Remote Heart Research Articles

Digital Home-Based Self-Monitoring System for People with Heart Failure: Protocol for Development of SmartHeart and Evaluation of Feasibility and Acceptability.

Heart failure (HF) is a chronic, progressive condition where the heart cannot pump enough blood to meet the body's needs. In addition to the daily challenges that HF poses, acute exacerbations can lead to costly hospitalizations and increased mortality. High health care costs and the burden of HF have led to the emerging application of new technologies to support people living with HF to stay well while living in the community. However, many digital solutions have not involved consumers and health care professionals in their design, leading to poor adoption. The SmartHeart project aimed to codevelop a smart health ecosystem to support the early detection of HF deterioration and encourage self-care, potentially preventing hospitalizations. This study aims to provide an overview of the SmartHeart project by describing our approach to designing the SmartHeart system, outlining its features, and describing the planned pilot study to determine the feasibility of the system. We used the Integrate, Design, Assess, and Share (IDEAS) framework to guide the development of the SmartHeart system, involving users (people with HF and their caregivers) and stakeholders (health care providers involved in the management of HF) in its design. SmartHeart is a complete remote heart health monitoring and automated feedback delivery system. It includes 2 user interfaces for patients: an Amazon Alexa conversational agent and a smartphone app. The system collects physiological, symptom, and behavioral data through wireless sensors and self-reports from users. These data are processed and analyzed to provide personalized health insights, self-care support, and alerts in case of health deterioration. The system also includes a web-based user interface for health care professionals, allowing them to access data, send messages to users, and receive notifications about potential health deterioration. A single-arm, multicenter pilot trial (N=20) is planned to determine the feasibility and acceptability of SmartHeart before evaluation through a randomized controlled trial. The primary outcome will be a description of the study's feasibility (recruitment, attrition, engagement, and changes in self-care). The SmartHeart study started in January 2021 on procurement of funding. Recruitment for the pilot trial started in August 2024 and will be completed by March 2025. We have currently enrolled 12 participants. Follow-up of all participants will be completed by the end of May 2025. We have co-designed and developed a complete remote heart health monitoring and automated feedback delivery system for the early detection of HF deterioration and prevention of HF-related hospitalizations. The next step is a pilot study, which will provide valuable information on feasibility and preliminary effects to inform a larger evaluation trial. SmartHeart has the potential to augment existing health services and help people with HF stay well while living in the community. DERR1-10.2196/62964.

Optimal facial regions for remote heart rate measurement during physical and cognitive activities

Remote photoplethysmography (rPPG) has gained prominence as a non-contact and real-time technology for heart rate monitoring. A critical factor in rPPG’s accuracy is the selection of regions of interest (ROI), as it can significantly influence prediction outcomes. Most studies typically use the forehead and cheeks as ROIs, but little research has explored other facial regions or how stable these ROIs are during physical movement and cognitive tasks. In this study, we analyzed 28 facial regions based on anatomical definitions using two mixed datasets derived from three public databases: LGI-PPGI, UBFC-rPPG, and UBFC-Phys. We applied rPPG algorithms such as orthogonal matrix image transformation (OMIT), plane-orthogonal-to-skin (POS), chrominance-based (CHROM), and local group invariance (LGI). Our findings show that the glabella, medial forehead, lateral forehead, malars, and upper nasal dorsum consistently perform well, with the glabella achieving the highest overall evaluation score. These results offer valuable insights for advancing remote heart rate monitoring technologies.

Advancements in wearable heart sounds devices for the monitoring of cardiovascular diseases

AbstractCardiovascular diseases remain a leading global cause of mortality, underscoring the urgent need for intelligent diagnostic tools to enhance early detection, prediction, diagnosis, prevention, treatment, and recovery. This demand has spurred the advancement of wearable and flexible technologies, revolutionizing continuous, noninvasive, and remote heart sound (HS) monitoring—a vital avenue for assessing heart activity. The conventional stethoscope, used to listen to HSs, has limitations in terms of its physical structure, as it is inflexible and bulky, which restricts its prospective applications. Recently, mechanoacoustic sensors have made remarkable advancements, evolving from primitive forms to soft, flexible, and wearable designs. This article provides an in‐depth review of the latest scientific and technological advancements by addressing various topics, including different types of sensors, sensing materials, design principles, denoising techniques, and clinical applications of flexible and wearable HS sensors. This transformative potential lies in the capacity for ongoing, remote, and personalized monitoring, promising enhanced patient outcomes, amplified remote monitoring capabilities, and timely diagnoses. Last, the article highlights current challenges and prospects for the future, suggesting techniques to advance HS sensing technologies for exciting real‐time applications.

