On the Variability of Heart Rate Variability-Evidence from Prospective Study of Healthy Young College Students

Tissue-adhesive, silk-based conductive hydrogel with high stretchable, transparent, healable and degradable properties for real-time, precise monitoring of tissue motions and electrocardiogram under sweaty condition

Developing bioelectronic sensors with exceptional physicochemical properties, such as strong adhesion to wet biological tissues, high mechanical strength and stretchability, transparency, self-healing ability, biocompatibility, and degradability remains a significant challenge in meeting the complex requirements of monitoring biological tissues. In this study, a novel silk fibroin/polyacrylamide/ferric ion (PAM-SF/Fe3+) double network hydrogel was developed by a self-assembly cross-linking strategy to address this challenge. Benefiting from the double network structure, reinforcement of random coils of SF, a large number of metal chelation and hydrogen bond interactions among SF, PAM, and Fe3+, the hydrogel demonstrates exceptional mechanical properties, including a maximum tensile strength of 71 kPa, elongation at break exceeding 1442 %, compressive stress over 0.66 MPa, Young's modulus of approximately 10 kPa, light transmittance of about 90 %, instant robust adhesion to various wet biological tissues even underwater, and excellent self-healing capability at room temperature. To the best of our knowledge, this is the highest stretchability and mechanical strength among the reported silk-based conductive hydrogels while simultaneously achieving adhesive performance on wet biological tissues. Additionally, the PAM-SF/Fe3+ hydrogel also exhibits good biocompatibility and degradability, enabling direct adhesion to wet biological tissue surfaces, such as pig lung and rat bladder, for real-time and reliable monitoring of their contractile movements. Furthermore, it serves as flexible conductive gel electrodes for long-term continuous monitoring of ECG signals under sweaty conditions and displays promising applications in implantable sensors, wearable devices, and personal healthcare and human-machine interfaces.

Developing a Five-Minute Normative Database of Heart Rate Variability for Diagnosing Cardiac Autonomic Dysregulation for Patients with Major Depressive Disorder

Heart rate variability (HRV) is related to cardiac vagal control and emotional regulation and an index for cardiac vagal control and cardiac autonomic activity. This study aimed to develop the Taiwan HRV normative database covering individuals aged 20 to 70 years and to assess its diagnosing validity in patients with major depressive disorder (MDD). A total of 311 healthy participants were in the HRV normative database and divided into five groups in 10-year age groups, and then the means and standard deviations of the HRV indices were calculated. We recruited 272 patients with MDD for cross-validation, compared their HRV indices with the normative database, and then converted them to Z-scores to explore the deviation of HRV in MDD patients from healthy groups. The results found a gradual decline in HRV indices with advancing age in the HC group, and females in the HC group exhibit higher cardiac vagal control and parasympathetic activity than males. Conversely, patients in the MDD group demonstrate lower HRV indices than those in the HC group, with their symptoms of depression and anxiety showing a negative correlation with HRV indices. The Taiwan HRV normative database has good psychometric characteristics of cross-validation.

Quantifying the accuracy of inter-beat intervals acquired from consumer-grade photoplethysmography wristbands using an electrocardiogram-aided information-based similarity approach

Objective. Although inter-beat intervals (IBI) and the derived heart rate variability (HRV) can be acquired through consumer-grade photoplethysmography (PPG) wristbands and have been applied in a variety of physiological and psychophysiological conditions, their accuracy is still unsatisfactory.Approach.In this study, 30 healthy participants concurrently wore two wristbands (E4 and Honor 5) and a gold-standard electrocardiogram (ECG) device under four conditions: resting, deep breathing with a frequency of 0.17 Hz and 0.1 Hz, and mental stress tasks. To quantitatively validate the accuracy of IBI acquired from PPG wristbands, this study proposed to apply an information-based similarity (IBS) approach to quantify the pattern similarity of the underlying dynamical temporal structures embedded in IBI time series simultaneously recorded using PPG wristbands and the ECG system. The occurrence frequency of basic patterns and their rankings were analyzed to calculate the IBS distance from gold-standard IBI, and to further calculate the signal-to-noise ratio (SNR) of the wristband IBI time series.Main results.The accuracies of both HRV and mental state classification were not satisfactory due to the low SNR in the wristband IBI. However, by rejecting data segments of SNR < 25, the Pearson correlation coefficients between the wristbands' HRV and the gold-standard HRV were increased from 0.542 ± 0.235 to 0.922 ± 0.120 for E4 and from 0.596 ± 0.227 to 0.859 ± 0.145 for Honor 5. The average accuracy of four-class mental state classification increased from 77.3% to 81.9% for E4 and from 79.3% to 83.3% for Honor 5.Significance.Consumer-grade PPG wristbands are acceptable for HR and HRV monitoring when removing low SNR segments. The proposed method can be applied for quantifying the accuracies of IBI and HRV indices acquired via any non-ECG system.

