD (p=0.004). More specifically, the nighttime RMSSD of adolescents with a SM at 5:30 (i.e., sleep onset at 2:00, sleep offset at 9:00), was 18.2ms (6.7) lower (p=0.007) than that of adolescents with a SM at 2:30 (i.e., sleep onset at 23:00, sleep offset at 6:00). Conclusions: A delayed sleep phase is associated with impaired CAM in adolescents. These data support that circadian misalignment of the sleep-wake cycle may independently contribute to CVD risk.

Association of Heart Rate Variability with Obstructive Sleep Apnea in Adults.

Background and Objectives: Heart rate variability (HRV) analysis is a noninvasive method used to examine autonomic system function, and the clinical applications of HRV analysis have been well documented. The aim of this study is to investigate the association between HRV and the apnea-hypopnea index (AHI) in patients referred for polysomnography (PSG) for obstructive sleep apnea (OSA) diagnosis. Materials and Methods: Patients underwent whole-night PSG. Data on nocturnal HRV and AHI were analyzed. We determined the correlation of time- and frequency-domain parameters of HRV with the AHI. Results: A total of 62 participants (50 men and 12 women) were enrolled. The mean age, body mass index (BMI), neck circumference, and AHI score of the patients were 44.4 ± 11.5 years, 28.7 ± 5.2, 40.2 ± 4.8 cm, and 32.1 ± 27.0, respectively. The log root mean square of successive differences between normal heartbeats (RMSSD) were negatively correlated with BMI (p = 0.034) and neck circumference (p = 0.003). The log absolute power of the low-frequency band over high-frequency band (LF/HF) ratio was positively correlated with the AHI (p = 0.006). A higher log LF/HF power ratio (β = 5.01, p = 0.029) and BMI (β = 2.20, p < 0.001) were associated with a higher AHI value in multiple linear regression analysis. Conclusions: A higher log LF/HF power ratio and BMI were positively and significantly associated with the AHI during whole-night PSG in adult patients.

Case Studies in Physiology: Is blackout in breath-hold diving related to cardiac arrhythmias?

Syncope or "blackout" (BO) in breath-hold diving (freediving) is generally considered to be caused by hypoxia. However, it has been suggested that cardiac arrhythmias affecting the pumping effectivity could contribute to BO. BO is fairly common in competitive freediving, where athletes aim for maximal performance. We recorded heart rate (HR) during a static apnea (STA) competition, to reveal if arrhythmias occur. Four male freedivers with STA personal best (PB) of 349 ± 43 s, volunteered during national championships, where they performed STA floating face down in a shallow indoor pool. A non-coded Polar T31 chest strap recorded R-R intervals and a water- and pressure-proof pulse oximeter arterial oxygen saturation. Three divers produced STA near their PB without problems, whereas one diver ended with BO at 5 min 17s, which was 12 s beyond his PB. He was immediately brought up by safety divers and resumed breathing within 10 s. All divers attained similar lowest diving HR (47 ± 4 beats/min), but HR recordings displayed a different pattern for the diver ending with BO. After a short tachycardia, the three successful divers developed bradycardia, which became more pronounced during the second half of the apnea. The fourth diver developed pronounced bradycardia earlier, and at 2.5 min into the apnea, HR started alternating between approximately 50 and 140 beats/min, until the diver lost consciousness. At resumed breathing, HR returned to baseline. Nadir oxygen saturation was similar for all divers. We speculate that arrhythmia could have contributed to BO, by lowering stroke volume leading to a systolic blood pressure drop, affecting brain perfusion.NEW & NOTEWORTHY Heart rate during prolonged breath-holding until the point of loss of consciousness has not previously been published. The recordings show that blackout was preceded by a period of persistent alterations in R-R intervals, whereby an ectopic beat followed every normal heartbeat. Explanations for this deviating heart rate pattern could be either premature atrial contractions or premature ventricular contractions following every atrial beat, i.e., bigeminy, which could have compromised cardiac pumping function and caused/contributed to blackout.

Towards Automated Optimization of Residual Convolutional Neural Networks for Electrocardiogram Classification.

