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Deep neural networks (DNNs) are increasingly important in the medical diagnosis of electrocardiogram (ECG) signals. However, research has shown that DNNs are highly vulnerable to adversarial examples, which can be created by carefully crafted perturbations. This vulnerability can lead to potential medical accidents. This poses new challenges for the application of DNNs in the medical diagnosis of ECG signals. This paper proposes a novel network Channel Activation Suppression with Lipschitz Constraints Net (CASLCNet), which employs the Channel-wise Activation Suppressing (CAS) strategy to dynamically adjust the contribution of different channels to the class prediction and uses the 1-Lipschitz's ℓ∞ distance network as a robust classifier to reduce the impact of adversarial perturbations on the model itself in order to increase the adversarial robustness of the model. The experimental results demonstrate that CASLCNet achieves A C C r o b u s t scores of 91.03% and 83.01% when subjected to PGD attacks on the MIT-BIH and CPSC2018 datasets, respectively, which proves that the proposed method in this paper enhances the model's adversarial robustness while maintaining a high accuracy rate. [ABSTRACT FROM AUTHOR]

The paper presents a system for analyzing cardiac activity with the possibility of continuous and remote monitoring. The created sensor mobile device monitors heart activity by means of the convenient and imperceptible registration of cardiac signals. At the same time, the behavior of the human body is also monitored through the accelerometer and gyroscope built into the device, thanks to which it is possible to signal in the event of loss of consciousness or fall (in patients with syncope). Conducting real-time cardio monitoring and the analysis of recordings using various mathematical methods (linear, non-linear, and graphical) enables the research, accurate diagnosis, timely assistance, and correct treatment of cardiovascular diseases. The paper examines the recordings of patients diagnosed with arrhythmia and syncope recorded by electrocardiography (ECG) sensors in real conditions. The obtained results are subjected to statistical analysis to determine the accuracy and significance of the obtained results. The studies show significant deviations in the patients with arrhythmia and syncope regarding the obtained values of the studied parameters of heart rate variability (HRV) from the accepted normal values (for example, the root mean square of successive differences between normal heartbeats (RMSSD) in healthy individuals is 24.02 ms, while, in patients with arrhythmia (6.09 ms) and syncope (5.21 ms), it is much lower). The obtained quantitative and graphic results identify some possible abnormalities and demonstrate disorders regarding the activity of the autonomic nervous system, which is directly related to the work of the heart. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach for wide angle monitoring of vital signs in smart home applications. The person is tracked using an indoor radar. Upon detecting the person to be static, the radar automatically focuses its beam on that location, and subsequently breathing and heart rates are extracted from the reflected signals using continuous wavelet transform ( C W T ) analysis. In this way, leveraging the radar's on-chip processor enables real-time monitoring of vital signs across varying angles. In our experiment, we employ a commercial multi-input multi-output (MIMO) millimeter-wave FMCW radar to monitor vital signs within a range of 1.15 to 2.3 m and an angular span of − 44.8 to + 44.8 deg. In the Bland–Altman plot, the measured results indicate the average difference of − 1.5 and 0.06 beats per minute (BPM) relative to the reference for heart rate and breathing rate, respectively. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative non-contact heart rate monitoring method based on Wi-Fi Channel State Information (CSI). This approach integrates both amplitude and phase information of the CSI signal through rotational projection, aiming to optimize the accuracy of heart rate estimation in home environments. We develop a frequency domain subcarrier selection algorithm based on Heartbeat to subcomponent ratio (HSR) and design a complete set of signal filtering and subcarrier selection processes to further enhance the accuracy of heart rate estimation. Heart rate estimation is conducted by combining the peak frequencies of multiple subcarriers. Extensive experimental validations demonstrate that our method exhibits exceptional performance under various environmental conditions. The experimental results show that our subcarrier selection method for heart rate estimation achieves an average accuracy of 96.8%, with a median error of only 0.8 bpm, representing an approximately 20% performance improvement over existing technologies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The 12-lead electrocardiogram (ECG) is crucial in assessing patient decisions. However, portable ECG devices capable of acquiring a complete 12-lead ECG are scarce. For the first time, a deep learning-based method is proposed to reconstruct the 12-lead ECG from Frank leads (VX, VY, and VZ) or EASI leads (VES, VAS, and VAI). The innovative ECG reconstruction network called M2Eformer is composed of a 2D-ECGblock and a ProbDecoder module. The 2D-ECGblock module adaptively segments EASI leads into multi-periods based on frequency energy, transforming the 1D time series into a 2D tensor representing within-cycle and between-cycle variations. The ProbDecoder module aims to extract Probsparse self-attention and achieve one-step output for the target leads. Experimental results from comparing recorded and reconstructed 12-lead ECG using Frank leads indicate that M2Eformer outperforms traditional ECG reconstruction methods on a public database. In this study, a self-constructed database (10 healthy individuals + 15 patients) was utilized for the clinical diagnostic validation of ECG reconstructed from EASI leads. Subsequently, both the ECG reconstructed using EASI and the recorded 12-lead ECG were subjected to a double-blind diagnostic experiment conducted by three cardiologists. The overall diagnostic consensus among three cardiology experts, reaching a rate of 96%, indicates the significant utility of EASI-reconstructed 12-lead ECG in facilitating the diagnosis of cardiac conditions. [ABSTRACT FROM AUTHOR]

In the rapidly evolving landscape of continuous electrocardiogram (ECG) monitoring systems, there is a heightened demand for non-invasive sensors capable of measuring ECGs and detecting heart rate variability (HRV) in diverse populations, ranging from cardiovascular patients to sports enthusiasts. Challenges like device accuracy, patient privacy, signal noise, and long-term safety impede the use of wearable devices in clinical practice. This scoping review aims to assess the performance and safety of novel multi-channel, sensor-based biopotential wearable devices in adults. A comprehensive search strategy was employed on four databases, resulting in 143 records and the inclusion of 12 relevant studies. Most studies focused on healthy adult subjects (n = 6), with some examining controlled groups with atrial fibrillation (AF) (n = 3), long QT syndrome (n = 1), and sleep apnea (n = 1). The investigated bio-sensor devices included chest-worn belts (n = 2), wrist bands (n = 2), adhesive chest strips (n = 2), and wearable textile smart clothes (n = 4). The primary objective of the included studies was to evaluate device performance in terms of accuracy, signal quality, comparability, and visual assessment of ECGs. Safety findings, reported in five articles, indicated no major side effects for long-term/continuous monitoring, with only minor instances of skin irritation. Looking forward, there are ample opportunities to enhance and test these technologies across various physical activity intensities and clinical conditions. [ABSTRACT FROM AUTHOR]

The use of radar technology for non-contact measurement of vital parameters is increasingly being examined in scientific studies. Based on a systematic literature search in the PubMed, German National Library, Austrian Library Network (Union Catalog), Swiss National Library and Common Library Network databases, the accuracy of heart rate and/or respiratory rate measurements by means of radar technology was analyzed. In 37% of the included studies on the measurement of the respiratory rate and in 48% of those on the measurement of the heart rate, the maximum deviation was 5%. For a tolerated deviation of 10%, the corresponding percentages were 85% and 87%, respectively. However, the quantitative comparability of the results available in the current literature is very limited due to a variety of variables. The elimination of the problem of confounding variables and the continuation of the tendency to focus on the algorithm applied will continue to constitute a central topic of radar-based vital parameter measurement. Promising fields of application of research can be found in particular in areas that require non-contact measurements. This includes infection events, emergency medicine, disaster situations and major catastrophic incidents. [ABSTRACT FROM AUTHOR]