Using heart rate data from wrist worn activity trackers to define thresholds for moderate to vigorous physical activity in children and young people with cystic fibrosis

BackgroundChildren and young people with cystic fibrosis (CYPwCF) are encouraged to do an average of 60 min of moderate-to-vigorous physical activity (MVPA) daily. However, there are no agreed heart rate (HR) thresholds for defining MVPA, so it is difficult to ascertain whether these targets are actually achieved. Wearable activity trackers enable continuous monitoring of fitness-related measures such as HR and could be used to measure duration and intensity of habitual MVPA. We aimed to define personalized and responsive MVPA thresholds from HR in CYPwCF, to determine habitual time spent in MVPA during childhood and adolescence. MethodsContinuous daily HR data were collected from 142 CYPwCF wearing activity trackers over 16 months. Linear mixed-effects models were used to develop personalised estimates of resting heart rate (RHR), peak heart rate (PHR) and MVPA thresholds, which were defined using the American College of Sports Medicine heart rate reserve (HRR) method. Results309,926 days of physical activity data showed that both RHR and PHR declined with age in CYPwCF, with considerable variability within and between individuals. The HRR method produced personalised MVPA thresholds for each CYPwCF based on age, which inherently accounted for individual demographic variability and personal factors such as cardiovascular fitness or disease severity. ConclusionsBy accounting for within and between person variability in RHR and PHR, our novel method provides more accurate age-related personalised MVPA thresholds for CYPwCF than existing estimates. Our findings provide population-based estimates for RHR, PHR and MVPA thresholds at different ages in CYPwCF. This approach may help guide development of international standards for objective MVPA measurement in the era of remote HR and activity monitoring and facilitate accurate measurement of habitual physical activity in children and young people.

Development and validation of a prognostic score integrating remote heart failure symptoms and clinical variables for mortality risk prediction after myocardial infarction. The PragueMi score

Abstract Background While heart failure (HF) symptoms are associated with adverse prognosis after myocardial infarction (MI), they are not routinely used for patients’ stratification. Purpose The primary objective of this study was to develop and validate a score to predict mortality risk after MI, combining remotely recorded HF symptoms and clinical risk factors, and to compare it against the guideline-recommended GRACE score. Methods A cohort study design using prospectively collected data from consecutive patients hospitalized for MI at a large tertiary heart centre between June 2017 and September 2022 was used. Results Data from 1,135 patients (aged 64±12 years, 26.7% women), were split into derivation (70%) and validation cohort (30%). Components of the 23-item Kansas City Cardiomyopathy Questionnaire (KCCQ) questionnaire and clinical variables were used as possible predictors. The best model included the following variables – age, heart failure history, admission creatinine and heart rate, ejection fraction at hospital discharge, and HF symptoms 1 month after discharge including walking impairment, leg swelling, and change in HF symptoms. Based on these variables, the PragueMi score was developed. In the validation cohort, the PragueMi score showed superior discrimination to the GRACE score for 6 months (AUC 90.1, 95% CI 81.8-98.4 vs. 77.4, 95% CI 62.2-92.5, p=0.04) and 1-year risk prediction (AUC 89.7, 95% CI 83.5-96.0 vs. 76.2, 95% CI 64.7-87.7, p=0.004). Conclusion The PragueMi score combining heart failure symptoms and clinical variables performs better than the currently recommended GRACE score.

Model-based estimation of heart movements using microwave Doppler radar sensor.

Heart rate is one of the most crucial vital signs and can be measured remotely using microwave Doppler radar. As the distance between the body and the Doppler radar sensor increases, the output signal weakens, making it difficult to extract heartbeat waveforms. In this study, we propose a new template-matching method that addresses this issue by simulating Doppler radar signals. This method extracts the heartbeat waveform with higher accuracy while the participant is naturally sitting in a chair. An extended triangular wave model was created as a mathematical representation of cardiac physiology, taking into account heart movements. The Doppler radar output signal was then simulated based on this model to automatically obtain a template for one cycle. The validity of the proposed method was confirmed by calculating the PPIs using the template and comparing their accuracy to the R-R intervals (RRIs) of the electrocardiogram for five participants and by analyzing the signals of eight participants in their natural state using the mathematical model of heart movements. All measurements were conducted from a distance of 500 mm. The correlation coefficients between the RRIs of the electrocardiogram and the PPIs using the proposed method were examined for five participants. The correlation coefficients were 0.93 without breathing and 0.70 with breathing. This demonstrates a higher correlation considering the long distance of 500 mm, and the fact that body movements were not specifically restricted, suggesting that the proposed method can successfully estimate RRI. The average correlation coefficients, calculated between the Doppler output signals and the templates for each of the eight participants, exceeded 0.95. Overall, the proposed method showed higher correlation coefficients than those reported in previous studies, indicating that our method performed well in extracting heartbeat waveforms. Our results indicate that the proposed method of remote heart monitoring using microwave Doppler radar demonstrates higher accuracy in estimating the RRI of the electrocardiogram while at rest sitting in a chair, and the ability to extract the heartbeat waveforms from the measured Doppler output signal, eliminating the need to create templates in advance as required by conventional template matching methods. This approach offers more flexibility in the measurement environment than conventional methods.

