MousePZT: A simple, reliable, low-cost device for vital sign monitoring and respiratory gating in mice under anesthesia.

Small animal studies in biomedical research often require anesthesia to reduce pain or stress experienced by research animals and to minimize motion artifact during imaging or other measurements. Anesthetized animals must be closely monitored for the safety of the animals and to prevent unintended effects of altered physiology on experimental outcomes. Many currently available monitoring devices are expensive, invasive, or interfere with experimental design. Here, we present MousePZT, a low-cost device based on a simple piezoelectric sensor, with a custom circuit and computer software that allows for measurements of both respiratory rate and heart rate in a non-invasive, minimal contact manner. We find the accuracy of the MousePZT device in measuring respiratory and heart rate matches those of commercial systems. Using the widely-used gas isoflurane and injectable ketamine/xylazine combination, we also demonstrate that changes in respiratory rate are more easily detected and can precede changes in heart rate associated with variations in anesthetic depth. Additional circuitry on the device outputs a respiration-locked trigger signal for respiratory-gating of imaging or other data acquisition and has high sensitivity and specificity for detecting respiratory cycles. We provide detailed instruction documents and all necessary microcontroller and computer software, enabling straightforward construction and utilization of this device.

Contactless face video based vital signs detection framework for continuous health monitoring using feature optimization and hybrid neural network.

Continuous monitoring of vital signs such as respiration and heart rate is essential to detect and predict conditions that may affect the patient's well-being. To detect these vital signs most medical systems use contact sensors. They are not feasible for long term monitoring and are not repeatable. Vital signs using facial video-noncontact monitoring are becoming increasingly important. Researchers in the last few years although considerable progress has been made, challenging datasets absence timing of assessment process and the technology still has some limitations such as time consuming nature and lack of computer portability. To solve those problems, we propose a contactless video based vital signs detection framework for continuous health monitoring using feature optimization and hybrid neural network. In the proposed technique, modified war strategy optimization algorithm is proposed to segment the face portion from the input video frames. Then, we utilize the known data acquisition models to extract vital signs from the segmented face portions are heart rate, blood pressure, respiratory rate and oxygen saturation. An improved neural network structure (Lifting Net) is further used to achieve the adaptive extraction of deep hidden features for specific signs, for realizing the high precision of human health monitoring. The Hughes effect or dimensionality issue affects detection accuracy in sign classification when there are fewer training instances relative to the number of spectral features. The problem can be overcome through feature optimization here Northern goshawk optimization algorithm is used to select optimal best features which reduces the data dimensionality issue. Furthermore, hybrid deep ensemble reinforcement learning classifier is proposed for the human vital sign detection and classification which ensures the early detection of patient abnormality. Finally, we validate our framework using benchmark video datasets such as TokyoTechrPPG, PURE and COHFACE. To proves the effectiveness of proposed technique using simulation results and comparative analysis.

Workload associated with manual assessment of vital signs as compared with continuous wireless monitoring.

BACKGROUND: Vital sign monitoring is considered an essential aspect of clinical care in hospitals. In general wards, this relies on intermittent manual assessments performed by clinical staff at intervals of up to 12 h. In recent years, continuous monitoring of vital signs has been introduced to the clinic, with improved patient outcomes being one of several potential benefits. The aim of this study was to determine the workload difference between continuous monitoring and manual monitoring of vital signs as part of the National Early Warning Score (NEWS). METHODS: Three wireless sensors continuously monitored blood pressure, heart rate, respiratory rate, and peripheral oxygen saturation in 20 patients admitted to the general hospital ward. The duration needed for equipment set-up and maintenance for continuous monitoring in a 24-h period was recorded and compared with the time spent on manual assessments and documentation of vital signs performed by clinical staff according to the NEWS. RESULTS: The time used for continuous monitoring was 6.0 (IQR 3.2; 7.2) min per patient per day vs. 14 (9.7; 32) min per patient per day for the NEWS. Median difference in duration for monitoring of vital signs was 9.9 (95% CI 5.6; 21) min per patient per day between NEWS and continuous monitoring (p < .001). Time used for continuous monitoring in isolated patients was 6.6 (4.6; 12) min per patient per day as compared with 22 (9.7; 94) min per patient per day for NEWS. CONCLUSION: The use of continuous monitoring was associated with a significant reduction in workload in terms of time for monitoring as compared with manual assessment of vital signs.