Technological development has boosted the use of multi-sensor devices to monitor athletes' performance, but the location and connectivity between devices have been shown to affect data reliability. This preliminary study aimed to determine whether the placement of a multi-sensor device (WIMU PROTM) could affect the heart rate signal reception (GARMINTM chest strap) and, therefore, data accuracy. Thirty-two physical education students (20 men and 12 women) performed 20 min of exercise in a cycle ergometer based on the warm-up of the Function Threshold Power 20 test in laboratory conditions, carrying two WIMU PROTM devices (Back: inter-scapula; Bicycle: bicycle's handlebar—20 cm from the chest) and two GARMINTM chest straps. A one-dimensional statistical parametric mapping test found full agreement between the two situations (inter-scapula vs. bicycle's handlebar). Excellent intra-class correlation values were obtained during the warm-up (ICC = 0.99, [1.00–1.00], p < 0.001), the time trial test (ICC = 0.99, [1.00–1.00], p < 0.001) and the cool-down (ICC = 0.99, [1.00–1.00], p < 0.001). The Bland–Altman plots confirmed the total agreement with a bias value of 0.00 ± 0.1 bpm. The interscapular back placement of the WIMU PROTM device does not affect heart rate measurement accuracy with a GARMINTM chest strap during cycling exercise in laboratory conditions. [ABSTRACT FROM AUTHOR]

Monitoring key physiological metrics, including heart rate and heart rate variability, has been shown to be of value in exercise science, disease management, and overall health. The purpose of this study was to investigate the diurnal variation of physiological responses and physical performances using digital biomarkers as a precise measurement tool during a walking football match (WFM) in higher-weight men. Nineteen males (mean age: 42.53 ± 12.18 years; BMI: 33.31 ± 4.31 kg·m−2) were engaged in a WFM at two different times of the day. Comprehensive evaluations of physiological parameters (e.g., cardiac autonomic function, lactate, glycemia, and oxygen saturation), along with physical performance, were assessed before, during, and after the match. Overall, there was a significant interaction (time of day x WFM) for mean blood pressure (MBP) (p = 0.007) and glycemia (p = 0.039). Glycemia decreased exclusively in the evening after WFM (p = 0.001), while mean blood pressure did not significantly change. Rating of perceived exertion was significantly higher in the evening than in the morning (p = 0.04), while the heart rate recovery after 1 min (HRR60s) of the match was lower in the evening than in the morning (p = 0.048). Overall, walking football practice seems to be safe, whatever the time of day. Furthermore, HRR60, glycemia, and (MBP) values were lower in the evening compared to the morning, suggesting that evening exercise practice could be safer for individuals with higher weight. The utilization of digital biomarkers for monitoring health status during WFM has been shown to be efficient. [ABSTRACT FROM AUTHOR]

Background: Combination devices to monitor heart rate/rhythms and physical activity are becoming increasingly popular in research and clinical settings. The Zio XT Patch (iRhythm Technologies, San Francisco, CA, USA) is US Food and Drug Administration (FDA)-approved for monitoring heart rhythms, but the validity of its accelerometer for assessing physical activity is unknown. Objective: To validate the accelerometer in the Zio XT Patch for measuring physical activity against the widely-used ActiGraph GT3X. Methods: The Zio XT and ActiGraph wGT3X-BT (Actigraph, Pensacola, FL, USA) were worn simultaneously in two separately-funded ancillary studies to Visit 6 of the Atherosclerosis Risk in Communities (ARIC) Study (2016–2017). Zio XT was worn on the chest and ActiGraph was worn on the hip. Raw accelerometer data were summarized using mean absolute deviation (MAD) for six different epoch lengths (1-min, 5-min, 10-min, 30-min, 1-h, and 2-h). Participants who had ≥3 days of at least 10 h of valid data between 7 a.m–11 p.m were included. Agreement of epoch-level MAD between the two devices was evaluated using correlation and mean squared error (MSE). Results: Among 257 participants (average age: 78.5 ± 4.7 years; 59.1% female), there were strong correlations between MAD values from Zio XT and ActiGraph (average r: 1-min: 0.66, 5-min: 0.90, 10-min: 0.93, 30-min: 0.93, 1-h: 0.89, 2-h: 0.82), with relatively low error values (Average MSE × 106: 1-min: 349.37 g, 5-min: 86.25 g, 10-min: 56.80 g, 30-min: 45.46 g, 1-h: 52.56 g, 2-h: 54.58 g). Conclusions: These findings suggest that Zio XT accelerometry is valid for measuring duration, frequency, and intensity of physical activity within time epochs of 5-min to 2-h. [ABSTRACT FROM AUTHOR]

Surgery is a common first-line treatment for many types of disease, including cancer. Mortality rates after general elective surgery have seen significant decreases whilst postoperative complications remain a frequent occurrence. Preoperative assessment tools are used to support patient risk stratification but do not always provide a precise and accessible assessment. Wearable sensors (WS) provide an accessible alternative that offers continuous monitoring in a non-clinical setting. They have shown consistent uptake across the perioperative period but there has been no review of WS as a preoperative assessment tool. This paper reviews the developments in WS research that have application to the preoperative period. Accelerometers were consistently employed as sensors in research and were frequently combined with photoplethysmography or electrocardiography sensors. Pre-processing methods were discussed and missing data was a common theme; this was dealt with in several ways, commonly by employing an extraction threshold or using imputation techniques. Research rarely processed raw data; commercial devices that employ internal proprietary algorithms with pre-calculated heart rate and step count were most commonly employed limiting further feature extraction. A range of machine learning models were used to predict outcomes including support vector machines, random forests and regression models. No individual model clearly outperformed others. Deep learning proved successful for predicting exercise testing outcomes but only within large sample-size studies. This review outlines the challenges of WS and provides recommendations for future research to develop WS as a viable preoperative assessment tool. [ABSTRACT FROM AUTHOR]

Heart rate is a key vital sign that can be used to understand an individual's health condition. Recently, remote sensing techniques, especially acoustic-based sensing, have received increasing attention for their ability to non-invasively detect heart rate via commercial mobile devices such as smartphones and smart speakers. However, due to signal interference, existing methods have primarily focused on monitoring a single user and required a large separation between them when monitoring multiple people. These limitations hinder many common use cases such as couples sharing the same bed or two or more people located in close proximity. In this paper, we present an approach that can minimize interference and thereby enable simultaneous heart rate monitoring of multiple individuals in close proximity using a commonly available smart speaker prototype. Our user study, conducted under various real-life scenarios, demonstrates the system's accuracy in sensing two users' heart rates when they are seated next to each other with a median error of 0.66 beats per minute (bpm). Moreover, the system can successfully monitor up to four people in close proximity. [ABSTRACT FROM AUTHOR]

