Diagnostic Performance of a Smart Device With Photoplethysmography Technology for Atrial Fibrillation Detection: Pilot Study (Pre-mAFA II Registry)

Evaluation of Wrist-Worn Photoplethysmography Trackers with an Electrocardiogram in Patients with Ischemic Heart Disease: A Validation Study

PURPOSE

Photoplethysmography measurement of heart rate with wrist-worn trackers has been introduced in healthy individuals. However, additional consideration is necessary for patients with ischemic heart disease, and the available evidence is limited. The study aims to evaluate the validity and reliability of heart rate measures by a wrist-worn photoplethysmography (PPG) tracker compared to an electrocardiogram (ECG) during incremental treadmill exercise among patients with ischemic heart disease.

METHODS

Fifty-one participants performed the standard incremental treadmill exercise in a controlled laboratory setting with 12-lead ECG attached to the patient's body and wearing wrist-worn PPG trackers.

RESULTS

At each stage, the absolute percentage error of the PPG was within 10% of the standard acceptable range. Further analysis using a linear mixed model, which accounts for individual variations, revealed that PPG yielded the best performance at the baseline low-intensity exercise. As the stages progressed, heart rate validity decreased but was regained during recovery. The reliability was moderate to excellent.

CONCLUSIONS

Low-cost trackers AMAZFIT Cor and Bip validity and reliability were within acceptable ranges, especially during low-intensity exercise among patients with ischemic heart disease recovering from cardiac procedures. Though using the tracker as part of the diagnosis tool still requires more supporting studies, it can potentially be used as a self-monitoring tool with precautions.

[Application of photoplethysmography for atrial fibrillation in early warning, diagnosis and integrated management]

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia. Early diagnosis and effective management are important to reduce atrial fibrillation-related adverse events. Photoplethysmography (PPG) is often used to assist wearables for continuous electrocardiograph monitoring, which shows its unique value. The development of PPG has provided an innovative solution to AF management. Serial studies of mobile health technology for improving screening and optimized integrated care in atrial fibrillation have explored the application of PPG in screening, diagnosing, early warning, and integrated management in patients with AF. This review summarizes the latest progress of PPG analysis based on artificial intelligence technology and mobile health in AF field in recent years, as well as the limitations of current research and the focus of future research.

Detection and Diagnostic Accuracy of Cardiac Arrhythmias Using Wearable Health Devices: A Systematic Review

Photoplethysmography (PPG) is the wearable devices' most widely used technology for monitoring heart rate. The systematic review used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards and guidelines. This systematic review seeks to establish the effects of wearable health devices on cardiac arrhythmias concerning their impact on the personalization of cardiac management, their refining effect on stroke prevention strategies, and their influence on research and preventive care of cardiac arrhythmias and their re-evaluation of the patient-physician relationship. The population, exposure, control, outcomes, and studies (PECOS) criteria were used in the systematic review. This review considered studies that covered the tests conducted on individuals who presented with cardiovascular diseases (CVD) and also healthy people. The intervention for studies included wearable health devices that could detect and diagnose cardiac arrhythmias.  The study considered articles that reported on the personalization of cardiac management, stroke prevention strategies, influence in research and preventive care of cardiac arrhythmias, and the re-evaluation of the patient-physician relationship. Two independent researchers were used in the extraction of the data. In case of dispute, the issue was resolved using a third party. The study's quality analysis was conducted using AXIS. The management of atrial fibrillation (AF) lies heavily in the prevention of stroke. The accuracy being reported in the prediction of arrhythmias and the monitoring of heart rates makes wearable devices an efficient means to personalize health care. Personalization of health and treatment in preventing and managing arrhythmias becomes possible due to the portability of smart wearable devices. However, limitations may be observed due to the high costs incurred in their purchase and use. Using smart wearable devices for the detection of cardiac arrhythmias was very significant.

