Contactless cardiac arrest detection using smart devices

Optimising telecommunicator recognition of out-of-hospital cardiac arrest: A scoping review

Aim

To summarize existing literature and identify knowledge gaps regarding barriers and enablers of telecommunicators' recognition of out-of-hospital cardiac arrest (OHCA).

Methods

This scoping review was undertaken by an International Liaison Committee on Resuscitation (ILCOR) Basic Life Support scoping review team and guided by the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR). Studies were eligible for inclusion if they were peer-reviewed and explored barriers and enablers of telecommunicator recognition of OHCA. We searched Ovid MEDLINE® and Embase and included articles from database inception till June 18th, 2024.

Results

We screened 9,244 studies and included 62 eligible studies on telecommunicator recognition of OHCA. The studies ranged in methodology. The majority were observational studies of emergency calls. The barriers most frequently described to OHCA recognition were breathing status and agonal breathing. The most frequently tested enabler for recognition was a variety of dispatch protocols focusing on breathing assessment. Only one randomized controlled trial (RCT) was identified, which found no difference in OHCA recognition with the addition of machine learning alerting telecommunicators in suspected OHCA cases.

Conclusion

Most studies were observational, assessed barriers to recognition of OHCA and compared different dispatch protocols. Only one RCT was identified. Randomized trials should be conducted to inform how to improve telecommunicator recognition of OHCA, including recognition of pediatric OHCAs and assessment of dispatch protocols.

Multiclass classification of Autism Spectrum Disorder, attention deficit hyperactivity disorder, and typically developed individuals using fMRI functional connectivity analysis

Neurodevelopmental conditions, such as Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD), present unique challenges due to overlapping symptoms, making an accurate diagnosis and targeted intervention difficult. Our study employs advanced machine learning techniques to analyze functional magnetic resonance imaging (fMRI) data from individuals with ASD, ADHD, and typically developed (TD) controls, totaling 120 subjects in the study. Leveraging multiclass classification (ML) algorithms, we achieve superior accuracy in distinguishing between ASD, ADHD, and TD groups, surpassing existing benchmarks with an area under the ROC curve near 98%. Our analysis reveals distinct neural signatures associated with ASD and ADHD: individuals with ADHD exhibit altered connectivity patterns of regions involved in attention and impulse control, whereas those with ASD show disruptions in brain regions critical for social and cognitive functions. The observed connectivity patterns, on which the ML classification rests, agree with established diagnostic approaches based on clinical symptoms. Furthermore, complex network analyses highlight differences in brain network integration and segregation among the three groups. Our findings pave the way for refined, ML-enhanced diagnostics in accordance with established practices, offering a promising avenue for developing trustworthy clinical decision-support systems.

Detecting cardiac states with wearable photoplethysmograms and implications for out-of-hospital cardiac arrest detection

Out-of-hospital cardiac arrest (OHCA) is a global health problem affecting approximately 4.4 million individuals yearly. OHCA has a poor survival rate, specifically when unwitnessed (accounting for up to 75% of cases). Rapid recognition can significantly improve OHCA survival, and consumer wearables with continuous cardiopulmonary monitoring capabilities hold potential to "witness" cardiac arrest and activate emergency services. In this study, we used an arterial occlusion model to simulate cardiac arrest and investigated the ability of infrared photoplethysmogram (PPG) sensors, often utilized in consumer wearable devices, to differentiate normal cardiac pulsation, pulseless cardiac (i.e., resembling a cardiac arrest), and non-physiologic (i.e., off-body) states. Across the classification models trained and evaluated on three anatomical locations, higher classification performances were observed on the finger (macro average F1-score of 0.964 on the fingertip and 0.954 on the finger base) compared to the wrist (macro average F1-score of 0.837). The wrist-based classification model, which was trained and evaluated using all PPG measurements, including both high- and low-quality recordings, achieved a macro average precision and recall of 0.922 and 0.800, respectively. This wrist-based model, which represents the most common form factor in consumer wearables, could only capture about 43.8% of pulseless events. However, models trained and tested exclusively on high-quality recordings achieved higher classification outcomes (macro average F1-score of 0.975 on the fingertip, 0.973 on the finger base, and 0.934 on the wrist). The fingertip model had the highest performance to differentiate arterial occlusion pulselessness from normal cardiac pulsation and off-body measurements with macro average precision and recall of 0.978 and 0.972, respectively. This model was able to identify 93.7% of pulseless states (i.e., resembling a cardiac arrest event), with a 0.4% false positive rate. All classification models relied on a combination of time-, power spectral density (PSD)-, and frequency-domain features to differentiate normal cardiac pulsation, pulseless cardiac, and off-body PPG recordings. However, our best model represented an idealized detection condition, relying on ensuring high-quality PPG data for training and evaluation of machine learning algorithms. While 90.7% of our PPG recordings from the fingertip were considered of high quality, only 53.2% of the measurements from the wrist passed the quality criteria. Our findings have implications for adapting consumer wearables to provide OHCA detection, involving advancements in hardware and software to ensure high-quality measurements in real-world settings, as well as development of wearables with form factors that enable high-quality PPG data acquisition more consistently. Given these improvements, we demonstrate that OHCA detection can feasibly be made available to anyone using PPG-based consumer wearables.

Cardiac arrest and cardiopulmonary resuscitation in the next decade: Predicting and shaping the impact of technological innovations

INTRODUCTION

Cardiac arrest (CA) is the third leading cause of death, with persistently low survival rates despite medical advancements. This article evaluates the potential of emerging technologies to enhance CA management over the next decade, using predictions from the AI tools ChatGPT-4 and Gemini Advanced.

METHODS

We conducted an exploratory literature review to envision the future of cardiopulmonary arrest (CA) management. Utilizing ChatGPT-4 and Gemini Advanced, we predicted implementation timelines for innovations in early recognition, CPR, defibrillation, and post-resuscitation care. We also consulted the AI to assess the consistency and reproducibility of the predictions.

RESULTS

We extrapolate that healthcare may embrace new technologies, such as comprehensive monitoring of vital signs to activate the emergency system (wireless detectors, smart speakers, and wearable devices), use new innovative early CPR and early AED devices (robot CPR, wearable AEDs, and immersive reality), and post-resuscitation care monitoring (brain-computer interface). These technologies could enhance timely life-saving interventions for cardiac arrest. However, there are many ethical and practical challenges, particularly in maintaining patient privacy and equity. The two AI tools made different predictions, with a horizon for implementation ranging between three and eight years.