Simple Summary: Two of the fundamental pillars of animal welfare are stress-free management and treatment. The extensive farming of cattle involves minimal human contact. However, regular veterinary treatments and vaccinations require restraining cattle with headlocks in crates in order to ensure the safety of workers. Since these animals are usually kept in herds and mainly graze freely, this procedure is a high-stress situation for them. Animals can vocalize a number of sounds as part of their behavior. For example, dams have a special nasal vocalization toward their calves with a calming effect. We hypothesized that, by removing noises, selecting the most representative part, and creating a repeated sound, this kind of nasal vocalization would have a calming effect on cattle restrained in headlocks. Our results show that the played processed nasal vocalization of a mother cow reduced the stress experienced by cattle during the test. Sound analysis is an important field of research for improving precision livestock farming systems. If the information carried by livestock sounds is interpreted correctly, it could be used to improve management and welfare assessment in this field. Therefore, we hypothesized that the nasal vocalization of a mother cow could have a calming effect on conspecifics. The nasal vocalization in our study was recorded from a mother cow (not part of the test herd) while it was licking its day-old calf. The raw sound was analyzed, cleaned from noises, and the most representative vocalization was lengthened to two minutes. Thirty cows having calves were randomly selected from eighty Hungarian grey cattle cows. Two test days were selected, one week apart; the weather circumstances in both days were similar. The herd was collected in a paddock, and the test site (a restraining crate with a headlock) was 21 m away from them. The cows from the herd were gently moved to the restraining crate, and, after the installation of the headlock, Polar® heart rate monitors were fixed on the animals. The recording of the RR intervals was carried out for two minutes. On day one of the test, the processed nasal sound was played to every second cow during the heart rate monitoring. When the sound ended, the heart rate monitor was removed. On test day two, the sound and no sound treatments were switched among the participating cows. At the end of the measurement, the headlock was opened, letting the animals out voluntarily, and a flight test was performed along a 5 m distance. The time needed to pass the 5 m length was measured with a stopwatch and divided by the distance. The RR intervals were analyzed with the Kubios HRV Standard (ver. 3.5.0) software. The following data were recorded for the entire measurement: average and maximum heart rate; SD1 and SD2; pNN50; VLF, LF, and HF. The quasi-periodic signal detected in the sound analyses can hardly be heard, even when it is enhanced to the maximum. This can be considered a vibration probably caused by the basis of articulation, such as a vibration of the tongue, for example. The SD2/SD1 ratio (0.97 vs. 1.07 for the animals having no sound and sound played, respectively, p = 0.0110) and the flight speed (0.92 vs. 1.08 s/m for the animals having no sound and sound played, respectively, p = 0.0409) indicate that the sound treatment had a calming effect on the restrained cows. The day of the test did not influence any of the measured parameters; therefore, no effect of the routine was observed. The yes–no sequence of the sound treatment significantly reduced the pNN50 and flight speed values, suggesting a somewhat more positive association with the headlock and the effectiveness of the processed nasal sound. In conclusion, we have demonstrated that, by means of sound analyses, not only information about individuals and the herd can be gathered but that, with proper processing, the sound obtained can be used to improve animal welfare. [ABSTRACT FROM AUTHOR]

Background: Individuals with rheumatoid arthritis (RA) are at a high risk of cardiovascular diseases (CVD). A reduced chronotropic response (CR), which produces exercise intolerance, is known to be a contributing factor to CVD and mortality. Studies have shown that patients with RA have a reduced CR. However, knowledge of CR-related factors in patients with RA is limited. This study aimed to explore CR-related factors, including CVD risk factors, inflammatory markers, and cardiorespiratory fitness (VO2PEAK). Methods: A total of 106 RA patients underwent a treadmill test, heart rate monitoring, and various assessments, including serological CVD risk factors, inflammatory markers, and VO2PEAK. Results: A total of 34% of participants demonstrated a reduced CR (≤80%). Body mass index, HOMA, hsCRP, and fibrinogen were inversely related to CR, while HDL, QUICKi, VO2PEAK, and RER exhibited a positive association. HDL and VO2PEAK emerged as independent CR-related factors in regression analysis. Conclusions: The current findings suggest that reduced CR in RA is associated with several CVD risk factors, inflammatory markers, and cardiorespiratory fitness. Future studies should investigate the effects of controlling these associated variables on CR in patients with RA. [ABSTRACT FROM AUTHOR]

The development of smart wearable solutions for monitoring daily life health status is increasingly popular, with chest straps and wristbands being predominant. This study introduces a novel sensorized T-shirt design with textile electrodes connected via a knitting technique to a Movesense device. We aimed to investigate the impact of stationary and movement actions on electrocardiography (ECG) and heart rate (HR) measurements using our sensorized T-shirt. Various activities of daily living (ADLs), including sitting, standing, walking, and mopping, were evaluated by comparing our T-shirt with a commercial chest strap. Our findings demonstrate measurement equivalence across ADLs, regardless of the sensing approach. By comparing ECG and HR measurements, we gained valuable insights into the influence of physical activity on sensorized T-shirt development for monitoring. Notably, the ECG signals exhibited remarkable similarity between our sensorized T-shirt and the chest strap, with closely aligned HR distributions during both stationary and movement actions. The average mean absolute percentage error was below 3%, affirming the agreement between the two solutions. These findings underscore the robustness and accuracy of our sensorized T-shirt in monitoring ECG and HR during diverse ADLs, emphasizing the significance of considering physical activity in cardiovascular monitoring research and the development of personal health applications. [ABSTRACT FROM AUTHOR]

Type 2 diabetes mellitus (T2D) poses a significant global health challenge and demands effective self-management strategies, including continuous blood glucose monitoring (CGM) and lifestyle adaptations. While CGM offers real-time glucose level assessment, the quest for minimizing trauma and enhancing convenience has spurred the need to explore non-invasive alternatives for monitoring vital signs in patients with T2D. Objective: This systematic review is the first that explores the current literature and critically evaluates the use and reporting of non-invasive wearable devices for monitoring vital signs in patients with T2D. Methods: Employing the PRISMA and PICOS guidelines, we conducted a comprehensive search to incorporate evidence from relevant studies, focusing on randomized controlled trials (RCTs), systematic reviews, and meta-analyses published since 2017. Of the 437 publications identified, seven were selected based on predetermined criteria. Results: The seven studies included in this review used various sensing technologies, such as heart rate monitors, accelerometers, and other wearable devices. Primary health outcomes included blood pressure measurements, heart rate, body fat percentage, and cardiorespiratory endurance. Non-invasive wearable devices demonstrated potential for aiding T2D management, albeit with variations in efficacy across studies. Conclusions: Based on the low number of studies with higher evidence levels (i.e., RCTs) that we were able to find and the significant differences in design between these studies, we conclude that further evidence is required to validate the application, efficacy, and real-world impact of these wearable devices. Emphasizing transparency in bias reporting and conducting in-depth research is crucial for fully understanding the implications and benefits of wearable devices in T2D management. [ABSTRACT FROM AUTHOR]

