Heart rate variability for medical decision support systems: A review

Investigating the course and predictors of desire to void based on heart rate variability

Urinary incontinence is closely related to the motor ability and toileting tasks. Some nursing home residents with limited mobility who cannot reach the bathroom in time highly depend on caregivers for toileting assistance. However, nursing home staffing is often insufficient to meet the needs of all residents. Monitoring the desire to urinate is vital to minimize functional dependence and improve the quality of life of older people. Improved reliability of the desire to void monitoring requires exploring more effective monitoring methods. In this paper, we observed the changes in heart rate variability (HRV) during bladder filling, established the mapping relationship between normal bladder filling degree and HRV, and evaluated the performance of different classification models in predicting the degree of desire to void using HRV characteristics at different bladder filling degrees. The results showed that the autonomic nervous system gradually shifted to sympathetic nerve activity with increased bladder filling. Meanwhile, the classification accuracy of the wide neural network model for the degree of desire to void was >98 %. HRV shows a significant application prospect in predicting the desire to void, which provides a new direction for the research and development of non-invasive voiding intention monitoring and intelligent rehabilitation equipment and is expected to promote technical progress and development in related fields.

Identification of Athleticism and Sports Profiles Throughout Machine Learning Applied to Heart Rate Variability

Heart rate variability (HRV) is a non-invasive health and fitness indicator, and machine learning (ML) has emerged as a powerful tool for analysing large HRV datasets. This study aims to identify athletic characteristics using the HRV test and ML algorithms. Two models were developed: Model 1 (M1) classified athletes and non-athletes using 856 observations from high-performance athletes and 494 from non-athletes. Model 2 (M2) identified an individual soccer player within a team based on 105 observations from the player and 514 from other team members. Three ML algorithms were applied -Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM)- and SHAP values were used to interpret the results. In M1, the SVM algorithm achieved the highest performance (accuracy = 0.84, ROC AUC = 0.91), while in M2 Random Forest performed best (accuracy = 0.92, ROC AUC = 0.94). Based on these results, we propose an athleticism index and a soccer identification index derived from HRV data. The findings suggest that ML algorithms, such as SVM and RF, can effectively generate indices based on HRV for identifying individuals with athletic characteristics or distinguishing athletes with specific sports profiles. These insights underscore the importance of integrating HRV assessments systematically into training regimens for enhanced athletic evaluation.

Dynamic Prediction of Physical Exertion: Leveraging AI Models and Wearable Sensor Data During Cycling Exercise

Background/Objectives: This study aimed to explore machine learning approaches for predicting physical exertion using physiological signals collected from wearable devices. Methods: Both traditional machine learning and deep learning methods for classification and regression were assessed. The research involved 27 healthy participants engaged in controlled cycling exercises. Physiological data, including ECG, heart rate, oxygen saturation, and pedal speed (RPM), were collected during these sessions, which were divided into eight two-minute segments. Heart rate variability (HRV) was also calculated to serve as a predictive indicator. We employed two feature selection algorithms to identify the most relevant features for model training: Minimum Redundancy Maximum Relevance (MRMR) for both classification and regression, and Univariate Feature Ranking for Classification. A total of 34 traditional models were developed using MATLAB's Classification Learner App, utilizing 20% of the data for testing. In addition, Long Short-Term Memory (LSTM) networks were trained on the top features selected by the MRMR and Univariate Feature Ranking algorithms to enhance model performance. Finally, the MRMR-selected features were used for regression to train the LSTM model for predicting continuous outcomes. Results: The LSTM model for regression demonstrated robust predictive capabilities, achieving a mean squared error (MSE) of 0.8493 and an R-squared value of 0.7757. The classification models also showed promising results, with the highest testing accuracy reaching 89.2% and an F1 score of 91.7%. Conclusions: These results underscore the effectiveness of combining feature selection algorithms with advanced machine learning (ML) and deep learning techniques for predicting physical exertion levels using wearable sensor data.

Auxiliary identification of depression patients using interpretable machine learning models based on heart rate variability: a retrospective study

OBJECTIVE

Depression has emerged as a global public health concern with high incidence and disability rates, which are timely imperative to identify and intervene in clinical practice. The objective of this study was to explore the association between heart rate variability (HRV) and depression, with the aim of establishing and validating machine learning models for the auxiliary diagnosis of depression.

METHODS

The data of 465 outpatients from the Affiliated Hospital of Southwest Medical University were selected for the study. The study population was then randomly divided into training and test sets in a 7:3 ratio. Logistic regression (LR), support vector machine (SVM), random forest (RF) and eXtreme gradient boosting (XGBoost) algorithm models were used to construct risk prediction models in the training set, and the model performance was verified in the test set. The four models were evaluated by the area under the receiver operating characteristic curve (ROC), calibration curve and the decision curve analysis (DCA). Furthermore, we employed the SHapley Additive exPlanations (SHAP) method to illustrate the effects of the features attributed to the model.