ECG Smart Monitoring versus Implantable Loop Recorders for Atrial Fibrillation Detection after Cryptogenic Stroke-An Overview for Decision Making

Up to 20% of patients with ischemic stroke or transient ischemic attack have a prior history of known atrial fibrillation (AF). Additionally, unknown AF can be detected by different monitoring strategies in up to 23% of patients with cryptogenic or non-cardioembolic stroke. However, most studies had substantial gaps in monitoring time, especially early after the index event. Following this, AF rates would be higher if patients underwent continuous monitoring early after stroke, avoiding any gaps in monitoring. The few existing randomized studies focused on patients with cryptogenic stroke but did not focus otherwise specifically on prevention strategies in patients at high risk for AF (patients at higher age or with high CHA2DS2-VASC scores). Besides invasive implantable loop recorders (ILRs), external loop recorders (ELRs) and mobile cardiac outpatient telemetry (MCOT) are non-invasive tools that are commonly used for long-term ECG monitoring in cryptogenic-stroke patients in the ambulatory setting. The role of MCOT and hand-held devices within ECG smart monitoring in the detection of AF for the prevention of and after cryptogenic stroke is currently unclear. This intense review provides an overview of current evidence, techniques, and gaps in knowledge and aims to advise which patients benefit most from the current available devices.

Novel Technologies in the Detection of Atrial Fibrillation: Review of Literature and Comparison of Different Novel Technologies for Screening of Atrial Fibrillation

Atrial fibrillation (AF) is globally the most common arrhythmia associated with significant morbidity and mortality. It impairs the quality of the patient's life, imposing a remarkable burden on public health, and the healthcare budget. The detection of AF is important in the decision to initiate anticoagulation therapy to prevent thromboembolic events. Nonetheless, AF detection is still a major clinical challenge as AF is often paroxysmal and asymptomatic. AF screening recommendations include opportunistic or systematic screening in patients ≥65 years of age or in those individuals with other characteristics pointing to an increased risk of stroke. The popularities of well-being and taking personal responsibility for one's own health are reflected in the continuous development and growth of mobile health technologies. These novel mobile health technologies could provide a cost-effective solution for AF screening and an additional opportunity to detect AF, particularly its paroxysmal and asymptomatic forms.

Wrist-worn device combining PPG and ECG can be reliably used for atrial fibrillation detection in an outpatient setting

Aims

The aim was to validate the performance of a monitoring system consisting of a wrist-worn device and a data management cloud service intended to be used by medical professionals in detecting atrial fibrillation (AF).

Methods

Thirty adult patients diagnosed with AF alone or AF with concomitant flutter were recruited. Continuous photoplethysmogram (PPG) and intermittent 30 s Lead I electrocardiogram (ECG) recordings were collected over 48 h. The ECG was measured four times a day at prescheduled times, when notified due to irregular rhythm detected by PPG, and when self-initiated based on symptoms. Three-channel Holter ECG was used as the reference.

Results

The subjects recorded a total of 1,415 h of continuous PPG data and 3.8 h of intermittent ECG data over the study period. The PPG data were analyzed by the system's algorithm in 5-min segments. The segments containing adequate amounts, at least ~30 s, of adequate quality PPG data for rhythm assessment algorithm, were included. After rejecting 46% of the 5-min segments, the remaining data were compared with annotated Holter ECG yielding AF detection sensitivity and specificity of 95.6 and 99.2%, respectively. The ECG analysis algorithm labeled 10% of the 30-s ECG records as inadequate quality and these were excluded from the analysis. The ECG AF detection sensitivity and specificity were 97.7 and 89.8%, respectively. The usability of the system was found to be good by both the study subjects and the participating cardiologists.

Conclusion

The system comprising of a wrist device and a data management service was validated to be suitable for use in patient monitoring and in the detection of AF in an ambulatory setting.Clinical Trial Registration: ClinicalTrials.gov/, NCT05008601.

Improving the management of patients with chronic cardiac and respiratory diseases by extending pulse-oximeter uses: the dynamic pulse-oximetry

Respiratory and cardio-vascular chronic diseases are among the most common noncommunicable diseases (NCDs) worldwide, accounting for a large portion of health-care costs in terms of mortality and disability. Their prevalence is expected to rise further in the coming years as the population ages. The current model of care for diagnosing and monitoring NCDs is out of date because it results in late medical interventions and/or an unfavourable cost-effectiveness balance based on reported symptoms and subsequent inpatient tests and treatments. Health projects and programs are being implemented in an attempt to move the time of an NCD's diagnosis, as well as its monitoring and follow up, out of hospital settings and as close to real life as possible, with the goal of benefiting both patients' quality of life and health system budgets. Following the SARS-CoV-2 pandemic, this implementation received additional impetus. Pulseoximeters (POs) are currently used in a variety of clinical settings, but they can also aid in the telemonitoring of certain patients. POs that can measure activities as well as pulse rate and oxygen saturation as proxies of cardio-vascular and respiratory function are now being introduced to the market. To obtain these data, the devices must be absolutely reliable, that is, accurate and precise, and capable of recording for a long enough period of time to allow for diagnosis. This paper is a review of current pulse-oximetry (POy) use, with the goal of investigating how its current use can be expanded to manage not only cardio-respiratory NCDs, but also acute emergencies with telemonitoring when hospitalization is not required but the patients' situation is debatable. Newly designed devices, both "consumer" and "professional," will be scrutinized, particularly those capable of continuously recording vital parameters on a 24-hour basis and coupling them with daily activities, a practice known as dynamic pulse-oximetry.

