Heart Rate Variability Monitoring Research Articles

Discriminatory ability of perioperative heart rate variability in predicting postoperative complications in major urologic surgery: a prospective cohort study

We aimed to determine if continuous perioperative heart rate variability (HRV) monitoring could improve risk stratification compared to a short preoperative measurement in radical cystectomy patients. Electrocardiography (ECG) recordings were collected continuously preoperatively to discharge in 83 patients. Two, 5-min ECG signal segments (preoperative and at 24-h post ECG placement) were analyzed offline to extract HRV metrics. HRV metric discriminatory ability to identify patients with 30-day postoperative complications were analyzed using receiver operating characteristics curves. Sixty participants were included for analysis of which 27 (45%) developed a complication within 30 days postoperative. HRV was reduced in patients with complications. Postoperative standard deviation NN intervals and root mean square of successive differences had area under the curves (AUC) of 0.67 (95% CI 0.54 to 0.81) and 0.68 (95% CI 0.54 to 0.82), respectively. Significant discriminatory abilities were also reported for postoperative frequency metrics of absolute low frequency (LF) [AUC = 0.65 (95% CI 0.51 to 0.79)] and high frequency (HF) powers [AUC = 0.69 (95% CI 0.55 to 0.83)] and total power [AUC = 0.66 (95% CI 0.53 to 0.80)]. Postoperative acquired HRV metrics demonstrated improved discriminatory ability. Our findings suggest that longer-term perioperative HRV monitoring presents with superior ability to stratify complication risk.

Camera-Based Seismocardiogram for Heart Rate Variability Monitoring.

Heart rate variability (HRV) is a crucial metric that quantifies the variation between consecutive heartbeats, serving as a significant indicator of autonomic nervous system (ANS) activity. It has found widespread applications in clinical diagnosis, treatment, and prevention of cardiovascular diseases. In this study, we proposed an optical model for defocused speckle imaging, to simultaneously incorporate out-of-plane translation and rotation-induced motion for highly-sensitive non-contact seismocardiogram (SCG) measurement. Using electrocardiogram (ECG) signals as the gold standard, we evaluated the performance of photoplethysmogram (PPG) signals and speckle-based SCG signals in assessing HRV. The results indicated that the HRV parameters measured from SCG signals extracted from laser speckle videos showed higher consistency with the results obtained from the ECG signals compared to PPG signals. Additionally, we confirmed that even when clothing obstructed the measurement site, the efficacy of SCG signals extracted from the motion of laser speckle patterns persisted in assessing the HRV levels. This demonstrates the robustness of camera-based non-contact SCG in monitoring HRV, highlighting its potential as a reliable, non-contact alternative to traditional contact-PPG sensors.

Heart rate variability profile changes associated with specialist police selection activities: A case study.

Police Tactical Groups (PTGs) are specialist police units tasked with rigorous physical and psychosocial duties. Consequently, selection courses (SCs) for service in these units must also be rigorous. Given the intensity of SCs, holistic monitoring for potential overstress may be beneficial. Heart Rate Variability (HRV) is one holistic stress measure that can be obtained in austere environments. The purpose of this study was to profile HRV during a PTG SC. Six (n = 6) qualified male police officers attempted a 36-hour PTG selection course held at an Australian state facility. HRV was obtained from Equivitaltrademark EQ02 + LifeMonitor bioharnesses. The selection course consisted of physically demanding events with minimal sleep (approx. 45 mins). Only one candidate completed the full selection course; whose results are reported here. A visual time-series of 384 consecutive 5 min HRV analyses was generated. Contextual analysis was applied to appreciate HRV changes between SC serials. HRV decline occurred during the planning of a navigation exercise and a pack march. Increases in HRV were observed throughout the pack march exercise and rest period. This case study demonstrates the potential utility for selection personnel to obtain additional insight into candidate responses to various occupational challenges throughout an SC. Information provided by HRV monitoring may support leadership decisions when evaluating personnel holistically. For example, the ability to continue occupational task execution even while experiencing potential overstress (as measured by HRV) and after food and sleep deprivation is desirable. HRV may potentially inform stakeholders regarding overstress in PTG candidates.

