Effectiveness of a Smartphone App-Based Intervention With Bluetooth-Connected Monitoring Devices and a Feedback System in Heart Failure (SMART-HF Trial): Randomized Controlled Trial.

BACKGROUND: Current heart failure (HF) guidelines recommend a multidisciplinary approach, discharge education, and self-management for HF. However, the recommendations are challenging to implement in real-world clinical settings. OBJECTIVE: We developed a mobile health (mHealth) platform for HF self-care to evaluate whether a smartphone app-based intervention with Bluetooth-connected monitoring devices and a feedback system can help improve HF symptoms. METHODS: In this prospective, randomized, multicenter study, we enrolled patients 20 years of age and older, hospitalized for acute HF, and who could use a smartphone from 7 tertiary hospitals in South Korea. In the intervention group (n=39), the apps were automatically paired with Bluetooth-connected monitoring devices. The patients could enter information on vital signs, HF symptoms, diet, medications, and exercise regimen into the app daily and receive feedback or alerts on their input. In the control group (n=38), patients could only enter their blood pressure, heart rate, and weight using conventional, non-Bluetooth devices and could not receive any feedback or alerts from the app. The primary end point was the change in dyspnea symptom scores from baseline to 4 weeks, assessed using a questionnaire. RESULTS: At 4 weeks, the change in dyspnea symptom score from baseline was significantly greater in the intervention group than in the control group (mean -1.3, SD 2.1 vs mean -0.3, SD 2.3; P=.048). A significant reduction was found in body water composition from baseline to the final measurement in the intervention group (baseline level mean 7.4, SD 2.5 vs final level mean 6.6, SD 2.5; P=.003). App adherence, which was assessed based on log-in or the percentage of days when symptoms were first observed, was higher in the intervention group than in the control group. Composite end points, including death, rehospitalization, and urgent HF visits, were not significantly different between the 2 groups. CONCLUSIONS: The mobile-based health platform with Bluetooth-connected monitoring devices and a feedback system demonstrated improvement in dyspnea symptoms in patients with HF. This study provides evidence and rationale for implementing mobile app-based self-care strategies and feedback for patients with HF. TRIAL REGISTRATION: ClinicalTrials.gov NCT05668000; https://clinicaltrials.gov/study/NCT05668000.

A Review of Patient Bed Sensors for Monitoring of Vital Signs.

The analysis of biomedical signals is a very challenging task. This review paper is focused on the presentation of various methods where biomedical data, in particular vital signs, could be monitored using sensors mounted to beds. The presented methods to monitor vital signs include those combined with optical fibers, camera systems, pressure sensors, or other sensors, which may provide more efficient patient bed monitoring results. This work also covers the aspects of interference occurrence in the above-mentioned signals and sleep quality monitoring, which play a very important role in the analysis of biomedical signals and the choice of appropriate signal-processing methods. The provided information will help various researchers to understand the importance of vital sign monitoring and will be a thorough and up-to-date summary of these methods. It will also be a foundation for further enhancement of these methods.

Human Activity Recognition in a Free-Living Environment Using an Ear-Worn Motion Sensor.

Human activity recognition (HAR) technology enables continuous behavior monitoring, which is particularly valuable in healthcare. This study investigates the viability of using an ear-worn motion sensor for classifying daily activities, including lying, sitting/standing, walking, ascending stairs, descending stairs, and running. Fifty healthy participants (between 20 and 47 years old) engaged in these activities while under monitoring. Various machine learning algorithms, ranging from interpretable shallow models to state-of-the-art deep learning approaches designed for HAR (i.e., DeepConvLSTM and ConvTransformer), were employed for classification. The results demonstrate the ear sensor's efficacy, with deep learning models achieving a 98% accuracy rate of classification. The obtained classification models are agnostic regarding which ear the sensor is worn and robust against moderate variations in sensor orientation (e.g., due to differences in auricle anatomy), meaning no initial calibration of the sensor orientation is required. The study underscores the ear's efficacy as a suitable site for monitoring human daily activity and suggests its potential for combining HAR with in-ear vital sign monitoring. This approach offers a practical method for comprehensive health monitoring by integrating sensors in a single anatomical location. This integration facilitates individualized health assessments, with potential applications in tele-monitoring, personalized health insights, and optimizing athletic training regimes.

Video plethysmography for contactless blood pressure and heart rate measurement in perioperative care.