CONCLUSION

Integrating advanced monitoring technologies and AI-driven tools offers hope in improving CA management. A balanced approach involving rigorous scientific validation and ethical oversight is necessary. Collaboration among technologists, medical professionals, ethicists, and policymakers is crucial to use these innovations ethically to reduce CA incidence and enhance outcomes. Further research is needed to enhance the reliability of AI predictive capabilities.

Cardiorespiratory Sensors and Their Implications for Out-of-Hospital Cardiac Arrest Detection: A Systematic Review

Out-of-hospital cardiac arrest (OHCA) is a major health problem, with a poor survival rate of 2-11%. For the roughly 75% of OHCAs that are unwitnessed, survival is approximately 2-4.4%, as there are no bystanders present to provide life-saving interventions and alert Emergency Medical Services. Sensor technologies may reduce the number of unwitnessed OHCAs through automated detection of OHCA-associated physiological changes. However, no technologies are widely available for OHCA detection. This review identifies research and commercial technologies developed for cardiopulmonary monitoring that may be best suited for use in the context of OHCA, and provides recommendations for technology development, testing, and implementation. We conducted a systematic review of published studies along with a search of grey literature to identify technologies that were able to provide cardiopulmonary monitoring, and could be used to detect OHCA. We searched MEDLINE, EMBASE, Web of Science, and Engineering Village using MeSH keywords. Following inclusion, we summarized trends and findings from included studies. Our searches retrieved 6945 unique publications between January, 1950 and May, 2023. 90 studies met the inclusion criteria. In addition, our grey literature search identified 26 commercial technologies. Among included technologies, 52% utilized electrocardiography (ECG) and 40% utilized photoplethysmography (PPG) sensors. Most wearable devices were multi-modal (59%), utilizing more than one sensor simultaneously. Most included devices were wearable technologies (84%), with chest patches (22%), wrist-worn devices (18%), and garments (14%) being the most prevalent. ECG and PPG sensors are heavily utilized in devices for cardiopulmonary monitoring that could be adapted to OHCA detection. Developers seeking to rapidly develop methods for OHCA detection should focus on using ECG- and/or PPG-based multimodal systems as these are most prevalent in existing devices. However, novel sensor technology development could overcome limitations in existing sensors and could serve as potential additions to or replacements for ECG- and PPG-based devices.

Wolf creek XVII part 4: Amplifying lay-rescuer response

Introduction

Amplifying lay-rescuer response is a key priority to increase survival from out-of-hospital cardiac arrest (OHCA). We describe the current state of lay-rescuer response, how we envision the future, and the gaps, barriers, and research priorities that will amplify response to OHCA.

Methods

'Amplifying Lay-Rescuer Response' was one of six focus topics for the Wolf Creek XVII Conference held on June 14-17, 2023, in Ann Arbor, Michigan, USA. Conference invitees included international thought leaders and scientists in the field of cardiac arrest resuscitation from academia and industry. Participants submitted via online survey knowledge gaps, barriers to translation and research priorities for each focus topic. Expert panels used the survey results and their own perspectives and insights to create and present a preliminary unranked list for each category that was debated, revised and ranked by all attendees to identify the top 5 for each category.

Results

The top five knowledge gaps as ranked by the panel, reflected a recognition of the need to better understand the psycho-social aspects of lay response. The top five barriers to translation reflected issues at the individual, community, societal, structural, and governmental levels. The top five research priorities were focused on understanding the social/psychological and emotional barriers to action, finding the most effective/cost-effective strategies to educate lay persons and implement community life-saving interventions, evaluation of new technological solutions and how to enhance the role of dispatch working with lay-rescuers.

Conclusion

Future research in lay rescuer response should incorporate technology innovations, understand the "humanity" of the situation, leverage implementation science and systems thinking to save lives. This will require the field of resuscitation to engage with scholars outside our traditional ranks and to be open to new ways of thinking about old problems.

Rationale and design of the BECA project: Smartwatch-based activation of the chain of survival for out-of-hospital cardiac arrest

Aim

Out-of-hospital cardiac arrest is a major health problem, and the overall survival rate is low (4.6%-16.4%). The initiation of the current chain of survival depends on the presence of a witness of the cardiac arrest, which is not present in 29.7%-63.4% of the cases. Furthermore, a delay in starting this chain is common in witnessed out-of-hospital cardiac arrest. This project aims to reduce morbidity and mortality due to out-of-hospital cardiac arrest by developing a smartwatch-based solution to expedite the chain of survival in the case of (un)witnessed out-of-hospital cardiac arrest.

Methods

Within the 'Beating Cardiac Arrest' project, we aim to develop a demonstrator product that detects out-of-hospital cardiac arrest using photoplethysmography and accelerometer analysis, and autonomously alerts emergency medical services. A target group study will be performed to determine who benefits the most from this product. Furthermore, several clinical studies will be conducted to capture or simulate data on out-of-hospital cardiac arrest cases, as to develop detection algorithms and validate their diagnostic performance. For this, the product will be worn by patients at high risk for out-of-hospital cardiac arrest, by volunteers who will temporarily interrupt blood flow in their arm by inflating a blood pressure cuff, and by patients who undergo cardiac electrophysiologic and implantable cardioverter defibrillator testing procedures. Moreover, studies on psychosocial and ethical acceptability will be conducted, consisting of surveys, focus groups, and interviews. These studies will focus on end-user preferences and needs, to ensure that important individual and societal values are respected in the design process.

Wolf Creek XVII part 9: Wolf Creek Innovator in Cardiac Arrest and Resuscitation Science Award

The Wolf Creek Conferences on Cardiac Arrest Resuscitation began in 1975, and have served as an important forum for thought leaders and scientists from industry and academia to come together with the common goal of advancing the field of cardiac arrest resuscitation. The Wolf Creek XVII Conference was hosted by the Max Harry Weil Institute of Critical Care Research and Innovation in Ann Arbor, Michigan on June 14-17, 2023. A new component of the conference was the Wolf Creek Innovator in Cardiac Arrest and Resuscitation Science Award competition. The competition was designed to recognize early career investigators from around the world who's science is challenging the current paradigms in the field. Finalists were selected by a panel of international experts and invited to present in-person at the conference. The winner was chosen by electronic vote of conference participants and awarded a $10,0000 cash prize. Finalists included Carolina Barbosa Maciel from the University of Florida, Adam Gottula from the University of Michigan, Rajat Kalra from the University of Minnesota, Ryan Morgan from the Children's Hospital of Philadelphia, Mitsuaki Nishikimi form Hiroshima University, and Jacob Sunshine from the University of Washington. Ryan Morgan from the Children's Hospital of Philadelphia was selected as the 2023 Wolf Creek Innovator Awardee. This manuscript provides a summary of the work presented by each of the finalists and provides a preview of the future of resuscitation science.