Continuous Long-Term Assessment of Heart Rate Variability in Adults with Cyanotic Congenital Heart Disease after Surgical Repair.

Background: Heart rate variability (HRV) is an established, non-invasive parameter for the assessment of cardiac autonomic nervous activity and the health status in general cardiology. However, there are few studies on HRV in adults with congenital heart defects (CHDs). The aim of the present study was to evaluate the use of long-term continuous HRV measurement for the assessment of global health status in adults with cyanotic CHD. Methods: This prospective study included 45 adults (40% female, mean age = 35.2 ± 9.2 [range: 19-58] years) after cardiac surgical repair. HRV parameters were calculated from continuous 24 h measurements using a Bittium Faros 180 sensor (Bittium Corp., Oulu, Finland). Results: Postoperative patients with transposition of the great arteries (TGA) (n = 18) achieved significantly higher values of standard deviation of NN intervals (SDNN) (175.4 ± 59.9 ms vs. 133.5 ± 40.6 ms; p = 0.013) compared with patients with other conotruncal anomalies (n = 22). Comparing patients with TGA after a Senning-Brom or Mustard operation (n = 13) with all other heart surgery patients (n = 32), significantly higher HRV parameters were found after atrial switch (root mean square of successive RR interval differences: 53.6 ± 20.7 ms vs. 38.4 ± 18.3 ms; p = 0.019; SDNN: 183.5 ± 58.4 ms vs. 136.3 ± 45.3 ms; p = 0.006). A higher SDNN was also measured after Senning-Brom or Mustard operations than after a Rastelli operations (n = 2) (SDNN: 183.5 ± 58.4 ms vs. 84.5 ± 5.2 ms; p = 0.037). When comparing atrial switch operations (n = 3) with Rastelli operations, the SDNN value was significantly shorter in the Rastelli group (p = 0.004). Conclusions: Our results suggest that continuous HRV monitoring may serve as a marker of cardiac autonomic dysfunction in adults with cyanotic CHD after surgical repair. Impaired cardiac autonomic nervous activity may be associated with an increased risk of adverse reactions in patients with repaired CHD. Therefore, a longitudinal assessment of HRV patterns and trends may provide a deeper insight into dynamic changes in their autonomic regulation and disease progression, lifestyle changes, or treatments. As each person has individual variability in heart rate, HRV may be useful in assessing intra-individual disease progression and may help to improve personalized medicine. Further studies are needed to better understand the underlying mechanisms and to explore the full potential of HRV analysis to optimize medical care for ACHDs.

Heart Rate Variability Monitoring Based on Doppler Radar Using Deep Learning.

The potential of microwave Doppler radar in non-contact vital sign detection is significant; however, prevailing radar-based heart rate (HR) and heart rate variability (HRV) monitoring technologies often necessitate data lengths surpassing 10 s, leading to increased detection latency and inaccurate HRV estimates. To address this problem, this paper introduces a novel network integrating a frequency representation module and a residual in residual module for the precise estimation and tracking of HR from concise time series, followed by HRV monitoring. The network adeptly transforms radar signals from the time domain to the frequency domain, yielding high-resolution spectrum representation within specified frequency intervals. This significantly reduces latency and improves HRV estimation accuracy by using data that are only 4 s in length. This study uses simulation data, Frequency-Modulated Continuous-Wave radar-measured data, and Continuous-Wave radar data to validate the model. Experimental results show that despite the shortened data length, the average heart rate measurement accuracy of the algorithm remains above 95% with no loss of estimation accuracy. This study contributes an efficient heart rate variability estimation algorithm to the domain of non-contact vital sign detection, offering significant practical application value.

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.