An Automatic Remote Health Risk Assessment system based on LSTM for elderly.

To address the challenges posed by the aging process, we designed and validated an LSTM-based automatic remote health risk assessment system for the elderly. This system consists of a wireless physiological parameter sensing unit, a vital sign prediction unit and a pre-defined risk scoring criteria unit. The vital sign prediction module is composed of five 5-input-1-output neural networks based on the LSTM architecture, which are responsible for predicting the vital signs collected by wireless sensors, including: systolic blood pressure (SBP), pulse rate (PR), respiratory rate (RR), temperature (TEMP), and oxygen saturation (SPO2). The pre-defined health risk scoring criteria is a simplified version of the National Early Warning Score (NEWS), which is responsible for calculating the risk level based on the predicted values. This allows the care team to respond to the medical needs of the elderly in a timely manner. Through experiments, our system can achieve a risk identification accuracy of 74% and MAEs of the predicted values for each parameter are in an acceptable range. Our results suggest that an automated remote health risk assessment system for the elderly using deep learning could be a viable new strategy for home-based monitoring systems.

A Wireless Wearable Patch for Remote Monitoring of Respiratory and Cardiac Rhythm in Children.

Remote patient monitoring (RPM) is an innovative strategy to promote health and improve patient management and care. Recent advances in healthcare technologies have seen the emergence of wearable sensors allowing longitudinal physiological measurements in any environment. This paper introduces a wireless wearable patch 'Leo' for continuous remote monitoring of physiological data at home and healthcare settings. This includes single lead ECG, chest impedance, heart rate (HR), respiration rate (RR) and body posture. To test Leo's ability to capture longitudinal physiological data at home, 15 children experiencing acute severe asthma exacerbations were recruited during their emergency department (ED) visits. Participants wore the Leo device for 7 (+/-2) days post-hospital discharge. Nocturnal RR and HR and variability were higher during the first half of the night on Day1 compared to Day7 (p<0.005). Participants also completed a usability questionnaire and reported the patch wear to be comfortable (average score of 3.3 out of 5) and easy to wear during the night (average score of 3.5 out of 5) with 5/15 (33%) reported very slight barely perceptible skin irritation/redness and 2 (13%) reported well defined skin irritation and redness.Clinical Relevance- These results highlight the potential use of the Leo device in clinical practice for continuous un-obstructive monitoring of diseased populations, such as asthma.

Assessment of Driver's Stress using Multimodal Biosignals and Regularized Deep Kernel Learning.

In this work, we classify the stress state of car drivers using multimodal physiological signals and regularized deep kernel learning. Using a driving simulator in a controlled environment, we acquire electrocardiography (ECG), electrodermal activity (EDA), photoplethysmography (PPG), and respiration rate (RESP) from N = 10 healthy drivers in experiments of 25min duration with different stress states (5min resting, 10min driving, 10min driving + answering cognitive questions). We manually remove unusable segments and approximately 4h of data remain. Multimodal time and frequency features are extracted and employed to regularized deep kernel machine learning based on a fusion framework. Task-specific representations of different physiological signals are combined using intermediate fusion. Subsequently, the fused multimodal features are fed a support vector machine (SVM) and a random forest (RF) for stress classification. The experimental results show that the proposed approach can discriminate between stress states. The combination of PPG and ECG using RF as classifier yields the highest F1-score of 0.97 in the test set. PPG only and RF yield a maximum F1-score of 0.90. Furthermore, subject-specific cross-validation improves performance. ECG and PPG signals are reliable in classifying the stress state of a car driver. In summary, the proposed framework could be extended to real-time stress state assessment in driving conditions.

A Portable Low-Cost Respiration Rate Measurement System for Sleep Apnea Detection.