Automated cardiac arrest detection using a photoplethysmography wristband: algorithm development and validation in patients with induced circulatory arrest in the DETECT-1 study

BACKGROUND

Unwitnessed out-of-hospital cardiac arrest is associated with low survival chances because of the delayed activation of the emergency medical system in most cases. Automated cardiac arrest detection and alarming using biosensor technology would offer a potential solution to provide early help. We developed and validated an algorithm for automated circulatory arrest detection using wrist-derived photoplethysmography from patients with induced circulatory arrests.

METHODS

In this prospective multicentre study in three university medical centres in the Netherlands, adult patients (aged 18 years or older) in whom short-lasting circulatory arrest was induced as part of routine practice (transcatheter aortic valve implantation, defibrillation testing, or ventricular tachycardia induction) were eligible for inclusion. Exclusion criteria were a known bilateral significant subclavian artery stenosis or medical issues interfering with the wearing of the wristband. After providing informed consent, patients were equipped with a photoplethysmography wristband during the procedure. Invasive arterial blood pressure and electrocardiography were continuously monitored as the reference standard. Development of the photoplethysmography algorithm was based on three consecutive training cohorts. For each cohort, patients were consecutively enrolled. When a total of 50 patients with at least one event of circulatory arrest were enrolled, that cohort was closed. Validation was performed on the fourth set of included patients. The primary outcome was sensitivity for the detection of circulatory arrest.

FINDINGS

Of 306 patients enrolled between March 14, 2022, and April 21, 2023, 291 patients were included in the data analysis. In the development phase (n=205), the first training set yielded a sensitivity for circulatory arrest detection of 100% (95% CI 94-100) and four false positive alarms; the second training set yielded a sensitivity of 100% (94-100), with six false positive alarms; and the third training set yielded a sensitivity of 100% (94-100), with two false positive alarms. In the validation phase (n=86), the sensitivity for circulatory arrest detection was 98% (92-100) and 11 false positive circulatory arrest alarms. The positive predictive value was 90% (95% CI 82-94).

INTERPRETATION

The automated detection of induced circulatory arrests using wrist-derived photoplethysmography is feasible with good sensitivity and low false positives. These promising findings warrant further development of this wearable technology to enable automated cardiac arrest detection and alarming in a home setting.

FUNDING

Dutch Heart Foundation (Hartstichting).

Ambient health sensing on passive surfaces using metamaterials

Ambient sensors can continuously and unobtrusively monitor a person's health and well-being in everyday settings. Among various sensing modalities, wireless radio-frequency sensors offer exceptional sensitivity, immunity to lighting conditions, and privacy advantages. However, existing wireless sensors are susceptible to environmental interference and unable to capture detailed information from multiple body sites. Here, we present a technique to transform passive surfaces in the environment into highly sensitive and localized health sensors using metamaterials. Leveraging textiles' ubiquity, we engineer metamaterial textiles that mediate near-field interactions between wireless signals and the body for contactless and interference-free sensing. We demonstrate that passive surfaces functionalized by these metamaterials can provide hours-long cardiopulmonary monitoring with accuracy comparable to gold standards. We also show the potential of distributed sensors and machine learning for continuous blood pressure monitoring. Our approach enables passive environmental surfaces to be harnessed for ambient sensing and digital health applications.

Wolf Creek XVII part 3: Automated cardiac arrest diagnosis

Introduction

Automated cardiac arrest diagnosis offers the possibility to significantly shorten the interval between onset of out-of-hospital cardiac arrest (OHCA) and notification of EMS, providing the opportunity for earlier resuscitation and possibly increased survival.

Methods

Automated cardiac arrest diagnosis was one of six focus topics for the Wolf Creek XVII Conference held on June 14-17 2023 in Ann Arbor, Michigan, USA. Conference invitees included international thought leaders and scientists in the field of cardiac arrest resuscitation from academia and industry. Participants submitted via online survey knowledge gaps, barriers to translation and research priorities for each focus topic. Expert panels used the survey results and their own perspectives and insights to create and present a preliminary unranked list for each category that was debated, revised and ranked by all attendees to identify the top 5 for each category.

Results

Top knowledge gaps include the accuracy of automated OHCA detection technologies and the feasibility and reliability of automated EMS activation. The main barriers to translation are the risk of false positives potentially overburdening EMS, development and application costs of technology and the challenge of integrating new technology in EMS IT systems. The top research priorities are large-scale evaluation studies to measure real world performance and user research regarding the willingness to adopt these technologies.

Conclusion

Automated cardiac arrest diagnosis has the potential to significantly impact time to resuscitation and survival of OHCA because it could convert unwitnessed events into witnessed events. Validation and feasibility studies are needed. The specificity of the technology must be high not to overburden limited EMS resources. If adequate event classification is achieved, future research could shift toward event prediction, focusing on identifying potential digital biomarkers and signatures of imminent cardiac arrest. Implementation could be challenging due to high costs of development, regulatory considerations and instantiation logistics.

Analysis of functional connectivity using machine learning and deep learning in different data modalities from individuals with schizophrenia

Objective. Schizophrenia(SCZ) is a severe mental disorder associated with persistent or recurrent psychosis, hallucinations, delusions, and thought disorders that affect approximately 26 million people worldwide, according to the World Health Organization. Several studies encompass machine learning (ML) and deep learning algorithms to automate the diagnosis of this mental disorder. Others study SCZ brain networks to get new insights into the dynamics of information processing in individuals suffering from the condition. In this paper, we offer a rigorous approach with ML and deep learning techniques for evaluating connectivity matrices and measures of complex networks to establish an automated diagnosis and comprehend the topology and dynamics of brain networks in SCZ individuals.Approach.For this purpose, we employed an functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) dataset. In addition, we combined EEG measures, i.e. Hjorth mobility and complexity, with complex network measurements to be analyzed in our model for the first time in the literature.Main results.When comparing the SCZ group to the control group, we found a high positive correlation between the left superior parietal lobe and the left motor cortex and a positive correlation between the left dorsal posterior cingulate cortex and the left primary motor. Regarding complex network measures, the diameter, which corresponds to the longest shortest path length in a network, may be regarded as a biomarker because it is the most crucial measure in different data modalities. Furthermore, the SCZ brain networks exhibit less segregation and a lower distribution of information. As a result, EEG measures outperformed complex networks in capturing the brain alterations associated with SCZ.Significance. Our model achieved an area under receiver operating characteristic curve (AUC) of 100% and an accuracy of 98.5% for the fMRI, an AUC of 95%, and an accuracy of 95.4% for the EEG data set. These are excellent classification results. Furthermore, we investigated the impact of specific brain connections and network measures on these results, which helped us better describe changes in the diseased brain.

