Development and validation of a smartwatch algorithm for differentiating physical activity intensity in health monitoring.

To develop and validate a machine learning based algorithm to estimate physical activity (PA) intensity using the smartwatch with the capacity to record PA and determine outdoor state. Two groups of participants, including 24 adults (13 males) and 18 children (9 boys), completed a sequential activity trial. During each trial, participants wore a smartwatch, and energy expenditure was measured using indirect calorimetry as gold standard. The support vector machine algorithm and the least squares regression model were applied for the metabolic equivalent (MET) estimation using raw data derived from the smartwatch. Exercise intensity was categorized based on MET values into sedentary activity (SED), light activity (LPA), moderate activity (MPA), and vigorous activity (VPA). The classification accuracy was evaluated using area under the ROC curve (AUC). The METs estimation accuracy were assessed via the mean absolute error (MAE), the correlation coefficient, Bland-Altman plots, and intraclass correlation (ICC). A total of 24 adults aged 21-34 years and 18 children aged 9-13 years participated in the study, yielding 1790 and 1246 data points for adults and children respectively for model building and validation. For adults, the AUC for classifying SED, MVPA, and VPA were 0.96, 0.88, and 0.86, respectively. The MAE between true METs and estimated METs was 0.75 METs. The correlation coefficient and ICC were 0.87 (p < 0.001) and 0.89, respectively. For children, comparable levels of accuracy were demonstrated, with the AUC for SED, MVPA, and VPA being 0.98, 0.89, and 0.85, respectively. The MAE between true METs and estimated METs was 0.80 METs. The correlation coefficient and ICC were 0.79 (p < 0.001) and 0.84, respectively. The developed model successfully estimated PA intensity with high accuracy in both adults and children. The application of this model enables independent investigation of PA intensity, facilitating research in health monitoring and potentially in areas such as myopia prevention and control.

Electrical Impedance Tomography as a monitoring tool during weaning from mechanical ventilation: an observational study during the spontaneous breathing trial.

BACKGROUND: Prolonged weaning from mechanical ventilation is associated with poor clinical outcome. Therefore, choosing the right moment for weaning and extubation is essential. Electrical Impedance Tomography (EIT) is a promising innovative lung monitoring technique, but its role in supporting weaning decisions is yet uncertain. We aimed to evaluate physiological trends during a T-piece spontaneous breathing trail (SBT) as measured with EIT and the relation between EIT parameters and SBT success or failure. METHODS: This is an observational study in which twenty-four adult patients receiving mechanical ventilation performed an SBT. EIT monitoring was performed around the SBT. Multiple EIT parameters including the end-expiratory lung impedance (EELI), delta Tidal Impedance (ΔZ), Global Inhomogeneity index (GI), Rapid Shallow Breathing Index (RSBIEIT), Respiratory Rate (RREIT) and Minute Ventilation (MVEIT) were computed on a breath-by-breath basis from stable tidal breathing periods. RESULTS: EELI values dropped after the start of the SBT (p < 0.001) and did not recover to baseline after restarting mechanical ventilation. The ΔZ dropped (p < 0.001) but restored to baseline within seconds after restarting mechanical ventilation. Five patients failed the SBT, the GI (p = 0.01) and transcutaneous CO2 (p < 0.001) values significantly increased during the SBT in patients who failed the SBT compared to patients with a successful SBT. CONCLUSION: EIT has the potential to assess changes in ventilation distribution and quantify the inhomogeneity of the lungs during the SBT. High lung inhomogeneity was found during SBT failure. Insight into physiological trends for the individual patient can be obtained with EIT during weaning from mechanical ventilation, but its role in predicting weaning failure requires further study.

Recommendations for asthma monitoring in children: A PeARL document endorsed by APAPARI, EAACI, INTERASMA, REG, and WAO.

