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The 12-lead electrocardiogram (ECG) is an integral component to the diagnosis of a multitude of cardiovascular conditions. It is performed using a complex set of skin surface electrodes, limiting its use outside traditional clinical settings. We developed an artificial intelligence algorithm, trained over 600,000 clinically acquired ECGs, to explore whether fewer leads as input are sufficient to reconstruct a 12-lead ECG. Two limb leads (I and II) and one precordial lead (V3) were required to generate a reconstructed 12-lead ECG highly correlated with the original ECG. An automatic algorithm for detection of ECG features consistent with acute myocardial infarction (MI) performed similarly for original and reconstructed ECGs (AUC = 0.95). When interpreted by cardiologists, reconstructed ECGs achieved an accuracy of 81.4 ± 5.0% in identifying ECG features of ST-segment elevation MI, comparable with the original 12-lead ECGs (accuracy 84.6 ± 4.6%). These results will impact development efforts to innovate ECG acquisition methods with simplified tools in non-specialized settings. [ABSTRACT FROM AUTHOR]

Prior studies have shown that sleep duration peri-vaccination influences an individual's antibody response. However, whether peri-vaccination sleep affects real-world vaccine effectiveness is unknown. Here, we tested whether objectively measured sleep around COVID-19 vaccination affected breakthrough infection rates. DETECT is a study of digitally recruited participants who report COVID-19-related information, including vaccination and illness data. Objective sleep data are also recorded through activity trackers. We compared the impact of sleep duration, sleep efficiency, and frequency of awakenings on reported breakthrough infection after the 2nd vaccination and 1st COVID-19 booster. Logistic regression models were created to examine if sleep metrics predicted COVID-19 breakthrough infection independent of age and gender. Self-reported breakthrough COVID-19 infection following 2nd COVID-19 vaccination and 1st booster. 256 out of 5265 individuals reported a breakthrough infection after the 2nd vaccine, and 581 out of 2583 individuals reported a breakthrough after the 1st booster. There was no difference in sleep duration between those with and without breakthrough infection. Increased awakening frequency was associated with breakthrough infection after the 1st booster with 3.01 ± 0.65 awakenings/hour in the breakthrough group compared to 2.82 ± 0.65 awakenings/hour in those without breakthrough (P < 0.001). Cox proportional hazards modeling showed that age < 60 years (hazard ratio 2.15, P < 0.001) and frequency of awakenings (hazard ratio 1.17, P = 0.019) were associated with breakthrough infection after the 1st booster. Sleep duration was not associated with breakthrough infection after COVID vaccination. While increased awakening frequency during sleep was associated with breakthrough infection beyond traditional risk factors, the clinical implications of this finding are unclear. [ABSTRACT FROM AUTHOR]

Early identification of atrial fibrillation (AF) can reduce the risk of stroke, heart failure, and other serious cardiovascular outcomes. However, paroxysmal AF may not be detected even after a two-week continuous monitoring period. We developed a model to quantify the risk of near-term AF in a two-week period, based on AF-free ECG intervals of up to 24 h from 459,889 patch-based ambulatory single-lead ECG (modified lead II) recordings of up to 14 days. A deep learning model was used to integrate ECG morphology data with demographic and heart rhythm features toward AF prediction. Observing a 1-day AF-free ECG recording, the model with deep learning features produced the most accurate prediction of near-term AF with an area under the curve AUC = 0.80 (95% confidence interval, CI = 0.79–0.81), significantly improving discrimination compared to demographic metrics alone (AUC 0.67; CI = 0.66–0.68). Our model was able to predict incident AF over a two-week time frame with high discrimination, based on AF-free single-lead ECG recordings of various lengths. Application of the model may enable a digital strategy for improving diagnostic capture of AF by risk stratifying individuals with AF-negative ambulatory monitoring for prolonged or recurrent monitoring, potentially leading to more rapid initiation of treatment. [ABSTRACT FROM AUTHOR]