The Lancet Commission to reduce the global burden of sudden cardiac death: a call for multidisciplinary action

Despite major advancements in cardiovascular medicine, sudden cardiac death (SCD) continues to be an enormous medical and societal challenge, claiming millions of lives every year. Efforts to prevent SCD are hampered by imperfect risk prediction and inadequate solutions to specifically address arrhythmogenesis. Although resuscitation strategies have witnessed substantial evolution, there is a need to strengthen the organisation of community interventions and emergency medical systems across varied locations and health-care structures. With all the technological and medical advances of the 21st century, the fact that survival from sudden cardiac arrest (SCA) remains lower than 10% in most parts of the world is unacceptable. Recognising this urgent need, the Lancet Commission on SCD was constituted, bringing together 30 international experts in varied disciplines. Consistent progress in tackling SCD will require a completely revamped approach to SCD prevention, with wide-sweeping policy changes that will empower the development of both governmental and community-based programmes to maximise survival from SCA, and to comprehensively attend to survivors and decedents' families after the event. International collaborative efforts that maximally leverage and connect the expertise of various research organisations will need to be prioritised to properly address identified gaps. The Commission places substantial emphasis on the need to develop a multidisciplinary strategy that encompasses all aspects of SCD prevention and treatment. The Commission provides a critical assessment of the current scientific efforts in the field, and puts forth key recommendations to challenge, activate, and intensify efforts by both the scientific and global community with new directions, research, and innovation to reduce the burden of SCD worldwide.

Perspective of Artificial Intelligence in Disease Diagnosis: A Review of Current and Future Endeavours in the Medical Field

Artificial intelligence (AI) has demonstrated significant promise for the present and future diagnosis of diseases. At the moment, AI-powered diagnostic technologies can help physicians decipher medical pictures like X-rays, magnetic resonance imaging, and computed tomography scans, resulting in quicker and more precise diagnoses. In order to make a prospective diagnosis, AI algorithms may also examine patient information, symptoms, and medical background. The application of AI in disease diagnosis is anticipated to grow as the field develops. In the future, AI may be used to find patterns in enormous volumes of medical data, aiding in disease prediction and prevention before symptoms appear. Additionally, by combining genetic data, lifestyle data, and environmental variables, AI may help in the diagnosis of complicated diseases. It is crucial to remember that while AI can be a powerful tool, it cannot take the place of qualified medical personnel. Instead, AI ought to support and improve diagnostic procedures, enhancing patient care and healthcare results. Future research and the use of AI for disease diagnosis must take ethical issues, data protection, and ongoing model validation into account.

Video characteristics for remote recognition of agonal respiration: A pilot study

Aim

The mobile network quality in ambulances can be variable and limited. This pilot study aimed to identify a suitable network setting for recognizing agonal respiration under limited network conditions.

Methods

We recruited five emergency medical technicians, and each participant viewed 30 real-life videos with different resolutions, frame rates, and network scenarios. Thereafter, they reported the respiration pattern of the patient and identified agonal respiration cases. The time at which agonal respiration was identified was also recorded. The answers provided by the five participants were compared with those of two emergency physicians to compare the accuracy and time delay in breathing pattern recognition.

Results

The overall accuracy for initial respiratory pattern recognition was 80.7% (121/150). The accuracy for normal breathing was 93.3% (28/30), for not breathing was 96% (48/50), and for agonal breathing was 64.3% (45/70). There was no significant difference in successful recognition between video resolutions. However, the rate of time delay in recognizing agonal respiration less than 10 seconds between 15-fps group and 30-fps group had statistical significance (21% vs 52%, p = 0.041).

Conclusion

The frame rate emerges as one of critical factors in agonal respiration recognition through telemedicine, outweighing the significance of video resolution.

Artificial intelligence and machine learning in prehospital emergency care: A scoping review

Our scoping review provides a comprehensive analysis of the landscape of artificial intelligence (AI) applications in prehospital emergency care (PEC). It contributes to the field by highlighting the most studied AI applications and identifying the most common methodological approaches across 106 included studies. The findings indicate a promising future for AI in PEC, with many unique use cases, such as prognostication, demand prediction, resource optimization, and the Internet of Things continuous monitoring systems. Comparisons with other approaches showed AI outperforming clinicians and non-AI algorithms in most cases. However, most studies were internally validated and retrospective, highlighting the need for rigorous prospective validation of AI applications before implementation in clinical settings. We identified knowledge and methodological gaps using an evidence map, offering a roadmap for future investigators. We also discussed the significance of explainable AI for establishing trust in AI systems among clinicians and facilitating real-world validation of AI models.

Diagnosis of autism spectrum disorder based on functional brain networks and machine learning

Autism is a multifaceted neurodevelopmental condition whose accurate diagnosis may be challenging because the associated symptoms and severity vary considerably. The wrong diagnosis can affect families and the educational system, raising the risk of depression, eating disorders, and self-harm. Recently, many works have proposed new methods for the diagnosis of autism based on machine learning and brain data. However, these works focus on only one pairwise statistical metric, ignoring the brain network organization. In this paper, we propose a method for the automatic diagnosis of autism based on functional brain imaging data recorded from 500 subjects, where 242 present autism spectrum disorder considering the regions of interest throughout Bootstrap Analysis of Stable Cluster map. Our method can distinguish the control group from autism spectrum disorder patients with high accuracy. Indeed the best performance provides an AUC near 1.0, which is higher than that found in the literature. We verify that the left ventral posterior cingulate cortex region is less connected to an area in the cerebellum of patients with this neurodevelopment disorder, which agrees with previous studies. The functional brain networks of autism spectrum disorder patients show more segregation, less distribution of information across the network, and less connectivity compared to the control cases. Our workflow provides medical interpretability and can be used on other fMRI and EEG data, including small data sets.

