Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an ...electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient when based on irregularity of beat-to-beat intervals estimated by the heart rate (HR) features. Automated classification of heartbeats into AF and non-AF by means of the Lagrangian Support Vector Machine has been proposed. The classifier input vector consisted of sixteen features, including four coefficients very sensitive to beat-to-beat heart changes, taken from the fetal heart rate analysis in perinatal medicine. Effectiveness of the proposed classifier has been verified on the MIT-BIH Atrial Fibrillation Database. Designing of the LSVM classifier using very large number of feature vectors requires extreme computational efforts. Therefore, an original approach has been proposed to determine a training set of the smallest possible size that still would guarantee a high quality of AF detection. It enables to obtain satisfactory results using only 1.39% of all heartbeats as the training data. Post-processing stage based on aggregation of classified heartbeats into AF episodes has been applied to provide more reliable information on patient risk. Results obtained during the testing phase showed the sensitivity of 98.94%, positive predictive value of 98.39%, and classification accuracy of 98.86%.
The commonly observed nocturnal attack of asthma is accompanied by circadian variations in airway inflammation and other physiological variables. It is also documented to present with a significantly ...higher risk of adverse cardiovascular events that are associated with lower heart rate variability (HRV) and depressed sympathetic and enhanced parasympathetic modulations. However, available literature is scarce with regard to the impact of alteration in circadian rhythmicity of long-term HRV and its day-night variation in asthmatic patients. Thus, 72-h continuous recording of RR interval and oxygen saturation was done to study the circadian variability of HRV (in terms of time and frequency domain indices) and also to assess the pattern of alterations in sympathetic and parasympathetic tones at different times of the day in asthmatic patients (n = 32) and healthy control subjects (n = 31). Repeated-measure analysis of variance and independent-samples t-test revealed significantly increased parasympathetic tone in terms of increased square root of the mean squared differences of successive NN intervals (RMSSD), percentage of number of pairs of adjacent RR interval differing by more than 50 ms (pNN50), standard deviation of NN intervals (SDNN), and high frequency (HF) with reduced sympathetic activity decreased low frequency (LF) and LF/HF ratio at early morning hours (between 04:00 and 10:00 h) in the asthma patients in contrast to the healthy subjects who had opposite response. Also, significant phase delay (p<0.05) of all the HRV indices and SpO
2
, was evident by cosinor analysis. Therefore, disturbed circadian rhythm of HRV indices and early morning increased parasympathetic tone points toward the possible pathophysiological basis of exacerbated asthmatic symptoms at late night/early morning hours and susceptibility of future cardiovascular pathologies. This also necessitates the assessment of HRV rhythm while dealing with the therapeutic management of asthma patients.
An exacerbated physiological response to stress is associated with the development of stress-related disorders (e.g., depression and anxiety disorders). Recently, it has been proposed that ...individuals with high expectancies of being able to deal with stressful situations will activate regulatory mechanisms during the anticipation of the stressful event that would improve stress regulation. To test this hypothesis, 52 women in young adulthood (M = 21.06; SD = 2.58) anticipated and performed a laboratory-based stress task after receiving positive or negative bogus feedback on their abilities to deal with stressful events. Heart rate variability and salivary cortisol were assessed throughout the experimental protocol. Participants receiving positive bogus feedback (i.e., High Expectancy group) showed a more positive anticipatory cognitive stress appraisal (i.e., they anticipated the stress task as less threatening/challenging, and they perceived that they were more able to deal with it), and they showed a lower cortisol response to stress. Moreover, a more positive anticipatory cognitive stress appraisal was associated with better anticipatory stress regulation (indexed as less decrease in heart rate variability), leading to a lower cortisol response. Our results indicate that people with positive expectancy initiate mechanisms of anticipatory stress regulation that enhance the regulation of the physiological stress response. Expectancy and anticipatory stress regulation may be key mechanisms in the development and treatment of stress-related disorders.
•We investigated the role of expectancy and stress anticipation in stress regulation.•Positive expectancy attenuates the cortisol response to stress.•Positive expectancy is associated with better anticipatory stress regulation.•Better anticipatory stress regulation leads to lower cortisol response to stress.