IoT system development for heart rhythm monitoring and cardiovascular risk estimation

The research focuses on addressing the global issue of cardiovascular diseases. The key variable under consideration for predicting cardiovascular diseases is heart rate variability (HRV). Leveraging the widespread adoption of IoT in various applications, particularly in the health sector, the study proposes the design and implementation of an IoT system for HRV monitoring. The research unfolded in four methodological phases: exploration and selection of technologies, definition of the IoT architecture, development of the prototype, and verification of its functionality. The implemented IoT system adheres to the conventional 4-layer IoT architecture: capture, storage, analysis, and visualization. Heart rate data is periodically acquired using a heart rate sensor and an Arduino-compatible board. The storage layer employs a non-relational database to store the captured data. The analysis layer extracts metrics related to HRV (High: RR &lt;750 ms, Moderate: RR 750–900 ms, Low: RR &gt;900 ms) by applying and delivering quantitative results from clustering algorithms such as machine learning models to evaluate data distribution. Risk levels indicate specific patient metrics. Thus, a 75-year-old patient exhibits an average HR of 75.56, Avg. RR of 795.42, falling into Cluster 1 with a risk value of 1.0. Similar detailed metrics and risk stratifications are presented for patients aged 68, 46, 37, and 18, demonstrating the system's robustness and efficacy in assessing cardiovascular risk. The visualization layer enables real-time observation of physiological variables, risk metrics, and results from data analytics models. The distinctive features of the results lie in the portability advantages of the IoT system, utilizing free hardware and software tools. This facilitates easy replication and utilization of the proposed system in medical campaigns, specifically for the early detection of cardiac conditions. The portable IoT system, leveraging free tools, enhances predictive capabilities for early cardiovascular risk detection globally

Assessment of the state of autonomic dysfunction in patients with tuberculosis of the respiratory system and in combination with HIV infection in modern conditions

The autonomic nervous system (ANS) is an important part of the human nervous system responsible for regulating internal organs and maintaining the internal environment of the body. Adequate functioning of the ANS supports homeostasis is the internal stability of the body and regulates many functions in it, such as cardiac activity, breathing, etc. TB-related stress and inflammation affect have influence at the balance of sympathetic and parasympathetic activity. Affecting the work of the peripheral nervous system, it becomes possible to influence at the system of homeostasis of the body through its regulatory systems. Improving the diagnostics of autonomic dysfunction and identifying its dysfunction will provide an integrated individualized approach to treatment and will make a significant contribution to increasing the effectiveness of therapy for patients with tuberculosis.Aim. Diagnosis of the state of the autonomic nervous system by its main indicators using available informative methods for assessing its functional state in patients with respiratory tuberculosis, including in combination with HIV infection at the beginning of treatment and detecting the level of its damage for further correction with the prescription of pathogenetic therapy.Materials and methods. The functional state of the autonomic nervous system has been investigated in 260 people. In the study, 2 groups were formed: a control group (CG) of 70 healthy persons and a monitoring group (MG) of 190 newly diagnosed patients with tuberculosis. There are 3 subgroups in MG. The 1 subgroup included 65 patients with pulmonary tuberculosis, in whom the function of ANS was studied by conventional examination methods, and in 64 patients with tuberculosis 2 subgroups and 61 patients with tuberculosis in combination with HIV infection 3 subgroups using Varikard 2.51. Calculations were carried out in the program SPSS Statistics v. 23.Results and discussion. Comparison of the frequency of autonomic dysfunction in patients with MG, subgroup 1 with that in the CG according to questionnaires No. 1 (χ2=36.346; p&lt;0.001) and No. 2 (χ2=50.885; p&lt;0.001), HR per minute (χ2=31.989; p&lt;0.001), Kerdo index (χ2=30.008; p&lt;0.001), cold sample (χ2=36.549; p&lt;0.001) revealed high reliability of differences and indicated the predominance of sympathicotension, which was determined 7 times more often in patients. At the beginning of treatment, in patients with concomitant HIV infection was detected a more pronounced dysfunction of ANS with a predominance of its sympathetic link, which was found 9.3 times more often than in healthy persons and 2.3 times more often than in patients without HIV infection. Comparing the frequency of PARS in the «normal zone» in the CG and in the 2 subgroup of MG (t1=5.01; p1&lt;0,001; t2=3,69; p2&gt;0.001), in CG and in MG subgroup 3 (t1=3.61; p1&lt;0.001; t2=3.61; p2&lt;0.001) high significant differences were identified, indicating a significant predominance of regulatory process disorders in patients with tuberculosis and especially when combined with HIV infection.Conclusion. The revealed disorders in the work of ANS patients with tuberculosis and in combination with HIV infection are associated with intoxication and asthenovegetative syndrome. Monitoring of heart rate variability can be used in medical research, as well as in clinical practice to assess the state of the autonomic nervous system in patients with tuberculosis and when combined with HIV infection, determine the application point for the effects of pathogenetic therapy in order to improve the results and effectiveness of the treatment.