Prediction models for lymph node metastasis in cervical cancer based on preoperative heart rate variability

Background

The occurrence of lymph node metastasis (LNM) is one of the critical factors in determining the staging, treatment and prognosis of cervical cancer (CC). Heart rate variability (HRV) is associated with LNM in patients with CC. The purpose of this study was to validate the feasibility of machine learning (ML) models constructed with preoperative HRV as a feature of CC patients in predicting CC LNM.

Methods

A total of 292 patients with pathologically confirmed CC admitted to the Department of Gynecological Oncology of the First Affiliated Hospital of Bengbu Medical University from November 2020 to September 2023 were included in the study. The patient' preoperative 5-min electrocardiogram data were collected, and HRV time-domain, frequency-domain and non-linear analyses were subsequently performed, and six ML models were constructed based on 32 parameters. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.

Results

Among the 6 ML models, the random forest (RF) model showed the best predictive performance, as specified by the following metrics on the test set: AUC (0.852), accuracy (0.744), sensitivity (0.783), and specificity (0.785).

Conclusion

The RF model built with preoperative HRV parameters showed superior performance in CC LNM prediction, but multicenter studies with larger datasets are needed to validate our findings, and the physiopathological mechanisms between HRV and CC LNM need to be further explored.

Heart rhythm complexity analysis in patients with inferior ST-elevation myocardial infarction

Heart rhythm complexity (HRC), a subtype of heart rate variability (HRV), is an important tool to investigate cardiovascular disease. In this study, we aimed to analyze serial changes in HRV and HRC metrics in patients with inferior ST-elevation myocardial infarction (STEMI) within 1 year postinfarct and explore the association between HRC and postinfarct left ventricular (LV) systolic impairment. We prospectively enrolled 33 inferior STEMI patients and 74 control subjects and analyzed traditional linear HRV and HRC metrics in both groups, including detrended fluctuation analysis (DFA) and multiscale entropy (MSE). We also analyzed follow-up postinfarct echocardiography for 1 year. The STEMI group had significantly lower standard deviation of RR interval (SDNN), and DFAα2 within 7 days postinfarct (acute stage) comparing to control subjects. LF power was consistently higher in STEMI group during follow up. The MSE scale 5 was higher at acute stage comparing to control subjects and had a trend of decrease during 1-year postinfarct. The MSE area under scale 1-5 showed persistently lower than control subjects and progressively decreased during 1-year postinfarct. To predict long-term postinfarct LV systolic impairment, the slope between MSE scale 1 to 5 (slope 1-5) had the best predictive value. MSE slope 1-5 also increased the predictive ability of the linear HRV metrics in both the net reclassification index and integrated discrimination index models. In conclusion, HRC and LV contractility decreased 1 year postinfarct in inferior STEMI patients, and MSE slope 1-5 was a good predictor of postinfarct LV systolic impairment.

