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BackgroundPhotoplethysmography (PPG) is a technology routinely used in clinical practice to assess blood oxygenation (SpO2) and pulse rate (PR). Skin pigmentation may influence accuracy, leading to health outcomes disparities. ObjectiveThis systematic review and meta-analysis primarily aimed to evaluate the accuracy of PPG-derived SpO2 and PR by skin pigmentation. Secondarily, we aimed to evaluate statistical biases and the clinical relevance of PPG-derived SpO2 and PR according to skin pigmentation. MethodsWe identified 23 pulse oximetry studies (n=59,684; 197,353 paired SpO2-arterial blood observations) and 4 wearable PR studies (n=176; 140,771 paired PPG-electrocardiography observations). We evaluated accuracy according to skin pigmentation group by comparing SpO2 accuracy root-mean-square values to the regulatory threshold of 3% and PR 95% limits of agreement values to +5 or –5 beats per minute (bpm), according to the standards of the American National Standards Institute, Association for the Advancement of Medical Instrumentation, and the International Electrotechnical Commission. We evaluated biases and clinical relevance using mean bias and 95% CI. ResultsFor SpO2, accuracy root-mean-square values were 3.96%, 4.71%, and 4.15%, and pooled mean biases were 0.70% (95% CI 0.17%-1.22%), 0.27% (95% CI –0.64% to 1.19%), and 1.27% (95% CI 0.58%-1.95%) for light, medium, and dark pigmentation, respectively. For PR, 95% limits of agreement values were from –16.02 to 13.54, from –18.62 to 16.84, and from –33.69 to 32.54, and pooled mean biases were –1.24 (95% CI –5.31 to 2.83) bpm, –0.89 (95% CI –3.70 to 1.93) bpm, and –0.57 (95% CI –9.44 to 8.29) bpm for light, medium, and dark pigmentation, respectively. ConclusionsSpO2 and PR measurements may be inaccurate across all skin pigmentation groups, breaching U.S. Food and Drug Administration guidance and industry standard thresholds. Pulse oximeters significantly overestimate SpO2 for both light and dark skin pigmentation, but this overestimation may not be clinically relevant. PRs obtained from wearables exhibit no statistically or clinically significant bias based on skin pigmentation.

Background Wearable devices may be useful for identification, quantification and characterization, and management of atrial fibrillation (AF). To date, consumer wrist‐worn devices for AF detection using photoplethysmography‐based algorithms perform only periodic checks when the user is stationary and are US Food and Drug Administration cleared for prediagnostic uses without intended use for clinical decision‐making. There is an unmet need for medical‐grade diagnostic wrist‐worn devices that provide long‐term, continuous AF monitoring. Methods and Results We evaluated the performance of a wrist‐worn device with lead‐I ECG and continuous photoplethysmography (Verily Study Watch) and photoplethysmography‐based convolutional neural network for AF detection and burden estimation in a prospective multicenter study that enrolled 117 patients with paroxysmal AF. A 14‐day continuous ECG monitor (Zio XT) served as the reference device to evaluate algorithm sensitivity and specificity for detection of AF in 15‐minute intervals. A total of 91 857 intervals were contributed by 111 subjects with evaluable reference and test data (18.3 h/d median watch wear time). The watch was 96.1% sensitive (95% CI, 92.7%–98.0%) and 98.1% specific (95% CI, 97.2%–99.1%) for interval‐level AF detection. Photoplethysmography‐derived AF burden estimation was highly correlated with the reference device burden (R2=0.986) with a mean difference of 0.8% (95% limits of agreement, −6.6% to 8.2%). Conclusions Continuous monitoring using a photoplethysmography‐based convolutional neural network incorporated in a wrist‐worn device has clinical‐grade performance for AF detection and burden estimation. These findings suggest that monitoring can be performed with wrist‐worn wearables for diagnosis and clinical management of AF. Registration Information URL: https://www.clinicaltrials.gov; Unique identifier: NCT04546763.