Stress influence on real-world driving identified by monitoring heart rate variability and morphologic variability of electrocardiogram signals: the case of intercity roads

Objectives. This study examines which of the heart rate variability (HRV) and morphologic variability (MV) metrics may have the highest accuracy in different stress detection during real-world driving. Methods. The cross-sectional study was carried out among 93 intercity mini-bus male drivers aged 22–67 years. The Trillium 5000 Holter Recorder and GARMIN Virb Elite camera were used to determine heart rate and vehicle speed measurements along the path, respectively. We considered the HRV and MV metrics of electrocardiogram (ECG) signals including the mean RR interval (mRR), mean heart rate (mHR), normalized low-frequency spectrum (nLF), normalized high-frequency spectrum (nHF), normalized very low-frequency spectrum (nVLF), difference of normalized low-frequency spectrum and normalized high-frequency spectrum (dLFHF), and sympathovagal balance index (SVI). Results. The analysis showed that the HRV metrics mHR, mRR, nVLF, nLF, nHF, dLFHF and SVI are effective in mental stress detection while driving as compared to rest time. We obtained a high accuracy of stress detection for MV metrics as compared to the traditional HRV analysis, of approximately 92%. Conclusions. Our findings indicate that driver stress could be detected with an accuracy of 92% using MV metrics as an accurate physiological index of the driver’s state.

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.

Wearable heart rate variability and atrial fibrillation monitoring to improve clinically relevant endpoints in cardiac surgery-a systematic review.

This systematic review aims to highlight the untapped potential of heart rate variability (HRV) and atrial fibrillation (AF) monitoring by wearable health monitoring devices as a critical diagnostic tool in cardiac surgery (CS) patients. We reviewed established predictive capabilities of HRV and AF monitoring in specific cardiosurgical scenarios and provide a perspective on additional predictive properties of wearable health monitoring devices that need to be investigated. After screening most relevant databases, we included 33 publications in this review. Perusing these publications on HRV's prognostic value, we could identify HRV as a predictor for sudden cardiac death, mortality after acute myocardial infarction (AMI), and post operative atrial fibrillation (POAF). With regards to standard AF assessment, which typically includes extensive periods of unrecorded cardiac activity, we demonstrated that continuous monitoring via wearables recorded significant cardiac events that would otherwise have been missed. Photoplethysmography and single-lead electrocardiogram (ECG) were identified as the most useful and convenient technical assessment modalities, and their advantages and disadvantages were described in detail. As a call to further action, we observed that the scientific community has relatively extensively explored wearable AF screening, whereas HRV assessment to improve relevant clinical outcomes in CS is rarely studied; it still has great potential to be leveraged. Therefore, risk assessment in CS would benefit greatly from earlier preoperative and postoperative AF detection, comprehensive and accurate assessment of cardiac health through HRV metrics, and continuous long-term monitoring. These should be achievable via commercially available wearables.

Visually assessing work performance using a smartwatch via day-to-day fluctuations in heart rate variability.

