Statistical Analysis of the Consistency of HRV Analysis Using BCG or Pulse Wave Signals

Ballistocardiography (BCG) is considered a good alternative to HRV analysis with its non-contact and unobtrusive acquisition characteristics. However, consensus about its validity has not yet been established. In this study, 50 healthy subjects (26.2 ± 5.5 years old, 22 females, 28 males) were invited. Comprehensive statistical analysis, including Coefficients of Variation (CV), Lin’s Concordance Correlation Coefficient (LCCC), and Bland-Altman analysis (BA ratio), were utilized to analyze the consistency of BCG and ECG signals in HRV analysis. If the methods gave different answers, the worst case was taken as the result. Measures of consistency such as Mean, SDNN, LF gave good agreement (the absolute value of CV difference 0.99, BA ratio 0.95, BA ratio < 0.2), while RMSSD, HF, LF/HF indicated poor agreement (the absolute value of CV difference ≥ 5% or LCCC ≤ 0.95 or BA ratio ≥ 0.2). Additionally, the R-R intervals were compared with P-P intervals extracted from the pulse wave (PW). Except for pNN50, which exhibited poor agreement in this comparison, the performances of the HRV indices estimated from the PW and the BCG signals were similar

A review of machine learning techniques in photoplethysmography for the non-invasive cuff-less measurement of blood pressure

Hypertension or high blood pressure is a leading cause of death throughout the world and a critical factor for increasing the risk of serious diseases, including cardiovascular diseases such as stroke and heart failure. Blood pressure is a primary vital sign that must be monitored regularly for the early detection, prevention and treatment of cardiovascular diseases. Traditional blood pressure measurement techniques are either invasive or cuff-based, which are impractical, intermittent, and uncomfortable for patients. Over the past few decades, several indirect approaches using photoplethysmogram (PPG) have been investigated, namely, pulse transit time, pulse wave velocity, pulse arrival time and pulse wave analysis, in an effort to utilise PPG for estimating blood pressure. Recent advancements in signal processing techniques, including machine learning and artificial intelligence, have also opened up exciting new horizons for PPG-based cuff less and continuous monitoring of blood pressure. Such a device will have a significant and transformative impact in monitoring patients’ vital signs, especially those at risk of cardiovascular disease. This paper provides a comprehensive review for non-invasive cuff-less blood pressure estimation using the PPG approach along with their challenges and limitations

Shear wave echocardiography

In this thesis we demonstrate that the assessment of the diastolic function of the left ventricle withclassical echocardiography remain

Cardiorespiratory Function in Young Adults With a History of Covid-19 Infection

Objective. Respiratory complications may persist several months into the recovery period following COVID-19 infection. This study evaluated respiratory function and oxygen saturation variability between young adults with a history of COVID-19 infection and controls. Associations between cardiorespiratory function with potential biobehavioral correlates of COVID-19 infection were also explored.Methods. 57 adults ages 18 to 65 participated in this study (24 COVID+, 33 Control). Spirometry was used to assess pulmonary function volumes of forced vital capacity (FVC), forced expiratory volume in 1 second (FEV1), FEV1/FVC and peak expiratory flow (PEF). Exhaled nitric oxide (FeNO) was measured using the NiOX VERO, a handheld electrochemical nitric oxide analyzer and taken as a proxy of airway inflammation. Systemic inflammation levels were assessed using salivary concentrations of inflammatory biomarkers. Oxygen saturation variability was quantified via extended continuous oxygen saturation (SpO2) monitoring using linear and nonlinear analyses. Network physiology analysis was conducted to evaluate cardiorespiratory control between SpO2, heart rate (HR), respiratory rate and skin temperature signals measured by continuous ambulatory monitoring with an Equivital EQO2 LifeMonitor. Physical activity levels and sedentary time were assessed using 9-day accelerometry. COVID-19 symptom severity was assessed by participant self-report via questionnaires. Results. No group differences were observed for pulmonary function of FVC (COVID+: 4.22±1.01, C: 4.43±1.06 L, p=.663), FEV1 (COVID+: 3.45±0.72, C: 3.57±0.92 L, p=.865), PEF (COVID+: 349.63±105.54, C: 373.73±140.61 L/min, p=.370), or FeNO (COVID+: 16.61±13.04, C: 20.03±20.11 ppb, p=.285). Linear and nonlinear oxygen saturation variability did not differ between adults with a history of COVID-19 infection and controls with no history of infection (p\u3e0.05). Cardiorespiratory function measured using network analysis of did not differ between recovering COVID-19 individuals and controls (p\u3e0.05). Sedentary time was inversely associated with FEV1 (r=-.392, p=.040), PEF (r=-.579, p=.003), and IL-6 concentrations (r=- .370, p=.049). COVID-19 disease severity was inversely associated with FVC (r=-.461, p=.012) and FEV1 (r=-.365, p=.040). Number of symptoms was inversely associated with FVC (r=-.404, p=.025). Conclusions. Pulmonary function, inflammation levels and oxygen saturation variability were similar between individuals with a history of COVID-19 infection and controls without a history of COVID-19 infection. Network interactions between regulatory components of the cardiorespiratory system were also similar between recovering COVID-19 individuals and controls. Findings suggest that cardiorespiratory function and dynamic control of SpO2 may not be impaired following COVID-19 infection in young adults. Moreover, increased sedentary time and disease severity may have negative effects on pulmonary function in individuals recovering from COVID-19