Purpose of Review: The purpose of this review is to discuss and explore selected clinically integrated and commercially available devices, the data supporting them, as well as the next phase of devices that are coming down the pipeline. This includes usage of devices such as the evolution of pacemaker in electrophysiology. Recent Findings: In the last 10 years alone, there has been a multitude of devices from invasive to noninvasive and prescribed to over the counter, with potential cardiovascular disease applications. Recent developments in remote monitoring include: the emergency of wireless PA catheters, wearable devices that can monitor heart rate and assess for atrial fibrillation, and leadless, wireless pacemakers, and patch monitoring systems. Remote patient monitoring is here to stay, but which devices get integrated into daily cardiovascular clinical practice is yet to be seen. Summary: Cardiology and sensor technology have significantly evolved since the turn of the twentieth century, transforming cardiac devices into small and less invasive forms catalyzing the field of remote monitoring. Remote patient monitoring is now a well-recognized term in care management of cardiovascular disease patients; however, there are few actual clinical integrations. [ABSTRACT FROM AUTHOR]

Abstract and full-text screen. Studies were excluded if they enrolled adolescents or children, used animal models, enrolled patients with another primary diagnosis other than psychiatric as outlined by the diagnostic and statistical manual of mental disorders (DSM) V, or if they assessed HRV in the context of treatment rather than diagnosis. Study quality assessment was conducted using a modified Downs and Blacks quality assessment tool for observational rather than interventional studies. Data were reported in four tables: 1) summarizing study characteristics, 2) methods of HRV detection, 3) key findings and statistics, and 4) quality assessment.Results: There is significant variability between studies in their methodology of recording as well as reporting HRV, which makes it difficult to meaningfully interpret data that is clinically applicable due to the presence of significant bias in existing studies. The presence of an association between HRV and the severity of various psychiatric disorders, however, remains promising.Conclusion: Future studies should be done to further explore how HRV parameters may be used to enhance the diagnosis and prognosis of several psychiatric disorders. [ABSTRACT FROM AUTHOR]

Smartphones, smartwatches, linked wearables, and associated wellness apps have had rapid uptake. These tools become ever ‘smarter’ in sensing intimate aspects of our surroundings and physiology over time, including activity, metabolites, electrical signals, blood pressure and oxygenation. Proposed EU law stipulates the ‘involuntary donation’ of depersonalized health and wellness data. There has been pushback against the ever-increasing gathering and sharing of wellness data in this context, increasing with every app purchased or updated. Is the potential of this data now lost to research? Consent-led COVID-19 data donation projects signpost a participative, standardized, and scalable approach to data sharing. [ABSTRACT FROM AUTHOR]

At the beginning of the COVID-19 pandemic, analog tools such as nasopharyngeal swabs for PCR tests were center stage and the major prevention tactics of masking and physical distancing were a throwback to the 1918 influenza pandemic. Overall, there has been scant regard for digital tools, particularly those based on smartphone apps, which is surprising given the ubiquity of smartphones across the globe. Smartphone apps, given accessibility in the time of physical distancing, were widely used for tracking, tracing and educating the public about COVID-19. Despite limitations, such as concerns around data privacy, data security, digital health illiteracy and structural inequities, there is ample evidence that apps are beneficial for understanding outbreak epidemiology, individual screening and contact tracing. While there were successes and failures in each category, outbreak epidemiology and individual screening were substantially enhanced by the reach of smartphone apps and accessory wearables. Continued use of apps within the digital infrastructure promises to provide an important tool for rigorous investigation of outcomes both in the ongoing outbreak and in future epidemics. An overview of apps for outbreak epidemiology, individual screening and contact tracing during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The ability to identify who does or does not experience the intended immune response following vaccination could be of great value in not only managing the global trajectory of COVID-19 but also helping guide future vaccine development. Vaccine reactogenicity can potentially lead to detectable physiologic changes, thus we postulated that we could detect an individual's initial physiologic response to a vaccine by tracking changes relative to their pre-vaccine baseline using consumer wearable devices. We explored this possibility using a smartphone app-based research platform that enabled volunteers (39,701 individuals) to share their smartwatch data, as well as self-report, when appropriate, any symptoms, COVID-19 test results, and vaccination information. Of 7728 individuals who reported at least one vaccination dose, 7298 received an mRNA vaccine, and 5674 provided adequate data from the peri-vaccine period for analysis. We found that in most individuals, resting heart rate (RHR) increased with respect to their individual baseline after vaccination, peaked on day 2, and returned to normal by day 6. This increase in RHR was greater than one standard deviation above individuals' normal daily pattern in 47% of participants after their second vaccine dose. Consistent with other reports of subjective reactogenicity following vaccination, we measured a significantly stronger effect after the second dose relative to the first, except those who previously tested positive to COVID-19, and a more pronounced increase for individuals who received the Moderna vaccine. Females, after the first dose only, and those aged <40 years, also experienced a greater objective response after adjusting for possible confounding factors. These early findings show that it is possible to detect subtle, but important changes from an individual's normal as objective evidence of reactogenicity, which, with further work, could prove useful as a surrogate for vaccine-induced immune response. [ABSTRACT FROM AUTHOR]