Diagnostic validation of smart wearable device embedded with single-lead electrocardiogram for arrhythmia detection

Objective

To validate a single-lead electrocardiogram algorithm for identifying atrial fibrillation, atrial premature beats, ventricular premature beats, and sinus rhythm.

Methods

A total of 656 subjects aged 19 to 94 years were enrolled. Participants were simultaneously tested with a wristwatch (Huawei Watch GT2 Pro, Huawei Technologies Co., Ltd, Shenzhen, China) and a 12-lead electrocardiogram for 3 minutes. A total of 1926 electrocardiogram signals from 628 subjects (282 men and 346 women) aged 19 to 94 years (median 64 years) were analyzed using an algorithm.

Results

The numbers of subjects with atrial fibrillation, atrial premature beats, ventricular premature beats, and sinus rhythm were 129, 141, 107, and 251, respectively, and together they had a total of 1926 electrocardiogram signals. For the three-class classification system, the recall, precision, and F1 score were 97.6%, 96.5%, 97.0% for sinus rhythm; 96.7%, 96.9%, 96.8% for atrial fibrillation; and 92.8%, 94.2%, 93.5% for ectopic beats, respectively. The macro-F1 score of the three-class classification system was 95.8%. For the four-class classification system, the recall, precision, and F1 score were 97.6%, 96.5%, 97.0% for sinus rhythm; 96.7%, 96.9%, 96.8% for atrial fibrillation; 90.5%, 89.4%, 89.9% for atrial premature beats; and 86.1%, 89.6%, 87.8% for ventricular premature beats, respectively. The macro-F1 score of the four-class classification system was 92.9%.

Conclusions

The single-lead electrocardiogram algorithm embedded into smart wearables demonstrated good performance in detecting atrial fibrillation, atrial/ventricular premature beats, and sinus rhythm, and thus would facilitate atrial fibrillation screening and management.

The "obesity paradox" in patients with atrial fibrillation: Insights from the Gulf SAFE registry

BACKGROUND

The prognostic impact of obesity on patients with atrial fibrillation (AF) remains under-evaluated and controversial.

METHODS

Patients with AF from the Gulf Survey of Atrial Fibrillation Events (Gulf SAFE) registry were included, who were recruited from six countries in the Middle East Gulf region and followed for 12 months. A multivariable model was established to investigate the association of obesity with clinical outcomes, including stroke or systemic embolism (SE), bleeding, admission for heart failure (HF) or AF, all-cause mortality, and a composite outcome. Restricted cubic splines were depicted to illustrate the relationship between body mass index (BMI) and outcomes. Sensitivity analysis was also conducted.

RESULTS

A total of 1,804 patients with AF and recorded BMI entered the final analysis (mean age 56.2 ± 16.1 years, 47.0% female); 559 (31.0%) were obese (BMI over 30 kg/m2). In multivariable analysis, obesity was associated with reduced risks of stroke/systematic embolism [adjusted odds ratio (aOR) 0.40, 95% confidence interval (CI), 0.18-0.89], bleeding [aOR 0.44, 95%CI, 0.26-0.74], HF admission (aOR 0.61, 95%CI, 0.41-0.90) and the composite outcome (aOR 0.65, 95%CI, 0.50-0.84). As a continuous variable, higher BMI was associated with lower risks for stroke/SE, bleeding, HF admission, all-cause mortality, and the composite outcome as demonstrated by the accumulated incidence of events and restricted cubic splines. This "protective effect" of obesity was more prominent in some subgroups of patients.

CONCLUSION

Among patients with AF, obesity and higher BMI were associated with a more favorable prognosis in the Gulf SAFE registry. The underlying mechanisms for this obesity "paradox" merit further exploration.