An ultralow power wearable vital sign sensor using an electromagnetically reactive near field

Despite coronavirus disease 2019, cardiovascular disease, the leading cause of global death, requires timely detection and treatment for a high survival rate, underscoring the 24 h monitoring of vital signs. Therefore, telehealth using wearable devices with vital sign sensors is not only a fundamental response against the pandemic but a solution to provide prompt healthcare for the patients in remote sites. Former technologies which measured a couple of vital signs had features that disturbed practical applications to wearable devices, such as heavy power consumption. Here, we suggest an ultralow power (100 μW) sensor that collects all cardiopulmonary vital signs, including blood pressure, heart rate, and the respiration signal. The small and lightweight (2 g) sensor designed to be easily embedded in the flexible wristband generates an electromagnetically reactive near field to monitor the contraction and relaxation of the radial artery. The proposed ultralow power sensor measuring noninvasively continuous and accurate cardiopulmonary vital signs at once will be one of the most promising sensors for wearable devices to bring telehealth to our lives.

Public access defibrillation: challenges and new solutions

PURPOSE OF REVIEW

The purpose of this article is to review the current status of public access defibrillation and the various utility modalities of early defibrillation.

RECENT FINDINGS

Defibrillation with on-site automated external defibrillators (AEDs) has been the conventional approach for public access defibrillation. This strategy is highly effective in cardiac arrests occurring in close proximity to on-site AEDs; however, only a few cardiac arrests will be covered by this strategy. During the last decades, additional strategies for public access defibrillation have developed, including volunteer responder programmes and drone assisted AED-delivery. These programs have increased chances of early defibrillation within a greater radius, which remains an important factor for survival after out-of-hospital cardiac arrest.

SUMMARY

Recent advances in the use of public access defibrillation show great potential for optimizing early defibrillation. With new technological solutions, AEDs can be transported to the cardiac arrest location reaching OHCAs in both public and private locations. Furthermore, new technological innovations could potentially identify and automatically alert the emergency medical services in nonwitnessed OHCA previously left untreated.

Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments

Ayahuasca is a blend of Amazonian plants that has been used for traditional medicine by the inhabitants of this region for hundreds of years. Furthermore, this plant has been demonstrated to be a viable therapy for a variety of neurological and mental diseases. EEG experiments have found specific brain regions that changed significantly due to ayahuasca. Here, we used an EEG dataset to investigate the ability to automatically detect changes in brain activity using machine learning and complex networks. Machine learning was applied at three different levels of data abstraction: (A) the raw EEG time series, (B) the correlation of the EEG time series, and (C) the complex network measures calculated from (B). Further, at the abstraction level of (C), we developed new measures of complex networks relating to community detection. As a result, the machine learning method was able to automatically detect changes in brain activity, with case (B) showing the highest accuracy (92%), followed by (A) (88%) and (C) (83%), indicating that connectivity changes between brain regions are more important for the detection of ayahuasca. The most activated areas were the frontal and temporal lobe, which is consistent with the literature. F3 and PO4 were the most important brain connections, a significant new discovery for psychedelic literature. This connection may point to a cognitive process akin to face recognition in individuals during ayahuasca-mediated visual hallucinations. Furthermore, closeness centrality and assortativity were the most important complex network measures. These two measures are also associated with diseases such as Alzheimer's disease, indicating a possible therapeutic mechanism. Moreover, the new measures were crucial to the predictive model and suggested larger brain communities associated with the use of ayahuasca. This suggests that the dissemination of information in functional brain networks is slower when this drug is present. Overall, our methodology was able to automatically detect changes in brain activity during ayahuasca consumption and interpret how these psychedelics alter brain networks, as well as provide insights into their mechanisms of action.

Wearables in Sports Cardiology

The expanding array and adoption of consumer health wearables is creating a new dynamic to the patient-health-care provider relationship. Providers are increasingly tasked with integrating the biometric data collected from their patients into clinical care. Further, a growing body of evidence is supporting the provider-driven utility of wearables in the screening, diagnosis, and monitoring of cardiovascular disease. Here we highlight existing and emerging wearable health technologies and the potential applications for use within sports cardiology. We additionally highlight how wearables can advance the remote cardiovascular care of patients within the context of the COVID-19 pandemic. Finally, despite these promising advances, we acknowledge some of the significant challenges that remain before wearables can be routinely incorporated into clinical care.

Emerging Artificial Intelligence–Empowered mHealth: Scoping Review

Background

Artificial intelligence (AI) has revolutionized health care delivery in recent years. There is an increase in research for advanced AI techniques, such as deep learning, to build predictive models for the early detection of diseases. Such predictive models leverage mobile health (mHealth) data from wearable sensors and smartphones to discover novel ways for detecting and managing chronic diseases and mental health conditions.

Objective

Currently, little is known about the use of AI-powered mHealth (AIM) settings. Therefore, this scoping review aims to map current research on the emerging use of AIM for managing diseases and promoting health. Our objective is to synthesize research in AIM models that have increasingly been used for health care delivery in the last 2 years.

Methods

Using Arksey and O’Malley’s 5-point framework for conducting scoping reviews, we reviewed AIM literature from the past 2 years in the fields of biomedical technology, AI, and information systems. We searched 3 databases, PubsOnline at INFORMS, e-journal archive at MIS Quarterly, and Association for Computing Machinery (ACM) Digital Library using keywords such as “mobile healthcare,” “wearable medical sensors,” “smartphones”, and “AI.” We included AIM articles and excluded technical articles focused only on AI models. We also used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) technique for identifying articles that represent a comprehensive view of current research in the AIM domain.

Results

We screened 108 articles focusing on developing AIM models for ensuring better health care delivery, detecting diseases early, and diagnosing chronic health conditions, and 37 articles were eligible for inclusion, with 31 of the 37 articles being published last year (76%). Of the included articles, 9 studied AI models to detect serious mental health issues, such as depression and suicidal tendencies, and chronic health conditions, such as sleep apnea and diabetes. Several articles discussed the application of AIM models for remote patient monitoring and disease management. The considered primary health concerns belonged to 3 categories: mental health, physical health, and health promotion and wellness. Moreover, 14 of the 37 articles used AIM applications to research physical health, representing 38% of the total studies. Finally, 28 out of the 37 (76%) studies used proprietary data sets rather than public data sets. We found a lack of research in addressing chronic mental health issues and a lack of publicly available data sets for AIM research.

Conclusions

The application of AIM models for disease detection and management is a growing research domain. These models provide accurate predictions for enabling preventive care on a broader scale in the health care domain. Given the ever-increasing need for remote disease management during the pandemic, recent AI techniques, such as federated learning and explainable AI, can act as a catalyst for increasing the adoption of AIM and enabling secure data sharing across the health care industry.