RESULTS

There were 237 people in the depressed group and 228 in the non-depressed group. In the training set (n = 325) and test set (n = 140), the area under of the curve(AUC) values of the XGBoost model are 0.92 [95% confidence interval (CI) 0.888,0.95] and 0.82 (95% CI 0.754,0.892)] respectively, which are higher than the other three models. The XGBoost model has excellent predictive efficacy and clinical utility. The SHAP method was ranked according to the importance of the degree of influence on the model, with age, heart rate, Standard deviation of the NN intervals (SDNN), two nonlinear parameters of HRV and sex considered to be the top 6 predictors.

CONCLUSION

We provided a feasibility study of HRV as a potential biomarker for depression. The proposed model based on HRV provides clinicians with a quantitative auxiliary diagnostic tool, which is assist to improving the accuracy and efficiency of depression diagnosis, and can also be utilized for the monitoring and prevention of depression.

TheU-shape association between on-admission resting heart rate and 60-day all-cause mortality of AIDS inpatients in Fujian China: a retrospective cohort study

BACKGROUND

An elevated resting heart rate (RHR) is associated with poor outcomes in both healthy individuals and those with human immunodeficiency virus (HIV) or acquired immunodeficiency syndrome (AIDS). This study aimed to investigated the association between on admission resting heart rate (RHR) and 60-day mortality.

METHODS

This single-center retrospective cohort study evaluated the effect of RHR on the 60-day mortality of patient with AIDS in Southeast China. A total of 2188 patients with AIDS admitted for the first time between January 2016 and December 2021 were included. The RHR was categorized into tertiles. Disease progression was estimated using 60-day mortality rates. Cox proportional hazards regression models were used to evaluate the RHR with disease progression, and a two-piecewise Cox regression model was used to reveal the RHR effect at admission on 60-day mortality.

RESULTS

We observed a U-shape relationship between RHR and 60-day mortality. For a above 90 bpm, the 60-day mortality rose rapidly with a multivariable adjusted odds ratio (OR) of 1.032 (95% confidence interval [CI 1.016-1.048, P < 0.001). Below the threshold, 60 days mortality decreased as the RHR increased to 90 bpm with a multivariate-adjusted OR of 0.943 (95% CI 0.904-0.984, P = 0.0065).

CONCLUSIONS

This study identified a U-shape relationship between RHR and 60-day mortality in HIV/AIDS patients. Further research is needed to characterize the role of RHR in the timely prevention of mortality in HIV/AIDS patients.

Examining the practical importance of nonstationary cardio-respiratory coupling detection in breathing training: a methodological appraisal

This study investigates changes in cardiorespiratory coupling during clinic breathing training and its impact on autonomic nervous functioning compared with heart rate variability (HRV). A total of 39 subjects undergoing dynamic electrocardiogram-recorded breathing training were analyzed. Subjects were divided into early- and late-training periods, and further categorized based on changes in HRV indexes. Subtypes were identified using time-frequency cardiorespiratory coupling diagrams. Significant differences were observed in the high-frequency (HF) index between training stages in the subgroup with increasing HF-HRV (p = 0.0335). Both unimodal and bimodal subtypes showed significant high-frequency coupling (HFC) in the mid-training period compared with early and late stages (both p < 0.0001), suggesting improved parasympathetic cardiac regulation or reduced sympathetic control. This study highlights the potential of nonstationary cardiorespiratory coupling analysis alongside traditional HRV in evaluating the therapeutic effect of breathing training on autonomic nervous function. Cardiorespiratory coupling analysis could provide valuable adjunctive information to HRV measures for assessing the impact of breathing training.

Development and Validation of Cloud-based Heart Rate Variability Monitor

Context

This article introduces a new cloud-based point-of-care system to monitor heart rate variability (HRV).

Aims

Medical investigations carried out at dispensaries or hospitals impose substantial physiological and psychological stress (white coat effect), disrupting cardiovascular homeostasis, which can be taken care by point-of-care cloud computing system to facilitate secure patient monitoring.

Settings and Design

The device employs MAX30102 sensor to collect peripheral pulse signal using photoplethysmography technique. The non-invasive design ensures patient compliance while delivering critical insights into Autonomic Nervous System activity. Preliminary validations indicate the system's potential to enhance clinical outcomes by supporting timely, data-driven therapeutic adjustments based on HRV metrics.

Subjects and Methods

This article explores the system's development, functionality, and reliability. System designed is validated with peripheral pulse analyzer (PPA), a research product of electronics division, Bhabha Atomic Research Centre.

Statistical Analysis Used

The output of developed HRV monitor (HRVM) is compared using Pearson's correlation and Mann-Whitney U-test with output of PPA. Peak positions and spectrum values are validated using Pearson's correlation, mean error, standard deviation (SD) of error, and range of error. HRV parameters such as total power, mean, peak amplitude, and power in very low frequency, low frequency, and high frequency bands are validated using Mann-Whitney U-test.