Individual smartwatch or fitness band sensor data in the setting of COVID-19 has shown promise to identify symptomatic and pre-symptomatic infection or the need for hospitalization, correlations between peripheral temperature and self-reported fever, and an association between changes in heart-rate-variability and infection. In our study, a total of 38,911 individuals (61% female, 15% over 65) have been enrolled between March 25, 2020 and April 3, 2021, with 1118 reported testing positive and 7032 negative for COVID-19 by nasopharyngeal PCR swab test. We propose an explainable gradient boosting prediction model based on decision trees for the detection of COVID-19 infection that can adapt to the absence of self-reported symptoms and to the available sensor data, and that can explain the importance of each feature and the post-test-behavior for the individuals. We tested it in a cohort of symptomatic individuals who exhibited an AUC of 0.83 [0.81–0.85], or AUC = 0.78 [0.75–0.80] when considering only data before the test date, outperforming state-of-the-art algorithm in these conditions. The analysis of all individuals (including asymptomatic and pre-symptomatic) when self-reported symptoms were excluded provided an AUC of 0.78 [0.76–0.79], or AUC of 0.70 [0.69–0.72] when considering only data before the test date. Extending the use of predictive algorithms for detection of COVID-19 infection based only on passively monitored data from any device, we showed that it is possible to scale up this platform and apply the algorithm in other settings where self-reported symptoms can not be collected. [ABSTRACT FROM AUTHOR]

In this study, we propose a post-hoc explainability framework for deep learning models applied to quasi-periodic biomedical time-series classification. As a case study, we focus on the problem of atrial fibrillation (AF) detection from electrocardiography signals, which has strong clinical relevance. Starting from a state-of-the-art pretrained model, we tackle the problem from two different perspectives: global and local explanation. With global explanation, we analyze the model behavior by looking at entire classes of data, showing which regions of the input repetitive patterns have the most influence for a specific outcome of the model. Our explanation results align with the expectations of clinical experts, showing that features crucial for AF detection contribute heavily to the final decision. These features include R-R interval regularity, absence of the P-wave or presence of electrical activity in the isoelectric period. On the other hand, with local explanation, we analyze specific input signals and model outcomes. We present a comprehensive analysis of the network facing different conditions, whether the model has correctly classified the input signal or not. This enables a deeper understanding of the network's behavior, showing the most informative regions that trigger the classification decision and highlighting possible causes of misbehavior. [ABSTRACT FROM AUTHOR]