The purpose of this study was to evaluate the feasibility and accuracy of remote Video Plethysmography (VPPG) for contactless measurements of blood pressure (BP) and heart rate (HR) in adult surgical patients in a hospital setting. An iPad Pro was used to record a 1.5-minute facial video of the participant's face and VPPG was used to extract vital signs measurements. A standard medical device (Welch Allyn) was used for comparison to measure BP and HR. Trial registration: NCT05165381. Two-hundred-sixteen participants consented and completed the contactless BP and HR monitoring (mean age 54.1 ± 16.8 years, 58% male). The consent rate was 75% and VPPG was 99% successful in capturing BP and HR. VPPG predicted SBP, DBP, and HR with a measurement bias ± SD, -8.18 ± 16.44 mmHg, - 6.65 ± 9.59 mmHg, 0.09 ± 6.47 beats/min respectively. Pearson's correlation for all measurements between VPPG and standard medical device was significant. Correlation for SBP was moderate (0.48), DBP was weak (0.29), and HR was strong (0.85). Most patients were satisfied with the non-contact technology with an average rating of 8.7/10 and would recommend it for clinical use. VPPG was highly accurate in measuring HR, and is currently not accurate in measuring BP in surgical patients. The VPPG BP algorithm showed limitations in capturing individual variations in blood pressure, highlighting the need for further improvements to render it clinically effective across all ranges. Contactless vital signs monitoring was well-received and earned a high satisfaction score.

Video plethysmography for contactless measurement of respiratory rate in surgical patients.

The accurate recording of respiratory rate (RR) without contact is important for patient care. The current methods for RR measurement such as capnography, pneumography, and plethysmography require patient contact, are cumbersome, or not accurate for widespread clinical use. Video Plethysmography (VPPG) is a novel automated technology that measures RR using a facial video without contact. The objective of our study was to determine whether VPPG can feasibly and accurately measure RR without contact in surgical patients at a clinical setting. After research ethics approval, 216 patients undergoing ambulatory surgery consented to the study. Patients had a 1.5 min video of their faces taken via an iPad preoperatively, which was analyzed using VPPG to obtain RR information. The RR prediction by VPPG was compared to 60-s manual counting of breathing by research assistants. We found that VPPG predicted RR with 88.8% accuracy and a bias of 1.40 ± 1.96 breaths per minute. A significant and high correlation (0.87) was observed between VPPG-predicted and manually recorded RR. These results did not change with the ethnicity of patients. The success rate of the VPPG technology was 99.1%. Contactless RR monitoring of surgical patients at a hospital setting using VPPG is accurate and feasible, making this technology an attractive alternative to the current approaches to RR monitoring. Future developments should focus on improving reliability of the technology.

Prognostic value of heart rate variability for risk of serious adverse events in continuously monitored hospital patients.

Technological advances allow continuous vital sign monitoring at the general ward, but traditional vital signs alone may not predict serious adverse events (SAE). This study investigated continuous heart rate variability (HRV) monitoring's predictive value for SAEs in acute medical and major surgical patients. Data was collected from four prospective observational studies and two randomized controlled trials using a single-lead ECG. The primary outcome was any SAE, secondary outcomes included all-cause mortality and specific non-fatal SAE groups, all within 30 days. Subgroup analyses of medical and surgical patients were performed. The primary analysis compared the last 24 h preceding an SAE with the last 24 h of measurements in patients without an SAE. The area under a receiver operating characteristics curve (AUROC) quantified predictive performance, interpretated as low prognostic ability (0.5-0.7), moderate prognostic ability (0.7-0.9), or high prognostic ability (> 0.9). Of 1402 assessed patients, 923 were analysed, with 297 (32%) experiencing at least one SAE. The best performing threshold had an AUROC of 0.67 (95% confidence interval (CI) 0.63-0.71) for predicting cardiovascular SAEs. In the surgical subgroup, the best performing threshold had an AUROC of 0.70 (95% CI 0.60-0.81) for neurologic SAE prediction. In the medical subgroup, thresholds for all-cause mortality, cardiovascular, infectious, and neurologic SAEs had moderate prognostic ability, and the best performing threshold had an AUROC of 0.85 (95% CI 0.76-0.95) for predicting neurologic SAEs. Predicting SAEs based on the accumulated time below thresholds for individual continuously measured HRV parameters demonstrated overall low prognostic ability in high-risk hospitalized patients. Certain HRV thresholds had moderate prognostic ability for prediction of specific SAEs in the medical subgroup.