The interpretation of biological data such as theElectroCardioGram (ECG) signalgives clinical information and helps to assess theheart function. There are distinct ECG patterns associated with a specific class of arrhythmia. The convolutional neural network, inspired by findings in the study of biological vision, is currently one of the most commonly employed deep neural network algorithms for ECG processing. However, deep neural network models require many hyperparameters to tune. Selecting the optimal or thebest hyperparameter for the convolutional neural network algorithm is a highly challenging task. Often, we end up tuning the model manually with different possible ranges of values until a best fit model is obtained. Automatic hyperparameters tuning using Bayesian Optimization (BO) and evolutionary algorithms can provide an effective solution to current labour-intensive manual configuration approaches. In this paper, we propose to optimize the Residual one Dimensional Convolutional Neural Network model (R-1D-CNN) at two levels. At the first level, a residual convolutional layer and one-dimensional convolutional neural layers are trained to learn patient-specific ECG features over which multilayer perceptron layers can learn to produce the final class vectors of each input. This level is manual and aims to limit the search space and select the most important hyperparameters to optimize. The second level is automatic and based on our proposed BO-based algorithm. Our optimized proposed architecture (BO-R-1D-CNN) is evaluated on two publicly available ECG datasets. Comparative experimental results demonstrate that our BO-based algorithm achieves an optimal rate of 99.95% for the MIT-BIH database to discriminate between five kinds of heartbeats, including normal heartbeats, left bundle branch block, atrial premature, right bundle branch block, and premature ventricular contraction. Moreover, experiments demonstrate that the proposed architecture fine-tuned with BO achieves a higher accuracy tested on the10,000 ECG patients datasetcompared to the other proposed architectures. Our optimized architecture achieves excellent results compared to previous works on the two benchmark datasets.

A novel time representation input based on deep learning for ECG classification

Electrocardiogram (ECG) is an important tool used to analyze abnormal heart activity and assess heart health, especially in remote cardiac health monitoring. Although deep learning has achieved significant results in automatic ECG classification, how to combine the characteristics of ECG physiological signals to construct inputs or features with differentiation is still a key point of classification. To this end, a novel representation input method with temporal characteristics was proposed in this paper. At first, the temporal characteristic of ECG signals was extracted and transformed into a time representation input with the original input. Subsequently, the deep learning network combining Convolutional Neural Network and Long Short-Term Memory was employed for feature extraction. Simultaneous attention mechanism was used to focus on feature differences. The proposed method was validated in the classification of five classes of heartbeats (Normal heartbeat, Left bundle branch block heartbeat, Right bundle branch block heartbeat, Atrial Premature Contraction, Premature ventricular contraction), achieving a higher average accuracy, precision, sensitivity, and specificity of 98.95%, 97.07%, 96.54%, and 99.33% respectively in the MIT-BIH arrhythmia database. The results show that our method is able to combine the periodic characteristics of ECG to construct a better temporal representation input than traditional feature fusion. This method can provide a new way to classify similar physiological signals with periodic characteristics.

Comparing prenatal and postpartum stress among women with previous adverse pregnancy outcomes and normal obstetric histories: A longitudinal cohort study

ObjectiveThe aim of this study was to compare subjectively and objectively measured stress during pregnancy and the three months postpartum in women with previous adverse pregnancy outcomes and women with normal obstetric histories. MethodsWe recruited two cohorts in southwestern Finland for this longitudinal study: (1) pregnant women (n = 32) with histories of preterm births or late miscarriages January–December 2019 and (2) pregnant women (n = 30) with histories of full-term births October 2019–March 2020. We continuously measured heart rate variability (HRV) using a smartwatch from 12 to 15 weeks of pregnancy until three months postpartum, and subjective stress was assessed with a smartphone application. ResultsWe recruited the women in both cohorts at a median of 14.2 weeks of pregnancy. The women with previous adverse pregnancy outcomes delivered earlier and more often through Caesarean section compared with the women with normal obstetric histories. We found differences in subjective stress between the cohorts in pregnancy weeks 29 and 34. The cohort of women with previous adverse pregnancy outcomes had a higher root mean square of successive differences between normal heartbeats (RMSSD), a well-known HRV parameter, compared with the other cohort in pregnancy weeks 26 (64.9 vs 55.0, p = 0.04) and 32 (63.0 vs 52.3, p = 0.04). Subjective stress did not correlate with HRV parameters. ConclusionsWomen with previous adverse pregnancy outcomes do not suffer from stress in subsequent pregnancies more than women with normal obstetric histories. Healthcare professionals need to be aware that interindividual variation in stress during pregnancy is considerable.