Continuous monitoring of breathing activity is vital in detecting respiratory-based diseases such as obstructive sleep apnea (OSA) and hypopnea. Sleep apnea (SA) is a potentially dangerous sleep problem that occurs when a person's breathing stops and begins periodically while they sleep. In addition, SA interrupts sleep, causing significant daytime sleepiness, weirdness, and irritability. This study aims to design a single inertial measurement unit (IMU) sensor-based system to analyze the respiratory rate of humans. The results of the developed system is validated with the Equivital Wireless Physiological Systems for different activities. Further, the experiment has been designed to identify the optimal sensor placement location for efficient respiration rate estimation during different activities. The performance of the developed model has been quantified using breathing rate estimation accuracy (% BREA) and mean absolute error (MAE). Among all sensor placement locations and activities combinations, a window size of 30sec resulted in the worst performance, whereas a window size ≥ 60sec resulted in a better performance (p-value<0.05). In addition, the performance of the model has been found consistent for window size ≥ 60sec (p-value>0.05). For activity 3 (lying straight), comparably similar performance, 0.52±0.24 and 0.52±0.12 (p-value>0.05) have been depicted by the sensor placement position 3 (Abdomen) and position 1 (chest), respectively. Further, for the other two activities, activity 1 (sitting and working) and activity 2 (sitting straight), the best performance has been depicted as 0.32±0.18, 0.49±0.21 respectively (p-value<0.05), by the sensor placement position 2 (left ribs). This research presents a reliable, cost-effective, portable respiration monitoring system that could detect SA during sleep.

XIII International Conference on Kangaroo Mother Care - Different opinions, experiences and related KMC issues: Good practices, stabilisation concept, nutrition and basic respiratory support.

AIM: This study aimed to summarise the views and experiences of the participants in the workshop of the XIII International Conference on Kangaroo Mother Care (KMC). METHODS: The results of the discussions held during the workshop of the XIII International Conference on KMC were summarised. There were 152 participants from 47 countries. Four main KMC topics were discussed: good practices, immediate implementation, nutrition and basic ventilation. RESULTS: Several agreements were reached, namely that professional societies and governments should develop official recommendations to promote KMC as standard care for preterm and low birth weight infants and that parents should be involved as active caregivers in neonatal care units. Moreover, the criteria for referring community-born infants to KMC require standardisation. Important inequalities in resource availability among high-, middle- and low-income countries were recognised for all topics. Specific needs were identified for parenteral nutrition and fortifiers, nasal continuous positive airway pressure (nCPAP) and oxygen blenders, which are rarely available in low- and middle-income countries. Immediate implementation of KMC was discussed as a new concept. Its benefits were recognised, but its application has some variability. CONCLUSION: Adequate preterm care requires a basic neonatal package, including KMC, nCPAP, immediate management protocols and adequate nutrition and feeding strategies. The differences in resources among high-, middle- and low-income countries highlight the wide disparities in neonatal care according to the place of birth.

Effective fall detection and post-fall breath rate tracking using a low-cost CW Doppler radar sensor.

Existing low-cost Doppler radar-based fall detection systems encounter challenges due to false alarms and the absence of post-fall health tracking, significantly impacting their accuracy and overall compatibility for fall detection. This paper presents a cost-effective, robust solution for a fall detection system with the post-fall health tracking facility using a 3.18 GHz continuous-wave Doppler radar sensor. The experimental data acquisition is conducted in-house under the guidance of a healthcare expert, involving various activities such as standing, sitting, sleeping, running, walking, falling, sit-to-stand, and stand-to-sit transitions. We propose an algorithm comprising four hierarchical stages, each with specific objectives. Considering the complexity, the model is trained differently for each stage to optimize the classification accuracy. The system architecture is designed to minimize computational costs and power consumption through modular implementation in stages, utilizing low-power equipment and incorporating traditional machine-learning algorithms. Experimental results demonstrate a fall detection accuracy of 93.24% and breath rate measurement error of 2.26%, which is competitive with recent state-of-the-art approaches. Obtained results highlight the effectiveness of the proposed system in addressing the challenges of false alarms and post-fall health tracking while maintaining cost-efficiency and accuracy in fall detection.

Oxygen uptake rate versus CO2 based respiration rate for assessment of the biological stability of peat, plant fibers and woody materials with high C:N ratio versus composts.