Monitoring is a major component of asthma management in children. Regular monitoring allows for diagnosis confirmation, treatment optimization, and natural history review. Numerous factors that may affect disease activity and patient well-being need to be monitored: response and adherence to treatment, disease control, disease progression, comorbidities, quality of life, medication side-effects, allergen and irritant exposures, diet and more. However, the prioritization of such factors and the selection of relevant assessment tools is an unmet need. Furthermore, rapidly developing technologies promise new opportunities for closer, or even "real-time," monitoring between visits. Following an approach that included needs assessment, evidence appraisal, and Delphi consensus, the PeARL Think Tank, in collaboration with major international professional and patient organizations, has developed a set of 24 recommendations on pediatric asthma monitoring, to support healthcare professionals in decision-making and care pathway design.

Prediction interval: A powerful statistical tool for monitoring patients and analytical systems.

Monitoring is indispensable for assessing disease prognosis and evaluating the effectiveness of treatment strategies, both of which rely on serial measurements of patients' data. It also plays a critical role in maintaining the stability of analytical systems, which is achieved through serial measurements of quality control samples. Accurate monitoring can be achieved through data collection, following a strict preanalytical and analytical protocol, and the application of a suitable statistical method. In a stable process, future observations can be predicted based on historical data collected during periods when the process was deemed reliable. This can be evaluated using the statistical prediction interval. Statistically, prediction interval gives an "interval" based on historical data where future measurement results can be located with a specified probability such as 95%. Prediction interval consists of two primary components: (i) the set point and (ii) the total variation around the set point which determines the upper and lower limits of the interval. Both can be calculated using the repeated measurement results obtained from the process during its steady-state. In this paper, (i) the theoretical bases of prediction intervals were outlined, and (ii) its practical application was explained through examples, aiming to facilitate the implementation of prediction intervals in laboratory medicine routine practice, as a robust tool for monitoring patients' data and analytical systems.

Development of a Web-Based Oxygenation Dashboard for Preterm Neonates: A Quality Improvement Initiative.

BACKGROUND: Preterm neonates are extensively monitored to require strict oxygen target attainment for optimal outcomes. In daily practice, detailed oxygenation data are hardly used and crucial patterns may be missed due to the snapshot presentations and subjective observations. This study aimed to develop a web-based dashboard with both detailed and summarized oxygenation data in real-time and to test its feasibility to support clinical decision making. METHODS: Data from pulse oximeters and ventilators were synchronized and stored to enable real-time and retrospective trend visualizations in a web-based viewer. The dashboard was designed based on interviews with clinicians. A preliminary version was evaluated during daily clinical rounds. The routine evaluation of the respiratory condition of neonates (gestational age < 32 weeks) with respiratory support at the NICU was compared to an assessment with the assistance of the dashboard. RESULTS: The web-based dashboard included data on the oxygen saturation (SpO2), fraction of inspired oxygen (FiO2), SpO2/FiO2 ratio, and area < 80% and > 95% SpO2 curve during time intervals that could be varied. The distribution of SpO2 values was visualized as histograms. In 65% of the patient evaluations (n = 86) the level of hypoxia was assessed differently with the use of the dashboard. In 75% of the patients the dashboard was judged to provide added value for the clinicians in supporting clinical decisions. CONCLUSIONS: A web-based customized oxygenation dashboard for preterm neonates at the NICU was developed and found feasible during evaluation. More clear and objective information was found supportive for clinicians during the daily rounds in tailoring treatment strategies.

Recent Advances in Two-Dimensional Nanomaterials for Healthcare Monitoring.

The development of advanced technologies for the fabrication of functional nanomaterials, nanostructures, and devices has facilitated the development of biosensors for analyses. Two-dimensional (2D) nanomaterials, with unique hierarchical structures, a high surface area, and the ability to be functionalized for target detection at the surface, exhibit high potential for biosensing applications. The electronic properties, mechanical flexibility, and optical, electrochemical, and physical properties of 2D nanomaterials can be easily modulated, enabling the construction of biosensing platforms for the detection of various analytes with targeted recognition, sensitivity, and selectivity. This review provides an overview of the recent advances in 2D nanomaterials and nanostructures used for biosensor and wearable-sensor development for healthcare and health-monitoring applications. Finally, the advantages of 2D-nanomaterial-based devices and several challenges in their optimal operation have been discussed to facilitate the development of smart high-performance biosensors in the future.

"NeuroVanguard": a contemporary strategy in neuromonitoring for severe adult brain injury patients.