Indicators of patient deterioration in poorly resourced private hospitals: Which vital sign to watch? A retrospective case-control study.

BACKGROUND: Patient vital signs are a measure of wellness if monitored regularly and accurately. Staff shortages in poorly resourced regional hospitals often result in inadequate patient monitoring, putting patients at risk of undetected deterioration. OBJECTIVE: This study aims to explore the pattern and completeness of vital sign monitoring and the contribution of each vital sign in predicting clinical deterioration events in resource-poor regional/rural hospitals. METHOD: Using a retrospective case-control study design, we compared 24 h of vital sign data from deteriorating and nondeteriorating patients from two poorly-resourced regional hospitals. Descriptive statistics, t-tests, and analysis of variance are used to compare patient-monitoring frequency and completeness. The contribution of each vital sign in predicting patient deterioration was determined using the Area Under the Receiver Operator Characteristic curve and binary logistical regression analysis. RESULTS: Deteriorating patients were monitored more frequently (9.58 [7.02] times) in the 24-h period than nondeteriorating patients (4.93 [2.66] times). However, the completeness of vital sign documentation was higher in nondeteriorating (85.2%) than in deteriorating patients (57.7%). Body temperature was the most frequently omitted vital sign. Patient deterioration was positively linked to the frequency of abnormal vital signs and the number of abnormal vital signs per set (Area Under the Receiver Operator Characteristic curve: 0.872 and 0.867, respectively). No single vital sign strongly predicts patient outcomes. However, a supplementary oxygen value of >3 L/min and a heart rate of >139 beats/min were the best predictors of patient deterioration. CONCLUSION: Given the poor resourcing and often geographical remoteness of small regional hospitals, it is prudent that the nursing staff are made aware of the vital signs that best indicate deterioration for the cohort of patients in their care. Tachycardic patients on supplementary oxygen are at high risk of deterioration.

Advanced wireless monitoring for children in the cardiac perioperative setting.

INTRODUCTION: More than 40,000 children undergo surgical interventions annually for the treatment of congenital heart defects. Intraoperative and postoperative vital sign monitoring is a cornerstone of pediatric care. METHODS: A single-arm prospective observational study was performed. Pediatric patients undergoing a procedure with a planned admission to the Cardiac Intensive Care Unit at Lurie Children's Hospital (Chicago, IL) were eligible for enrollment. Participant vital signs were monitored using standard equipment and an FDA-cleared experimental device (ANNE® ) consisting of a wireless patch positioned at the suprasternal notch and index finger or foot. The primary goal of the study was to assess real-world feasibility of wireless sensors in pediatric patients with congenital cardiac defects. RESULTS: A total of 13 patients were enrolled, ranging in age from 4 months to 16 years with a median age of 4 years. Overall, 54% (n = 7) were female and the most common anomaly in the cohort was an atrial septal defect (n = 6). The mean admission length was 3 days (range 2-6), resulting in more than 1000 h of vital sign monitoring (⟩60,000 data points). Bland-Altman plots were generated for heart rate and respiratory rate to assess beat-to-beast differences between the standard equipment and the experimental sensors. CONCLUSIONS: Novel, wireless, flexible sensors demonstrated comparable performance to standard monitoring equipment in a cohort of pediatric patients with congenital cardiac heart defects undergoing surgery.

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

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

SSA-VMD for UWB Radar Sensor Vital Sign Extraction.

The combination of advanced radar sensor technology and smart grid has broad prospects. It is meaningful to monitor the respiration and heartbeat of grid employees under resting state through radar sensors to ensure that they are in a healthy working state. Ultra-wideband (UWB) radar sensor is suitable for this application because of its strong penetration ability, high range resolution and low average power consumption. However, due to weak heartbeat amplitude and measurement noise, the accurate measurement of the target heart rate is a challenge. In this paper, singular spectrum analysis (SSA) is proposed to reconstruct the eigenvalues of noisy vital signs to eliminate noise peaks around the heartbeat rate; combined with the variational modal decomposition (VMD), the target vital signs can be extracted with high accuracy. The experiment confirmed that the target vital sign information can be extracted with high accuracy from ten subjects at different distances, which can play an important role in short distance human detection and vital sign monitoring.