The effect of exercise using incentive spirometry on heart rate variability in patients after COVID-19 infection

Background: COVID-19 infection causes various sequelae and complications after recovery. Changes in heart rate variability (HRV) were found in patients with COVID-19 infection, suggesting a disturbance in the autonomic system. Breathing exercises with diaphragmatic breathing and incentive spirometry have been shown to increase HRV by increasing lung capacity, respiratory muscle strength, and pulmonary O2 pressure, which can affect baroreflex signals. Incentive spirometry is one of the easy-to-use, safe, inexpensive pulmonary rehabilitation exercises that can be done at home without supervision and are accompanied by a visual display as a guide to the patient. The purpose of the study was to determine the effect of giving breathing exercises using Incentive Spirometry for four weeks on Heart Rate Variability in patients post COVID-19 infection Method: This research is an experimental study with a pre-post-test control group design. The treatment group was given breathing exercises using Incentive Spirometry, while the control group used diaphragmatic breathing five times a day, seven times per week, for four weeks in each group. HRV measurement was performed before and after four weeks of intervention, using the parameter Root Mean Square of Successive Differences between normal heartbeats (RMSSD), Standard Deviation of N-N intervals (SDNN), and LF/HF ratio of Heart Rate Variability (HRV). Result: There subject of this study was 20 post-COVID-19 patients divided into a treatment group (n=10) and a control group (n=10). There was no significant increase in RMSSD, SDNN, and LF/HF ratio pre and post-intervention in both groups, suggesting no significant increase in HRV. Conclusion: Exercise with Incentive Spirometry and Diaphragmatic Breathing for four weeks did not increase the Heart Rate Variability value in post-COVID-19 patients.

Root mean square of successive differences is not a valid measure of parasympathetic reactivity during slow deep breathing.

Deep breathing exercises are the second most used complementary health approach in the United States. Two heart rate variability (HRV) parameters, the root mean square of successive differences (RMSSD) and the respiratory sinus arrhythmia (RSA), are used to assess parasympathetic reactivity to deep breathing, but they are often not in agreement. Our purpose was to determine the cause of the disagreement. We investigated HRV parameters in 38 subjects during baseline, deep breathing, and recovery. Here we show that RMSSD as a measure of parasympathetic reactivity is unreliable; it does not reflect the increase in HRV during deep breathing as determined by RSA. We observed a decrease in RMSSD values despite a marked increase in HRV as determined by RSA and the standard deviation of normal heartbeat interval (SDNN) in healthy subjects and patients with functional bowel disorders. We show that RSA captures all aspects of HRV, whereas successive differences in heart rate intervals are only a small part of HRV, with decreasing variability during deep breathing in most subjects. We present a new measure of calculating RSA during deep breathing that may become an essential tool for researchers and clinicians. We also provide a unique visualization of the increased heart rate variability during deep breathing. Hence, RMSSD cannot be used to assess parasympathetic reactivity during deep breathing; using RSA is recommended. The use of RMSSD in previous influential studies may have led to erroneous conclusions about parasympathetic reactivity during deep breathing. Its continued use may undervalue the effects of the autonomic nervous system in slow deep breathing.

A nonlinear analysis of cardiovascular diseases using multi-scale analysis and generalized hurst exponent

Congestive heart failure (CHF) and arrhythmia (ARR) are common heart diseases that affect a growing population of patients worldwide. In this work, we employ multi-scale analysis (MSA) to estimate generalized Hurst exponent (GHE) from electrocardiogram (ECG) records under CHF, ARR, and normal sinus rhythm (NSR). As a result, fractal correlations in short and long fluctuations of CHF, ARR, and NSR are measured. Then, a set of six statistical tests are applied to GHE estimates to check how they are different at each time scale between two different ECG conditions. Particularly, the goal is verify if two different ECG conditions can be statistically differentiated by short or by long fluctuations. The battery of statistical tests includes Kolmogorov–Smirnov, Kruskal–Wallis, Wilcoxon rank sum, Student t-test, Ansari–Bradley, and F-test. The results from MSA show evidence that CHF, ARR, and NSR all exhibit multi-fractal properties. Besides, the results from statistical tests revealed that long fluctuations statistically differentiate CHF and ARR, ARR and NSR, and CHF and NSR. Therefore, long fluctuations account most for the characterization of CHF, ARR, and NSR. Our findings are helpful to better understand the mechanics of heart disease and normal heart beats, and also promising to eventually designing computer-aided diagnosis systems for CHF and ARR classification.