A low-power long-term ambulatory ECG monitor was developed for the acquisition, storage and processing of three simultaneous leads DI, aVF and V2 with a beat-to-beat heart rate measurement in real time. It provides long-term continuous ECG recordings until 84 h. The monitor uses a QRS complex detection algorithm based on the continuous wavelet transform with splines, which automatically selects the scale for the analysis of ECG records with different sampling frequencies. It includes a lead-off detection to continuously monitor the electrode connections and a real-time system of visual and acoustic alarms to alert users of abnormal conditions in its operation. The monitor presented is based in an ADS1294 analogue front end with four channels, 24-bit analog-to-digital converters and programmable gain amplifiers, a low-power dual-core ESP32 microcontroller, a microSD memory for data storage in a range of 4 GB to 32 GB and a 1.4 in thin-film transistor liquid crystal display (LCD) variant with a resolution of 128 × 128 pixels. It has programmable sampling rates of 250, 500 and 1000 Hz; a bandwidth of 0 Hz to 50% of the selected sampling rate; a CMRR of −105 dB; an input margin of ±2.4 V; a resolution of 286 nV; and a current consumption of 50 mA for an average battery life of 84 h. The ambulatory ECG monitor was evaluated with the commercial data-acquisition system BIOPAC MP36 and its module for ECG LABEL SS2LB, simultaneously comparing the morphologies of two ECG records and obtaining a correlation of 91.78%. For the QRS detection in real time, the implemented algorithm had an error less than 5%. The developed ambulatory ECG monitor can be used for the analysis of the dynamics of the heart rate variability in long-term ECG records and for the development of one's own databases of ECG recordings of normal subjects and patients with cardiovascular and noncardiovascular diseases. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Noncontact heart rate monitoring techniques based on millimeter-wave radar have advantages in unique medical scenarios. However, the accuracy of the existing noncontact heart rate estimation methods is still limited by interference, such as DC offsets, respiratory harmonics, and environmental noise. Additionally, these methods still require longer observation times. Most deep learning methods related to heart rate estimation still need to collect more heart rate marker data for training. To address the above problems, this paper introduces a radar signal-based heart rate estimation network named the "masked phase autoencoders with a vision transformer network" (MVN). This network is grounded on masked autoencoders (MAEs) for self-supervised pretraining and a vision transformer (ViT) for transfer learning. During the phase preprocessing stage, phase differencing and interpolation smoothing are performed on the input phase signal. In the self-supervised pretraining step, masked self-supervised training is performed on the phase signal using the MAE network. In the transfer learning stage, the encoder segment of the MAE network is integrated with the ViT network to enable transfer learning using labeled heart rate data. The innovative MVN offers a dual advantage—it not only reduces the cost associated with heart rate data acquisition but also adeptly addresses the issue of respiratory harmonic interference, which is an improvement over conventional signal processing methods. The experimental results show that the process in this paper improves the accuracy of heart rate estimation while reducing the requisite observation time. [ABSTRACT FROM AUTHOR]

Unobtrusive monitoring of children's heart rate (HR) and respiratory rate (RR) can be valuable for promoting the early detection of potential health issues, improving communication with healthcare providers and reducing unnecessary hospital visits. A promising solution for wireless vital sign monitoring is radar technology. This paper presents a novel approach for the simultaneous estimation of children's RR and HR utilizing ultra-wideband (UWB) radar using a deep transfer learning algorithm in a cohort of 55 children. The HR and RR are calculated by processing radar signals via spectrogram from time epochs of 10 s (25 sample length of hamming window with 90% overlap) and then transforming the resultant representation into 2-dimensional images. These images were fed into a pre-trained Visual Geometry Group-16 (VGG-16) model (trained on ImageNet dataset), with weights of five added layers fine-tuned using the proposed data. The prediction on the test data achieved a mean absolute error (MAE) of 7.3 beats per minute (BPM < 6.5% of average HR) and 2.63 breaths per minute (BPM < 7% of average RR). We also achieved a significant Pearson's correlation of 77% and 81% between true and extracted for HR and RR, respectively. HR and RR samples are extracted every 10 s. [ABSTRACT FROM AUTHOR]

Background: Heart rate is an essential diagnostic parameter indicating a patient's condition. The assessment of heart rate is also a crucial parameter in the diagnostics of various sleep disorders, including sleep apnoea, as well as sleep/wake pattern analysis. It is usually measured using an electrocardiograph (ECG)—a device monitoring the electrical activity of the heart using several electrodes attached to a patient's upper body—or photoplethysmography (PPG). Methods: The following paper investigates an alternative method for heart rate detection and monitoring that operates on tracheal audio recordings. Datasets for this research were obtained from six participants along with ECG Holter (for validation), as well as from fifty participants undergoing a full night polysomnography testing, during which both heart rate measurements and audio recordings were acquired. Results: The presented method implements a digital filtering and peak detection algorithm applied to audio recordings obtained with a wireless sensor using a contact microphone attached in the suprasternal notch. The system was validated using ECG Holter data, achieving over 92% accuracy. Furthermore, the proposed algorithm was evaluated against whole-night polysomnography-derived HR using Bland-Altman's plots and Pearson's Correlation Coefficient, reaching the average of 0.82 (0.93 maximum) with 0 BPM error tolerance and 0.89 (0.97 maximum) at ±3 BPM. Conclusions: The results prove that the proposed system serves the purpose of a precise heart rate monitoring tool that can conveniently assess HR during sleep as a part of a home-based sleep disorder diagnostics process. [ABSTRACT FROM AUTHOR]

This study proposes a novel method for obtaining the electrocardiogram (ECG) derived respiration (EDR) from a single lead ECG and respiration-derived cardiogram (RDC) from a respiratory stretch sensor. The research aims to reconstruct the respiration waveform, determine the respiration rate from ECG QRS heartbeat complexes data, locate heartbeats, and calculate a heart rate (HR) using the respiration signal. The accuracy of both methods will be evaluated by comparing located QRS complexes and inspiration maxima to reference positions. The findings of this study will ultimately contribute to the development of new, more accurate, and efficient methods for identifying heartbeats in respiratory signals, leading to better diagnosis and management of cardiovascular diseases, particularly during sleep where respiration monitoring is paramount to detect apnoea and other respiratory dysfunctions linked to a decreased life quality and known cause of cardiovascular diseases. Additionally, this work could potentially assist in determining the feasibility of using simple, no-contact wearable devices for obtaining simultaneous cardiology and respiratory data from a single device. [ABSTRACT FROM AUTHOR]

Motivation: The advancement of preventive medicine and, subsequently, telemedicine drives the need for noninvasive and remote measurements in patients' natural environments. Heart rate (HR) measurements are particularly promising and extensively researched due to their quick assessment and comprehensive representation of patients' conditions. However, in scenarios such as endurance training or emergencies, where HR measurement was not anticipated and direct access to victims is limited, no method enables obtaining HR results that are suitable even for triage. Methods: This paper presents the possibility of remotely measuring of human HR from a series of in-flight videos using videoplethysmography (VPG) along with skin detection, human pose estimation and image stabilization methods. An unmanned aerial vehicle (UAV) equipped with a camera captured ten segments of video footage featuring volunteers engaged in free walking and running activities in natural sunlight. The human pose was determined using the OpenPose algorithm, and subsequently, skin areas on the face and forearms were identified and tracked in consecutive frames. Ultimately, HR was estimated using several VPG methods: the green channel (G), green-red difference (GR), excess green (ExG), independent component analysis (ICA), and a plane orthogonal to the skin (POS). Results: When compared to simultaneous readings from a reference ECG-based wearable recorder, the root-mean-squared error ranged from 17.7 (G) to 27.7 (POS), with errors of less than 3.5 bpm achieved for the G and GR methods. Conclusions: These results demonstrate the acceptable accuracy of touchless human pulse measurement with the accompanying UAV-mounted camera. The method bridges the gap between HR-transmitting wearables and emergency HR recorders, and it has the potential to be advantageous in training or rescue scenarios in mountain, water, disaster, or battlefield settings. [ABSTRACT FROM AUTHOR]