Convolutional Neural Networks for the Real-Time Monitoring of Vital Signs Based on Impulse Radio Ultrawide-Band Radar during Sleep.

Vital signs provide important biometric information for managing health and disease, and it is important to monitor them for a long time in a daily home environment. To this end, we developed and evaluated a deep learning framework that estimates the respiration rate (RR) and heart rate (HR) in real time from long-term data measured during sleep using a contactless impulse radio ultrawide-band (IR-UWB) radar. The clutter is removed from the measured radar signal, and the position of the subject is detected using the standard deviation of each radar signal channel. The 1D signal of the selected UWB channel index and the 2D signal applied with the continuous wavelet transform are entered as inputs into the convolutional neural-network-based model that then estimates RR and HR. From 30 recordings measured during night-time sleep, 10 were used for training, 5 for validation, and 15 for testing. The average mean absolute errors for RR and HR were 2.67 and 4.78, respectively. The performance of the proposed model was confirmed for long-term data, including static and dynamic conditions, and it is expected to be used for health management through vital-sign monitoring in the home environment.

Multi-Layer Beam Scanning Leaky Wave Antenna for Remote Vital Signs Detection at 60 GHz.

A multi-layer beam-scanning leaky wave antenna (LWA) for remote vital sign monitoring (RVSM) at 60 GHz using a single-tone continuous-wave (CW) Doppler radar has been developed in a typical dynamic environment. The antenna's components are: a partially reflecting surface (PRS), high-impedance surfaces (HISs), and a plain dielectric slab. A dipole antenna works as a source together with these elements to produce a gain of 24 dBi, a frequency beam scanning range of 30°, and precise remote vital sign monitoring (RVSM) up to 4 m across the operating frequency range (58-66 GHz). The antenna requirements for the DR are summarised in a typical dynamic scenario where a patient is to have continuous monitoring remotely, while sleeping. During the continuous health monitoring process, the patient has the freedom to move up to one meter away from the fixed sensor position.The proposed multi-layer LWA system was placed at a distance of 2 m and 4 m from the test subject to confirm the suitability of the developed antenna for dynamic RVSM applications. A proper setting of the operating frequency range (58 to 66 GHz) enabled the detection of both heart beats and respiration rates of the subject within a 30° angular range.

Understanding the effects of overnight vital signs monitoring on sleep duration and disruptions in hospitalized children: A scoping review.

PROBLEM: Overnight vital signs are typically taken every four hours on pediatric acute care units, despite limited evidence supporting the efficacy of this practice. Vital signs are often ordered and collected without considering the patient's clinical status or potential impact that they may have on sleep. We sought to understand the impact that overnight vital sign monitoring has on sleep duration and disruptions among hospitalized children in an acute care setting. ELIGIBILITY CRITERIA: We conducted a scoping review using the Preferred Reporting Items for Systematic Reviews and Meta-analysis Protocols extension for scoping reviews (PRISMA-ScR). Studies were included if they addressed the relationship between vital signs monitoring and sleep among children hospitalized in an acute care unit. SAMPLE: Eleven studies from 2012 to 2022 were included in the final review. RESULTS: Vital signs monitoring is the most common sleep disruptor among hospitalized children in acute care units and early evidence suggests that minimizing overnight vital signs may be a safe intervention for clinically stable children. Methods for measuring sleep duration and disruptions are heterogenous and validated tools are not often used. Finally, nurses report comfort with forgoing overnight vital signs when their patient's clinical status is stable. CONCLUSION: Despite a lack of evidence regarding the efficacy of every 4 h vital signs, overnight vital signs monitoring is consistently the greatest disruptor to sleep for hospitalized children. IMPLICATIONS: Nurses should play a central role in guiding vital signs monitoring that maintains safety and improves sleep in hospitalized children.

The Role of Unobtrusive Home-Based Continuous Sensing in the Management of Postacute Sequelae of SARS CoV-2.