Results

Pearson's correlation for spectrum values has been found to be more than 0.97 in all the subjects. Mean error, SD of error, and range of error are found to be in acceptable range.

Conclusions

Statistical results validate the new HRVM system against PPA for use in cloud computing and point-of-care testing.

The ECG abnormalities in persons with chronic disorders of consciousness

We aimed to investigate the electrocardiogram (ECG) features in persons with chronic disorders of consciousness (DOC, ≥ 29 days since injury, DSI) resulted from the most severe brain damages. The ECG data from 30 patients with chronic DOC and 18 healthy controls (HCs) were recorded during resting wakefulness state for about five minutes. The patients were classified into vegetative state (VS) and minimally conscious state (MCS). Eight ECG metrics were extracted for comparisons between the subject subgroups, and regression analysis of the metrics were conducted on the DSI (29-593 days). The DOC patients exhibit a significantly higher heart rate (HR, p = 0.009) and lower values for SDNN (p = 0.001), CVRR (p = 0.009), and T-wave amplitude (p < 0.001) compared to the HCs. However, there're no significant differences in QRS, QT, QTc, or ST amplitude between the two groups (p > 0.05). Three ECG metrics of the DOC patients-HR, SDNN, and CVRR-are significantly correlated with the DSI. The ECG abnormalities persist in chronic DOC patients. The abnormalities are mainly manifested in the rhythm features HR, SDNN and CVRR, but not the waveform features such as QRS width, QT, QTc, ST and T-wave amplitudes.

Heart rate variability during sleep onset in patients with insomnia with or without comorbid sleep apnea

OBJECTIVES

Pre-sleep stress or hyperarousal is a known key etiological component in insomnia disorder. Despite this, physiological alterations during the sleep onset are not well-understood. In particular, insomnia and obstructive sleep apnea (OSA) are highly prevalent co-morbid conditions, where autonomic regulation may be altered. We aimed to characterize heart rate variability (HRV) during sleep onset as a potential measure of pre-sleep hyperarousal.

METHODS

We described the profile of pre-sleep HRV measures and explore autonomic differences in participants with self-reported insomnia disorder (with no OSA, n = 69; with mild OSA, n = 70; with moderate or severe OSA, n = 66), compared to normal sleep controls (n = 123). Heart rate data during the sleep onset process were extracted for HRV analyses.

RESULTS

During the sleep onset process, compared to normal sleep controls, participants with insomnia had altered HRV, indicated by higher heart rate (p = 0.004), lower SDNN (p = 0.003), reduced pNN20 (p < 0.001) and pNN50 (p = 0.010) and lower powers (p < 0.001). Participants with insomnia and moderate/severe OSA may have further deteriorated HRV outcomes compared to no/mild OSA patients with insomnia but differences were not significant. Insomnia itself was associated with significantly higher heart rate, lower pNN20, and lower high frequency power even after adjustment for age, gender, BMI and OSA severity.

CONCLUSIONS

Participants with insomnia had lower vagal activity during the sleep onset period, which may be compounded by OSA, reflected in higher heart rates and lower HRV. These altered heart rate dynamics may serve as a physiological biomarker for insomnia during bedtime wakefulness, or as a potential tool to evaluate the efficacy of behavioral interventions which target bedtime stress.

Heart Diseases Recognition Model Based on HRV Feature Extraction over 12-Lead ECG Signals

Heart Rate Variability (HRV) refers to the capability of the heart rhythm to vary at different times, typically reflecting the regulation of the heart by the autonomic nervous system. In recent years, with advancements in Electrocardiogram (ECG) signal processing technology, HRV features reflect various aspects of cardiac activity, such as variability in heart rate, cardiac health status, and responses. We extracted key features of HRV and used them to develop and evaluate an automatic recognition model for cardiac diseases. Consequently, we proposed the HRV Heart Disease Recognition (HHDR) method, employing the Spectral Magnitude Quantification (SMQ) technique for feature extraction. Firstly, the HRV signals are extracted through electrocardiogram signal processing. Then, by analyzing parts of the HRV signal within various frequency ranges, the SMQ method extracts rich features of partial information. Finally, the Random Forest (RF) classification computational method is employed to classify the extracted information, achieving efficient and accurate cardiac disease recognition. Experimental results indicate that this method surpasses current technologies in recognizing cardiac diseases, with an average accuracy rate of 95.1% for normal/diseased classification, and an average accuracy of 84.8% in classifying five different disease categories. Thus, the proposed HHDR method effectively utilizes the local information of HRV signals for efficient and accurate cardiac disease recognition, providing strong support for cardiac disease research in the medical field.