Severe acute brain injuries, stemming from trauma, ischemia or hemorrhage, remain a significant global healthcare concern due to their association with high morbidity and mortality rates. Accurate assessment of secondary brain injuries severity is pivotal for tailor adequate therapies in such patients. Together with neurological examination and brain imaging, monitoring of systemic secondary brain injuries is relatively straightforward and should be implemented in all patients, according to local resources. Cerebral secondary injuries involve factors like brain compliance loss, tissue hypoxia, seizures, metabolic disturbances and neuroinflammation. In this viewpoint, we have considered the combination of specific noninvasive and invasive monitoring tools to better understand the mechanisms behind the occurrence of these events and enhance treatment customization, such as intracranial pressure monitoring, brain oxygenation assessment and metabolic monitoring. These tools enable precise intervention, contributing to improved care quality for severe brain injury patients. The future entails more sophisticated technologies, necessitating knowledge, interdisciplinary collaboration and resource allocation, with a focus on patient-centered care and rigorous validation through clinical trials.

Augmenting authenticity for non-invasive in vivo detection of random blood glucose with photoacoustic spectroscopy using Kernel-based ridge regression.

Photoacoustic Spectroscopy (PAS) is a potential method for the noninvasive detection of blood glucose. However random blood glucose testing can help to diagnose diabetes at an early stage and is crucial for managing and preventing complications with diabetes. In order to improve the diagnosis, control, and treatment of Diabetes Mellitus, an appropriate approach of noninvasive random blood glucose is required for glucose monitoring. A polynomial kernel-based ridge regression is proposed in this paper to detect random blood glucose accurately using PAS. Additionally, we explored the impact of the biological parameter BMI on the regulation of blood glucose, as it serves as the primary source of energy for the body's cells. The kernel function plays a pivotal role in kernel ridge regression as it enables the algorithm to capture intricate non-linear associations between input and output variables. Using a Pulsed Laser source with a wavelength of 905 nm, a noninvasive portable device has been developed to collect the Photoacoustic (PA) signal from a finger. A collection of 105 individual random blood glucose samples was obtained and their accuracy was assessed using three metrics: Root Mean Square Error (RMSE), Mean Absolute Difference (MAD), and Mean Absolute Relative Difference (MARD). The respective values for these metrics were found to be 10.94 (mg/dl), 10.15 (mg/dl), and 8.86%. The performance of the readings was evaluated through Clarke Error Grid Analysis and Bland Altman Plot, demonstrating that the obtained readings outperformed the previously reported state-of-the-art approaches. To conclude the proposed IoT-based PAS random blood glucose monitoring system using kernel-based ridge regression is reported for the first time with more accuracy.

Neuromonitoring in the ICU - what, how and why?

PURPOSE OF REVIEW: We selectively review emerging noninvasive neuromonitoring techniques and the evidence that supports their use in the ICU setting. The focus is on neuromonitoring research in patients with acute brain injury. RECENT FINDINGS: Noninvasive intracranial pressure evaluation with optic nerve sheath diameter measurements, transcranial Doppler waveform analysis, or skull mechanical extensometer waveform recordings have potential safety and resource-intensity advantages when compared to standard invasive monitors, however each of these techniques has limitations. Quantitative electroencephalography can be applied for detection of cerebral ischemia and states of covert consciousness. Near-infrared spectroscopy may be leveraged for cerebral oxygenation and autoregulation computation. Automated quantitative pupillometry and heart rate variability analysis have been shown to have diagnostic and/or prognostic significance in selected subtypes of acute brain injury. Finally, artificial intelligence is likely to transform interpretation and deployment of neuromonitoring paradigms individually and when integrated in multimodal paradigms. SUMMARY: The ability to detect brain dysfunction and injury in critically ill patients is being enriched thanks to remarkable advances in neuromonitoring data acquisition and analysis. Studies are needed to validate the accuracy and reliability of these new approaches, and their feasibility and implementation within existing intensive care workflows.

Pacemaker remote monitoring challenges in pediatric population: Single center long term experience.