The choice of the algorithm used for detecting the heartbeats within the PPG signal is of crucial importance in order to extract a PRV that is a reliable surrogate of the heart rate variability (HRV) obtained from the ECG signal. Photoplethysmography (PPG) is a popular, non-invasive, and low-cost optical technique that can provide useful information about the cardiovascular system, aiming to reveal autonomic dysfunctions and peripheral vascular diseases during daily life. According to their findings, after the application of these techniques, the feature availability from PPG waveforms achieved more than 98.6%, with a significant correlation of up to 0.92 between finger and wrist PPG signals concerning the properties of the third and fifth component waves. The PPG-derived heart rate series were provided by Apple Watch during relaxing and stress-inducing experimental conditions. [Extracted from the article]

Over the past few years, there has been increased interest in photoplethysmography (PPG) technology, which has revealed that, in addition to heart rate and oxygen saturation, the pulse shape of the PPG signal contains much more valuable information. Lately, the wearable market has shifted towards a multi-wavelength and multichannel approach to increase signal robustness and facilitate the extraction of other intrinsic information from the signal. This transition presents several challenges related to complexity, accuracy, and reliability of algorithms. To address these challenges, anomaly detection stages can be employed to increase the accuracy and reliability of estimated parameters. Powerful algorithms, such as lightweight machine learning (ML) algorithms, can be used for anomaly detection in multi-wavelength PPG (MW-PPG). The main contributions of this paper are (a) proposing a set of features with high information gain for anomaly detection in MW-PPG signals in the classification context, (b) assessing the impact of window size and evaluating various lightweight ML models to achieve highly accurate anomaly detection, and (c) examining the effectiveness of MW-PPG signals in detecting artifacts. [ABSTRACT FROM AUTHOR]

Incorporating technology into healthcare processes is necessary to ensure the availability of high-quality care in the future. Wearable sensors are an example of such technology that could decrease workload, enable early detection of patient deterioration, and support clinical decision making by healthcare professionals. These sensors unlock continuous monitoring of vital signs, such as heart rate, respiration rate, blood oxygen saturation, temperature, and physical activity. However, broad and successful application of wearable sensors on the surgical ward is currently lacking. This may be related to the complexity, especially when it comes to replacing manual measurements by healthcare professionals. This report provides practical guidance to support peers before starting with the clinical application of wearable sensors in the surgical ward. For this purpose, the Non-Adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework of technology adoption and innovations in healthcare organizations is used, combining existing literature and our own experience in this field over the past years. Specifically, the relevant topics are discussed per domain, and key lessons are subsequently summarized. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Lung carcinoma is one of the most common malignancies worldwide. At present, unfortunately, there are no markers that would allow early identification of this tumor in the preclinical or early clinical stage. The use of sniffer dogs has been reported to show some promise in early diagnosis of this type of cancer Aim: This study aimed to evaluate the possibility of utilizing changes in the heart rate of sniffer dogs (which increases when finding a positive sample) in tumor detection. Methods: This double-blinded pilot study included two sniffer dogs. A chest strap was fastened on the dog's chests for heart rate monitoring while they were examining samples and heart rate was recorded. Test parameters (sensitivity, specificity, positive and negative predictive values) were then calculated, evaluating performances based on (i) the dog's indications according to their training and (ii) the changes in their heart rates. Results: Calculation according to the dog's indications revealed an overall sensitivity of 95.2% accompanied by a specificity of 81.8%, a PPV of 93.7%, and an NPV of 85.7%, respectively. These results were not significantly different from those evaluated by heart rate; heart rate monitoring was, however, burdened with a relatively high proportion of invalid experiments in which heart rate measurement failed. When the method of calculation was changed from rounds to individual samples, the test parameters further increased. Conclusions: This pilot study confirmed the hypothesis that heart rate increases in trained sniffer dogs when encountering samples from tumor-positive patients but remains unchanged when only negative samples are present. The reliability of results based on heart rate increase is similar to that obtained by a dog's indications and, if the limitation represented by technical issues is overcome, it could serve as a valuable verification method. [ABSTRACT FROM AUTHOR]

Continuous, real-time monitoring of occupational health and safety in high-risk workplaces such as construction sites can substantially improve the safety of workers. However, introducing such systems in practice is associated with a number of challenges, such as scaling up the solution while keeping its cost low. In this context, this work investigates the use of an off-the-shelf, low-cost smartwatch to detect health issues based on heart rate monitoring in a privacy-preserving manner. To improve the smartwatch's low measurement quality, a novel, frugal machine learning method is proposed that corrects measurement errors, along with a new dataset for this task. This method's integration with the smartwatch and the remaining parts of the health and safety monitoring system (built on the ASSIST-IoT reference architecture) are presented. This method was evaluated in a laboratory environment in terms of its accuracy, computational requirements, and frugality. With an experimentally established mean absolute error of 8.19 BPM, only 880 bytes of required memory, and a negligible impact on the performance of the device, this method meets all relevant requirements and is expected to be field-tested in the coming months. To support reproducibility and to encourage alternative approaches, the dataset, the trained model, and its implementation on the smartwatch were published under free licenses. [ABSTRACT FROM AUTHOR]

Instantaneous heart rate (IHR) has been investigated for sleep applications, such as sleep apnea detection and sleep staging. To ensure the comfort of the patient during sleep, it is desirable for IHR to be measured in a contact-free fashion. In this work, we use speckle vibrometry (SV) to perform on-skin and on-textile IHR monitoring in a sleep setting. Minute motions on the laser-illuminated surface can be captured by a defocused camera, enabling the detection of cardiac motions even on textiles. We investigate supine, lateral, and prone sleeping positions. Based on Bland–Altman analysis between SV cardiac measurements and electrocardiogram (ECG), with respect to each position, we achieve the best limits of agreement with ECG values of [−8.65, 7.79] bpm, [−9.79, 9.25] bpm, and [−10.81, 10.23] bpm, respectively. The results indicate the potential of using speckle vibrometry as a contact-free monitoring method for instantaneous heart rate in a setting where the participant is allowed to rest in a spontaneous position while covered by textile layers. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Detection of fatigue is extremely important in the development of different kinds of preventive systems (such as driver monitoring or operator monitoring for accident prevention). The presence of fatigue for this task should be determined with physiological and objective behavioral indicators. To develop an effective model of fatigue detection, it is important to record a dataset with people in a state of fatigue as well as in a normal state. We carried out data collection using an eye tracker, a video camera, a stage camera, and a heart rate monitor to record a different kind of signal to analyze them. In our proposed dataset, 10 participants took part in the experiment and recorded data 3 times a day for 8 days. They performed different types of activity (choice reaction time, reading, correction test Landolt rings, playing Tetris), imitating everyday tasks. Our dataset is useful for studying fatigue and finding indicators of its manifestation. We have analyzed datasets that have public access to find the best for this task. Each of them contains data of eye movements and other types of data. We evaluated each of them to determine their suitability for fatigue studies, but none of them fully fit the fatigue detection task. We evaluated the recorded dataset by calculating the correspondences between eye-tracking data and CRT (choice reaction time) that show the presence of fatigue. [ABSTRACT FROM AUTHOR]