Higher habitual lying time is inversely associated with vagal-related heart rate variability outcomes in younger adults

Heart rate variability (HRV) is a well-established noninvasive marker of autonomic cardiac control. We test whether time spent sitting (negatively) versus lying (positively) influences vagal HRV outcomes. HRV (10 min supine electrocardiogram) and free-living postures (dual-accelerometer configuration, 7 days) were measured in 31 young healthy adults (15♀, age: 23 ± 3 years). Habitual lying (66 ± 61 min/day), but not sitting time (558 ± 109 min/day), total sedentary time (623 ± 132 min/day), nor step counts (10 752 ± 3200 steps/day; all, p > 0.090), was associated with root mean square of successive cardiac interval differences (ρ = -0.409, p = 0.022) and normalized high-frequency HRV (ρ = -0.361, p = 0.046). These findings document a paradoxical negative impact of waking lying time on cardioautonomic function.   Take home message Using a multi-accelerometer configuration, we demonstrated that more habitual waking time lying, but not sitting or total sedentary time, was associated with worse vagally mediated cardiac control.

Autonomic complexity dynamically indexes affect regulation in everyday life

Affect regulation often is disrupted in depression. Understanding biomarkers of affect regulation in ecologically valid contexts is critical for identifying moments when interventions can be delivered to improve regulation and may have utility for identifying which individuals are vulnerable to psychopathology. Autonomic complexity, which includes linear and nonlinear indices of heart rate variability, has been proposed as a novel marker of neurovisceral integration. However, it is not clear how autonomic complexity tracks with regulation in everyday life, and whether low complexity serves as a marker of related psychopathology. To measure regulation phenotypes with diminished influence of current symptoms, 37 young adults with remitted major depressive disorder (rMDD) and 28 healthy comparisons (HCs) completed ambulatory assessments of autonomic complexity and affect regulation across one week in everyday life. Multilevel models indicated that in HCs, but not rMDD, autonomic complexity fluctuated in response to regulation cues, increasing in response to reappraisal and distraction and decreasing in response to negative affect. Higher complexity across the week predicted greater everyday regulation success, whereas greater variability of complexity predicted lower (and less variable) negative affect, rumination, and mind-wandering. Results suggest that ambulatory assessment of autonomic complexity can passively index dynamic aspects of real-world affect and regulation, and that dynamic physiological reactivity to regulation is restricted in rMDD. These results demonstrate how intensive sampling of dynamic, nonlinear regulatory processes can advance our understanding of potential mechanisms underlying psychopathology. Such measurements might inform how to test interventions to enhance neurovisceral complexity and affect regulation success in real time. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A meta-analysis investigating the outcomes and correlation between heart rate variability biofeedback training on depressive symptoms and heart rate variability outcomes versus standard treatment in comorbid adult populations

BACKGROUND AND AIM

Heart rate variability biofeedback (HRVB) has previously been used to ameliorate depressive symptoms but its uses for tackling depressive symptoms in an array of comorbid adult patients is less established. This meta-analysis aims to evaluate whether HRVB is a useful tool to reduce depressive symptoms and improve HRV relative to standard treatment in adult comorbid populations, while also attempting to establish the association between the two outcomes.

METHODS

An extensive literature review was conducted using several databases including PubMed, Cinahl, Medline, Web of science and clinical.gov/UK register. A total of 149 studies were identified with 9 studies, totalling 428 participants were analysed using a random effects model.

RESULTS

Depressive outcomes yielded a mean effect size g=0.478 (CI 95% 0.212, 0.743) with HRV outcomes, yielding a mean effect size of g=0.223 (95% CI 0.036 to 0.411). Total heterogeneity was non-significant for depressive outcomes (Q= 13.77, p=0.088 I^=42.86%) and HRV (Q= 1.598, p=0.991, I^=0.000%) which indicates that little variance existed for the included studies.

CONCLUSIONS

In summary, the outcomes demonstrate that HRVB can improve both clinically relevant depressive symptoms and physiological HRV outcomes in various comorbid conditions in adult populations, while the correlation between the two was moderately negative, but non-significant.