BACKGROUND: Remote monitoring (RM) of cardiac implantable electronic devices for adults offers improved treatment efficacy and, consequently, better patient clinical outcomes. There is scant data on the value and prognosis of RM in the pediatric population. AIMS: The goal of this study was to determine the efficacy of RM by analyzing the connectivity of bedside transmitters, adherence to planned automatic follow-ups, and occurrence of alert-based events. METHODS: We evaluated the pediatric population with implanted pacemakers for congenital AV block or after surgically corrected congenital heart diseases. RESULTS: A total of 69 patients were included in our study. The median (Q1-Q3) patient age was 6.0 (2.0-11.0) years. All patients received bedside transmitters and were enrolled in the RM system. Among them, 95.7% of patients had their first scheduled follow-up successfully sent. Patients were followed up remotely over a median time of 33.0 (13-45) months. Only 42% of patients were continuously monitored, and all scheduled transmissions were delivered on time. Further analysis revealed that 34.8% of patients missed transmissions between June and September (holiday season). Alert-based events were observed in 40.6% patients, mainly related to epicardial lead malfunction and arrhythmic events. Overall compliance was also compromised by socioeconomic factors. CONCLUSIONS: Our findings are in concordance with recently published results by PACES regarding a high level of compliance in patient enrollment to RM and time to initial transmission. However, a lower level of adherence was observed during the holiday season due to interrupted connectivity of bedside transmitters. Importantly, a relatively low occurrence of alert transmissions was observed, mainly related to epicardial lead malfunction and arrhythmic events.

A personalized earbud for non-invasive long-term EEG monitoring.

Objective. The primary objective of this study was to evaluate the reliability, comfort, and performance of a custom-fit, non-invasive long-term electrophysiologic headphone, known as Aware Hearable, for the ambulatory recording of brain activities. These recordings play a crucial role in diagnosing neurological disorders such as epilepsy and in studying neural dynamics during daily activities.Approach.The study uses commercial manufacturing processes common to the hearing aid industry, such as 3D scanning, computer-aided design modeling, and 3D printing. These processes enable the creation of the Aware Hearable with a personalized, custom-fit, thereby ensuring complete and consistent contact with the inner surfaces of the ear for high-quality data recordings. Additionally, the study employs a machine learning data analysis approach to validate the recordings produced by Aware Hearable, by comparing them to the gold standard intracranial electroencephalography recordings in epilepsy patients.Main results.The results indicate the potential of Aware Hearable to expedite the diagnosis of epilepsy by enabling extended periods of ambulatory recording.Significance.This offers significant reductions in burden to patients and their families. Furthermore, the device's utility may extend to a broader spectrum, making it suitable for other applications involving neurophysiological recordings in real-world settings.

Integrating Wearable Textiles Sensors and IoT for Continuous sEMG Monitoring.

Surface electromyography is a technique used to measure the electrical activity of muscles. sEMG can be used to assess muscle function in various settings, including clinical, academic/industrial research, and sports medicine. The aim of this study is to develop a wearable textile sensor for continuous sEMG monitoring. Here, we have developed an integrated biomedical monitoring system that records sEMG signals through a textile electrode embroidered within a smart sleeve bandage for telemetric assessment of muscle activities and fatigue. We have taken an "Internet of Things"-based approach to acquire the sEMG, using a Myoware sensor and transmit the signal wirelessly through a WiFi-enabled microcontroller unit (NodeMCU; ESP8266). Using a wireless router as an access point, the data transmitted from ESP8266 was received and routed to the webserver-cum-database (Xampp local server) installed on a mobile phone or PC for processing and visualization. The textile electrode integrated with IoT enabled us to measure sEMG, whose quality is similar to that of conventional methods. To verify the performance of our developed prototype, we compared the sEMG signal recorded from the biceps, triceps, and tibialis muscles, using both the smart textile electrode and the gelled electrode. The root mean square and average rectified values of the sEMG measured using our prototype for the three muscle types were within the range of 1.001 ± 0.091 mV to 1.025 ± 0.060 mV and 0.291 ± 0.00 mV to 0.65 ± 0.09 mV, respectively. Further, we also performed the principal component analysis for a total of 18 features (15 time domain and 3 frequency domain) for the same muscle position signals. On the basis on the hierarchical clustering analysis of the PCA's score, as well as the one-way MANOVA of the 18 features, we conclude that the differences observed in the data for the different muscle types as well as the electrode types are statistically insignificant.