Development of Electrocardiogram Signal Generator for Fibrillation Detector

Fibrillation is one of the abnormalities in the heart where it beats irregularly, which can cause sudden death. Fortunately, these abnormalities can be detected using an electrocardiogram (ECG) fibrillation detector. An ECG can be detected as biopotential body signal that arises as a result of electrical activity of the heart muscle represented by the electrical signals consisting of P, Q, R, S and T waves that carry information about someone’s heart condition through graph pattern. However, a test in humans with the potential for fibrillation is needed to determine whether the fibrillation detector works correctly. Testing this way is ineffective because we cannot predict when and where a person will experience fibrillation. On the other hand, a site called PhysioNet provides various medical records from patients, including ECG signals of normal heartbeats, atrial fibrillations, and ventricular fibrillations. Because of the ineffectiveness of testing a fibrillation detector directly on a living person, this study focused on research to develop an ECG signal generator using microcontroller Arduino Due for reconstructing the heart signal in normal and fibrillated heart conditions collected from the PhysioNet database. The research focused on obtaining an R-peak value and RR interval time that resembled a heart ECG wave. This study compared the reconstructed signal with references from PhysioNet and measured the peak level of R and R-R interval time using an oscilloscope to calculate the accuracy of the ECG signal generator. The generated ECG signal during Atrial Fibrillation and Normal Sinus Rhythm has an overall accuracy of 88.65% for the R-peak level and 97.24% for the RR-Interval time. While Ventricular Fibrillation has been reproduced by achieving amplitude errors of less than 5.63% and 10.22% during first and second samples, respectively.

Heart rate variability responses to cognitive stress in fibromyalgia are characterised by inadequate autonomous system stress responses: a clinical trial

Fibromyalgia (FM) is associated with sympathetically dominant dysautonomia, but the connection between dysautonomia and FM symptoms is unclear. Dysautonomia can be analysed with heart rate variability (HRV) and it has been proposed that FM patients comprise subgroups with differing profiles of symptom severity. In our study, 51 female FM patients aged 18 to 65 years and 31 age-matched healthy female controls followed a 20-min protocol of alternating relaxation and cognitive stress (mental arithmetic). Heart rates and electrocardiograms were registered. The HRV measures of heart rate (HR), mean interval between heart beats (RRmean), root mean squared interval differences of successive beats (RMSSD), and the standard deviation of intervals between normal heart beats (SDNN) were analysed with generalized linear modelling. Features in HRV reactivity which differed between FM patients and controls were used to cluster the FM patients and cluster characteristics were analysed. FM patients had higher baseline HR (72.3 [SD 12.7] vs 64.5 [7.80], p < 0.001) and lower RRmean (0.844 [0.134] vs 0.934 [0.118], p = 0.002), compared with controls. They also reacted to repeated cognitive stress with an attenuated rise in HR (− 4.41 [95% CI − 7.88 to − 0.93], p = 0.013) and attenuated decrease of RRmean (0.06 [95 CI 0.03 to 0.09], p < 0.001), compared with controls. Clustering of FM patients by HRV reactivity resulted in three clusters characterised by (1) normal levels of HRV and HRV reactivity with low levels of depressive mood and anxiety, (2) reduced levels of HRV and impaired HRV reactivity with increased levels of depressive mood and high levels of anxiety, and (3) lowest HRV and most impaired HRV reactivity with the highest scores for depressive mood and anxiety. Our results show that FM patients have lower HRV than healthy controls and their autonomous reactions to cognitive stress are attenuated. Dysautonomia in FM associates with mood disturbance.Trial registration ClinicalTrials.gov (NCT03300635). Registered October 3 2017—Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT03300635.