How technology can save lives in cardiac arrest

PURPOSE OF REVIEW

Technology is being increasingly implemented in the fields of cardiac arrest and cardiopulmonary resuscitation. In this review, we describe how recent technological advances have been implemented in the chain of survival and their impact on outcomes after cardiac arrest. Breakthrough technologies that are likely to make an impact in the future are also presented.

RECENT FINDINGS

Technology is present in every link of the chain of survival, from prediction, prevention, and rapid recognition of cardiac arrest to early cardiopulmonary resuscitation and defibrillation. Mobile phone systems to notify citizen first responders of nearby out-of-hospital cardiac arrest have been implemented in numerous countries with improvement in bystanders' interventions and outcomes. Drones delivering automated external defibrillators and artificial intelligence to support the dispatcher in recognising cardiac arrest are already being used in real-life out-of-hospital cardiac arrest. Wearables, smart speakers, surveillance cameras, and artificial intelligence technologies are being developed and studied to prevent and recognize out-of-hospital and in-hospital cardiac arrest.

SUMMARY

This review highlights the importance of technology applied to every single step of the chain of survival to improve outcomes in cardiac arrest. Further research is needed to understand the best role of different technologies in the chain of survival and how these may ultimately improve outcomes.

Responsible nudging for social good: new healthcare skills for AI-driven digital personal assistants

Traditional medical practices and relationships are changing given the widespread adoption of AI-driven technologies across the various domains of health and healthcare. In many cases, these new technologies are not specific to the field of healthcare. Still, they are existent, ubiquitous, and commercially available systems upskilled to integrate these novel care practices. Given the widespread adoption, coupled with the dramatic changes in practices, new ethical and social issues emerge due to how these systems nudge users into making decisions and changing behaviours. This article discusses how these AI-driven systems pose particular ethical challenges with regards to nudging. To confront these issues, the value sensitive design (VSD) approach is adopted as a principled methodology that designers can adopt to design these systems to avoid harming and contribute to the social good. The AI for Social Good (AI4SG) factors are adopted as the norms constraining maleficence. In contrast, higher-order values specific to AI, such as those from the EU High-Level Expert Group on AI and the United Nations Sustainable Development Goals, are adopted as the values to be promoted as much as possible in design. The use case of Amazon Alexa's Healthcare Skills is used to illustrate this design approach. It provides an exemplar of how designers and engineers can begin to orientate their design programs of these technologies towards the social good.

Smart Speakers: The Next Frontier in mHealth

The rapid dissemination and adoption of smart speakers has enabled substantial opportunities to improve human health. Just as the introduction of the mobile phone led to considerable health innovation, smart speaker computing systems carry several unique advantages that have the potential to catalyze new fields of health research, particularly in out-of-hospital environments. The recent rise and ubiquity of these smart computing systems holds significant potential for enhancing chronic disease management, enabling passive identification of unwitnessed medical emergencies, detecting subtle changes in human behavior and cognition, limiting isolation, and potentially allowing widespread, passive, remote monitoring of respiratory diseases that impact public health. There are 3 broad mechanisms for how a smart speaker can interact with a person to improve health. These include (1) as an intelligent conversational agent, (2) as a passive identifier of medically relevant diagnostic sounds, and (3) by active sensing using the device's internal hardware to measure physiologic parameters, such as with active sonar, radar, or computer vision. Each of these different modalities has specific clinical use cases, all of which need to be balanced against potential privacy concerns, equity concerns related to system access, and regulatory frameworks which have not yet been developed for this unique type of passive data collection.

Optimizing Outcomes After Out-of-Hospital Cardiac Arrest With Innovative Approaches to Public-Access Defibrillation: A Scientific Statement From the International Liaison Committee on Resuscitation

Out-of-hospital cardiac arrest is a global public health issue experienced by ≈3.8 million people annually. Only 8% to 12% survive to hospital discharge. Early defibrillation of shockable rhythms is associated with improved survival, but ensuring timely access to defibrillators has been a significant challenge. To date, the development of public-access defibrillation programs, involving the deployment of automated external defibrillators into the public space, has been the main strategy to address this challenge. Public-access defibrillator programs have been associated with improved outcomes for out-of-hospital cardiac arrest; however, the devices are used in <3% of episodes of out-of-hospital cardiac arrest. This scientific statement was commissioned by the International Liaison Committee on Resuscitation with 3 objectives: (1) identify known barriers to public-access defibrillator use and early defibrillation, (2) discuss established and novel strategies to address those barriers, and (3) identify high-priority knowledge gaps for future research to address. The writing group undertook systematic searches of the literature to inform this statement. Innovative strategies were identified that relate to enhanced public outreach, behavior change approaches, optimization of static public-access defibrillator deployment and housing, evolved automated external defibrillator technology and functionality, improved integration of public-access defibrillation with existing emergency dispatch protocols, and exploration of novel automated external defibrillator delivery vectors. We provide evidence- and consensus-based policy suggestions to enhance public-access defibrillation and guidance for future research in this area.

Optimizing outcomes after out-of-hospital cardiac arrest with innovative approaches to public-access defibrillation: A scientific statement from the International Liaison Committee on Resuscitation

Out-of-hospital cardiac arrest is a global public health issue experienced by ≈3.8 million people annually. Only 8% to 12% survive to hospital discharge. Early defibrillation of shockable rhythms is associated with improved survival, but ensuring timely access to defibrillators has been a significant challenge. To date, the development of public-access defibrillation programs, involving the deployment of automated external defibrillators into the public space, has been the main strategy to address this challenge. Public-access defibrillator programs have been associated with improved outcomes for out-of-hospital cardiac arrest; however, the devices are used in <3% of episodes of out-of-hospital cardiac arrest. This scientific statement was commissioned by the International Liaison Committee on Resuscitation with 3 objectives: (1) identify known barriers to public-access defibrillator use and early defibrillation, (2) discuss established and novel strategies to address those barriers, and (3) identify high-priority knowledge gaps for future research to address. The writing group undertook systematic searches of the literature to inform this statement. Innovative strategies were identified that relate to enhanced public outreach, behavior change approaches, optimization of static public-access defibrillator deployment and housing, evolved automated external defibrillator technology and functionality, improved integration of public-access defibrillation with existing emergency dispatch protocols, and exploration of novel automated external defibrillator delivery vectors. We provide evidence- and consensus-based policy suggestions to enhance public-access defibrillation and guidance for future research in this area.