The biological stability of organic materials predicts their performance when used as either a soil improver or as an ingredient in growing media. CO2 release in a static measurement and O2 consumption rate (OUR) were compared for seven groups of growing media components. The ratio between CO2 release and OUR was matrix-specific. This ratio was highest for plant fibers high in C:N and with a high risk of N immobilization, intermediate for wood fiber and woody composts, and lowest for peat and other compost types. The effect of variable test conditions in the OUR setup was assessed for plant fibers, where addition of mineral N and/or nitrification inhibitor had no effect on the OUR measurements. Testing at 30 °C instead of 20 °C resulted in higher OUR values as expected, but did not change the effect of mineral N dose. A strong increase in the CO2 flux was measured when plant fibers were mixed with mineral fertilizer; in contrast, addition of mineral N or fertilizer before or during the OUR test had no effect. The present experimental setup did not allow for differentiation between a higher CO2 release as a result of increased microbial respiration after adding mineral N versus an underestimation of stability due to N limitation in the dynamic OUR setup. Results indicate that type of material, C:N ratio and risk of N immobilization all appear to affect the OUR results. The OUR criteria may therefore require clear differentiation based on the different materials used in horticultural substrates.

Tracheal sound-based apnea detection using hidden Markov model in sedated volunteers and post anesthesia care unit patients.

The current method of apnea detection based on tracheal sounds is limited in certain situations. In this work, the Hidden Markov Model (HMM) algorithm based on segmentation is used to classify the respiratory and non-respiratory states of tracheal sounds, to achieve the purpose of apnea detection. Three groups of tracheal sounds were used, including two groups of data collected in the laboratory and a group of patient data in the post anesthesia care unit (PACU). One was used for model training, and the others (laboratory test group and clinical test group) were used for testing and apnea detection. The trained HMMs were used to segment the tracheal sounds in laboratory test data and clinical test data. Apnea was detected according to the segmentation results and respiratory flow rate/pressure which was the reference signal in two test groups. The sensitivity, specificity, and accuracy were calculated. For the laboratory test data, apnea detection sensitivity, specificity, and accuracy were 96.9%, 95.5%, and 95.7%, respectively. For the clinical test data, apnea detection sensitivity, specificity, and accuracy were 83.1%, 99.0% and 98.6%. Apnea detection based on tracheal sound using HMM is accurate and reliable for sedated volunteers and patients in PACU.

Assessment of a Multi-Sensor FBG-Based Wearable System in Sitting Postures Recognition and Respiratory Rate Evaluation of Office Workers.

Due to prolonged incorrect sitting posture, upper body musculoskeletal disorders (UBMDs) are largely widespread among sedentary workers. Monitoring employees' sitting behaviors could be of great help in minimizing UBMDs' occurrence. In addition, being primarily influenced by psycho-physical stress conditions, respiratory rate (RR) would be a further useful parameter to delineate the workers' state of health. Wearable systems have emerged as a viable option for sitting posture and RR monitoring since enable continuous data collecting with no posture disturbances. Nevertheless, the main limits are poor fit, cumbersomeness, and movement restriction resulting in discomfort for the user. In addition, only few wearable solutions can track both these parameters contextually. To address these problems, in this study a flexible wearable system composed of seven modular sensing elements based on fiber Bragg grating (FBG) technology and designed to be worn on the back has been proposed to recognize the most common sitting postures (i.e., kyphotic, upright and lordotic) and estimate RR. The assessment was performed on ten volunteers showing good performances in postures recognition via Naïve Bayes classificator (accuracy >96.9%) and agreement with the benchmark in RR estimation (MAPE ranging between 0.74% and 3.83%, MODs close to zero, and LOAs between 0.76 bpm and 3.63 bpm). The method was then successfully tested on three additional subjects under different breathing conditions. The wearable system could offer great support for a better understanding of the workers' posture attitudes and contribute to gathering RR information to depict an overall picture of the users' state of health.

Accuracy of breathing and radial pulse assessment by non-medical persons: an observational cross-sectional study.