Abstract Background Rapid and irregular ventricular rates (RVR) are an important consequence of atrial fibrillation (AF). Raw accelerometry data in combination with electrocardiogram (ECG) data have the potential to distinguish inappropriate from appropriate tachycardia in AF. This can allow for the development of a just-in-time intervention for clinical treatments of AF events. The objective of this study is to develop a machine learning algorithm that can distinguish episodes of AF with RVR that are associated with low levels of activity. Methods This study involves 45 patients with persistent or paroxysmal AF. The ECG and accelerometer data were recorded continuously for up to 3 weeks. The prediction of AF episodes with RVR and low activity was achieved using a deterministic probabilistic finite-state automata (DPFA)-based approach. Rapid and irregular ventricular rate (RVR) is defined as having heart rates (HR) greater than 110 beats per minute (BPM) and high activity is defined as greater than 0.75 quantile of the activity level. The AF events were annotated using the FDA-cleared BeatLogic algorithm. Various time intervals prior to the events were used to determine the longest prediction intervals for predicting AF with RVR episodes associated with low levels of activity. Results Among the 961 annotated AF events, 292 met the criterion for RVR episode. There were 176 and 116 episodes with low and high activity levels respectively. Out of the 961 AF episodes, 770 (80.1%) were used in the training data set and the remaining 191 intervals were held out for testing. The model was able to predict AF with RVR and low activity up to 4.5 min before the events. The mean prediction performance gradually decreased as the time to events increased. The overall Area under the ROC Curve (AUC) for the model lies within the range of 0.67–0.78. Conclusion The DPFA algorithm can predict AF with RVR associated with low levels of activity up to 4.5 min before the onset of the event. This would enable the development of just-in-time interventions that could reduce the morbidity and mortality associated with AF and other similar arrhythmias.

Background: Current symptom management approaches for patients with atrial fibrillation (AF) focus on addressing heart rhythm and do not include management of behavioral or emotional contributors to symptom manifestation or severity. Objective: To inform content development of a digitally delivered AF symptom self-management program by exploring patients’ experiences of the impact of AF symptoms and their perspectives on behavioral approaches to symptom management. Methods: This was a qualitative study of 3 focus groups composed of adults living with symptomatic AF. Group transcripts underwent thematic content analysis to identify themes and subthemes. Themes were matched to available self-management strategies that could be adapted for use in a digitally delivered AF symptom self-management program. Results: Six major themes (with subthemes) were identified: symptoms (anxiety, fatigue, stress/other negative emotions, AF-specific symptoms, heart rhythm); social aspects (social impact, social support); AF treatments (medication, procedures); health behaviors (sleep, physical activity, hydration, breathing/mindfulness/relaxation); positive emotions; and AF education and information gathering. Symptom self-management strategies were identified that could be used to address these symptom-related themes. Conclusion: Patients with AF reported a wide range of emotional, physical, and social impacts of the condition. They endorsed attempts to self-manage symptoms and an interest in learning more about how to effectively self-manage. Findings indicate the potential for a digital self-management program to address existing gaps in AF symptom–related care.

This collaborative statement from the Digital Health Committee of the Heart Rhythm Society provides everyday clinical scenarios in which wearables may be utilized by patients for cardiovascular health and arrhythmia management. We describe herein the spectrum of wearables that are commercially available for patients, and their benefits, shortcomings and areas for technological improvement. Although wearables for rhythm diagnosis and management have not been examined in large randomized clinical trials, undoubtedly the usage of wearables has quickly escalated in clinical practice. This document is the first of a planned series in which we will update information on wearables as they are revised and released to consumers.

The particle size of lake sediments integrates important environmental information, and the detection of changes in this variable over time provides important information for understanding ecosystem and sedimentary processes. Although standard machine learning regression algorithms especially random forest (RF) have shown great potential for particle size mapping in sediment cores using hyperspectral imaging, no research has yet applied deep learning approaches. One-dimensional convolutional neural networks (CNN) have recently been developed and applied in several applications in the field of spectroscopy. This study addresses this issue by developing and applying a new methodology based on a 1D convolutional autoencoder as the feature extractor and a 1D CNN architecture for regression. The proposed architecture was applied to hyperspectral images of nine lake sediment cores across Canada and evaluated against results of the RF algorithm. However, in order for the results to be comparable, the RF algorithm was performed on features that also resulted from the convolutional autoencoder. According to the leave-one-out evaluation method, the proposed CNN method represented an improvement of 14%, 4.58, 5.45, and 0.83 for R2, MAE, RMSE, and RPD, respectively, relative to the best RF algorithm. Our findings show that the proposed method can be reliably used to reconstruct particle size in sediment cores from lakes of varying climatic and environmental characteristics.