Modification of SMWT for remote heart’s functional capacity determination from oxygen consumption rate

Cardiovascular disease (CVD) is series of diseases affects blood circulation and heart which are fatal to human being. CVD patients recorded that every each of them must have at least one among the following behavioural risks: smoking, diabetes, obesity, hypertension, high cholesterol, and sedentary lifestyle. The diagnosis for CVD is commonly through cardiac stress test. It is a test where electrocardiogram will be used to monitor heart activity under induced stress such as walking on treadmill. However, the facilities for the cardiac stress test are very limited and increasing number of patients with potential CVD caused a long queue for an appointment. This situation makes it hard for healthy people living with behavioral risk wanting for their heart to be checked. Therefore, in this paper, we proposed to determine heart functional capacity which can be done anywhere and sufficient to access heart condition. For this proposal, despite using treadmill-test, Six-Minutes Walking Test (SMWT) is utilized. SMWT induces stress while the heart’s endurance parameters such as heart rate is extracted. This study developed an integrated sensor device which comprises of a sensor to detect step count calculate distance, and a pulse sensor that detect heart rate. Then, oxygen consumption rate during the SMWT, is computed. Metabolic equivalent of task (MET) is equal to the oxygen consumption rate during activity over resting oxygen consumption rate. Heart functional capacity classification is about exercise intensity and the MET of SMWT can be compared to the classification which allows this paper to determine heart functional capacity remotely using SMWT. Based on the results, the heart’s functional capacity classification after performing SMWT using sensor device is Class 1, the same when using manual calculation for distance and heart rate. The device sensor somehow produce error at 20.63% when calculating oxygen uptake. Nevertheless, this paper is identified that the SMWT is able to classify the heart’s functional capacity.

A Comparison Between Two Sensor Devices for Measuring Heart Rate Using IOT

Heart health has become a critical issue in our modern world. The elevated risk of heart disease is an important problem for every person. The Internet of Things is among the most innovative new technologies to appear in recent years. An example of such a development is the heart rate monitor. In this paper, we propose a remote heart rate monitoring system based on Internet of Things technology to facilitate communication between the doctor and the patient. This system is designed using electronic sensors (Pulse Sensor, Easy Pulse Sensor V1.1) that measure the heart rate and then send it to the NodeMcu board, which in turn sends the data to the Firebase database, where an Android application was created and designed using the Android Studio program, which uses the Java programming language to receive data from the database in real time. We tested the system and compared it with both sensors, revealing that the Easy Pulse V1.1 sensor outperforms the pulse sensor in terms of accuracy.

Remote heart rate monitoring with contactless ambient technology using machine learning for aging population

Remote heart rate monitoring with contactless ambient technology using machine learning for aging population

Association of Smartwatch-Based Heart Rate and Physical Activity With Cardiorespiratory Fitness Measures in the Community: Cohort Study.