Bias-Free Cardiac Monitoring Capsule

Cardiovascular disease (CVD) remains the leading cause of death worldwide. Patients often fail to recognize the early signs of CVDs, which display irregularities in cardiac contractility and may ultimately lead to heart failure. Therefore, continuously monitoring the abnormal changes in cardiac contractility may represent a novel approach to long-term CVD surveillance. Here, a zero-power consumption and implantable bias-free cardiac monitoring capsule (BCMC) is introduced based on the triboelectric effect for cardiac contractility monitoring in situ. The output performance of BCMC is improved over 10 times with nanoparticle self-adsorption method. This device can be implanted into the right ventricle of swine using catheter intervention to detect the change of cardiac contractility and the corresponding CVDs. The physiological signals can be wirelessly transmitted to a mobile terminal for analysis through the acquisition and transmission module. This work contributes to a new option for precise monitoring and early diagnosis of CVDs.

Assessment of parameters reflecting the reactivity of the autonomic nervous system of Polish firefighters on the basis of a test in a smoke chamber

Objective

Measurement and analysis of heart rate variability in a population of professional firefighters based on heart rate (RR) recording. Assessment based on a smoke chamber test in correlation with age, length of service, body mass index.

Materials and methods

The smoke chamber test for the officers of the State Fire Service (SFS) is aimed at improving the skills and techniques of working in special clothing and in a respiratory protection set (RPS) under high psychophysical burden. The study was divided into 3 stages: 1. measurement of parameters at rest - sitting position for 5 min, 2. measurement of parameters during the firefighter's activity, effort related to the training path and the test in the smoke chamber, indefinite time (different for each firefighter), 3. measurement of parameters at rest after exercise - sitting position for 5 min. Each firefighter included in the study had fitted onto his chest a Polar H10 band with a sensor (size XXL) that measures parameters HR, HRV (sensor connected via Bluetooth to an application on the phone of a person controlling the test).

Results

The study involved 96 firefighters aged 19-45 (Mean 27.9; SD 7.4), with 1-19 years of service (Mean 5.2; SD 4.6). The study included 75 firefighters who completed the entire activity and their results were recorded completely in a way that allowed for analysis and interpretation. Results of 17 firefighters were selected (parameters describing HRV changes was carried out, which are important from the authors' experience: RMSSD, HF ms2, DFA α1).

Conclusion

The presence of excessive body weight did not affect HR parameters, which may be related to the limited possibilities of using the BMI index among people with high muscle mass. Longer work experience has a health-promoting effect on heart rate values through increased adaptation of the circulatory system to increased effort and stress. HRV parameter and ANS activity have a wide range of clinical applications, in addition to monitoring health status in the course of diseases, ANS activity can be analyzed in correlation with occupational risk factors.

An effective cross-scenario remote heart rate estimation network based on global-local information and video transformer

Remote photoplethysmography (rPPG) technology is a non-contact physiological signal measurement method, characterized by non-invasiveness and ease of use. It has broad application potential in medical health, human factors engineering, and other fields. However, current rPPG technology is highly susceptible to variations in lighting conditions, head pose changes, and partial occlusions, posing significant challenges for its widespread application. In order to improve the accuracy of remote heart rate estimation and enhance model generalization, we propose PulseFormer, a dual-path network based on transformer. By integrating local and global information and utilizing fast and slow paths, PulseFormer effectively captures the temporal variations of key regions and spatial variations of the global area, facilitating the extraction of rPPG feature information while mitigating the impact of background noise variations. Heart rate estimation results on the popular rPPG dataset show that PulseFormer achieves state-of-the-art performance on public datasets. Additionally, we establish a dataset containing facial expressions and synchronized physiological signals in driving scenarios and test the pre-trained model from the public dataset on this collected dataset. The results indicate that PulseFormer exhibits strong generalization capabilities across different data distributions in cross-scenario settings. Therefore, this model is applicable for heart rate estimation of individuals in various scenarios.

Long-COVID-19 autonomic dysfunction: An integrated view in the framework of inflammaging

The recently identified syndrome known as Long COVID (LC) is characterized by a constellation of debilitating conditions that impair both physical and cognitive functions, thus reducing the quality of life and increasing the risk of developing the most common age-related diseases. These conditions are linked to the presence of symptoms of autonomic dysfunction, in association with low cortisol levels, suggestive of reduced hypothalamic-pituitary-adrenal (HPA) axis activity, and with increased pro-inflammatory condition. Alterations of dopamine and serotonin neurotransmitter levels were also recently observed in LC. Interestingly, at least some of the proposed mechanisms of LC development overlap with mechanisms of Autonomic Nervous System (ANS) imbalance, previously detailed in the framework of the aging process. ANS imbalance is characterized by a proinflammatory sympathetic overdrive, and a concomitant decreased anti-inflammatory vagal parasympathetic activity, associated with reduced anti-inflammatory effects of the HPA axis and cholinergic anti-inflammatory pathway (CAP). These neuro-immune-endocrine system imbalanced activities fuel the vicious circle of chronic inflammation, i.e. inflammaging. Here, we refine our original hypothesis that ANS dysfunction fuels inflammaging and propose that biomarkers of ANS imbalance could also be considered biomarkers of inflammaging, recognized as the main risk factor for developing age-related diseases and the sequelae of viral infections, i.e. LC.