Since the rise of deep learning in the past few years, convolutional neural networks (CNNs) have quickly found their place within the remote sensing (RS) community. As a result, they have transitioned away from other machine learning techniques, achieving unprecedented improvements in many specific RS applications. This article presents a meta-analysis of 416 peer-reviewed journal articles, summarizes CNN advancements, and its current status under RS applications. The review process includes a statistical and descriptive analysis of a database comprised of 23 fields, including: 1) general characteristics, such as various applications, study objectives, sensors, and data types, and 2) algorithm specifications, such as different types of CNN models, parameter settings, and reported accuracies. This review begins with a comprehensive survey of the relevant articles without considering any specific criteria to give readers an idea of general trends, and then investigates CNNs within different RS applications to provide specific directions for the researchers. Finally, a conclusion summarizes potentialities, critical issues, and challenges related to the observed trends.

Background: Many digital health technologies capable of atrial fibrillation (AF) detection are directly available to patients. However, adaptation into clinical practice by heart rhythm healthcare practitioners (HCPs) is unclear. Objective: To examine HCP perspectives on use of commercial technologies for AF detection and management. Methods: We created an electronic survey for HCPs assessing practice demographics and perspectives on digital devices for AF detection and management. The survey was distributed electronically to all members of 3 heart rhythm professional societies. Results: We received 1601 responses out of 73,563 e-mails sent, with 43.6% from cardiac electrophysiologists, 12.8% from fellows, and 11.6% from advanced practice practitioners. Most respondents (62.3%) reported having recommended patient use of a digital device for AF detection. Those who did not had concerns about their accuracy (29.6%), clinical utility of results (22.8%), and integration into electronic health records (19.8%). Results from a 30-second single-lead electrocardiogram were sufficient for 42.7% of HCPs to recommend oral anticoagulation for patients at high risk for stroke. Respondents wanted more data comparing the accuracy of digital devices to conventional devices for AF monitoring (64.9%). A quarter (27.3%) of HCPs had no reservations recommending digital devices for AF detection, and most (53.4%) wanted guidelines from their professional societies providing guidance on their optimal use. Conclusion: Many HCPs have already integrated digital devices into their clinical practice. However, HCPs reported facing challenges when using digital technologies for AF detection, and professional society recommendations on their use are needed.

Several machine-learning algorithms have been proposed for remote sensing image classification during the past two decades. Among these machine learning algorithms, Random Forest (RF) and Support Vector Machines (SVM) have drawn attention to image classification in several remote sensing applications. This article reviews RF and SVM concepts relevant to remote sensing image classification and applies a meta-analysis of 251 peer-reviewed journal papers. A database with more than 40 quantitative and qualitative fields was constructed from these reviewed papers. The meta-analysis mainly focuses on 1) the analysis regarding the general characteristics of the studies, such as geographical distribution, frequency of the papers considering time, journals, application domains, and remote sensing software packages used in the case studies, and 2) a comparative analysis regarding the performances of RF and SVM classification against various parameters, such as data type, RS applications, spatial resolution, and the number of extracted features in the feature engineering step. The challenges, recommendations, and potential directions for future research are also discussed in detail. Moreover, a summary of the results is provided to aid researchers to customize their efforts in order to achieve the most accurate results based on their thematic applications.

Healthcare providers are expected to deliver care improvement solutions that not only provide high quality patient care, but also improve outcomes, reduce costs, ensure safety, and increase patient satisfaction. Human-centered design methodologies, such as design thinking, allow providers to collaboratively ideate solutions with patients and family members. We describe a pilot workshop designed to teach providers the stages of design thinking while working on improving patient-provider communication. Twenty-four providers (physicians, nurses, technical staff, and administrative staff) from multiple cardiovascular units attended the workshop with five former patients and family members from those units. The workshop educated on and guided teams of providers patients and family members through the stages of design thinking (empathy, define, ideate, prototype, test). Pre- and post-event assessments indicated an increase in knowledge of the design thinking methodology and participants’ ability to apply it to a clinical problem. We also present recommendations for designing a successful design thinking workshop.