Heart rate variability as an indicator of COVID-19 induced myocardial injury: a retrospective cohort study

BackgroundHeart rate variability (HRV) is a valuable indicator of autonomic nervous system integrity and can be a prognostic tool of COVID-19 induced myocardial affection. This study aimed to compare HRV indices between patients who developed myocardial injury and those without myocardial injury in COVID-19 patients who were admitted to intensive care unit (ICU).MethodsIn this retrospective study, the data from 238 COVID-19 adult patients who were admitted to ICU from April 2020 to June 2021 were collected. The patients were assigned to myocardial injury and non-myocardial injury groups. The main collected data were R-R intervals, standard deviation of NN intervals (SDANN) and the root mean square of successive differences between normal heartbeats (RMSSD) that were measured daily during the first five days of ICU admission.ResultsThe R-R intervals, the SDANN and the RMSSD were significantly shorter in the myocardial injury group than the non-myocardial group at the first, t second, third, fourth and the fifth days of ICU admission. There were no significant differences between the myocardial injury and the non-myocardial injury groups with regard the number of patients who needed mechanical ventilation, ICU length of stay and the number of ICU deaths.ConclusionsFrom the results of this retrospective study, we concluded that the indices of HRV were greatly affected in COVID-19 patients who developed myocardial injury.

Association of Autonomic Balance With Phone Call Duration in Healthy Individuals.

Background This study aimed to estimate the association of autonomic balance with the duration of phone calls in healthy individuals. Methodology A total of 30 subjects aged between 18 and 30 years without any established systemic disease and using mobile phones for more than five years with minimum daily usage of 30 minutes were included in this analytical study. Heart rate variability (HRV) was recorded using a three-channel physiograph (AD Instruments South Asia (India) Pvt. Ltd., New Delhi, India) with the software LabChart PROV8.1.8 with HRV Module version 2.0.3 for 10 minutes. Time domain parameters were recorded in terms of the standard deviation of normal to normal interval (SDNN), root mean square of successive differencesbetween normal heartbeats (RMSSD), R-R intervals greater than 50 ms (pRR50), and mean heart rate (MHR), and frequency domain parameters were total power,low-frequency power (LF), high-frequency power (HF), and the ratio of low-frequency to high-frequency power (LF/HF). HRV was recorded three times in each subject that included baseline HRV, HRV during the use of a mobile phone, and HRV after the use of a mobile phone. Results A total of 30 subjects (14 males and 16 females) participated in this study. The mean age of participants was 31.93 ± 8.59 years (32.07 ± 9.87 years for males, and 31.81 ± 7.64 years for females). There were no findings of significant arrhythmiain any of the participants. There was a significant difference in pRR50 on comparing all three phases (p = 0.036). However, there was no significant variation in other parameters such as very low frequency (VLF, ms2), VLF (%), LF (ms2), LF (%), HF (ms2), HF (%), LF/HF, SDNN (ms), RMSSD (ms), Poincare plot standard deviation perpendicular to the line of identity (ms), Poincare plot standard deviation along the line of identity (ms), systolic blood pressure (mmHg), and diabolic blood pressure (mmHg) during, before, and after exposure to mobile phone calls. There was no significant difference in the value of all parameters between males and females (p < 0.05). Conclusions Mobile phone calls may influence HRV and autonomic balance. This change may be affected by the electromagnetic field and by speaking as well.

Heart Rate Estimation from Incomplete Electrocardiography Signals.

As one of the most remarkable indicators of physiological health, heart rate (HR) has become an unfailing investigation for researchers. Unlike many existing methods, this article proposes an approach to implement short-time HR estimation from electrocardiography in time series missing patterns. Benefiting from the rapid development of deep learning, we adopted a bidirectional long short-term memory model (Bi-LSTM) and temporal convolution network (TCN) to recover complete heartbeat signals from those with durations are less than one cardiac cycle, and the estimated HR from recovered segment combining the input and the predicted output. We also compared the performance of Bi-LSTM and TCN in PhysioNet dataset. Validating the method over a resting heart rate range of 60−120 bpm in the database without significant arrhythmias and a corresponding range of 30−150 bpm in the database with arrhythmias, we found that networks provide an estimated approach for incomplete signals in a fixed format. These results are consistent with real heartbeats in the normal heartbeat dataset (γ > 0.7, RMSE < 10) and in the arrhythmia database (γ > 0.6, RMSE < 30), verifying that HR could be estimated by models in advance. We also discussed the short-time limits for the predictive model. It could be used for physiological purposes such as mobile sensing in time-constrained scenarios, and providing useful insights for better time series analyses in missing data patterns.

Machine learning approaches for predicting sleep arousal response based on heart rate variability, oxygen saturation, and body profiles.