Automatic identification of hypertension and assessment of its secondary effects using artificial intelligence: A systematic review (2013-2023)

Artificial Intelligence (AI) techniques are increasingly used in computer-aided diagnostic tools in medicine. These techniques can also help to identify Hypertension (HTN) in its early stage, as it is a global health issue. Automated HTN detection uses socio-demographic, clinical data, and physiological signals. Additionally, signs of secondary HTN can also be identified using various imaging modalities. This systematic review examines related work on automated HTN detection. We identify datasets, techniques, and classifiers used to develop AI models from clinical data, physiological signals, and fused data (a combination of both). Image-based models for assessing secondary HTN are also reviewed. The majority of the studies have primarily utilized single-modality approaches, such as biological signals (e.g., electrocardiography, photoplethysmography), and medical imaging (e.g., magnetic resonance angiography, ultrasound). Surprisingly, only a small portion of the studies (22 out of 122) utilized a multi-modal fusion approach combining data from different sources. Even fewer investigated integrating clinical data, physiological signals, and medical imaging to understand the intricate relationships between these factors. Future research directions are discussed that could build better healthcare systems for early HTN detection through more integrated modeling of multi-modal data sources.

Vitamin C as Scavenger of Reactive Oxygen Species during Healing after Myocardial Infarction

Currently, coronary artery bypass and reperfusion therapies are considered the gold standard in long-term treatments to restore heart function after acute myocardial infarction. As a drawback of these restoring strategies, reperfusion after an ischemic insult and sudden oxygen exposure lead to the exacerbated synthesis of additional reactive oxidative species and the persistence of increased oxidation levels. Attempts based on antioxidant treatment have failed to achieve an effective therapy for cardiovascular disease patients. The controversial use of vitamin C as an antioxidant in clinical practice is comprehensively systematized and discussed in this review. The dose-dependent adsorption and release kinetics mechanism of vitamin C is complex; however, this review may provide a holistic perspective on its potential as a preventive supplement and/or for combined precise and targeted therapeutics in cardiovascular management therapy.

Heart rate variability in school-age children born moderate-to-late preterm

BACKGROUND

Prematurity is associated with reduced cardiac autonomic function. This study aimed to investigate the heart rate variability (HRV) in school-age children born moderately to late preterm (MLPT).

METHODS

This cross-sectional study investigated school-age children, aged 5 to 10 years, born moderate-to-late preterm. Electrocardiograms recordings were performed during fifteen-minutes. Time and frequency domain parameters were calculated, corrected for heart rate and compared between the groups.

RESULTS

A total of 123 children were evaluated and 119 were included in this study. HRV measures, studied in the time and frequency domains, were similar in both groups. Corrected values of root mean square of successive differences between normal cycles (RMSSD), percentage of successive cycles with a duration difference >50 ms (pNN50%), and high frequency (HF), indices that predominantly represent the parasympathetic activity of the autonomic nervous system, were 1.6E-7 and 1.8E-7 (p=0.226); 1.6E-13 and 1.6E-13 (p=0.506); 6.9E-12 and 7.4E-12 (p=0.968) in the preterm and control groups, respectively.

CONCLUSION

This study did not find differences in heart rate variability between school-age children born MLPT and those born at term, suggesting that plasticity of cardiac autonomic modulation continues to occur in children up to school age or there is less impairment of the autonomic system in MLPT.

An extensive quantitative analysis of the effects of errors in beat-to-beat intervals on all commonly used HRV parameters

Heart rate variability (HRV) analysis is often used to estimate human health and fitness status. More specifically, a range of parameters that express the variability in beat-to-beat intervals are calculated from electrocardiogram beat detections. Since beat detection may yield erroneous interval data, these errors travel through the processing chain and may result in misleading parameter values that can lead to incorrect conclusions. In this study, we utilized Monte Carlo simulation on real data, Kolmogorov-Smirnov tests and Bland-Altman analysis to carry out extensive analysis of the noise sensitivity of different HRV parameters. The used noise models consider Gaussian and student-t distributed noise. As a result we observed that commonly used HRV parameters (e.g. pNN50 and LF/HF ratio) are especially sensitive to noise and that all parameters show biases to some extent. We conclude that researchers should be careful when reporting different HRV parameters, consider the distributions in addition to mean values, and consider reference data if applicable. The analysis of HRV parameter sensitivity to noise and resulting biases presented in this work generalizes over a wide population and can serve as a reference and thus provide a basis for the decision about which HRV parameters to choose under similar conditions.