Noise and distractions are commonly associated with stress. Our objective was to identify the impact of noise and distracting environments on the stress levels of library patrons, as measured by ...wearable devices. In this study, we explored the relationship between heart rate variability (HRV) and skin temperature measures using consumer wearable devices. Additionally, we analyzed our data through the perspective of established personas for library patrons to determine if purpose in visiting the library had any effect on observed stress. For those who were expected to be more stressed, there was no significant difference between loud and quiet conditions. Our results showed that patrons in both quiet and loud conditions were significantly more stressed than their baseline measurements outside of the library, but equally so. This was consistent even accounting for differing purpose in library attendance. Our findings suggest that noisy environments in the library may not be as problematic for library patrons as is often perceived.
Long-COVID is a syndrome persisting 12+ weeks after COVID-19 infection, impacting life and work ability. Autonomic nervous system imbalance has been hypothesised as the cause. This study aims to ...investigate cardiovascular autonomic function in health care workers (HCWs) with Long-COVID and the effectiveness of slow paced breathing SPB on autonomic modulation.
From 1st December 2022 to 31th March 2023, 6655 HCWs of the University Hospitals of Trieste (Northeast Italy) were asked to participate the study by company-email. Inclusion/exclusion criteria were assessed. Global health status and psychosomatic disorders were evaluated through validated questionnaires. Heart rate variability was assessed by finger-photoplethysmography during spontaneous breathing (SB) and SPB, which stimulate vagal response. Long-COVID-HCWs (G1) were contrasted with never infected (G2) and fully recovered COVID-19 workers (G3).
126 HCWs were evaluated. The. 58 Long-COVID were assessed at a median time since COVID-19 of 419.5 days (IQR 269-730) and had significantly more psychosomatic symptoms and lower detectability of spontaneous systolic pressure oscillation at 0.1 Hz (Mayer wave - baroreflex arc) during SB compared to 53 never-infected and 14 fully-recovered HCWs (19%, 42% and 40%, respectively, p=0.027). During SPB, the increase in this parameter was close to controls (91.2%, 100% and 100%, respectively, p=0.09). No other differences in HRV parameters were found among groups.
Resting vascular modulation was reduced in Long-COVID, while during SPB baroreflex sensitivity effectively improved. Long-term studies are needed to evaluate whether multiple sessions of breathing exercises can restore basal vascular reactivity and reduce cardiovascular risk in these patients.
•We examined respiratory sinus arrhythmia (RSA) in post traumatic stress disorder (PTSD).•We conducted a meta-analysis of 56 studies including 6725 participants.•Results support a small negative ...association between RSA and PTSD.•Heterogeneity of this effect may be explained by age, RSA measure, and DSM version.•Results suggest some smaller parasympathetic dysfunction in PTSD.
Respiratory sinus arrhythmia (RSA) has been examined as a psychophysiological marker of stress vulnerability. Research indicates that low resting RSA is associated with physical and mental health problems, including posttraumatic stress disorder (PTSD). Some research suggests that people diagnosed with PTSD have lower RSA than people without PTSD, but findings have been mixed and the overall magnitude of this effect is unknown, indicating the need for a comprehensive meta-analysis. This meta-analysis examined the association between PTSD and baseline RSA in 55 studies, including 12 unpublished studies, with a total sample size of 6689. Studies were included if they used a PTSD measure, a baseline measure of RSA, and involved humans. Studies were excluded if they were not available in English, did not present quantitative data, presented duplicate data, were a case series, or did not provide results required for computing an effect size. The meta-analysis indicated there is a small but significant association between PTSD and RSA (g = −0.26; 95% CI = −0.35, −0.16) with moderate heterogeneity. Moderator analyses suggested that effects are larger for adults than for children and for DSM-5 PTSD measures than for non-DSM referenced measures. We found some evidence for publication bias among the meta-analysis findings. Overall, there is a small but reliable association between PTSD and lower resting RSA, providing support for further research examining the complex relationship between parasympathetic activity and PTSD.
Some evidence suggests that heart rate variability (HRV) biofeedback might be an effective way to treat anxiety and stress symptoms. To examine the effect of HRV biofeedback on symptoms of anxiety ...and stress, we conducted a meta-analysis of studies extracted from PubMed, PsycINFO and the Cochrane Library.