Aims: Screening for asymptomatic atrial fibrillation (AF) could prevent strokes and save lives, but the AF burden of those detected can impact prognosis. New technologies enable continuous monitoring or intermittent electrocardiogram (ECG) snapshots, however, the relationship between AF detection rates and the burden of AF found with intermittent strategies is unknown. We simulated the likelihood of detecting AF using real-world 2-week continuous ECG recordings and developed a generalizable model for AF detection strategies.Methods and Results: From 1738 asymptomatic screened individuals, ECG data of 69 individuals (mean age 76.3, median burden 1.9%) with new AF found during 14 days continuous monitoring were used to simulate 30 seconds ECG snapshots one to four times daily for 14 days. Based on this simulation, 35-66% of individuals with AF would be detected using intermittent screening. Twice-daily snapshots for 2 weeks missed 48% of those detected by continuous monitoring, but mean burden was 0.68% vs. 4% in those detected (P < 0.001). In a cohort of 6235 patients (mean age 69.2, median burden 4.6%) with paroxysmal AF during clinically indicated monitoring, simulated detection rates were 53-76%. The Markovian model of AF detection using mean episode duration and mean burden simulated actual AF detection with ≤9% error across the range of screening frequencies and durations.Conclusion: Using twice-daily ECG snapshots over 2 weeks would detect only half of individuals discovered to have AF by continuous recordings, but AF burden of those missed was low. A model predicting AF detection, validated using real-world data, could assist development of optimized AF screening programmes. [ABSTRACT FROM AUTHOR]

Background: Heart rate is routinely measured as part of the clinical examination but is rarely acted upon unless it is well outside a population-based normal range. With wearable sensor technologies, heart rate can now be continuously measured, making it possible to accurately identify an individual's "normal" heart rate and potentially important variations in it over time. Our objective is to describe inter- and intra-individual variability in resting heart rate (RHR) collected over the course of two years using a wearable device, studying the variations of resting heart rate as a function of time of year, as well as individuals characteristics like age, sex, average sleep duration, and body mass index (BMI). Methods and findings: Our retrospective, longitudinal cohort study includes 92,457 de-identified individuals from the United States (all 50 states), who consistently—over at least 35 weeks in the period from March 2016 to February 2018, for at least 2 days per week, and at least 20 hours per day—wore a heart rate wrist-worn tracker. In this study, we report daily RHR and its association with age, BMI, sex, and sleep duration, and its variation over time. Individual daily RHR was available for a median of 320 days, providing nearly 33 million daily RHR values. We also explored the range in daily RHR variability between individuals, and the long- and short-term changes in the trajectory of an individual's daily RHR. Mean daily RHR was 65 beats per minute (bpm), with a range of 40 to 109 bpm among all individuals. The mean RHR differed significantly by age, sex, BMI, and average sleep duration. Time of year variations were also noted, with a minimum in July and maximum in January. For most subjects, RHR remained relatively stable over the short term, but 20% experienced at least 1 week in which their RHR fluctuated by 10 bpm or more. Conclusions: Individuals have a daily RHR that is normal for them but can differ from another individual's normal by as much as 70 bpm. Within individuals, RHR was much more consistent over time, with a small but significant seasonal trend, and detectable discrete and infrequent episodes outside their norms. [ABSTRACT FROM AUTHOR]

The article offers information on activities and conference report of the Institute of Electrical & Electronics Engineers (IEEE) Communications Society. Topics discussed include Spanish law for electronic access to public services; 22nd edition of the Innovation in Clouds, Internet and Networks (ICIN) conference; and IEEE Distinguished Lecturer tour on machine learning in digital medicine in Norway and Finland by Giorgio Quer.

Proactive caching will enable the 5G networks of the future to meet the challenge of continuously increasing wireless data traffic. Thanks to proactive caching, we are observing a shift from standard cellular networks, with one base station providing wireless connectivity to all the users in the cell, to heterogeneous networks, where several small base stations can assist the macro base station. Going one step further, users can also share their local content via device-to-device communications, avoiding multiple requests to the base station. In this complex network scenario, we propose a proactive caching policy to exploit all these communication opportunities and reduce congestion on the backhaul link, with the goal of minimizing the system cost, e.g., in terms of energy or bandwidth wastage. We provide a closed-form expression for the average system cost in the case of user mobility. In this scenario, we also consider heterogeneous file popularity profiles for different users. We present a robust optimization framework and show significant performance gains compared to a static caching policy, in which the caching decisions do not depend on the evolution of the network scenario. [ABSTRACT FROM AUTHOR]