ECG-Free Heartbeat Detection in Seismocardiography and Gyrocardiography Signals Provides Acceptable Heart Rate Variability Indices in Healthy and Pathological Subjects

Cardio-mechanical monitoring techniques, such as Seismocardiography (SCG) and Gyrocardiography (GCG), have received an ever-growing interest in recent years as potential alternatives to Electrocardiography (ECG) for heart rate monitoring. Wearable SCG and GCG devices based on lightweight accelerometers and gyroscopes are particularly appealing for continuous, long-term monitoring of heart rate and its variability (HRV). Heartbeat detection in cardio-mechanical signals is usually performed with the support of a concurrent ECG lead, which, however, limits their applicability in standalone cardio-mechanical monitoring applications. The complex and variable morphology of SCG and GCG signals makes the ECG-free heartbeat detection task quite challenging; therefore, only a few methods have been proposed. Very recently, a template matching method based on normalized cross-correlation (NCC) has been demonstrated to provide very accurate detection of heartbeats and estimation of inter-beat intervals in SCG and GCG signals of pathological subjects. In this study, the accuracy of HRV indices obtained with this template matching method is evaluated by comparison with ECG. Tests were performed on two public datasets of SCG and GCG signals from healthy and pathological subjects. Linear regression, correlation, and Bland-Altman analyses were carried out to evaluate the agreement of 24 HRV indices obtained from SCG and GCG signals with those obtained from ECG signals, simultaneously acquired from the same subjects. The results of this study show that the NCC-based template matching method allowed estimating HRV indices from SCG and GCG signals of healthy subjects with acceptable accuracy. On healthy subjects, the relative errors on time-domain indices ranged from 0.25% to 15%, on frequency-domain indices ranged from 10% to 20%, and on non-linear indices were within 8%. The estimates obtained on signals from pathological subjects were affected by larger errors. Overall, GCG provided slightly better performances as compared to SCG, both on healthy and pathological subjects. These findings provide, for the first time, clear evidence that monitoring HRV via SCG and GCG sensors without concurrent ECG is feasible with the NCC-based template matching method for heartbeat detection.

Cardiovascular autonomic dysfunction in "Long COVID": pathophysiology, heart rate variability, and inflammatory markers

Long COVID is characterized by persistent signs and symptoms that continue or develop for more than 4 weeks after acute COVID-19 infection. Patients with Long COVID experience a cardiovascular autonomic imbalance known as dysautonomia. However, the underlying autonomic pathophysiological mechanisms behind this remain unclear. Current hypotheses include neurotropism, cytokine storms, and inflammatory persistence. Certain immunological factors indicate autoimmune dysfunction, which can be used to identify patients at a higher risk of Long COVID. Heart rate variability can indicate autonomic imbalances in individuals suffering from Long COVID, and measurement is a non-invasive and low-cost method for assessing cardiovascular autonomic modulation. Additionally, biochemical inflammatory markers are used for diagnosing and monitoring Long COVID. These inflammatory markers can be used to improve the understanding of the mechanisms driving the inflammatory response and its effects on the sympathetic and parasympathetic pathways of the autonomic nervous system. Autonomic imbalances in patients with Long COVID may result in lower heart rate variability, impaired vagal activity, and substantial sympathovagal imbalance. New research on this subject must be encouraged to enhance the understanding of the long-term risks that cardiovascular autonomic imbalances can cause in individuals with Long COVID.

A Real-Time Evaluation Algorithm for Noncontact Heart Rate Variability Monitoring

Noncontact vital sign monitoring based on radar has attracted great interest in many fields. Heart Rate Variability (HRV), which measures the fluctuation of heartbeat intervals, has been considered as an important indicator for general health evaluation. This paper proposes a new algorithm for HRV monitoring in which frequency-modulated continuous-wave (FMCW) radar is used to separate echo signals from different distances, and the beamforming technique is adopted to improve signal quality. After the phase reflecting the chest wall motion is demodulated, the acceleration is calculated to enhance the heartbeat and suppress the impact of respiration. The time interval of each heartbeat is estimated based on the smoothed acceleration waveform. Finally, a joint optimization algorithm was developed and is used to precisely segment the acceleration signal for analyzing HRV. Experimental results from 10 participants show the potential of the proposed algorithm for obtaining a noncontact HRV estimation with high accuracy. The proposed algorithm can measure the interbeat interval (IBI) with a root mean square error (RMSE) of 14.9 ms and accurately estimate HRV parameters with an RMSE of 3.24 ms for MEAN (the average value of the IBI), 4.91 ms for the standard deviation of normal to normal (SDNN), and 9.10 ms for the root mean square of successive differences (RMSSD). These results demonstrate the effectiveness and feasibility of the proposed method in emotion recognition, sleep monitoring, and heart disease diagnosis.