Traditional training and funding mechanisms in academic health centers often do not support its faculty, staff, and trainees in evaluating and implementing innovative ideas, necessitating supplemental innovation programming. The University of Michigan (U-M) Frankel Cardiovascular Center partnered with U-M Fast Forward Medical Innovation (FMMI), a biomedical innovation and commercialization unit funded in part by the Clinical and Translational Science Award awarded to the Michigan Institute for Clinical & Health Research, to provide training and resources to advance ideas toward impacting patients. The program recruited faculty, trainees, staff, patients, and family members from multidisciplinary backgrounds. Engaging patients and family members expanded the ideas generated and furthered clinical relevance. Over two years, 11 project teams completed an 11-week, 16-session course on innovation and entrepreneurship concepts that incorporated workshops to progress ideas and develop a pitch for development funding. An increase in knowledge was reported in key innovation topics, such as customer discovery, assessing markets, and intellectual property. Participants reported an increase in project preparation, including obtaining stakeholder support, preparation of a development plan, readiness to apply for funding, and filing invention disclosures. This program can serve as a model for implementing training and funding mechanisms to advance innovative ideas.

OBJECTIVES/GOALS: The University of Michigan Frankel Cardiovascular Center (FCVC) Innovation Challenge is an annual competition offering funding for innovative ideas to improve cardiovascular care. Due to the COVID-19 pandemic, administrators converted the recruitment process and pitch event to fully virtual. METHODS/STUDY POPULATION: We detail the process of converting the event from a hybrid process (virtual and in-person recruiting and in-person event) to a fully virtual one. Changes to the event included implementing a virtual recruiting process utilizing short video recordings as submission format; a new tool for storing and displaying submissions; fully virtual finalist selection and coaching; and a fully virtual pitch and judging event. The submission process tracked information about submissions that include the type of idea (process or product), role of team lead, and department of team lead. RESULTS/ANTICIPATED RESULTS: The FCVC Innovation Challenge was successfully converted to a fully virtual event. Methods and tools will be shared to allow similar institutions to replicate a successful virtual pitch event. These include methods and tools utilized to allow participants to describe their ideas, strategies to select and coach finalists, and to host a virtual pitch event. Data will be shared on the number of ideas and category (product/process) of projects submitted, and number and category of finalists selected. DISCUSSION/SIGNIFICANCE: This case review can demonstrate how institutions can use a similar virtual idea submission and pitch process to (1) catalyze innovative ideas that can impact patient care by accessing its communitys ideas and (2) fund innovative ideas that do not fit traditional mechanisms.

This paper presents a novel method for reliable and efficient spatial-spectral classification of hyperspectral data. This algorithm is based on the Bayesian labelling by combining the results of the Gaussian mixture model (GMM) with spatial-contextual information extracted by Markov random fields (MRF). Moreover, a new fuzzy segmentation-based function was defined and incorporated into the spatial energy involved to improve the performance of MRF. To evaluate the proposed algorithm in real analysis scenarios, three benchmark hyperspectral datasets, i.e. Indian Pines, Pavia University and Salinas, were used. Experimental results demonstrated that the proposed method could considerably improve the classification’s overall accuracies when compared to conventional MRF-based approaches.

Atrial fibrillation (AF) usually starts as paroxysmal but can evolve relentlessly to the persistent and permanent forms. However, the mechanisms governing such a transition are unknown. The authors show that intracardiac serum levels of galectin (Gal)-3 are greater in patients with persistent than paroxysmal AF and that Gal-3 independently predicts atrial tachyarrhythmia recurrences after a single ablation procedure. Using a sheep model of persistent AF the authors further demonstrate that upstream therapy targeting Gal-3 diminishes both electrical remodeling and fibrosis by impairing transforming growth factor beta–mediated signaling and reducing myofibroblast activation. Accordingly, Gal-3 inhibition therapy increases the probability of AF termination and reduces the overall burden of AF. Therefore the authors postulate that Gal-3 inhibition is a potential new upstream therapy to prevent AF progression.