Consumer-Led Screening for Atrial Fibrillation: A Report From the mAFA-II Trial Long-Term Extension Cohort

BACKGROUND

There are limited data on mobile health detection of prevalent atrial fibrillation (AF) and its related risk factors over time.

OBJECTIVES

This study aimed to report the trends on prevalent AF detection over time and risk factors, with a consumer-led photoplethysmography screening approach.

METHODS

3,499,461 subjects aged over 18 years, who use smart devices (Huawei Technologies Co.) were enrolled between October 26, 2018, and December 1, 2021.

RESULTS

Among 2,852,217 subjects for AF screening, 12,244 subjects (0.43%; 83.2% male, mean age 57 ± 15 years) detected AF episodes. When compared with 2018, the risk (adjusted HRs, 95% CI) for monitored prevalent AF increased significantly for subjects when monitoring started in 2020 (adjusted HR: 1.34; 95% CI: 1.27-1.40; P < .001) or in 2021 (adjusted HR: 1.67; 95% CI: 1.59-1.76; P < 0.001). Of the 961,931 subjects who screening for both AF and OSA, 18,032 (1.9%, 97.8% male, mean age 44 ±17 years) were identified as high risk for OSA, which resulted in a 1.5-fold increase (95% CI: 1.30-fold to 1.75-fold) in the prevalent AF. A total of 5,227 (53.3%, 5,227/9,797) subjects were effectively followed up, from which 4,903 (93.8%, 4,903/5,227) subjects were confirmed with the diagnosis of AF, by the mAFA Telecare Team health providers.

CONCLUSIONS

Photoplethysmography-based smart devices can facilitate screening for AF with >93% confirmation of detected AF episodes even for the low-risk general population, highlighting the increased risk for detecting prevalent AF and the need for modification of OSA that increase AF susceptibility. (Mobile Health [mHealth] Technology for Improved Screening, Patient Involvement and Optimizing Integrated Care in Atrial Fibrillation [mAFA (mAF-App) II study]; ChiCTR-OOC-17014138).

Photoplethysmography-Based Smart Devices for Detection of Atrial Fibrillation

Atrial fibrillation is the most commonly experienced type of cardiac arrhythmia and is the most associated with substantial clinical occurrences and expenses. This arrhythmia often occurs in its "silent" asymptomatic form, revealed only after complications such as a stroke or congestive heart failure have transpired. New smart devices confer effective advantages in the detection of this heart arrhythmia, of which photoplethysmography-based smart devices have shown great potential, according to previous research. However, the solution becomes a problem as widespread use and high availability of various applications and smart devices may lead to substantial amounts of false and misleading recordings and information, causing unnecessary anxiety regarding arrhythmic occurrences diagnosed by the devices but not professionally confirmed. Thus, with most of the devices being photoplethysmography based for detection of atrial fibrillation, it is important to research devices studied up to this point to find the best smart device to detect the aforementioned arrhythmias.

Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review

Cardiovascular disease (CVD) is the world's leading cause of mortality. There is significant interest in using Artificial Intelligence (AI) to analyse data from novel sensors such as wearables to provide an earlier and more accurate prediction and diagnosis of heart disease. Digital health technologies that fuse AI and sensing devices may help disease prevention and reduce the substantial morbidity and mortality caused by CVD worldwide. In this review, we identify and describe recent developments in the application of digital health for CVD, focusing on AI approaches for CVD detection, diagnosis, and prediction through AI models driven by data collected from wearables. We summarise the literature on the use of wearables and AI in cardiovascular disease diagnosis, followed by a detailed description of the dominant AI approaches applied for modelling and prediction using data acquired from sensors such as wearables. We discuss the AI algorithms and models and clinical applications and find that AI and machine-learning-based approaches are superior to traditional or conventional statistical methods for predicting cardiovascular events. However, further studies evaluating the applicability of such algorithms in the real world are needed. In addition, improvements in wearable device data accuracy and better management of their application are required. Lastly, we discuss the challenges that the introduction of such technologies into routine healthcare may face.

Smartphone detection of atrial fibrillation using photoplethysmography: a systematic review and meta-analysis

OBJECTIVES

Timely diagnosis of atrial fibrillation (AF) is essential to reduce complications from this increasingly common condition. We sought to assess the diagnostic accuracy of smartphone camera photoplethysmography (PPG) compared with conventional electrocardiogram (ECG) for AF detection.