A dataset on 24-h electrocardiograph, sleep and metabolic function of male type 2 diabetes mellitus

This dataset provides a collection of 24 h electrocardiograph (ECG) signals, ECG analysis results based on circadian rhythm and R-peak detection, results of sleep quality assessment and clinical indicators of metabolic function acquired from 60 male type 2 diabetes mellitus (T2DM) inpatients. Upon admission, a fasting blood draw and urinary sample were obtained the next morning for routine glucose, lipid and renal panels. Subjects were also involved in investigation for diabetic complications. On the second day of hospitalization, subjects were monitored in hospital for 24-h ECG starting at 10 pm. Subjective sleep quality was assessed by Pittsburgh Sleep Quality Index and a brief sleep log was used to record sleep duration for the studied night. Objective sleep quality and sleep staging were assessed by cardiopulmonary coupling analysis. This dataset could be utilized to conduct conjoint research on the relationships among sleep, metabolic function, and function of cardiovascular system and autonomic nervous system derived from ECG analysis in T2DM, and further investigate the information in ECG signals based on circadian rhythm and physiological status, providing new insights into long term physiological signal processing.

Heart rate variability in different sleep stages is associated with metabolic function and glycemic control in type 2 diabetes mellitus

Introduction: Autonomic nervous system (ANS) plays an important role in the exchange of metabolic information between organs and regulation on peripheral metabolism with obvious circadian rhythm in a healthy state. Sleep, a vital brain phenomenon, significantly affects both ANS and metabolic function. Objectives: This study investigated the relationships among sleep, ANS and metabolic function in type 2 diabetes mellitus (T2DM), to support the evaluation of ANS function through heart rate variability (HRV) metrics, and the determination of the correlated underlying autonomic pathways, and help optimize the early prevention, post-diagnosis and management of T2DM and its complications. Materials and methods: A total of 64 volunteered inpatients with T2DM took part in this study. 24-h electrocardiogram (ECG), clinical indicators of metabolic function, sleep quality and sleep staging results of T2DM patients were monitored. Results: The associations between sleep quality, 24-h/awake/sleep/sleep staging HRV and clinical indicators of metabolic function were analyzed. Significant correlations were found between sleep quality and metabolic function (|r| = 0.386 ± 0.062, p < 0.05); HRV derived ANS function showed strengthened correlations with metabolic function during sleep period (|r| = 0.474 ± 0.100, p < 0.05); HRV metrics during sleep stages coupled more tightly with clinical indicators of metabolic function [in unstable sleep: |r| = 0.453 ± 0.095, p < 0.05; in stable sleep: |r| = 0.463 ± 0.100, p < 0.05; in rapid eye movement (REM) sleep: |r| = 0.453 ± 0.082, p < 0.05], and showed significant associations with glycemic control in non-linear analysis [fasting blood glucose within 24 h of admission (admission FBG), |r| = 0.420 ± 0.064, p < 0.05; glycated hemoglobin (HbA1c), |r| = 0.417 ± 0.016, p < 0.05]. Conclusions: HRV metrics during sleep period play more distinct role than during awake period in investigating ANS dysfunction and metabolism in T2DM patients, and sleep rhythm based HRV analysis should perform better in ANS and metabolic function assessment, especially for glycemic control in non-linear analysis among T2DM patients.

Fractal Properties of Heart Rate Dynamics: A New Biomarker for Anesthesia-Biphasic Changes in General Anesthesia and Decrease in Spinal Anesthesia

Processed electroencephalogram (EEG) has been considered a useful tool for measuring the depth of anesthesia (DOA). However, because of its inability to detect the activities of the brain stem and spinal cord responsible for most of the vital signs, a new biomarker for measuring the multidimensional activities of the central nervous system under anesthesia is required. Detrended fluctuation analysis (DFA) is a new technique for detecting the scaling properties of nonstationary heart rate (HR) behavior. This study investigated the changes in fractal properties of heart rate variability (HRV), a nonlinear analysis, under intravenous propofol, inhalational desflurane, and spinal anesthesia. We compared the DFA method with traditional spectral analysis to evaluate its potential as an alternative biomarker under different levels of anesthesia. Eighty patients receiving elective procedures were randomly allocated different anesthesia. HRV was measured with spectral analysis and DFA short-term (4-11 beats) scaling exponent (DFAα1). An increase in DFAα1 followed by a decrease at higher concentrations during propofol or desflurane anesthesia is observed. Spinal anesthesia decreased the DFAα1 and low-/high-frequency ratio (LF/HF ratio). DFAα1 of HRV is a sensitive and specific method for distinguishing changes from baseline to anesthesia state. The DFAα1 provides a potential real-time biomarker to measure HRV as one of the multiple dimensions of the DOA.