The Impact of Heart Rate Variability Monitoring on Preventing Severe Cardiovascular Events

The increase in the incidence of cardiovascular diseases worldwide raises concerns about the urgent need to increase definite measures for the self-determination of different parameters, especially those defining cardiac function. Heart rate variability (HRV) is a non-invasive method used to evaluate autonomic nervous system modulation on the cardiac sinus node, thus describing the oscillations between consecutive electrocardiogram R-R intervals. These fluctuations are undetectable except when using specialized devices, with ECG Holter monitoring considered the gold standard. HRV is considered an independent biomarker for measuring cardiovascular risk and for screening the occurrence of both acute and chronic heart diseases. Also, it can be an important predictive factor of frailty or neurocognitive disorders, like anxiety and depression. An increased HRV is correlated with rest, exercise, and good recovery, while a decreased HRV is an effect of stress or illness. Until now, ECG Holter monitoring has been considered the gold standard for determining HRV, but the recent decade has led to an accelerated development of technology using numerous devices that were created specifically for the pre-hospital self-monitoring of health statuses. The new generation of devices is based on the use of photoplethysmography, which involves the determination of blood changes at the level of blood vessels. These devices provide additional information about heart rate (HR), blood pressure (BP), peripheral oxygen saturation (SpO²), step counting, physical activity, and sleep monitoring. The most common devices that have this technique are smartwatches (used on a large scale) and chest strap monitors. Therefore, the use of technology and the self-monitoring of heart rate and heart rate variability can be an important first step in screening cardiovascular pathology and reducing the pressure on medical services in a hospital. The use of telemedicine can be an alternative, especially among elderly patients who are associated with walking disorders, frailty, or neurocognitive disorders.

Heart rate variability changes with respect to time and exercise intensity during heart-rate-controlled steady-state treadmill running

The aim of this work was to investigate the time and exercise intensity dependence of heart rate variability (HRV). Time-dependent, cardiovascular-drift-related increases in heart rate (HR) were inhibited by enforcing a constant heart rate throughout the exercise with a feedback control system. Thirty-two healthy adults performed HR-stabilised treadmill running exercise at two distinct exercise intensity levels. Standard time and frequency domain HRV metrics were computed and served as outcomes. Significant decreases were detected in 8 of the 14 outcomes for the time dependence analysis and in 6 of the 7 outcomes for the exercise intensity dependence analysis (excluding the experimental speed-signal frequency analysis). Furthermore, metrics that have been reported to reach an intensity-dependent near-zero minimum rapidly (usually at moderate intensity) were found to be near constant over time and only barely decreased with intensity. Taken together, these results highlight that HRV generally decreases with time and with exercise intensity. The intensity-related reductions were found to be greater in value and significance compared to the time-related reductions. Additionally, the results indicate that decreases in HRV metrics with time or exercise intensity are only detectable as long as their metric-specific near-zero minimum has not yet been reached.

ECG-Free Heartbeat Detection in Seismocardiography Signals via Template Matching

Cardiac monitoring can be performed by means of an accelerometer attached to a subject's chest, which produces the Seismocardiography (SCG) signal. Detection of SCG heartbeats is commonly carried out by taking advantage of a simultaneous electrocardiogram (ECG). SCG-based long-term monitoring would certainly be less obtrusive and easier to implement without an ECG. Few studies have addressed this issue using a variety of complex approaches. This study proposes a novel approach to ECG-free heartbeat detection in SCG signals via template matching, based on normalized cross-correlation as heartbeats similarity measure. The algorithm was tested on the SCG signals acquired from 77 patients with valvular heart diseases, available from a public database. The performance of the proposed approach was assessed in terms of sensitivity and positive predictive value (PPV) of the heartbeat detection and accuracy of inter-beat intervals measurement. Sensitivity and PPV of 96% and 97%, respectively, were obtained by considering templates that included both systolic and diastolic complexes. Regression, correlation, and Bland-Altman analyses carried out on inter-beat intervals reported slope and intercept of 0.997 and 2.8 ms (R² > 0.999), as well as non-significant bias and limits of agreement of ±7.8 ms. The results are comparable or superior to those achieved by far more complex algorithms, also based on artificial intelligence. The low computational burden of the proposed approach makes it suitable for direct implementation in wearable devices.

Cardiovascular and autonomic dysfunction in long-COVID syndrome and the potential role of non-invasive therapeutic strategies on cardiovascular outcomes