In this paper, a beat-based autoencoder is proposed for mapping photoplethysmography (PPG) to a single-lead electrocardiogram (single-lead ECG) signal. The main limiting factors represented in uncleaned data, subject dependency, and erroneous beat segmentation are regarded. The dataset is cleaned by a two-stage clustering approach. Rather than complete single–lead ECG signal reconstruction, a beat-based PPG-to-single-lead-ECG (PPG2ECG) conversion is introduced for providing a simple lightweight model that meets the computational capabilities of wearable devices. In addition, peak-to-peak segmentation is employed for alleviating errors in PPG onset detection. Furthermore, subject-dependent training is highlighted as a critical factor in training procedures because most existing work includes different beats/signals from the same subject's record in both training and testing sets. So, we provide a completely subject-independent model where the testing subjects' records are hidden in the training stage entirely, i.e., a subject record appears once either in the training or testing set, but testing beats/signals belong to records that never appear in the training set. The proposed deep learning model is designed for providing efficient feature extraction that attains high reconstruction quality over subject-independent scenarios. The achieved performance is about 0.92 for the correlation coefficient and 0.0086 for the mean square error for the dataset extracted/cleaned from the MIMIC II dataset. [ABSTRACT FROM AUTHOR]

The wireless capture of analog differential signals from fully passive (battery-less) sensors is technically challenging but it can allow for the seamless capture of differential biosignals such as an electrocardiogram (ECG). This paper presents a novel design for the wireless capture of analog differential signals using a novel conjugate coil pair for a wireless resistive analog passive (WRAP) ECG sensor. Furthermore, we integrate this sensor with a new type of dry electrode, namely conductive polymer polypyrrole (PPy)-coated patterned vertical carbon nanotube (pvCNT) electrodes. The proposed circuit uses dual-gate depletion-mode MOSFETs to convert the differential biopotential signals to correlated drain-source resistance changes and the conjugate coil wirelessly transmits the differences of the two input signals. The circuit rejects (17.24 dB) common mode signals and passing only differential signals. We have integrated this novel design with our previously reported PPy-coated pvCNT dry ECG electrodes, fabricated on a stainless steel substrate with a diameter of 10 mm, which provided a zero-power (battery-less) ECG capture system for long duration monitoring. The scanner transmits an RF carrier signal at 8.37 MHz. The proposed ECG WRAP sensor uses only two complementary biopotential amplifier circuits, each of which has a single-depletion MOSFET. The amplitude-modulated RF signal is envelope-detected, filtered, amplified, and transmitted to a computer for signal processing. ECG signals are collected using this WRAP sensor and compared with a commercial counterpart. Due to the battery-less nature of the ECG WRAP sensor, it has the potential to be a body-worn electronic circuit patch with dry pvCNT electrodes that stably operate for a long period of time. [ABSTRACT FROM AUTHOR]

This study introduces a novel method for detecting the post-COVID state using ECG data. By leveraging a convolutional neural network, we identify "cardiospikes" present in the ECG data of individuals who have experienced a COVID-19 infection. With a test sample, we achieve an 87 percent accuracy in detecting these cardiospikes. Importantly, our research demonstrates that these observed cardiospikes are not artifacts of hardware–software signal distortions, but rather possess an inherent nature, indicating their potential as markers for COVID-specific modes of heart rhythm regulation. Additionally, we conduct blood parameter measurements on recovered COVID-19 patients and construct corresponding profiles. These findings contribute to the field of remote screening using mobile devices and heart rate telemetry for diagnosing and monitoring COVID-19. [ABSTRACT FROM AUTHOR]

The use of IoT technology is rapidly increasing in healthcare development and smart healthcare system for fitness programs, monitoring, data analysis, etc. To improve the efficiency of monitoring, various studies have been conducted in this field to achieve improved precision. The architecture proposed herein is based on IoT integrated with a cloud system in which power absorption and accuracy are major concerns. We discuss and analyze development in this domain to improve the performance of IoT systems related to health care. Standards of communication for IoT data transmission and reception can help to understand the exact power absorption in different devices to achieve improved performance for healthcare development. We also systematically analyze the use of IoT in healthcare systems using cloud features, as well as the performance and limitations of IoT in this field. Furthermore, we discuss the design of an IoT system for efficient monitoring of various healthcare issues in elderly people and limitations of an existing system in terms of resources, power absorption and security when implemented in different devices as per requirements. Blood pressure and heartbeat monitoring in pregnant women are examples of high-intensity applications of NB-IoT (narrowband IoT), technology that supports widespread communication with a very low data cost and minimum processing complexity and battery lifespan. This article also focuses on analysis of the performance of narrowband IoT in terms of delay and throughput using single- and multinode approaches. We performed analysis using the message queuing telemetry transport protocol (MQTTP), which was found to be efficient compared to the limited application protocol (LAP) in sending information from sensors. [ABSTRACT FROM AUTHOR]

Sleep is essential to physical and mental health. However, the traditional approach to sleep analysis—polysomnography (PSG)—is intrusive and expensive. Therefore, there is great interest in the development of non-contact, non-invasive, and non-intrusive sleep monitoring systems and technologies that can reliably and accurately measure cardiorespiratory parameters with minimal impact on the patient. This has led to the development of other relevant approaches, which are characterised, for example, by the fact that they allow greater freedom of movement and do not require direct contact with the body, i.e., they are non-contact. This systematic review discusses the relevant methods and technologies for non-contact monitoring of cardiorespiratory activity during sleep. Taking into account the current state of the art in non-intrusive technologies, we can identify the methods of non-intrusive monitoring of cardiac and respiratory activity, the technologies and types of sensors used, and the possible physiological parameters available for analysis. To do this, we conducted a literature review and summarised current research on the use of non-contact technologies for non-intrusive monitoring of cardiac and respiratory activity. The inclusion and exclusion criteria for the selection of publications were established prior to the start of the search. Publications were assessed using one main question and several specific questions. We obtained 3774 unique articles from four literature databases (Web of Science, IEEE Xplore, PubMed, and Scopus) and checked them for relevance, resulting in 54 articles that were analysed in a structured way using terminology. The result was 15 different types of sensors and devices (e.g., radar, temperature sensors, motion sensors, cameras) that can be installed in hospital wards and departments or in the environment. The ability to detect heart rate, respiratory rate, and sleep disorders such as apnoea was among the characteristics examined to investigate the overall effectiveness of the systems and technologies considered for cardiorespiratory monitoring. In addition, the advantages and disadvantages of the considered systems and technologies were identified by answering the identified research questions. The results obtained allow us to determine the current trends and the vector of development of medical technologies in sleep medicine for future researchers and research. [ABSTRACT FROM AUTHOR]