Heart Rate Variability in Healthy Subjects During Monitored, Short-Term Stress Followed by 24-hour Cardiac Monitoring

Heart Rate Variability (HRV) can be a useful metric to capture meaningful information about heart function. One of the non-linear indices used to analyze HRV, Detrended Fluctuation Analysis (DFA), finds short and long-term correlations in RR intervals to capture quantitative information about variability. This study focuses on the impact of visual and mental stimulation on HRV as expressed via DFA within healthy adults. Visual stimulation can activate the automatic nervous system to directly impact physiological behavior such as heart rate. In this investigation of HRV, 70 participants (21 males) viewed images on a screen followed by a math and recall task. Each viewing segment lasted 2 min and 18 s. The math and memory recall task segment lasted 4 min total. This process was repeated 9 times during which the participants' electrocardiogram was recorded. 37 participants (12 males) opted in for an additional 24-h Holter recording after the viewing and task segments of the study were complete. Participants were randomly assigned to either a pure (organized image presentation) or mixed (random image presentation) image regime for the viewing portion of the study to investigate the impact of the external environment on HRV. DFA α1 was extracted from the RR intervals. Our findings suggest that DFA α1 can differentiate between the viewing [DFA α1 range from 0.96 (SD = 0.25) to 1.08 (SD = 0.22)] and the task segments [DFA α1 range from 1.17 (SD = 0.21) to 1.26 (SD = 0.25)], p < 0.0006 for all comparisons. However, DFA α1 was not able to distinguish between the two image regimes. During the 24-hour follow up, participants had an average DFA α1 = 1.09 (SD = 0.14). In conclusion, our findings suggest a graded response in DFA during short term stimulation and a responsiveness in participants to adjust physiologically to their external environment expressed through the DFA exponent.

Longitudinal Changes and Recovery in Heart Rate Variability of Young Healthy Subjects When Exposure to a Hypobaric Hypoxic Environment

Background: The autonomic nervous system (ANS) is crucial for acclimatization. Investigating the responses of acute exposure to a hypoxic environment may provide some knowledge of the cardiopulmonary system’s adjustment mechanism.

Objective: The present study investigates the longitudinal changes and recovery in heart rate variability (HRV) in a young healthy population when exposed to a simulated plateau environment.

Methods: The study followed a strict experimental paradigm in which physiological signals were collected from 33 healthy college students (26 ± 2 years, 171 cm ± 7 cm, 64 ± 11 kg) using a medical-grade wearable device. The subjects were asked to sit in normoxic (approximately 101 kPa) and hypoxic (4,000 m above sea level, about 62 kPa) environments. The whole experimental process was divided into four stable resting measurement segments in chronological order to analyze the longitudinal changes of physical stress and recovery phases. Seventy-six time-domain, frequency-domain, and non-linear indicators characterizing rhythm variability were analyzed in the four groups.

Results: Compared to normobaric normoxia, participants in hypobaric hypoxia had significantly lower HRV time-domain metrics, such as RMSSD, MeanNN, and MedianNN (p &lt; 0.01), substantially higher frequency domain metrics such as LF/HF ratio (p &lt; 0.05), significantly lower Poincaré plot parameters such as SD1/SD2 ratio and other Poincaré plot parameters are reduced considerably (p &lt; 0.01), and Refined Composite Multi-Scale Entropy (RCMSE) curves are reduced significantly (p &lt; 0.01).

Conclusion: The present study shows that elevated heart rates, sympathetic activation, and reduced overall complexity were observed in healthy subjects exposed to a hypobaric and hypoxic environment. Moreover, the results indicated that Multiscale Entropy (MSE) analysis of RR interval series could characterize the degree of minor physiological changes. This novel index of HRV can better explain changes in the human ANS.