Early recognition of cardiopulmonary arrest (CPA) expedites emergency calls and resuscitation and improves the survival rate of unresponsive individuals. However, the accuracy of breathing and radial artery pulse assessment by non-medical persons is poorly understood. The aim of this study was to determine the accuracy of breathing assessment and radial pulse palpation among 450 non-medical personnel using a high-fidelity simulator. We examined the accuracy of 10 second's assessment for breathing and radial pulse using a high-fidelity mannequin simulator, included 496 non-medical participants (school teachers) between 2016-2018. For a primary results, the sensitivity for the detection of the presence of the breathing and radial pulse was 96.2% (97.5% for sensitivity and 92.0% for specificity) and 91.7% (99.1% for sensitivity and 56.8% for specificity), respectively. Futher, breathing rate and radial pulse rate were strongly correlated with the assessments, with Spearman's correlation coefficients of 0.813 (P < 0.001) and 0.719 (P < 0.001), respectively. In contrast, radial pulse strength was weakly correlated with the assessment (coefficient of 0.288, P < 0.001). Our results suggested that non-medical persons would show high accuracy in detecting and measuring respiration and radial pulse, although they did not accurately determine radial pulse strength for the early recognition of CPA.

Preliminary Assessment of a Flexible Multi-Sensor Wearable System Based on Fiber Bragg Gratings for Respiratory Monitoring of Hemiplegic Patients.

Respiratory diseases are common in post-stroke hemiplegic patients and represent a major social problem as they worsen the quality of life and reduce the life span. As a consequence, being able to monitor respiratory parameters such as the respiratory rate (RR) and assess the presence of respiratory asynchronies could be of paramount importance to define hemiplegics' health status. Moreover, RR is a useful parameter to investigate the level of fatigue and distress that these patients undergo during rehabilitation processes. Although motion capture systems and flowmeters are the leading instruments for respiratory pattern evaluation, smart wearable systems are gaining ever more acceptance since they allow continuous monitoring by detecting chest wall breathing displacements, ensuring reduced costs and no need for dedicated spaces. Among other sensing technologies, fiber Bragg grating (FBG) sensors have emerged thanks to their high sensitivity to strain, lightness, and multiplexing capability. In this work, a wearable system composed of four flexible dumbbell-shaped sensing modules is proposed for respiratory monitoring in hemiplegic patients. The system is light and easy to wear and can be adapted to any anthropometry thanks to the modular anchoring system. Its feasibility assessment in RR evaluation was performed on seven hemiplegic volunteers in eupnea and tachypnea breathing conditions. In addition, an explorative investigation was conducted to assess the system's ability to detect asynchronies between torso compartments. The good results suggest that this device could be a useful instrument to support clinicians and operators in hemiplegic patients' management.

Forecasting of Continuous Vital Sign Using Multivariate Auto-Regressive Models.

This project assessed the use of multivariate auto-regressive (MAR) models to create forecasts of continuous vital signs in hospitalized patients. A total of 20 hours continuous (1/60Hz) heart rate and respiration rate from eight postoperative patients, where used to fit a centered MAR model for forecasting in windows of 15 minutes. The model was fitted using Markov Chain Monte Carlo sampling, and the model was evaluated on data from five additional patients. The results demonstrate an average RMSE in the forecast window of 11.4 (SD: 7.30) beats per minute for heart rate and 3.3 (SD:1.3) breaths per minute for respiration rate. These results indicate potential for forecasting vital signs in a clinical setting.

Pulse oximetry respiratory monitoring for assessment of acute childhood wheeze.

OBJECTIVE: There is a lack of objective measures to assess children with acute wheezing episodes. Increased respiratory rate (RR) and pulsus paradoxus (PP) are recognised markers, but poorly recorded in practice. We examined whether they can be reliably assessed from a pulse oximeter plethysmogram ('pleth') trace and predict clinical outcome. PATIENTS AND METHODS: We studied 44 children aged 1-7 years attending hospital with acute wheeze, following initial 'burst' bronchodilator therapy (BT), and used custom software to measure RR and assess PP from oximeter pleth traces. Traces were examined for quality, and the accuracy of the RR measurement was validated against simultaneous respiratory inductive plethysmography (RIP). RR and PP at 1 hour after BT were compared with clinical outcomes. RESULTS: RR from pleth and RIP showed excellent agreement, with a mean difference (RIP minus pleth) of -0.5 breaths per minute (limits of agreement -3.4 to +2.3). 52% of 1 min epochs contained 10 s or more of pleth artefact. At 1 hour after BT, children who subsequently required intravenous bronchodilators had significantly higher RR (median (IQR) 63 (62-66) vs 43 (37-51) breaths per minute) than those who did not, but their heart rate and oxygen saturation were similar. Children with RR ≥55 per minute spent longer in hospital: median (IQR) 30 (22-45) vs 10 (7-21) hours. All children who subsequently required hospital admission had PP-analogous pleth waveforms 1 hour after BT. CONCLUSION: RR can be reliably measured and PP detected from the pulse oximeter pleth trace in children with acute wheeze and both markers predict clinical outcome. TRIAL REGISTRATION NUMBER: UKCRN15742.