An elevated heart rate at admission or discharge is known to be associated with poor cardiovascular outcomes in patients with acute myocardial infarction (AMI). The association between post-discharge average office-visit heart rate and cardiovascular outcomes in patients with AMI has rarely been studied. We analyzed data for 7840 patients from the COREA-AMI registry who had their heart rates measured at least three times after hospital discharge. The office-visit heart rates were averaged and categorized into four groups by quartiles (<68, 68–74, 74–80, and >80 beats per minute). The primary end point was a composite of cardiovascular death, myocardial infarction, and ischemic stroke. During a median of 5.7 years of follow-up, major adverse cardiovascular events (MACE) affected 1357 (17.3%) patients. An average heart rate higher than 80 bpm was associated with an increased incidence of MACE compared to the reference average heart rate of 68–74 bpm. When dichotomized into <74 or ≥74 bpm, a lower average heart rate was not associated with MACE in patients with LV systolic dysfunction, in contrast to those without LV systolic dysfunction. An elevated average heart rate at office visits after AMI was associated with an increased risk of cardiovascular outcomes. Heart rate monitoring at office visits after discharge provides an important predictor related to cardiovascular events. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) remain a leading cause of death globally. According to the American Heart Association, approximately 19.1 million deaths were attributed to CVDs in 2020, in particular, ischemic heart disease and stroke. Several known risk factors for CVDs include smoking, alcohol consumption, lack of regular physical activity, and diabetes. The last decade has been characterized by widespread diffusion in the use of wristband-style wearable devices which can monitor and collect heart rate data, among other information. Wearable devices allow the analysis and interpretation of physiological and activity data obtained from the wearer and can therefore be used to monitor and prevent potential CVDs. However, these data are often provided in a manner that does not allow the general user to immediately comprehend possible health risks, and often require further analytics to draw meaningful conclusions. In this paper, we propose a disentangled variational autoencoder (β-VAE) with a bidirectional long short-term memory network (BiLSTM) backend to detect in an unsupervised manner anomalies in heart rate data collected during sleep time with a wearable device from eight heterogeneous participants. Testing was performed on the mean heart rate sampled both at 30 s and 1 min intervals. We compared the performance of our model with other well-known anomaly detection algorithms, and we found that our model outperformed them in almost all considered scenarios and for all considered participants. We also suggest that wearable devices may benefit from the integration of anomaly detection algorithms, in an effort to provide users more processed and straightforward information. [ABSTRACT FROM AUTHOR]

With the growing incidence of cardiovascular disease (CVD) in recent decades, the demand for out-of-hospital real-time ECG monitoring is increasing day by day, which promotes the research and development of portable ECG monitoring equipment. At present, two main categories of ECG monitoring devices are "limb lead ECG recording devices" and "chest lead ECG recording devices", which both require at least two electrodes. The former needs to complete the detection by means of a two-hand lap joint. This will seriously affect the normal activities of users. The electrodes used by the latter also need to be kept at a certain distance, usually more than 10 cm, to ensure the accuracy of the detection results. Decreasing the electrode spacing of the existing ECG detection equipment or reducing the area required for detection will be more conducive to improving the integration of the out-of-hospital portable ECG technologies. Therefore, a single-position ECG system based on charge induction is proposed to realize ECG detection on the surface of the human body with only one electrode with a diameter of less than 2 cm. Firstly, the ECG waveform detected in a single location is simulated by analyzing the electrophysiological activities of the human heart on the human body surface with COMSOL Multiphysics 5.4 software. Then, the hardware circuit design of the system and the host computer are developed and the test is performed. Finally, experiments for static and dynamic ECG monitoring are carried out and the heart rate correlation coefficients are 0.9698 and 0.9802, respectively, which proves the reliability and data accuracy of the system. [ABSTRACT FROM AUTHOR]

(1) Background: Consumer smartwatches may be a helpful tool to screen for atrial fibrillation (AF). However, validation studies on older stroke patients remain scarce. The aim of this pilot study from RCT NCT05565781 was to validate the resting heart rate (HR) measurement and the irregular rhythm notification (IRN) feature in stroke patients in sinus rhythm (SR) and AF. (2) Methods: Resting clinical HR measurements (every 5 min) were assessed using continuous bedside ECG monitoring (CEM) and the Fitbit Charge 5 (FC5). IRNs were gathered after at least 4 h of CEM. Lin's concordance correlation coefficient (CCC), Bland-Altman analysis, and mean absolute percentage error (MAPE) were used for agreement and accuracy assessment. (3) Results: In all, 526 individual pairs of measurements were obtained from 70 stroke patients—age 79.4 years (SD ± 10.2), 63% females, BMI 26.3 (IQ 22.2–30.5), and NIHSS score 8 (IQR 1.5–20). The agreement between the FC5 and CEM was good (CCC 0.791) when evaluating paired HR measurements in SR. Meanwhile, the FC5 provided weak agreement (CCC 0.211) and low accuracy (MAPE 16.48%) when compared to CEM recordings in AF. Regarding the accuracy of the IRN feature, analysis found a low sensitivity (34%) and high specificity (100%) for detecting AF. (4) Conclusion: The FC5 was accurate at assessing the HR during SR, but the accuracy during AF was poor. In contrast, the IRN feature was acceptable for guiding decisions regarding AF screening in stroke patients. [ABSTRACT FROM AUTHOR]

Detecting vital signs by using a contactless camera-based approach can provide several advantages over traditional clinical methods, such as lower financial costs, reduced visit times, increased comfort, and enhanced safety for healthcare professionals. Specifically, Eulerian Video Magnification (EVM) or Remote Photoplethysmography (rPPG) methods can be utilised to remotely estimate heart rate and respiratory rate biomarkers. In this paper two contactless camera-based health monitoring architectures are developed using EVM and rPPG, respectively; to this end, two different CNNs, (Mediapipe's BlazeFace and FaceMesh) are used to extract suitable regions of interest from incoming video frames. These two methods are implemented and deployed on four off-the-shelf edge devices as well as on a PC and evaluated in terms of latency (in each stage of the application's pipeline), throughput (FPS), power consumption (Watt), efficiency (throughput/Watt), and value (throughput/cost). This work provides important insights about the computational costs and bottlenecks of each method on each hardware platform, as well as which platform to use depending on the target metric. One of our insights shows that the Jetson Xavier NX platform is the best platform in terms of throughput and efficiency, while Raspberry Pi 4 8 GB is the best platform in terms of value. [ABSTRACT FROM AUTHOR]

Recently, various sophisticated methods, including machine learning and artificial intelligence, have been employed to examine health-related data. Medical professionals are acquiring enhanced diagnostic and treatment abilities by utilizing machine learning applications in the healthcare domain. Medical data have been used by many researchers to detect diseases and identify patterns. In the current literature, there are very few studies that address machine learning algorithms to improve healthcare data accuracy and efficiency. We examined the effectiveness of machine learning algorithms in improving time series healthcare metrics for heart rate data transmission (accuracy and efficiency). In this paper, we reviewed several machine learning algorithms in healthcare applications. After a comprehensive overview and investigation of supervised and unsupervised machine learning algorithms, we also demonstrated time series tasks based on past values (along with reviewing their feasibility for both small and large datasets). [ABSTRACT FROM AUTHOR]