METHODS

This is a systematic review of MEDLINE, EMBASE and Cochrane (1980-December 2020), including any study or abstract, where smartphone PPG was compared with a reference ECG (1, 3 or 12-lead). Random effects meta-analysis was performed to pool sensitivity/specificity and identify publication bias, with study quality assessed using the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2) risk of bias tool.

RESULTS

28 studies were included (10 full-text publications and 18 abstracts), providing 31 comparisons of smartphone PPG versus ECG for AF detection. 11 404 participants were included (2950 in AF), with most studies being small and based in secondary care. Sensitivity and specificity for AF detection were high, ranging from 81% to 100%, and from 85% to 100%, respectively. 20 comparisons from 17 studies were meta-analysed, including 6891 participants (2299 with AF); the pooled sensitivity was 94% (95% CI 92% to 95%) and specificity 97% (96%-98%), with substantial heterogeneity (p<0.01). Studies were of poor quality overall and none met all the QUADAS-2 criteria, with particular issues regarding selection bias and the potential for publication bias.

CONCLUSION

PPG provides a non-invasive, patient-led screening tool for AF. However, current evidence is limited to small, biased, low-quality studies with unrealistically high sensitivity and specificity. Further studies are needed, preferably independent from manufacturers, in order to advise clinicians on the true value of PPG technology for AF detection.

Cost-effectiveness of Screening for Atrial Fibrillation Using Wearable Devices

Question

Is population-based atrial fibrillation (AF) screening using wearable devices cost-effective?

Findings

In this economic evaluation of 30 million simulated individuals with an age, sex, and comorbidity profile matching the US population aged 65 years or older, AF screening using wearable devices was cost-effective, with the overall preferred strategy identified as wearable photoplethysmography, followed conditionally by wearable electrocardiography with patch monitor confirmation (incremental cost-effectiveness ratio, $57 894 per quality-adjusted life-year). The cost-effectiveness of screening was consistent across multiple scenarios, including strata of sex, screening at earlier ages, and with variation in the association of anticoagulation with risk of stroke associated with screening-detected AF.

Meaning

This study suggests that contemporary AF screening using wearable devices may be cost-effective.

Importance

Undiagnosed atrial fibrillation (AF) is an important cause of stroke. Screening for AF using wrist-worn wearable devices may prevent strokes, but their cost-effectiveness is unknown.

Objective

To evaluate the cost-effectiveness of contemporary AF screening strategies, particularly wrist-worn wearable devices.

Design, Setting, and Participants

This economic evaluation used a microsimulation decision-analytic model and was conducted from September 8, 2020, to May 23, 2022, comprising 30 million simulated individuals with an age, sex, and comorbidity profile matching the US population aged 65 years or older.

Interventions

Eight AF screening strategies, with 6 using wrist-worn wearable devices (watch or band photoplethysmography, with or without watch or band electrocardiography) and 2 using traditional modalities (ie, pulse palpation and 12-lead electrocardiogram) vs no screening.

Main Outcomes and Measures

The primary outcome was the incremental cost-effectiveness ratio, defined as US dollars per quality-adjusted life-year (QALY). Secondary measures included rates of stroke and major bleeding.

Results

In the base case analysis of this model, the mean (SD) age was 72.5 (7.5) years, and 50% of the individuals were women. All 6 screening strategies using wrist-worn wearable devices were estimated to be more effective than no screening (range of QALYs gained vs no screening, 226-957 per 100 000 individuals) and were associated with greater relative benefit than screening using traditional modalities (range of QALYs gained vs no screening, −116 to 93 per 100 000 individuals). Compared with no screening, screening using wrist-worn wearable devices was associated with a reduction in stroke incidence by 20 to 23 per 100 000 person-years but an increase in major bleeding by 20 to 44 per 100 000 person-years. The overall preferred strategy was wearable photoplethysmography, followed conditionally by wearable electrocardiography with patch monitor confirmation, which had an incremental cost-effectiveness ratio of $57 894 per QALY, meeting the acceptability threshold of $100 000 per QALY. The cost-effectiveness of screening was consistent across multiple scenarios, including strata of sex, screening at earlier ages (eg, ≥50 years), and with variation in the association of anticoagulation with risk of stroke in the setting of screening-detected AF.