A multimodal stacked ensemble model for cardiac output prediction utilizing cardiorespiratory interactions during general anesthesia.

This study examined the possibility of estimating cardiac output (CO) using a multimodal stacking model that utilizes cardiopulmonary interactions during general anesthesia and outlined a retrospective application of machine learning regression model to a pre-collected dataset. The data of 469 adult patients (obtained from VitalDB) with normal pulmonary function tests who underwent general anesthesia were analyzed. The hemodynamic data in this study included non-invasive blood pressure, plethysmographic heart rate, and SpO2. CO was recorded using Vigileo and EV1000 (pulse contour technique devices). Respiratory data included mechanical ventilation parameters and end-tidal CO2 levels. A generalized linear regression model was used as the metalearner for the multimodal stacking ensemble method. Random forest, generalized linear regression, gradient boosting machine, and XGBoost were used as base learners. A Bland-Altman plot revealed that the multimodal stacked ensemble model for CO prediction from 327 patients had a bias of - 0.001 L/min and - 0.271% when calculating the percentage of difference using the EV1000 device. Agreement of model CO prediction and measured Vigileo CO in 142 patients reported a bias of - 0.01 and - 0.333%. Overall, this model predicts CO compared to data obtained by the pulse contour technique CO monitors with good agreement.

Critical Care Alarm Fatigue and Monitor Customization: Alarm Frequencies and Context Factors.

BACKGROUND: Alarm fatigue among nurses working in the intensive care unit has garnered considerable attention as a national patient safety priority. A viable solution for reducing the frequency of alarms and unnecessary noise is intensive care unit alarm monitor customization. LOCAL PROBLEM: A 24-bed cardiovascular and thoracic surgery intensive care unit in a large academic medical center identified a high rate of alarms and associated noise as a problem contributing to nurse alarm fatigue. METHODS: An alarm monitor quality improvement project used both alarm frequency and nurse surveys before and after implementation to determine the effectiveness of interventions. Multimodal interventions included nurse training sessions, informational flyers, organizational policies, and an alarm monitor training video. Unexpected results inspired an extensive investigation and secondary analysis, which included examining the data-capturing capabilities of the alarm monitors and the impact of context factors. RESULTS: Alarm frequencies unexpectedly increased after the intervention. The software data-capturing features of the alarm monitors for determining frequency did not accurately measure nurse interactions with monitors. Measured increases in patient census, nurse staffing, and data input from medical devices from before to after the intervention substantially affected project results. CONCLUSIONS: Alarm frequencies proved an unreliable measure of nurse skills and practices in alarm customization. Documented changes in context factors provided strong anecdotal evidence of changed circumstances that clarified project results and underscored the critical importance of contemporaneous collection of context data. Designs and methods used in quality improvement projects must include reliable outcome measures to achieve meaningful results.

The current status of noninvasive intracranial pressure monitoring: A literature review.

Elevated intracranial pressure (ICP) is a life-threatening condition that must be promptly diagnosed. However, the gold standard methods for ICP monitoring are invasive, time-consuming, and they involve certain risks. To address these risks, many noninvasive approaches have been proposed. This study undertakes a literature review of the existing noninvasive methods, which have reported promising results. The experimental base on which they are established, however, prevents their application in emergency conditions and thus none of them are capable of replacing the traditional invasive methods to date. On the other hand, contemporary methods leverage Machine Learning (ML) which has already shown unprecedented results in several medical research areas. That said, only a few publications exist on ML-based approaches for ICP estimation, which are not appropriate for emergency conditions due to their restricted capability of employing the medical imaging data available in intensive care units. The lack of such image-based ML models to estimate ICP is attributed to the scarcity of annotated datasets requiring directly measured ICP data. This ascertainment highlights an active and unexplored scientific frontier, calling for further research and development in the field of ICP estimation, particularly leveraging the untapped potential of ML techniques.

Advancements in optical fiber-based wearable sensors for smart health monitoring.