Hyperspectral imaging has recently emerged in the geosciences as a technology that provides rapid, accurate, and high-resolution information from lake sediment cores. Here we introduce a new methodology to infer particle size distribution, an insightful proxy that tracks past changes in aquatic ecosystems and their catchments, from laboratory hyperspectral images of lake sediment cores. The proposed methodology includes data preparation, spectral preprocessing and transformation, variable selection, and model fitting. We evaluated random forest regression and other commonly used statistical methods to find the best model for particle size determination. We tested the performance of combinations of spectral transformation techniques, including absorbance, continuum removal, and first and second derivatives of the reflectance and absorbance, along with different regression models including partial least squares, multiple linear regression, principal component regression, and support vector regression, and evaluated the resulting root mean square error (RMSE), R-squared, and mean relative error (MRE). Our results show that a random forest regression model built on spectra absorbance significantly outperforms all other models. The new workflow demonstrated herein represents a much-improved method for generating inferences from hyperspectral imagery, which opens many new opportunities for advancing the study of sediment archives.

Cellulose wastes of a wood and paper factory were used to produce activated carbon. Several chemical agents under various conditions were used for production of activated carbon and hence their adsorption properties have been evaluated. In addition the effect of process parameters such as raw material, chemical agent concentration, impregnation ratio, carbonization temperature, carbonization time, activation temperature, activation time, particle size and soaking time on adsorption properties and product yield have been studied. Based on experimental results, the optimum values of process parameters were determined. As an achievement, samples of activated carbon with specific surface area up to 1100 m2 /g and iodine adsorption number up to 1080 mg/g were prepared.

Intensive Care Units (ICUs) are equipped with many sophisticated sensors and monitoring devices to provide the highest quality of care for critically ill patients. However, these devices might generate false alarms that reduce standard of care and result in desensitization of caregivers to alarms. Therefore, reducing the number of false alarms is of great importance. Many approaches such as signal processing and machine learning, and designing more accurate sensors have been developed for this purpose. However, the significant intrinsic correlation among the extracted features from different sensors has been mostly overlooked. A majority of current data mining techniques fail to capture such correlation among the collected signals from different sensors that limits their alarm recognition capabilities. Here, we propose a novel information-theoretic predictive modeling technique based on the idea of coalition game theory to enhance the accuracy of false alarm detection in ICUs by accounting for the synergistic power of signal attributes in the feature selection stage. This approach brings together techniques from information theory and game theory to account for inter-features mutual information in determining the most correlated predictors with respect to false alarm by calculating Banzhaf power of each feature. The numerical results show that the proposed method can enhance classification accuracy and improve the area under the ROC (receiver operating characteristic) curve compared to other feature selection techniques, when integrated in classifiers such as Bayes-Net that consider inter-features dependencies.

این مقاله به بررسی یکی از مهمترین مشکلاتی می پردازد که بازارهای مالی و بازارهای اوراق بهادار در عصر حاضر با آن روبه رو هستند. لیبرالیسم و آزادسازی بازارهای مالی موجب آن شده است که حرکت سرمایه از بازارهای یک کشور به کشور دیگر به سادگی انجام پذیرد. این امر به نوبه خود موجب آن گشته است که بازارهای سرمایه هر کشور نسبت به اتفاقاتی که در کشور دیگر رخ می دهد حساسیت و آسیب پذیری بیشتری داشته باشند. بحران مالی اخیر کشورهای غربی که دامنه و آثار آن طی زمان اندکی به کشورهای دیگر نیز سرایت نموده است، شاهدی بر این و ابستگی و یکپارچگی بازارهای سرمایه است. بدین سبب نیاز به یک سری راهکارهای قانونی و مقرراتی برای همسان سازی و تنظیم و کنترل رفتارهای بازیگران بازارهای بین المللی سرمایه احساس می شود. در این باره راهکارهای گوناگونی پیشنهاد شده است. از یکسان سازی مقررات مربوط به اوراق بهادار گرفته تا همسان سازی آنها یا شناسایی متقابل این مقررات در کشورهای گوناگون یا ایجاد نهادهایی با صلاحیت جهانی برای وضع مقررات در این زمینه. مقاله حاضر به بررسی راهکارهایی گوناگون می پردازد که در این خصوص پیشنهاد شده است و در این میان بر نقش دوگانگی حقوق خصوصی و حقوق عمومی و منطق متفاوتی را تاکید می نماید که بر هر عرصه حکومت می نماید