A Smart System for the Contactless Measurement of Energy Expenditure

Energy Expenditure (EE) (kcal/day), a key element to guide obesity treatment, is measured from CO2 production, VCO2 (mL/min), and/or O2 consumption, VO2 (mL/min). Current technologies are limited due to the requirement of wearable facial accessories. A novel system, the Smart Pad, which measures EE via VCO2 from a room's ambient CO2 concentration transients was evaluated. Resting EE (REE) and exercise VCO2 measurements were recorded using Smart Pad and a reference instrument to study measurement duration's influence on accuracy. The Smart Pad displayed 90% accuracy (±1 SD) for 14-19 min of REE measurement and for 4.8-7.0 min of exercise, using known room's air exchange rate. Additionally, the Smart Pad was validated measuring subjects with a wide range of body mass indexes (BMI = 18.8 to 31.4 kg/m2), successfully validating the system accuracy across REE's measures of ~1200 to ~3000 kcal/day. Furthermore, high correlation between subjects' VCO2 and λ for CO2 accumulation was observed (p < 0.00001, R = 0.785) in a 14.0 m3 sized room. This finding led to development of a new model for REE measurement from ambient CO2 without λ calibration using a reference instrument. The model correlated in nearly 100% agreement with reference instrument measures (y = 1.06x, R = 0.937) using an independent dataset (N = 56).

Responding to Cardiac Arrest in the Community in the Digital Age

Sudden cardiac arrest (SCA) is a common event, affecting almost 400,000 individuals annually in North America. Initiation of cardiopulmonary resuscitation (CPR) and early defibrillation using an automated external defibrillator (AED) are critical for survival, yet many bystanders are reluctant to intervene. Digital technologies, including mobile devices, social media and crowdsourcing may help play a role to improve survival from SCA. In this article we review the current digital tools and strategies available to increase rates of bystander recognition of SCA, prompt immediate activation of Emergency Medical Services (EMS), initiate high quality CPR and to locate, retrieve and operate AEDs. Smartphones can help to both educate and connect bystanders with EMS dispatchers, through text messaging or video-calling, to encourage the initiation of CPR and retrieval of the closest AED. Wearable devices and household smartspeakers could play a future role in continuous vital signs monitoring in individuals at-risk of lethal arrhythmias and send an alert to either chosen contacts or EMS. Machine learning algorithms and mathematical modeling may aid EMS dispatchers with better recognition of SCA as well as policymakers with where to best place AEDs for optimal accessibility. There are challenges with the use of digital tech, including the need for government regulation and issues with data ownership, accessibility and interoperability. Future research will include smart cities, e-linkages, new technologies and using social media for mass education. Together or in combination, these emerging digital technologies may represent the next leap forward in SCA survival.

Moderating effects of out-of-hospital cardiac arrest characteristics on the association between EMS response time and survival

BACKGROUND

Although several Utstein variables are known to independently improve survival, how they moderate the effect of emergency medical service (EMS) response times on survival is unknown.

OBJECTIVES

To quantify how public location, witnessed status, bystander CPR, and bystander AED shock individually and jointly moderate the effect of EMS response time delays on OHCA survival.

METHODS

This retrospective cohort study was a secondary analysis of the Resuscitation Outcomes Consortium Epistry-Cardiac Arrest database (December 2005 to June 2015). We included all adult, non-traumatic, non-EMS witnessed, and EMS-treated OHCAs from eleven sites across the US and Canada. We trained a logistic regression model with standard Utstein control variables and interaction terms between EMS response time and the four aforementioned OHCA characteristics.

RESULTS

102,216 patients were included. Three of the four characteristics - witnessed OHCAs (OR = 0.962), bystander CPR (OR = 0.968) and public location (OR = 0.980) - increased the negative effect of a one-minute delay on the odds of survival. In contrast, a bystander AED shock decreased the negative effect of a one-minute response time delay on the odds of survival (OR = 1.064). The magnitude of the effect of a one-minute delay in EMS response time on the odds of survival ranged from 1.3% to 9.8% (average: 5.3%), depending on the underlying OHCA characteristics.

CONCLUSIONS

Delays in EMS response time had the largest reduction in survival odds for OHCAs that did not receive a bystander AED shock but were witnessed, occurred in public, and/or received bystander CPR. A bystander AED shock appears to be protective against a delay in EMS response time.

Out of hospital cardiac arrest: Past, present, and future

Advances in resuscitation following out-of-hospital cardiac arrest (OHCA) provide an opportunity to improve public health. This review reflects on past developments, present status, and future possibilities using the science-education-implementation framework of the Utstein Formula and the clinical framework of the links in the chain of survival. With the discovery of CPR and defibrillation in the mid 20th century, resuscitation developed a scientific construct for progress. Systems of emergency community response provided operational efficiency to treat OHCA. Contemporary resuscitation involves integrated interventions in the chain of survival: early recognition, early CPR, early defibrillation, expert and timely advanced life support and hospital care, and multidimensional rehabilitation. Implementation of scientific advances is especially challenging given the unexpected nature of OHCA, the need for time-sensitive interventions, and the substantial collective of stakeholders involved in the chain of survival. Systematic measurement provides the foundation to evaluate performance and guide implementation initiatives. For many systems, telecommunicator CPR and high-performance CPR by emergency professionals are accessible, near-term programs to improve OHCA outcome. Smart technologies that activate, coordinate, and/or coach community "volunteers" to accelerate early CPR and defibrillation have conceptual promise, though robust implementation has been achieved by only a handful of systems. Longer-term strategies may leverage technology to develop a high-fidelity "life-detector" or engineer and disseminate a specialized consumer defibrillator designed to bridge care until arrival of professional response.

Feasibility of Incorporating Voice Technology and Virtual Assistants in Cardiovascular Care and Clinical Trials

PURPOSE OF REVIEW

With the rising cost of cardiovascular clinical trials, there is interest in determining whether new technologies can increase cost effectiveness. This review focuses on current and potential uses of voice-based technologies, including virtual assistants, in cardiovascular clinical trials.

RECENT FINDINGS

Numerous potential uses for voice-based technologies have begun to emerge within cardiovascular medicine. Voice biomarkers, subtle changes in speech parameters, have emerged as a potential tool to diagnose and monitor many cardiovascular conditions, including heart failure, coronary artery disease, and pulmonary hypertension. With the increasing use of virtual assistants, numerous pilot studies have examined whether these devices can supplement initiatives to promote transitional care, physical activity, smoking cessation, and medication adherence with promising initial results. Additionally, these devices have demonstrated the ability to streamline data collection by administering questionnaires accurately and reliably. With the use of these technologies, there are several challenges that must be addressed before wider implementation including respecting patient privacy, maintaining regulatory standards, acceptance by patients and healthcare providers, determining the validity of voice-based biomarkers and endpoints, and increased accessibility.