Healthcare system is undergoing a significant transformation from a traditional hospital-centered to an individual-centered one, as a result of escalating chronic diseases, ageing populations, and ever-increasing healthcare costs,. Wearable sensors have become widely used in health monitoring systems since the COVID-19 pandemic. They enable continuous measurement of important health indicators like body temperature, wrist pulse, respiration rate, and non-invasive bio fluids like saliva and perspiration. Over the last few decades, the development has mostly concentrated on electrochemical and electrical wearable sensors. However, due to the drawbacks of such sensors, such as electronic waste, electromagnetic interference, non-electrical security, and poor performance, researchers are exhibiting a strong interest in optical principle-based systems. Fiber-based optical wearables are among the most promising healthcare systems because of advancements in high-sensitivity, durable, multiplexed sensing, and simple integration with flexible materials to improve wearability and simplicity. We present an overview of recent developments in optical fiber-based wearable sensors, focusing on two mechanisms: wavelength interrogation and intensity modulation for the detection of body temperature, pulse rate, respiration rate, body movements, and biomedical noninvasive fluids, with a thorough examination of their benefits and drawbacks. This review also focuses on improving working performance and application techniques for healthcare systems, including the integration of nanomaterials and the usage of the Internet of Things (IoT) with signal processing. Finally, the review concludes with a discussion of the future possibilities and problems for optical fiber-based wearables.

From wires to waves, a novel sensor system for in vivo pressure monitoring.

Pressure monitoring in various organs of the body is essential for appropriate diagnostic and therapeutic purposes. In almost all situations, monitoring is performed in a hospital setting. Technological advances not only promise to improve clinical pressure monitoring systems, but also engage toward the development of fully implantable systems in ambulatory patients. Such systems would not only provide longitudinal time monitoring to healthcare personnel, but also to the patient who could adjust their way-of-life in response to the measurements. In the past years, we have developed a new type of piezoresistive pressure sensor system. Different bench tests have demonstrated that it delivers precise and reliable pressure measurements in real-time. The potential of this system was confirmed by a continuous recording in a patient that lasted for almost a day. In the present study, we further characterized the functionality of this sensor system by conducting in vivo implantation experiments in nine female farm pigs. To get a step closer to a fully implantable system, we also adapted two different wireless communication solutions to the sensor system. The communication protocols are based on MICS (Medical Implant Communication System) and BLE (Bluetooth Low Energy) communication. As a proof-of-concept, implantation experiments in nine female pigs demonstrated the functionality of both systems, with a notable technical superiority of the BLE.

ARTEMIS: An alarm threshold and policy mining system for the intensive care unit.

BACKGROUND: Alarm fatigue is a major technology-induced hazard for patients and staff in intensive care units. Too many - mostly unnecessary - alarms cause desensitisation and lack of response in medical staff. Unsuitable alarm policies are one reason for alarm fatigue. But changing alarm policies is a delicate issue since it concerns patient safety. OBJECTIVE: We present ARTEMIS, a novel, computer-aided clinical decision support system for policy makers that can help to considerably improve alarm policies using data from hospital information systems. METHODS: Policy makers can use different policy components from ARTEMIS' internal library to assemble tailor-made alarm policies for their intensive care units. Alternatively, policy makers can provide even more highly customised policy components as Python functions using data the hospital information systems. This can even include machine learning models - for example for setting alarm thresholds. Finally, policy makers can evaluate their system of policies and compare the resulting alarm loads. RESULTS: ARTEMIS reports and compares numbers of alarms caused by different alarm policies for an easily adaptable target population. ARTEMIS can compare policies side-by-side and provides grid comparisons and heat maps for parameter optimisation. For example, we found that the utility of alarm delays varies based on target population. Furthermore, policy makers can introduce virtual parameters that are not in the original data by providing a formula to compute them. Virtual parameters help measuring and alarming on the right metric, even if the patient monitors do not directly measure this metric. CONCLUSION: ARTEMIS does not release the policy maker from assessing the policy from a medical standpoint. But as a knowledge discovery and clinical decision support system, it provides a strong quantitative foundation for medical decisions. At comparatively low cost of implementation, ARTEMIS can have a substantial impact on patients and staff alike - with organisational, economic, and clinical benefits for the implementing hospital.