Obstructive sleep apnea is a global health concern, and several tools have been developed to screen its severity. However, most tools focus on respiratory events instead of sleep arousal, which can also affect sleep efficiency. This study employed easy-to-measure parameters-namely heart rate variability, oxygen saturation, and body profiles-to predict arousal occurrence. Body profiles and polysomnography recordings were collected from 659 patients. Continuous heart rate variability and oximetry measurements were performed and then labeled based on the presence of sleep arousal. The dataset, comprising five body profiles, mean heart rate, six heart rate variability, and five oximetry variables, was then split into 80% training/validation and 20% testing datasets. Eight machine learning approaches were employed. The model with the highest accuracy, area under the receiver operating characteristic curve, and area under the precision recall curve values in the training/validation dataset was applied to the testing dataset and to determine feature importance. InceptionTime, which exhibited superior performance in predicting sleep arousal in the training dataset, was used to classify the testing dataset and explore feature importance. In the testing dataset, InceptionTime achieved an accuracy of 76.21%, an area under the receiver operating characteristic curve of 84.33%, and an area under the precision recall curve of 86.28%. The standard deviations of time intervals between successive normal heartbeats and the square roots of the means of the squares of successive differences between normal heartbeats were predominant predictors of arousal occurrence. The established models can be considered for screening sleep arousal occurrence or integrated in wearable devices for home-based sleep examination.

A post-exercise infrared sauna session improves recovery of neuromuscular performance and muscle soreness after resistance exercise training.

The aim of this study was to investigate effects of a single infrared sauna (IRS) session on post-exercise recovery of neuromuscular performance, autonomic nervous system function, subjective sleep quality, and muscle soreness. Male basketball players (n = 16) performed two trials consisting of a complex resistance exercise protocol (maximal strength with plyometrics), followed by either 20 min passive recovery (PAS) or IRS (temperature 43 ± 5°C), in a randomized crossover design, with trials separated by one week. Recovery of neuromuscular performance was assessed using 20 m maximal sprint, maximal countermovement-jump (CMJ), and isometric leg press tests, performed 14 hours after exercise. Heart rate (HR), heart rate variability (HRV), sleep diary, muscle soreness, and indirect muscle damage markers were measured pre and post exercise. The decrease in CMJ performance from pre- to post-exercise was attenuated after IRS compared to PAS (p < 0.01). The IRS session resulted in higher HR and lower root mean square of successive differences between normal heartbeats (RMSSD), and high and low frequency power, compared to PAS (p < 0.002). Post-exercise night-time HR and HRV did not differ following IRS vs. PAS. Muscle soreness was less severe, and perceived recovery was higher after IRS compared to PAS (p < 0.01). Post-exercise IRS attenuated the drop in explosive performance and decreased subjective muscle soreness after resistance training, which may enhance mood, readiness, and physical performance of an athlete. A single IRS session had no detrimental effects on recovery of the autonomic nervous system.

Prediction of intrapartum fever using continuously monitored vital signs and heart rate variability.

Neonatal early onset sepsis (EOS), bacterial infection during the first seven days of life, is difficult to diagnose because presenting signs are non-specific, but early diagnosis before birth can direct life-saving treatment for mother and baby. Specifically, maternal fever during labor from placental infection is the strongest predictor of EOS. Alterations in maternal heart rate variability (HRV) may precede development of intrapartum fever, enabling incipient EOS detection. The objective of this work was to build a predictive model for intrapartum fever. Continuously measured temperature, heart rate, and beat-to-beat RR intervals were obtained from wireless sensors on women (n = 141) in labor; traditional manual vital signs were taken every 3-6 hours. Validated measures of HRV were calculated in moving 5-minute windows of RR intervals: standard deviation of normal-to-normal intervals (SDNN) and root mean square of successive differences (RMSSD) between normal heartbeats. Fever (>38.0 °C) was detected by manual or continuous measurements in 48 women. Compared to afebrile mothers, average SDNN and RMSSD in febrile mothers decreased significantly (p < 0.001) at 2 and 3 hours before fever onset, respectively. This observed HRV divergence and raw recorded vitals were applied to a logistic regression model at various time horizons, up to 4-5 hours before fever onset. Model performance increased with decreasing time horizons, and a model built using continuous vital signs as input variables consistently outperformed a model built from episodic vital signs. HRV-based predictive models could identify mothers at risk for fever and infants at risk for EOS, guiding maternal antibiotic prophylaxis and neonatal monitoring.