Signal processing methodologies for an acoustic fetal heart rate monitor

Research and development is presented of real time signal processing methodologies for the detection of fetal heart tones within a noise-contaminated signal from a passive acoustic sensor. A linear predictor algorithm is utilized for detection of the heart tone event and additional processing derives heart rate. The linear predictor is adaptively 'trained' in a least mean square error sense on generic fetal heart tones recorded from patients. A real time monitor system is described which outputs to a strip chart recorder for plotting the time history of the fetal heart rate. The system is validated in the context of the fetal nonstress test. Comparisons are made with ultrasonic nonstress tests on a series of patients. Comparative data provides favorable indications of the feasibility of the acoustic monitor for clinical use

Wearable and Nearable Biosensors and Systems for Healthcare

Biosensors and systems in the form of wearables and “nearables” (i.e., everyday sensorized objects with transmitting capabilities such as smartphones) are rapidly evolving for use in healthcare. Unlike conventional approaches, these technologies can enable seamless or on-demand physiological monitoring, anytime and anywhere. Such monitoring can help transform healthcare from the current reactive, one-size-fits-all, hospital-centered approach into a future proactive, personalized, decentralized structure. Wearable and nearable biosensors and systems have been made possible through integrated innovations in sensor design, electronics, data transmission, power management, and signal processing. Although much progress has been made in this field, many open challenges for the scientific community remain, especially for those applications requiring high accuracy. This book contains the 12 papers that constituted a recent Special Issue of Sensors sharing the same title. The aim of the initiative was to provide a collection of state-of-the-art investigations on wearables and nearables, in order to stimulate technological advances and the use of the technology to benefit healthcare. The topics covered by the book offer both depth and breadth pertaining to wearable and nearable technology. They include new biosensors and data transmission techniques, studies on accelerometers, signal processing, and cardiovascular monitoring, clinical applications, and validation of commercial devices

Nonlinear Systems in Medicine

Many achievements in medicine have come from applying linear theory to problems. Most current methods of data analysis use linear models, which are based on proportionality between two variables and/or relationships described by linear differential equations. However, nonlinear behavior commonly occurs within human systems due to their complex dynamic nature; this cannot be described adequately by linear models. Nonlinear thinking has grown among physiologists and physicians over the past century, and non-linear system theories are beginning to be applied to assist in interpreting, explaining, and predicting biological phenomena. Chaos theory describes elements manifesting behavior that is extremely sensitive to initial conditions, does not repeat itself and yet is deterministic. Complexity theory goes one step beyond chaos and is attempting to explain complex behavior that emerges within dynamic nonlinear systems. Nonlinear modeling still has not been able to explain all of the complexity present in human systems, and further models still need to be refined and developed. However, nonlinear modeling is helping to explain some system behaviors that linear systems cannot and thus will augment our understanding of the nature of complex dynamic systems within the human body in health and in disease states

Wearable armband device for daily life electrocardiogram monitoring

A wearable armband electrocardiogram (ECG) monitor has been used for daily life monitoring. The armband records three ECG channels, one electromyogram (EMG) channel, and tri-axial accelerometer signals. Contrary to conventional Holter monitors, the armband-based ECG device is convenient for long-term daily life monitoring because it uses no obstructive leads and has dry electrodes (no hydrogels), which do not cause skin irritation even after a few days. Principal component analysis (PCA) and normalized least mean squares (NLMS) adaptive filtering were used to reduce the EMG noise from the ECG channels. An artifact detector and an optimal channel selector were developed based on a support vector machine (SVM) classifier with a radial basis function (RBF) kernel using features that are related to the ECG signal quality. Mean HR was estimated from the 24-hour armband recordings from 16 volunteers in segments of 10 seconds each. In addition, four classical HR variability (HRV) parameters (SDNN, RMSSD, and powers at low and high frequency bands) were computed. For comparison purposes, the same parameters were estimated also for data from a commercial Holter monitor. The armband provided usable data (difference less than 10% from Holter-estimated mean HR) during 75.25%/11.02% (inter-subject median/interquartile range) of segments when the user was not in bed, and during 98.49%/0.79% of the bed segments. The automatic artifact detector found 53.85%/17.09% of the data to be usable during the non-bed time, and 95.00%/2.35% to be usable during the time in bed. The HRV analysis obtained a relative error with respect to the Holter data not higher than 1.37% (inter-subject median/interquartile range). Although further studies have to be conducted for specific applications, results suggest that the armband device has a good potential for daily life HR monitoring, especially for applications such as arrhythmia or seizure detection, stress assessment, or sleep studies