SUMMARY

Voice technology represents a novel and promising tool for cardiovascular clinical trials; however, research is still required to understand how it can be best harnessed.

[Basic life support]

The European Resuscitation Council has produced these basic life support guidelines, which are based on the 2020 International Consensus on Cardiopulmonary Resuscitation Science with Treatment Recommendations. The topics covered include cardiac arrest recognition, alerting emergency services, chest compressions, rescue breaths, automated external defibrillation (AED), cardiopulmonary resuscitation (CPR) quality measurement, new technologies, safety, and foreign body airway obstruction.

Activation of citizen responders to out-of-hospital cardiac arrest

PURPOSE OF REVIEW

To discuss different approaches to citizen responder activation and possible future solutions for improved citizen engagement in out-of-hospital cardiac arrest (OHCA) resuscitation.

RECENT FINDINGS

Activating volunteer citizens to OHCA has the potential to improve OHCA survival by increasing bystander cardiopulmonary resuscitation (CPR) and early defibrillation. Accordingly, citizen responder systems have become widespread in numerous countries despite very limited evidence of their effect on survival or cost-effectiveness. To date, only one randomized trial has investigated the effect of citizen responder activation for which the outcome was bystander CPR. Recent publications are of observational nature with high risk of bias. A scoping review published in 2020 provided an overview of available citizen responder systems and their differences in who, when, and how to activate volunteer citizens. These differences are further discussed in this review.

SUMMARY

Implementation of citizen responder programs holds the potential to improve bystander intervention in OHCA, with advancing technology offering new improvement possibilities. Information on how to best activate citizen responders as well as the effect on survival following OHCA is warranted to evaluate the cost-effectiveness of citizen responder programs.

Smart Wearables for Cardiac Monitoring-Real-World Use beyond Atrial Fibrillation

The possibilities and implementation of wearable cardiac monitoring beyond atrial fibrillation are increasing continuously. This review focuses on the real-world use and evolution of these devices for other arrhythmias, cardiovascular diseases and some of their risk factors beyond atrial fibrillation. The management of nonatrial fibrillation arrhythmias represents a broad field of wearable technologies in cardiology using Holter, event recorder, electrocardiogram (ECG) patches, wristbands and textiles. Implementation in other patient cohorts, such as ST-elevation myocardial infarction (STEMI), heart failure or sleep apnea, is feasible and expanding. In addition to appropriate accuracy, clinical studies must address the validation of clinical pathways including the appropriate device and clinical decisions resulting from the surrogate assessed.

2021 ISHNE/HRS/EHRA/APHRS collaborative statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society

This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society describes the current status of mobile health ("mHealth") technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self-management are novel aspects of mHealth. The promises of predictive analytics but also operational challenges in embedding mHealth into routine clinical care are explored.

European Resuscitation Council Guidelines 2021: Basic Life Support

The European Resuscitation Council has produced these basic life support guidelines, which are based on the 2020 International Consensus on Cardiopulmonary Resuscitation Science with Treatment Recommendations. The topics covered include cardiac arrest recognition, alerting emergency services, chest compressions, rescue breaths, automated external defibrillation (AED), CPR quality measurement, new technologies, safety, and foreign body airway obstruction.

Using smart speakers to contactlessly monitor heart rhythms

Heart rhythm assessment is indispensable in diagnosis and management of many cardiac conditions and to study heart rate variability in healthy individuals. We present a proof-of-concept system for acquiring individual heart beats using smart speakers in a fully contact-free manner. Our algorithms transform the smart speaker into a short-range active sonar system and measure heart rate and inter-beat intervals (R-R intervals) for both regular and irregular rhythms. The smart speaker emits inaudible 18–22 kHz sound and receives echoes reflected from the human body that encode sub-mm displacements due to heart beats. We conducted a clinical study with both healthy participants and hospitalized cardiac patients with diverse structural and arrhythmic cardiac abnormalities including atrial fibrillation, flutter and congestive heart failure. Compared to electrocardiogram (ECG) data, our system computed R-R intervals for healthy participants with a median error of 28 ms over 12,280 heart beats and a correlation coefficient of 0.929. For hospitalized cardiac patients, the median error was 30 ms over 5639 heart beats with a correlation coefficient of 0.901. The increasing adoption of smart speakers in hospitals and homes may provide a means to realize the potential of our non-contact cardiac rhythm monitoring system for monitoring of contagious or quarantined patients, skin sensitive patients and in telemedicine settings.

Anran Wang et al. present a contact-free method of monitoring individual heart beats by converting smart-speakers into active sonar systems. Their approach is capable of measuring heart rhythms with high accuracy in both healthy participants and hospitalized patients, and may be a useful healthcare tool for remote diagnosis or patient monitoring.

2021 ISHNE/ HRS/ EHRA/ APHRS collaborative statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society

This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology/ Heart Rhythm Society/ European Heart Rhythm Association/ Asia Pacific Heart Rhythm Society describes the current status of mobile health ("mHealth") technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self-management are novel aspects of mHealth. The promises of predictive analytics but also operational challenges in embedding mHealth into routine clinical care are explored.

2021 ISHNE / HRS / EHRA / APHRS Collaborative Statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology / Heart Rhythm Society / European Heart Rhythm Association / Asia Pacific Heart Rhythm Society

This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology / Heart Rhythm Society / European Heart Rhythm Association / Asia Pacific Heart Rhythm Society describes the current status of mobile health ("mHealth") technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self-management are novel aspects of mHealth. The promises of predictive analytics but also operational challenges in embedding mHealth into routine clinical care are explored.

2021 ISHNE/HRS/EHRA/APHRS Collaborative Statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society

This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society describes the current status of mobile health ("mHealth") technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self-management are novel aspects of mHealth. The promises of predictive analytics but also operational challenges in embedding mHealth into routine clinical care are explored.

2021 ISHNE/HRS/EHRA/APHRS Expert Collaborative Statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia-Pacific Heart Rhythm Society

This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia-Pacific Heart Rhythm Society describes the current status of mobile health technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self-management are novel aspects of mobile health. The promises of predictive analytics but also operational challenges in embedding mobile health into routine clinical care are explored.