BACKGROUND Using the data from 56,365 individuals, from 185 countries, and a Nokia Health Wireless blood pressure (BP) monitor, we investigated real-world characteristics of BP variability (BPV). METHODS All included individuals self-measured and uploaded their BP using Bluetooth at least 20 times over a period of ≥1 month at a frequency and duration of their choosing. In total, 16,904,844 BP measurements were analyzed, with a median of 146 measurements per person (interquartile range [IQR] 73–321) over a median of 14 months (IQR 7–31). SD, coefficient of variation, maximum BP, and maximum minus minimum BP difference were all calculated as measures of BPV. RESULTS BPV showed a distinct pattern, influenced by season of year, day of week, and time of day. BPV index was higher in females compared with males (P < 0.001) and increased with age (P < 0.001). Compared to the weekend, the weekday BPV index was significantly higher, and this finding was more prominent in females (P = 0.001). In multivariate analysis, BPV index were significantly associated with age, gender, geographic location, and mean BP values. CONCLUSION Using the largest BP data set we are aware of, with the benefits and limitations of real-world measurement, we could show the pattern of BPV and provide reference values that may be helpful in understanding the nature of BPV as self-measurement at home becomes more common, and help guide individualized management. [ABSTRACT FROM AUTHOR]

Wireless devices running the Android operating system offer a novel opportunity to study network behaviors, and to observe and modify, in real time, key networking parameters. This opens up an unprecedented opportunity to study, test, and evaluate the performance of techniques operating at different layers of the protocol stack and adopting the cognitive networking paradigm. In this article, we describe our novel IEEE 802.11 mesh network testbed that integrates Android-based devices. The aim is to build a flexible testbed to observe in-stack and out-of-stack parameters of interest that, with some modifications, can be used to test networking techniques in both civilian and emergency scenarios. We provide the implementation details to create an ad hoc network among these inexpensive commercial devices, and specify how to observe and modify the networking parameters at different layers of the protocol stack. We also run some standard protocols on the network, and we obtain some repeatable results, which are similar to other results from the literature and confirm our testbed as a valid tool for ad hoc network performance evaluation. [ABSTRACT FROM PUBLISHER]

Relay sharing has been recently investigated to increase the performance of coexisting wireless multi—hop networks. In this paper, we analyze a scenario where two wireless ad hoc networks are willing to share some of their nodes, acting as relays, in order to gain benefits in terms of lower packet delivery delay and reduced loss probability. Bayesian network analysis is exploited to compute the probabilistic relationships between local parameters and overall performance, whereas the selection of the nodes to share is made by means of a game theoretic approach. Our results are then validated through the use of a system level simulator, which shows that an accurate selection of the shared nodes can significantly increase the performance gain with respect to a random selection scheme. [ABSTRACT FROM AUTHOR]

We address the problem of compressing large and distributed signals monitored by a Wireless Sensor Network (WSN) and recovering them through the collection of a small number of samples. We propose a sparsity model that allows the use of Compressive Sensing (CS) for the online recovery of large data sets in real WSN scenarios, exploiting Principal Component Analysis (PCA) to capture the spatial and temporal characteristics of real signals. Bayesian analysis is utilized to approximate the statistical distribution of the principal components and to show that the Laplacian distribution provides an accurate representation of the statistics of real data. This combined CS and PCA technique is subsequently integrated into a novel framework, namely, SCoRe1: Sensing, Compression and Recovery through ON-line Estimation for WSNs. SCoRe1 is able to effectively self-adapt to unpredictable changes in the signal statistics thanks to a feedback control loop that estimates, in real time, the signal reconstruction error. We also propose an extensive validation of the framework used in conjunction with CS as well as with standard interpolation techniques, testing its performance for real world signals. The results in this paper have the merit of shedding new light on the performance limits of CS when used as a recovery tool in WSNs. [ABSTRACT FROM PUBLISHER]