Amid the COVID-19 pandemic, it has been reported that greater than 35% of patients with confirmed or suspected COVID-19 develop postacute sequelae of SARS CoV-2 (PASC). PASC is still a disease for which preliminary medical data are being collected-mostly measurements collected during hospital or clinical visits-and pathophysiological understanding is yet in its infancy. The disease is notable for its prevalence and its variable symptom presentation, and as such, management plans could be more holistically made if health care providers had access to unobtrusive home-based wearable and contactless continuous physiologic and physical sensor data. Such between-hospital or between-clinic data can quantitatively elucidate a majority of the temporal evolution of PASC symptoms. Although not universally of comparable accuracy to gold standard medical devices, home-deployed sensors offer great insights into the development and progression of PASC. Suitable sensors include those providing vital signs and activity measurements that correlate directly or by proxy to documented PASC symptoms. Such continuous, home-based data can give care providers contextualized information from which symptom exacerbation or relieving factors may be classified. Such data can also improve the collective academic understanding of PASC by providing temporally and activity-associated symptom cataloging. In this viewpoint, we make a case for the utilization of home-based continuous sensing that can serve as a foundation from which medical professionals and engineers may develop and pursue long-term mitigation strategies for PASC.

Advantage of Vital Sign Monitoring Using a Wireless Wearable Device for Predicting Septic Shock in Febrile Patients in the Emergency Department: A Machine Learning-Based Analysis.

Intermittent manual measurement of vital signs may not rapidly predict sepsis development in febrile patients admitted to the emergency department (ED). We aimed to evaluate the predictive performance of a wireless monitoring device that continuously measures heart rate (HR) and respiratory rate (RR) and a machine learning analysis in febrile but stable patients in the ED. We analysed 468 patients (age, ≥18 years; training set, n = 277; validation set, n = 93; test set, n = 98) having fever (temperature >38 °C) and admitted to the isolation care unit of the ED. The AUROC of the fragmented model with device data was 0.858 (95% confidence interval [CI], 0.809−0.908), and that with manual data was 0.841 (95% CI, 0.789−0.893). The AUROC of the accumulated model with device data was 0.861 (95% CI, 0.811−0.910), and that with manual data was 0.853 (95% CI, 0.803−0.903). Fragmented and accumulated models with device data detected clinical deterioration in febrile patients at risk of septic shock 9 h and 5 h 30 min earlier, respectively, than those with manual data. Continuous vital sign monitoring using a wearable device could accurately predict clinical deterioration and reduce the time to recognise potential clinical deterioration in stable ED patients with fever.

Development of a New Integrated System for Vital Sign Monitoring in Small Animals.

Monitoring the vital signs of mice is an essential practice during imaging procedures to avoid populational losses and improve image quality. For this purpose, a system based on a set of devices (piezoelectric sensor, optical module and thermistor) able to detect the heart rate, respiratory rate, body temperature and arterial blood oxygen saturation (SpO2) in mice anesthetized with sevoflurane was implemented. Results were validated by comparison with the reported literature on similar anesthetics. A new non-invasive electrocardiogram (ECG) module was developed, and its first results reflect the viability of its integration in the system. The sensors were strategically positioned on mice, and the signals were acquired through a custom-made printed circuit board during imaging procedures with a micro-PET (Positron Emission Tomography). For sevoflurane concentration of 1.5%, the average values obtained were: 388 bpm (beats/minute), 124 rpm (respirations/minute) and 88.9% for the heart rate, respiratory rate and SpO2, respectively. From the ECG information, the value obtained for the heart rate was around 352 bpm for injectable anesthesia. The results compare favorably to the ones established in the literature, proving the reliability of the proposed system. The ECG measurements show its potential for mice heart monitoring during imaging acquisitions and thus for integration into the developed system.

Soft Transducer for Patient's Vitals Telemonitoring with Deep Learning-Based Personalized Anomaly Detection.

This work addresses the design, development and implementation of a 4.0-based wearable soft transducer for patient-centered vitals telemonitoring. In particular, first, the soft transducer measures hypertension-related vitals (heart rate, oxygen saturation and systolic/diastolic pressure) and sends the data to a remote database (which can be easily consulted both by the patient and the physician). In addition to this, a dedicated deep learning algorithm, based on a Long-Short-Term-Memory Autoencoder, was designed, implemented and tested for providing an alert when the patient's vitals exceed certain thresholds, which are automatically personalized for the specific patient. Furthermore, a mobile application (EcO2u) was developed to manage the entire data flow and facilitate the data fruition; this application also implements an innovative face-detection algorithm that ensures the identity of the patient. The robustness of the proposed soft transducer was validated experimentally on five individuals, who used the system for 30 days. The experimental results demonstrated an accuracy in anomaly detection greater than 93%, with a true positive rate of more than 94%.