Resting heart rate (HR) and routine physical activity are associated with cardiorespiratory fitness levels. Commercial smartwatches permit remote HR monitoring and step count recording in real-world settings over long periods of time, but the relationship between smartwatch-measured HR and daily steps to cardiorespiratory fitness remains incompletely characterized in the community. This study aimed to examine the association of nonactive HR and daily steps measured by a smartwatch with a multidimensional fitness assessment via cardiopulmonary exercise testing (CPET) among participants in the electronic Framingham Heart Study. Electronic Framingham Heart Study participants were enrolled in a research examination (2016-2019) and provided with a study smartwatch that collected longitudinal HR and physical activity data for up to 3 years. At the same examination, the participants underwent CPET on a cycle ergometer. Multivariable linear models were used to test the association of CPET indices with nonactive HR and daily steps from the smartwatch. We included 662 participants (mean age 53, SD 9 years; n=391, 59% women, n=599, 91% White; mean nonactive HR 73, SD 6 beats per minute) with a median of 1836 (IQR 889-3559) HR records and a median of 128 (IQR 65-227) watch-wearing days for each individual. In multivariable-adjusted models, lower nonactive HR and higher daily steps were associated with higher peak oxygen uptake (VO2), % predicted peak VO2, and VO2 at the ventilatory anaerobic threshold, with false discovery rate (FDR)-adjusted P values <.001 for all. Reductions of 2.4 beats per minute in nonactive HR, or increases of nearly 1000 daily steps, corresponded to a 1.3 mL/kg/min higher peak VO2. In addition, ventilatory efficiency (VE/VCO2; FDR-adjusted P=.009), % predicted maximum HR (FDR-adjusted P<.001), and systolic blood pressure-to-workload slope (FDR-adjusted P=.01) were associated with nonactive HR but not associated with daily steps. Our findings suggest that smartwatch-based assessments are associated with a broad array of cardiorespiratory fitness responses in the community, including measures of global fitness (peak VO2), ventilatory efficiency, and blood pressure response to exercise. Metrics captured by wearable devices offer a valuable opportunity to use extensive data on health factors and behaviors to provide a window into individual cardiovascular fitness levels.

Channel-Wise Interactive Learning for Remote Heart Rate Estimation From Facial Video

Channel-Wise Interactive Learning for Remote Heart Rate Estimation From Facial Video

TransPPG: two-stream transformer for remote heart rate estimate

TransPPG: two-stream transformer for remote heart rate estimate

Beetaloo or bust: the route to commercial success for an Australian shale play

There is a huge unconventional shale gas resource in the remote heart of the Northern Territory with the potential to trigger big new gas developments in Darwin, including export liquefied natural gas (LNG), and underpin the chronically undersupplied East Coast market. And yet despite this promise, the Beetaloo is not universally viewed as a viable commercial project. Nonetheless, the Beetaloo is one of the hottest topics in Australia’s upstream sector, and two of its key operators, Tamboran and Empire, are closing in on final investment decision (FID) for initial gas production. So why are many in the market still sceptical the play will work? To create a definitive answer, we investigate the key factors required to make the Beetaloo commercially viable. We will evaluate what a Beetaloo gas project could look like across a range of resource sizes, estimated ultimate recovery (EUR) from analogue production profiles, and well costs. The work aims to understand the key cost barriers and price hurdles that need to be broached for economic success. From here we can determine the full range of economic pitfalls and potential rewards that lie in wait for ambitious Beetaloo operators.

Advancement in AI-Based Devices for Monitoring Heart Health in Patients with Cardiac Conditions

In the contemporary landscape of cardiac healthcare, the integration of Artificial Intelligence (AI) into monitoring devices represents a groundbreaking shift towards enhancing patient outcomes and streamlining the management of heart health. This review paper delves into the burgeoning domain of AI-based devices designed for the meticulous monitoring of heart patients, elucidating the methodologies behind their operation, the breadth of their applications, and the palpable impact they have had on both clinical practices and patient experiences. Through a meticulous selection and analysis of current studies, we uncover the transformative capabilities of AI technologies in detecting and predicting heart-related anomalies, offering a personalized approach to cardiac care. The findings underscore the paramount importance of AI in facilitating early intervention, reducing the incidence of heart-related morbacies, and ultimately, charting a course towards a more responsive and adaptive healthcare ecosystem. The significance of this review lies not only in its comprehensive synthesis of existing research but also in its exploration of future potentials, highlighting the ongoing evolution of AI as a pivotal ally in the quest for improved heart health monitoring. This paper aims to contribute to the academic and medical discourse surrounding AI applications in healthcare, offering insights that may guide future innovations and research directions. Keywords--- AI-driven Cardiac Monitoring, Wearable Health Technology, Remote Heart Health Monitoring, Smart Health Devices, Cardiovascular Disease Management, Machine Learning in, Cardiology, Digital Health Solutions, Real-time Heart Health Analytics, Sensor Technology in Cardiac Care, Patient-Centric Heart Monitoring, AI-based Cardiac Diagnostics, Continuous Heart Rate Monitoring, Mobile Health Applications, Personalized Cardiac Care, Telemedicine in Cardiology.

Emotion Classification Based on Pulsatile Images Extracted from Short Facial Videos via Deep Learning.