Non invasive jacketed telemetry in socially-housed rats for a combined assessment of respiratory system, electrocardiogram and activity using the DECRO system.

Respiratory and cardiovascular systems are among the vital organ systems that should be studied in safety pharmacology core battery test. Non-invasive jacketed external telemetry technology that enables concomitant monitoring of both systems has been available and used widely for non-rodent species. Recently, the DECRO system, a miniaturized technology system in line with the "3Rs" principles, has been developed to provide a similar approach in rats. However, data to evaluate this system in socially-housed rats is lacking. Therefore, the objectives of this study were to determine the tolerability and the material integrity of this novel solution in pair-housed rats in two conditions: i) in a single session of 22 h simulating a stand-alone safety pharmacology study design, and ii) in three repeated sessions of 22 h each, simulating the inclusion of safety pharmacology endpoints in a 1-month toxicology study. In both conditions, the GABAB receptor agonist baclofen was used as a reference compound inducing cardiorespiratory changes. Our results provided evidence that this novel solution was well tolerated, the material was resistant to deterioration and that it allowed the accurate recording, in a non-invasive manner, of cardiorespiratory parameters and activity level in freely moving, pair-housed rats in the above two conditions. In addition, the expected respiratory depressant effects of baclofen were recorded. These results pave the way for considering this novel solution as an enhanced approach for nonclinical safety assessment in rats.

Remote investigation and assessment of vital signs (RIA-VS)-proof of concept for contactless estimation of blood pressure, pulse, respiratory rate, and oxygen saturation in patients with suspicion of COVID-19.

BACKGROUND: Vital signs are critical in assessing the severity and prognosis of infections, for example, COVID-19, influenza, sepsis, and pneumonia. This study aimed to evaluate a new method for rapid camera-based non-contact measurement of heart rate, blood oxygen saturation, respiratory rate, and blood pressure. METHODS: Consecutive adult patients attending a hospital emergency department for suspected COVID-19 infection were invited to participate. Vital signs measured with a new camera-based method were compared to the corresponding standard reference methods. The camera device observed the patient's face for 30 s from ∼1 m. RESULTS: Between 1 April and 1 October 2020, 214 subjects were included in the trial, 131 female (61%) and 83 male (39%). The mean age was 44 years (range 18-81 years). The new camera-based device's vital signs measurements were, on average, very close to the gold standard but the random variation was larger than the reference methods. CONCLUSIONS: The principle of contactless measurement of blood pressure, pulse, respiratory rate, and oxygen saturation works, which is very promising. However, technical improvements to the equipment used in this study to reduce its random variability is required before clinical implementation. This will likely be a game changer once this is sorted out. CLINICAL TRIAL REGISTRATION: Universal Trial Number (UTN) U1111-1251-4114 and the ClinicalTrials.gov Identifier NCT04383457.

Non-Fatal Drowning: Measuring Respiratory Rate as Part of "Beach-Side" Clinical Assessment of Physiological Respiratory Stress.

An average of 115 people drown in Ireland each year. Drowning is the third commonest cause of unintentional injury-related death globally. The United Nations 'Global Drowning Prevention' resolution was adopted in April 2021. The WHO recognises almost 360,000 drownings annually. There are an estimated 8-10 non-fatal drownings for each fatal drowning. The evidence base underpinning clinical management of non-fatal drowning is sparse. The use of respiratory rate in non-fatal drowning may provide a "beach-side" objective, quantifiable clinical measure of evolving physiological respiratory stress.