To optimize workplace health promotion, a simple method for quantifying allostatic load response is needed. This study examines the feasibility of optimizing objective anxiety and presenteeism monitoring using daily smartwatch-measured ultra-short heart rate variability (HRV). Office workers without diagnosed disease prospectively performed 30 s HRV self-measurement each morning for two months and responded to the State-Trait Anxiety Inventory (STAI) and Work Limitation Questionnaire (WLQ). Logistic regression analysis examined daily HRV parameters in the high-trait anxiety group (HTA, STAI ≥ 40) using mean and variance HRV, age, self-reported gender, and body mass index (BMI). The ideal cutoff value enabled comparison of WLQ using the Mann-Whitney U test. Heart rate variability data were collected for 279 participants (male ratio, 83.9%; age, 42 ± 10 years) who completed questionnaires and monitored HRV for 30+ days. Compared to the low-trait anxiety group, HTA exhibited higher variance of the log-transformed coefficient of component variance of high-frequency component (LnccvHF) and low-frequency per HF (Lnccv L/H), in addition to differences in the means of these HRV parameters. In addition to BMI (odds ratio [OR] = 0.92, p = 0.02) and mean LnccvL/H (OR = 10.75, p < 0.01), the variance of Lnccv L/H was an independent predictor of HTA (OR = 2.39E + 8, p = 0.011). The daily Lnccv L/H dispersion group had a lower WLQ productivity loss score (p = 0.02, r = 0.17). By focusing on HRV dispersion status, this simple and instantly applicable daily HRV monitoring system enables optimized quantitative monitoring of anxiety and productivity.

Utilizing Real-Time Heart Rate Variability during Psychological Intervention Program for Complex Post-Traumatic Stress Disorder: A Case Study

This study explores the use of real-time heart rate variability (HRV) monitoring as an innovative tool in a psychological intervention program for complex post-traumatic stress disorder (CPTSD). The research focuses on a 25-year-old male subject, presenting severe symptoms of CPTSD resulting from prolonged exposure to traumatic events. The intervention program, conducted over four sessions, integrated cognitive and physical therapies, including bottom-up and top-down approaches. Real-time HRV data, reflecting autonomic nervous system activity, were collected using a wearable heart rate sensor and analyzed alongside qualitative data from session transcripts and observational notes. The findings demonstrated a significant correlation between HRV changes and the subject’s psychological state during therapeutic interventions. Key HRV metrics, such as the change ratio of the RMSSD and SDNN, responded notably to traumatic event recounting and stabilization techniques, suggesting their potential as indicators of emotional and physiological states during therapy. The study, while limited by its single-subject design, lays the groundwork for further investigations into HRV-assisted psychotherapy for a broader CPTSD patient cohort.

Heart Rate Variability Measurement through a Smart Wearable Device: Another Breakthrough for Personal Health Monitoring?

Heart rate variability (HRV) is a measurement of the fluctuation of time between each heartbeat and reflects the function of the autonomic nervous system. HRV is an important indicator for both physical and mental status and for broad-scope diseases. In this review, we discuss how wearable devices can be used to monitor HRV, and we compare the HRV monitoring function among different devices. In addition, we have reviewed the recent progress in HRV tracking with wearable devices and its value in health monitoring and disease diagnosis. Although many challenges remain, we believe HRV tracking with wearable devices is a promising tool that can be used to improve personal health.

Dysregulation of the autonomic nervous system in ME/CFS and post-COVID syndrome: insights from 48-h heart rate variability monitoring

Dysregulation of the autonomic nervous system in ME/CFS and post-COVID syndrome: insights from 48-h heart rate variability monitoring

Time-domain heart rate variability features for automatic congestive heart failure prediction.