This study examines the potential utility of using continuous glucose monitoring (CGM) to prescribe an exercise time to target peak hyperglycaemia in people with type 2 diabetes (T2D). The main aim is to test the feasibility of prescribing an individualised daily exercise time, based on the time of CGM-derived peak glucose, for people with T2D. Thirty-five individuals with T2D (HbA1c: 7.2 ± 0.8%; age: 64 ± 7 y; BMI: 29.2 ± 5.2 kg/m2) were recruited and randomised to one of two 14 d exercise interventions: i) ExPeak (daily exercise starting 30 min before peak hyperglycaemia) or placebo active control NonPeak (daily exercise starting 90 min after peak hyperglycaemia). The time of peak hyperglycaemia was determined via a two-week baseline CGM. A CGM, accelerometer, and heart rate monitor were worn during the free-living interventions to objectively measure glycaemic control outcomes, moderate-to-vigorous intensity physical activity (MVPA), and exercise adherence for future translation in a clinical trial. Participation in MVPA increased 26% when an exercise time was prescribed compared to habitual baseline (p < 0.01), with no difference between intervention groups (p > 0.26). The total MVPA increased by 10 min/day during the intervention compared to the baseline (baseline: 23 ± 14 min/d vs. intervention: 33 ± 16 min/d, main effect of time p = 0.03, no interaction). The change in peak blood glucose (mmol/L) was similar between the ExPeak (−0.44 ± 1.6 mmol/L, d = 0.21) and the NonPeak (−0.39 ± 1.5 mmol/L, d = 0.16) intervention groups (p = 0.92). Prescribing an exercise time based on CGM may increase daily participation in physical activity in people with type 2 diabetes; however, further studies are needed to test the long-term impact of this approach. [ABSTRACT FROM AUTHOR]

With higher levels of automation in vehicles, the need for robust driver monitoring systems increases, since it must be ensured that the driver can intervene at any moment. Drowsiness, stress and alcohol are still the main sources of driver distraction. However, physiological problems such as heart attacks and strokes also exhibit a significant risk for driver safety, especially with respect to the ageing population. In this paper, a portable cushion with four sensor units with multiple measurement modalities is presented. Capacitive electrocardiography, reflective photophlethysmography, magnetic induction measurement and seismocardiography are performed with the embedded sensors. The device can monitor the heart and respiratory rates of a vehicle driver. The promising results of the first proof-of-concept study with twenty participants in a driving simulator not only demonstrate the accuracy of the heart (above 70% of medical-grade heart rate estimations according to IEC 60601-2-27) and respiratory rate measurements (around 30% with errors below 2 BPM), but also that the cushion might be useful to monitor morphological changes in the capacitive electrocardiogram in some cases. The measurements can potentially be used to detect drowsiness and stress and thus the fitness of the driver, since heart rate variability and breathing rate variability can be captured. They are also useful for the early prediction of cardiovascular diseases, one of the main reasons for premature death. The data are publicly available in the UnoVis dataset. [ABSTRACT FROM AUTHOR]

Clinical alarm and decision support systems that lack clinical context may create non-actionable nuisance alarms that are not clinically relevant and can cause distractions during the most difficult moments of a surgery. We present a novel, interoperable, real-time system for adding contextual awareness to clinical systems by monitoring the heart-rate variability (HRV) of clinical team members. We designed an architecture for real-time capture, analysis, and presentation of HRV data from multiple clinicians and implemented this architecture as an application and device interfaces on the open-source OpenICE interoperability platform. In this work, we extend OpenICE with new capabilities to support the needs of the context-aware OR including a modularized data pipeline for simultaneously processing real-time electrocardiographic (ECG) waveforms from multiple clinicians to create estimates of their individual cognitive load. The system is built with standardized interfaces that allow for free interchange of software and hardware components including sensor devices, ECG filtering and beat detection algorithms, HRV metric calculations, and individual and team alerts based on changes in metrics. By integrating contextual cues and team member state into a unified process model, we believe future clinical applications will be able to emulate some of these behaviors to provide context-aware information to improve the safety and quality of surgical interventions. [ABSTRACT FROM AUTHOR]

Heart rate variability is a useful measure for monitoring the autonomic nervous system. Heart rate variability measurements have gained significant demand not only in science, but also in the public due to the fairly low price and wide accessibility of the Internet of things. The scientific debate about one of the measures of heart rate variability, i.e., what low-frequency power is reflecting, has been ongoing for decades. Some schools reason that it represents the sympathetic loading, while an even more compelling reasoning is that it measures how the baroreflex modulates the cardiac autonomic outflow. However, the current opinion manuscript proposes that the discovery of the more precise molecular characteristics of baroreceptors, i.e., that the Piezo2 ion channel containing vagal afferents could invoke the baroreflex, may possibly resolve this debate. It is long known that medium- to high-intensity exercise diminishes low-frequency power to almost undetectable values. Moreover, it is also demonstrated that the stretch- and force-gated Piezo2 ion channels are inactivated in a prolonged hyperexcited state in order to prevent pathological hyperexcitation. Accordingly, the current author suggests that the almost undetectable value of low-frequency power at medium- to high-intensity exercise reflects the inactivation of Piezo2 from vagal afferents in the baroreceptors with some Piezo1 residual activity contribution. Consequently, this opinion paper highlights how low-frequency power of the heart rate variability could represent the activity level of Piezo2 in baroreceptors. [ABSTRACT FROM AUTHOR]

Heart rate monitoring is especially important for aging individuals because it is associated with longevity and cardiovascular risk. Typically, this vital parameter can be measured using wearable sensors, which are widely available commercially. However, wearable sensors have some disadvantages in terms of acceptability, especially when used by elderly people. Thus, contactless solutions have increasingly attracted the scientific community in recent years. Camera-based photoplethysmography (also known as remote photoplethysmography) is an emerging method of contactless heart rate monitoring that uses a camera and a processing unit on the hardware side, and appropriate image processing methodologies on the software side. This paper describes the design and implementation of a novel pipeline for heart rate estimation using a commercial and low-cost camera as the input device. The pipeline's performance was tested and compared on a desktop PC, a laptop, and three different ARM-based embedded platforms (Raspberry Pi 4, Odroid N2+, and Jetson Nano). The results showed that the designed and implemented pipeline achieved an average accuracy of about 96.7% for heart rate estimation, with very low variance (between 1.5% and 2.5%) across processing platforms, user distances from the camera, and frame resolutions. Furthermore, benchmark analysis showed that the Odroid N2+ platform was the most convenient in terms of CPU load, RAM usage, and average execution time of the algorithmic pipeline. [ABSTRACT FROM AUTHOR]

Wearable Heart Rate monitors are used in sports to provide physiological insights into athletes' well-being and performance. Their unobtrusive nature and ability to provide reliable heart rate measurements facilitate the estimation of cardiorespiratory fitness of athletes, as quantified by maximum consumption of oxygen uptake. Previous studies have employed data-driven models which use heart rate information to estimate the cardiorespiratory fitness of athletes. This signifies the physiological relevance of heart rate and heart rate variability for the estimation of maximal oxygen uptake. In this work, the heart rate variability features that were extracted from both exercise and recovery segments were fed to three different Machine Learning models to estimate maximal oxygen uptake of 856 athletes performing Graded Exercise Testing. A total of 101 features from exercise and 30 features from recovery segments were given as input to three feature selection methods to avoid overfitting of the models and to obtain relevant features. This resulted in the increase of model's accuracy by 5.7% for exercise and 4.3% for recovery. Further, post-modelling analysis was performed to remove the deviant points in two cases, initially in both training and testing and then only in training set, using k-Nearest Neighbour. In the former case, the removal of deviant points led to a reduction of 19.3% and 18.0% in overall estimation error for exercise and recovery, respectively. In the latter case, which mimicked the real-world scenario, the average R value of the models was observed to be 0.72 and 0.70 for exercise and recovery, respectively. From the above experimental approach, the utility of heart rate variability to estimate maximal oxygen uptake of large population of athletes was validated. Additionally, the proposed work contributes to the utility of cardiorespiratory fitness assessment of athletes through wearable heart rate monitors. [ABSTRACT FROM AUTHOR]