Most human emotion recognition methods largely depend on classifying stereotypical facial expressions that represent emotions. However, such facial expressions do not necessarily correspond to actual emotional states and may correspond to communicative intentions. In other cases, emotions are hidden, cannot be expressed, or may have lower arousal manifested by less pronounced facial expressions, as may occur during passive video viewing. This study improves an emotion classification approach developed in a previous study, which classifies emotions remotely without relying on stereotypical facial expressions or contact-based methods, using short facial video data. In this approach, we desire to remotely sense transdermal cardiovascular spatiotemporal facial patterns associated with different emotional states and analyze this data via machine learning. In this paper, we propose several improvements, which include a better remote heart rate estimation via a preliminary skin segmentation, improvement of the heartbeat peaks and troughs detection process, and obtaining a better emotion classification accuracy by employing an appropriate deep learning classifier using an RGB camera input only with data. We used the dataset obtained in the previous study, which contains facial videos of 110 participants who passively viewed 150 short videos that elicited the following five emotion types: amusement, disgust, fear, sexual arousal, and no emotion, while three cameras with different wavelength sensitivities (visible spectrum, near-infrared, and longwave infrared) recorded them simultaneously. From the short facial videos, we extracted unique high-resolution spatiotemporal, physiologically affected features and examined them as input features with different deep-learning approaches. An EfficientNet-B0 model type was able to classify participants' emotional states with an overall average accuracy of 47.36% using a single input spatiotemporal feature map obtained from a regular RGB camera.

Optimizing remote heart failure management: First experiences with the HeartInsight score for implanted defibrillators

Optimizing remote heart failure management: First experiences with the HeartInsight score for implanted defibrillators

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.

Development and validation of a prognostic score integrating remote heart failure symptoms and clinical variables in mortality risk prediction after myocardial infarction: the PragueMi score.

While heart failure (HF) symptoms are associated with adverse prognosis after myocardial infarction (MI), they are not routinely used for patients' stratification. The primary objective of this study was to develop and validate a score to predict mortality risk after MI, combining remotely recorded HF symptoms and clinical risk factors, and to compare it against the guideline-recommended Global Registry of Acute Coronary Events (GRACE) score. A cohort study design using prospectively collected data from consecutive patients hospitalized for MI at a large tertiary heart centre between June 2017 and September 2022 was used. Data from 1135 patients (aged 64 ± 12 years, 26.7% women), were split into derivation (70%) and validation cohort (30%). Components of the 23-item Kansas City Cardiomyopathy Questionnaire and clinical variables were used as possible predictors. The best model included the following variables: age, HF history, admission creatinine and heart rate, ejection fraction at hospital discharge, and HF symptoms 1 month after discharge including walking impairment, leg swelling, and change in HF symptoms. Based on these variables, the PragueMi score was developed. In the validation cohort, the PragueMi score showed superior discrimination to the GRACE score for 6 months [the area under the receiver operating curve (AUC) 90.1, 95% confidence interval (CI) 81.8-98.4 vs. 77.4, 95% CI 62.2-92.5, P = 0.04) and 1-year risk prediction (AUC 89.7, 95% CI 83.5-96.0 vs. 76.2, 95% CI 64.7-87.7, P = 0.004). The PragueMi score combining HF symptoms and clinical variables performs better than the currently recommended GRACE score.

Remote Heart Rate Monitoring in Smart Environments From Videos With Self-Supervised Pretraining

Remote Heart Rate Monitoring in Smart Environments From Videos With Self-Supervised Pretraining

A self-supervised learning network for remote heart rate measurement

The rPPG technology is a facial video-based non-contact physiological parameter detection technology, which has been widely used in the measurement of heart rate, respiration, blood oxygen and other physiological parameters. Most of the current research hotspots in this field are based on deep learning methods, which typically necessitate a significant amount of data samples for training the neural network to obtain accurate measurement results. However, the high cost required to label the data samples limits the diffusion and application of the technique. To address the above problems, we propose a new self-supervised aimed at acquiring the ability to estimate rPPG signals from facial videos, eliminating the need for labeled data. The framework expands the unlabeled video samples into multiple positive/negative samples and uses contrast learning to obtain an rPPG signal estimation network, which outputs the corresponding rPPG signals of face videos. To promote the convergence of network training, a new frequency loss function is designed. This function can effectively shorten the distance between the frequencies of similar sample signals and push the distance between the frequencies of different sample signals far away, so as to enhance the frequency consistency between sample signals and make the model easier to learn the differences between different sample pairs. Our method is evaluated on four standard rPPG datasets, the experimental results show that the accuracy is close to the current best supervised method, and is superior to the previous self-supervised method without using any labeled data.