Heart failure is a serious condition that often goes undiagnosed in primary care due to the lack of reliable diagnostic tools and the similarity of its symptoms with other diseases. Non-invasive monitoring of heart rate variability (HRV), which reflects the activity of the autonomic nervous system, could offer a novel and accurate way to detect and manage heart failure patients. This study aimed to assess the feasibility of using machine learning techniques on HRV data as a non-invasive biomarker to classify healthy adults and those with heart failure. We used digitized electrocardiogram recordings from 54 adults with normal sinus rhythm and 44 adults categorized into New York Heart Association classes 1, 2, and 3, suffering from congestive heart failure. All recordings were sourced from the PhysioNet database. Following data pre-processing, we performed time-domain HRV analysis on all individual recordings, including root mean square of the successive difference in adjacent RR interval (RRi) (RMSSD), the standard deviation of RRi (SDNN, the NN stands for natural or sinus intervals), the standard deviation of the successive differences between successive RRi (SDSD), the number or percentage of RRi longer than 50ms (NN50 and pNN50), and the average value of RRi [mean RR interval (mRRi)]. In our experimental classification performance evaluation, on the computed HRV parameters, we optimized hyperparameters and performed five-fold cross-validation using four machine learning classification algorithms: support vector machine, k-nearest neighbour (KNN), naïve Bayes, and decision tree (DT). We evaluated the prediction accuracy of these models using performance criteria, namely, precision, recall, specificity, F1 score, and overall accuracy. For added insight, we also presented receiver operating characteristic (ROC) plots and area under the ROC curve (AUC) values. The overall best performance accuracy of 77% was achieved when KNN and DT were trained on computed HRV parameters with a 5min time window. KNN obtained an AUC of 0.77, while DT attained 0.78. Additionally, in the classification of severe congestive heart failure, KNN and DT had the best accuracy of 91%, with KNN achieving an AUC of 0.88 and DT obtaining 0.92. The results show that HRV can accurately predict severe congestive heart failure. The findings of this study could inform the use of machine learning approaches on non-invasive HRV, to screen congestive heart failure individuals in primary care.

Improving ballistocardiogram-based continuous heart rate variability monitoring: A self-supervised learning approach

Heart rate variability (HRV) is a reliable measure of an individual’s physical and mental fitness. Monitoring HRV over time provides crucial indicators of various health issues and helps in the early implementation of preventive measures. While the ballistocardiogram (BCG) serves as an alternative to the electrocardiogram (ECG) for convenient at-home HRV monitoring, it is inevitable to face the presence of undesirable artifacts and irregular waveforms in BCG signals that can hinder accurate monitoring when appropriate interventions are unavailable. To address this challenge, we propose a self-supervised learning approach consisting of three stages: signal separation, heartbeat pattern detection, and peak identification. We introduce convolutional kernels of different sizes to construct adaptive soft filters that effectively extract the heartbeat waveform from the original signal. To discover the heartbeat patterns, we utilize the k-means algorithm for clustering the encoded peaks obtained from the masked autoencoder and then compute Kullback–Leibler divergences to determine the optimal number of clusters. Subsequently, a lightweight multi-feature cross-attention classifier is employed to locate the heartbeats. Although the classifier is trained using pseudo-labels generated in the previous stage, the inherent similarity of heartbeats can enhance its performance. Our approach accommodates diverse BCG signal modes and does not require an annotated dataset for model training. Through evaluations conducted on 24 healthy subjects using synchronized ECG references, our prototype system demonstrated an accuracy of approximately 99% in heartbeat identification and an interbeat interval RMSE of under 15ms. These results suggest the system’s practicality and suitability for real-world HRV monitoring.

Real-time heart rate variability according to ambulatory glucose profile in patients with diabetes mellitus.

Autonomic neuropathy commonly occurs as a long-term complication of diabetes mellitus (DM) and can be diagnosed based on heart rate variability (HRV), calculated from electrocardiogram (ECG) recordings. There are limited data on HRV using real-time ECG and ambulatory glucose monitoring in patients with DM. The aim of this study was to investigate real-time HRV according to ambulatory glucose levels in patients with DM. A total of 43 patients (66.3 ± 7.5 years) with DM underwent continuous real-time ECG monitoring (225.7 ± 107.3 h) for HRV and ambulatory glucose monitoring using a remote monitoring system. We compared the HRV according to the ambulatory glucose profile. Data were analyzed according to the target in glucose range (TIR). There were no significant differences in the baseline characteristics of the patients according to the TIR. During monitoring, we checked ECG and ambulatory glucose levels (a total of 15,090 times) simultaneously for all patients. Both time- and frequency-domain HRVs were lower when the patients had poorly controlled glucose levels (TIR < 70%) compared with well controlled glucose levels (TIR > 70%). In addition, heart and respiratory rates increased with real-time glucose levels (P < 0.001). Poorly controlled glucose levels were independently associated with lower HRV in patients with DM. This was further substantiated by the independent continuous association between real-time measurements of hyperglycemia and lower HRV. These data strongly suggest that cardiac autonomic dysfunction is caused by elevated blood sugar levels.