A significant percentage of COVID-19 survivors develop long-lasting cardiovascular sequelae linked to autonomic nervous system dysfunction, including fatigue, arrhythmias, and hypertension. This post-COVID-19 cardiovascular syndrome is one facet of "long-COVID," generally defined as long-term health problems persisting/appearing after the typical recovery period of COVID-19. Despite the fact that this syndrome is not fully understood, it is urgent to develop strategies for diagnosing/managing long-COVID due to the immense potential for future disease burden. New diagnostic/therapeutic tools should provide health personnel with the ability to manage the consequences of long-COVID and preserve/improve patient quality of life. It has been shown that cardiovascular rehabilitation programs (CRPs) stimulate the parasympathetic nervous system, improve cardiorespiratory fitness (CRF), and reduce cardiovascular risk factors, hospitalization rates, and cognitive impairment in patients suffering from cardiovascular diseases. Given their efficacy in improving patient outcomes, CRPs may have salutary potential for the treatment of cardiovascular sequelae of long-COVID. Indeed, there are several public and private initiatives testing the potential of CRPs in treating fatigue and dysautonomia in long-COVID subjects. The application of these established rehabilitation techniques to COVID-19 cardiovascular syndrome represents a promising approach to improving functional capacity and quality of life. In this brief review, we will focus on the long-lasting cardiovascular and autonomic sequelae occurring after COVID-19 infection, as well as exploring the potential of classic and novel CRPs for managing COVID-19 cardiovascular syndrome. Finally, we expect this review will encourage health care professionals and private/public health organizations to evaluate/implement non-invasive techniques for the management of COVID-19 cardiovascular sequalae.

Wearable Devices for Remote Monitoring of Heart Rate and Heart Rate Variability-What We Know and What Is Coming

Heart rate at rest and exercise may predict cardiovascular risk. Heart rate variability is a measure of variation in time between each heartbeat, representing the balance between the parasympathetic and sympathetic nervous system and may predict adverse cardiovascular events. With advances in technology and increasing commercial interest, the scope of remote monitoring health systems has expanded. In this review, we discuss the concepts behind cardiac signal generation and recording, wearable devices, pros and cons focusing on accuracy, ease of application of commercial and medical grade diagnostic devices, which showed promising results in terms of reliability and value. Incorporation of artificial intelligence and cloud based remote monitoring have been evolving to facilitate timely data processing, improve patient convenience and ensure data security.

A review of automated sleep disorder detection

Automated sleep disorder detection is challenging because physiological symptoms can vary widely. These variations make it difficult to create effective sleep disorder detection models which support hu-man experts during diagnosis and treatment monitoring. From 2010 to 2021, authors of 95 scientific papers have taken up the challenge of automating sleep disorder detection. This paper provides an expert review of this work. We investigated whether digital technology and Artificial Intelligence (AI) can provide automated diagnosis support for sleep disorders. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines during the content discovery phase. We compared the performance of proposed sleep disorder detection methods, involving differ-ent datasets or signals. During the review, we found eight sleep disorders, of which sleep apnea and insomnia were the most studied. These disorders can be diagnosed using several kinds of biomedical signals, such as Electrocardiogram (ECG), Polysomnography (PSG), Electroencephalogram (EEG), Electromyogram (EMG), and snore sound. Subsequently, we established areas of commonality and distinctiveness. Common to all reviewed papers was that AI models were trained and tested with labelled physiological signals. Looking deeper, we discovered that 24 distinct algorithms were used for the detection task. The nature of these algorithms evolved, before 2017 only traditional Machine Learning (ML) was used. From 2018 onward, both ML and Deep Learning (DL) methods were used for sleep disorder detection. The strong emergence of DL algorithms has considerable implications for future detection systems because these algorithms demand significantly more data for training and testing when compared with ML. Based on our review results, we suggest that both type and amount of labelled data is crucial for the design of future sleep disorder detection systems because this will steer the choice of AI algorithm which establishes the desired decision support. As a guiding principle, more labelled data will help to represent the variations in symptoms. DL algorithms can extract information from these larger data quantities more effectively, therefore; we predict that the role of these algorithms will continue to expand.

Environmental Benefits of Sleep Apnoea Detection in the Home Environment

Sleep Apnoea (SA) is a common chronic illness that affects nearly 1 billion people around the world, and the number of patients is rising. SA causes a wide range of psychological and physiological ailments that have detrimental effects on a patient’s wellbeing. The high prevalence and negative health effects make SA a public health problem. Whilst the current gold standard diagnostic procedure, polysomnography (PSG), is reliable, it is resource-expensive and can have a negative impact on sleep quality, as well as the environment. With this study, we focus on the environmental impact that arises from resource utilisation during SA detection, and we propose remote monitoring (RM) as a potential solution that can improve the resource efficiency and reduce travel. By reusing infrastructure technology, such as mobile communication, cloud computing, and artificial intelligence (AI), RM establishes SA detection and diagnosis support services in the home environment. However, there are considerable barriers to a widespread adoption of this technology. To gain a better understanding of the available technology and its associated strength, as well as weaknesses, we reviewed scientific papers that used various strategies for RM-based SA detection. Our review focused on 113 studies that were conducted between 2018 and 2022 and that were listed in Google Scholar. We found that just over 50% of the proposed RM systems incorporated real time signal processing and around 20% of the studies did not report on this important aspect. From an environmental perspective, this is a significant shortcoming, because 30% of the studies were based on measurement devices that must travel whenever the internal buffer is full. The environmental impact of that travel might constitute an additional need for changing from offline to online SA detection in the home environment.