This study compared physiological and perceptual responses between cycling prescribed using fixed‐power (PWR) and fixed rating of perceived exertion (RPE), when performed with blood flow restricted (BFRPWR and BFRRPE) and unrestricted (CONPWR and CONRPE). Endurance cyclists/triathletes cycled for 10 min in four separate randomized conditions; that is, two methods of prescribed exercise intensity (power at the first ventilatory threshold or RPE matched to CONPWR) combined with two occlusion levels (with BFR or without). Cardiorespiratory and perceptual variables were recorded every 2 min. Blood lactate concentration was measured pre‐, immediately and 2‐min postexercise. Power output during BFRRPE was lower than CONRPE (−13 ± 13%). The greatest physiological and perceptual responses were achieved during BFRPWR. Heart rate during BFRRPE was not different compared with CONPWR, yet was greater than CONRPE (+4 ± 11%). Muscular discomfort during BFRRPE was greater than CONPWR (+43 ± 18%) and CONRPE (+65 ± 58%). Cuff pain was greater during BFRPWR than BFRRPE (+14 ± 21%). Blood lactate concentration was not different between BFRRPE, CONPWR, and CONRPE at any timepoint. The reduction in power (fixed‐RPE trials; BFR minus unrestricted) correlated with changes in the respiratory rate (r = 0.85, confidence intervals [CI] = 0.51, 0.96) and postexercise lactate (r = 0.75, CI = 0.27, 0.93) but not muscular discomfort (r = 0.18, CI = −0.47, 0.71). Cardiorespiratory and metabolic stress, muscular discomfort, and cuff pain likely mediated self‐regulating fixed‐RPE cycling with BFR. While cycling with BFR at a fixed‐RPE resulted in less physiological stress compared to BFRPWR, it still provided a heightened level of physiological stress, with less pain and discomfort. As such, fixed‐RPE can be a suitable alternative for prescribing BFR to trained cyclists. Highlights: Applying BFR at the fixed‐power associated with the first ventilatory threshold can increase the heart rate from the moderate to high exercise intensity domain for some individuals, indicating BFR exercise may not necessarily be low intensity.Fixed‐power cycling with BFR can be used at low work rates, perhaps following injury, yet practitioners should consider the balance between the physiological demands, discomfort, and pain at any given work rate.Discomfort and pain associated with BFR is alleviated in a fixed‐RPE model, which presents a suitable method of prescribing aerobic BFR cycling, yet physiological stress is lower compared to BFR cycling at a fixed‐power. [ABSTRACT FROM AUTHOR]

The publication offers a note of thanks and lists its reviewers. [ABSTRACT FROM PUBLISHER]

A subject and author index for the January 2014 issue of "IEEE Transactions on Communications" is presented.

Medical errors have a huge impact on clinical practice in terms of economic and human costs. As a result, technology-based solutions, such as those grounded in artificial intelligence (AI) or collective intelligence (CI), have attracted increasing interest as a means of reducing error rates and their impacts. Previous studies have shown that a combination of individual opinions based on rules, weighting mechanisms, or other CI solutions could improve diagnostic accuracy with respect to individual doctors. We conducted a study to investigate the potential of this approach in cardiology and, more precisely, in electrocardiogram (ECG) reading. To achieve this aim, we designed and conducted an experiment involving medical students, recent graduates, and residents, who were asked to annotate a collection of 10 ECGs of various complexity and difficulty. For each ECG, we considered groups of increasing size (from three to 30 members) and applied three different CI protocols. In all cases, the results showed a statistically significant improvement (ranging from 9% to 88%) in terms of diagnostic accuracy when compared to the performance of individual readers; this difference held for not only large groups, but also smaller ones. In light of these results, we conclude that CI approaches can support the tasks mentioned above, and possibly other similar ones as well. We discuss the implications of applying CI solutions to clinical settings, such as cases of augmented 'second opinions' and decision-making. [ABSTRACT FROM AUTHOR]