The search identified 24 studies totaling 484 participants who received HRV biofeedback training for stress and anxiety. We conducted a random-effects meta-analysis.
The pre-post within-group effect size (Hedges' g) was 0.81. The between-groups analysis comparing biofeedback to a control condition yielded Hedges' g = 0.83. Moderator analyses revealed that treatment efficacy was not moderated by study year, risk of study bias, percentage of females, number of sessions, or presence of an anxiety disorder.
HRV biofeedback training is associated with a large reduction in self-reported stress and anxiety. Although more well-controlled studies are needed, this intervention offers a promising approach for treating stress and anxiety with wearable devices.
Driver drowsiness is a major cause of fatal accidents, injury, and property damage, and has become an area of substantial research attention in recent years. The present study proposes a method to ...detect drowsiness in drivers which integrates features of electrocardiography (ECG) and electroencephalography (EEG) to improve detection performance. The study measures differences between the alert and drowsy states from physiological data collected from 22 healthy subjects in a driving simulator-based study. A monotonous driving environment is used to induce drowsiness in the participants. Various time and frequency domain feature were extracted from EEG including time domain statistical descriptors, complexity measures and power spectral measures. Features extracted from the ECG signal included heart rate (HR) and heart rate variability (HRV), including low frequency (LF), high frequency (HF) and LF/HF ratio. Furthermore, subjective sleepiness scale is also assessed to study its relationship with drowsiness. We used paired
-tests to select only statistically significant features (
< 0.05), that can differentiate between the alert and drowsy states effectively. Significant features of both modalities (EEG and ECG) are then combined to investigate the improvement in performance using support vector machine (SVM) classifier. The other main contribution of this paper is the study on channel reduction and its impact to the performance of detection. The proposed method demonstrated that combining EEG and ECG has improved the system's performance in discriminating between alert and drowsy states, instead of using them alone. Our channel reduction analysis revealed that an acceptable level of accuracy (80%) could be achieved by combining just two electrodes (one EEG and one ECG), indicating the feasibility of a system with improved wearability compared with existing systems involving many electrodes. Overall, our results demonstrate that the proposed method can be a viable solution for a practical driver drowsiness system that is both accurate and comfortable to wear.
Wearable physiological monitors are becoming increasingly commonplace in the consumer domain, but in literature there exists no substantive studies of their performance when measuring the physiology ...of ambulatory patients. In this Letter, the authors investigate the reliability of the heart-rate (HR) sensor in an exemplar ‘wearable’ wrist-worn monitoring system (the Microsoft Band 2); their experiments quantify the propagation of error from (i) the photoplethysmogram (PPG) acquired by pulse oximetry, to (ii) estimation of HR, and (iii) subsequent calculation of HR variability (HRV) features. Their experiments confirm that motion artefacts account for the majority of this error, and show that the unreliable portions of HR data can be removed, using the accelerometer sensor from the wearable device. The experiments further show that acquired signals contain noise with substantial energy in the high-frequency band, and that this contributes to subsequent variability in standard HRV features often used in clinical practice. The authors finally show that the conventional use of long-duration windows of data is not needed to perform accurate estimation of time-domain HRV features.
The purpose of this investigation was to cross-validate the ithlete
heart rate variability smart phone application with an electrocardiograph for determining ultra-short-term root mean square of ...successive R-R intervals. The root mean square of successive R-R intervals was simultaneously determined via electrocardiograph and ithlete
at rest in twenty five healthy participants. There were no significant differences between the electrocardiograph and ithlete
derived root mean square of successive R-R interval values (p > 0.05) and the correlation was near perfect (r = 0.99, p < 0.001). In addition, the ithlete
revealed a Standard Error of the Estimate of 1.47 and Bland Altman plot showed that the limits of agreement ranged from 2.57 below to 2.63 above the constant error of -0.03. In conclusion, the ithlete
appeared to provide a suitably accurate measure of root mean square of successive R-R intervals when compared to the electrocardiograph measures obtained in the laboratory within the current sample of healthy adult participants. The current study lays groundwork for future research determining the efficacy of ithlete
for reflecting athletic training status over a chronic conditioning period.
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