Nurses' experiences with continuous vital sign monitoring on the general surgical ward: a qualitative study based on the Behaviour Change Wheel.

BACKGROUND: To support early recognition of clinical deterioration on a general ward continuous vital signs monitoring (CMVS) systems using wearable devices are increasingly being investigated. Although nurses play a crucial role in successful implementation, reported nurse adoption and acceptance scores vary significantly. In-depth insight into the perspectives of nurses regarding CMVS is lacking. To this end, we applied a theoretical approach for behaviour change derived from the Behaviour Change Wheel (BCW). AIM: To provide insight in the capability, opportunity and motivation of nurses working with CMVS, in order to inform future implementation efforts. METHODS: A qualitative study was conducted, including twelve nurses of a surgical ward in a tertiary teaching hospital with previous experience of working with CMVS. Semi-structured interviews were audiotaped, transcribed verbatim, and analysed using thematic analysis. The results were mapped onto the Capability, Opportunity, Motivation - Behaviour (COM-B) model of the BCW. RESULTS: Five key themes emerged. The theme 'Learning and coaching on the job' linked to Capability. Nurses favoured learning about CVSM by dealing with it in daily practice. Receiving bedside guidance and coaching was perceived as important. The theme 'interpretation of vital sign trends' also linked to Capability. Nurses mentioned the novelty of monitoring vital sign trends of patients on wards. The theme 'Management of alarms' linked to Opportunity. Nurses perceived the (false) alarms generated by the system as excessive resulting in feelings of irritation and uncertainty. The theme 'Integration and compatibility with clinical workflow' linked to Opportunity. CVSM was experienced as helpful and easy to use, although integration in mobile devices and the EMR was highly favoured and the management of clinical workflows would need improvement. The theme 'Added value for nursing care' linked to Motivation. All nurses recognized the potential added value of CVSM for postoperative care. CONCLUSION: Our findings suggest all parts of the COM-B model should be considered when implementing CVSM on general wards. When the themes in Capability and Opportunity are not properly addressed by selecting interventions and policy categories, this may negatively influence the Motivation and may compromise successful implementation.

Day-to-day progression of vital-sign circadian rhythms in the intensive care unit.

BACKGROUND: Disrupted vital-sign circadian rhythms in the intensive care unit (ICU) are associated with complications such as immune system disruption, delirium and increased patient mortality. However, the prevalence and extent of this disruption is not well understood. Tools for its detection are currently limited. METHODS: This paper evaluated and compared vital-sign circadian rhythms in systolic blood pressure, heart rate, respiratory rate and temperature. Comparisons were made between the cohort of patients who recovered from the ICU and those who did not, across three large, publicly available clinical databases. This comparison included a qualitative assessment of rhythm profiles, as well as quantitative metrics such as peak-nadir excursions and correlation to a demographically matched 'recovered' profile. RESULTS: Circadian rhythms were present at the cohort level in all vital signs throughout an ICU stay. Peak-nadir excursions and correlation to a 'recovered' profile were typically greater throughout an ICU stay in the cohort of patients who recovered, compared to the cohort of patients who did not. CONCLUSIONS: These results suggest that vital-sign circadian rhythms are typically present at the cohort level throughout an ICU stay and that quantitative assessment of these rhythms may provide information of prognostic use in the ICU.

Radar Recorded Child Vital Sign Public Dataset and Deep Learning-Based Age Group Classification Framework for Vehicular Application.

The ongoing intense development of short-range radar systems and their improved capability of measuring small movements make these systems reliable solutions for the extraction of human vital signs in a contactless fashion. The continuous contactless monitoring of vital signs can be considered in a wide range of applications, such as remote healthcare solutions and context-aware smart sensor development. Currently, the provision of radar-recorded datasets of human vital signs is still an open issue. In this paper, we present a new frequency-modulated continuous wave (FMCW) radar-recorded vital sign dataset for 50 children aged less than 13 years. A clinically approved vital sign monitoring sensor was also deployed as a reference, and data from both sensors were time-synchronized. With the presented dataset, a new child age-group classification system based on GoogLeNet is proposed to develop a child safety sensor for smart vehicles. The radar-recorded vital signs of children are divided into several age groups, and the GoogLeNet framework is trained to predict the age of unknown human test subjects.