The correlation between heart rate variability index and vulnerability prognosis in patients with acute decompensated heart failure.

To explore the correlation between Heart Rate Variability Index (HRV) and poor prognosis in patients with acute decompensated heart failure (ADHF). A retrospective compilation of clinical data encompassed 128 cases of patients afflicted with acute decompensated heart failure (ADHF) who were admitted to and discharged from our hospital between April 2019 and July 2022. Subsequent to assessing their follow-up progress during the tracking period, the subjects were categorized into two cohorts: the poor prognosis group (n = 31) and the good prognosis group (n = 97). Comparative analysis of clinical data and Heart Rate Variability (HRV) parameters was executed between these two groups. Moreover, a multiple linear regression analysis was employed to identify the contributing factors associated with adverse prognoses in ADHF patients. Furthermore, the receiver operating characteristic (ROC) curve was employed to evaluate the prognostic predictive capability of HRV parameters among ADHF patients. The levels of SDNN (t = 3.924, P < 0.001), SDANN (t = 4.520, P < 0.001) and LF (t = 2.676, P = 0.018) in the poor prognosis group were significantly higher than those in the good prognosis group, and the differences were statistically significant (P < 0.05). The levels of PNN50 (t = 2.132, P = 0.035), HF (t = 11.781, P < 0.001) and LF/HF (t = 11.056, P < 0.001) in the poor prognosis group were significantly lower than those in the good prognosis group (P < 0.05). The results of multiple linear regression analysis indicated that SDNN, SDANN, LF, PNN50, and HF were factors influencing poor prognosis in ADHF patients (P < 0.05). The results of the ROC curve analysis indicate that the area under the curve (AUC) for predicting poor prognosis in ADHF patients using HRV parameters were as follows: SDNN (AUC = 0.818, 95% CI [0.722-0.914]), SDANN (AUC = 0.684, 95% CI [0.551-0.816]), PNN50 (AUC = 0.754, 95% CI [0.611-0.841]), LF/HF (AUC = 0.787, 95% CI [0.679-0.896]), and combined diagnosis (AUC = 0.901, 95% CI [0.832-0.970]). Among these, the combined diagnosis exhibited the highest AUC, sensitivity, and specificity for predicting poor prognosis in ADHF patients (P < 0.001). The HRV parameters of SDNN, SDANN, PNN50 and LF/HF are closely related to the prognosis of ADHF patients. The combined detection of the above HRV parameters can improve the efficacy of predicting the poor prognosis of ADHF patients. This suggests that clinical staff can identify ADHF patients at risk of poor prognosis by long-term monitoring of HRV in the future.

Psychophysiological Data Harmonization for the Sustainability of Outdoor Activities

Prolonged sedentary behavior is considered a risk factor for health throughout the human lifespan. Although outdoor activities, such as walking and hiking, can be effective in reducing sedentary behavior, there is a lack of data harmonization on the psychophysiological characteristics of hiking trails. Therefore, this research protocol aims to provide an innovative and uniform methodology to provide a psychophysiological characterization of hiking. Enrolled subjects will be allocated in groups equally distributed for age, physical activity level (physically active vs. sedentary), and sex (male vs. female). Subjects will perform two treadmill tests in laboratory sessions and two hiking tests in field sessions. The Ruffier test will be performed before each session to assess subjects’ exercise capacity. During each session, body mass measurement, cardiometabolic evaluation, heart rate and heart rate variability monitoring, rating of perceived exertion, and physical activity enjoyment rate will be assessed. To measure breath-by-breath ventilation, oxygen consumption, and energy expenditure, subjects will be equipped with a portable gas analyzer during one laboratory session and one field session. Findings from the present study protocol have the potential to fill a gap in assessing hiking-related fitness, promoting physical and mental health, and offering a practical way to evaluate fitness for hiking, encouraging outdoor activity. These findings will have the potential to impact tourism, health, and well-being through outdoor experiences.