Conclusions and Relevance

This economic evaluation of AF screening using a microsimulation decision-analytic model suggests that screening using wearable devices is cost-effective compared with either no screening or AF screening using traditional methods.

Effect of temporal resolution on the detection of cardiac arrhythmias using HRV features and machine learning

Objective.Arrhythmia is an abnormal cardiac rhythm that affects the pattern and rate of the heartbeat. Wearable devices with the functionality to measure and store heart rate (HR) data are growing in popularity and enable diagnosing and monitoring arrhythmia on a large scale. The typical sampling resolution of HR data available from non-medical grade wearable devices varies from seconds to several minutes depending on the device and its settings. However, the impact of sampling resolution on the performance and quality of arrhythmia detection has not yet been quantified.Approach.In this study, we investigated the detection and classification of three arrhythmias, namely atrial fibrillation, bradycardia, tachycardia, from down-sampled HR data with various temporal resolution (5-, 15-, 30- and 60 s averages) in 1 h segments extracted from an annotated Holter ECG database acquired at the University of Virginia Heart Station. For the classification task, a total of 15 common heart rate variability (HRV) features were engineered based on the HR time series of each patient. Three different types of machine learning classifiers were evaluated, namely logistic regression, support vector machine and random forest.Main results.A decrease in temporal resolution drastically impacted the detection of atrial fibrillation but did not substantially affect the detection of bradycardia and tachycardia. A HR resolution up to 15 s average demonstrated reasonable performance with a sensitivity of 0.92 and a specificity of 0.86 for a multiclass random forest classifier.Significance.HRV features extracted from low resolution long HR recordings have the potential to increase the early detection of arrhythmias in undiagnosed individuals.

Mobile health technology in atrial fibrillation

INTRODUCTION

Mobile health (mHealth) solutions in atrial fibrillation (AF) are becoming widespread, thanks to everyday life devices such as smartphones. Their use is validated both in monitoring and in screening scenarios. In the published literature, the diagnostic accuracy of mHealth solutions wide differs, and their current clinical use is not well established in principal guidelines.

AREAS COVERED

mHealth solutions have progressively built an AF-detection chain to guide patients from the device's alert signal to the health care practitioners' (HCPs) attention. This review aims to critically evaluate the latest evidence regarding mHealth devices and the future possible patient's uses in everyday life.

EXPERT OPINION

The patients are the first to be informed of the rhythm anomaly, leading to the urgency of increasing the patients' AF self-management. Furthermore, HCPs need to update themselves about mHealth devices use in clinical practice. Nevertheless, these are promising instruments in specific populations, such as post-stroke patients, to promote an early arrhythmia diagnosis in the post-ablation/cardioversion period, allowing checks on the efficacy of the treatment or intervention.

Impacts on Context Aware Systems in Evidence-Based Health Informatics: A Review

Background: The application of Context Aware Computing (CAC) can be an effective, useful, feasible, and acceptable way to advance medical research and provide health services. Methods: This review was conducted in accordance with the principles of the development of a mixed methods review and existing knowledge in the field via the Synthesis Framework for the Assessment of Health Information Technology to evaluate CAC implemented by Evidence-Based Health Informatics (EBHI). A systematic search of the literature was performed during 18 November 2021–22 January 2022 in Cochrane Library, IEEE Xplore, PUBMED, Scopus and in the clinical registry platform Clinicaltrials.gov. The author included the articles in the review if they were implemented by EBHI and concerned with CAC technologies. Results: 29 articles met the inclusion criteria and refer to 26 trials published between 2011 and 2022. The author noticed improvements in healthcare provision using EBHI in the findings of CAC application. She also confirmed that CAC systems are a valuable and reliable method in health care provision. Conclusions: The use of CAC systems in healthcare is a promising new area of research and development. The author presented that the evaluation of CAC systems in EBHI presents positive effects on the state of health and the management of long-term diseases. These implications are presented in this article in a detailed, clear, and reliable manner.

Application of photoplethysmography signals for healthcare systems: An in-depth review

BACKGROUND AND OBJECTIVES

Photoplethysmography (PPG) is a device that measures the amount of light absorbed by the blood vessel, blood, and tissues, which can, in turn, translate into various measurements such as the variation in blood flow volume, heart rate variability, blood pressure, etc. Hence, PPG signals can produce a wide variety of biological information that can be useful for the detection and diagnosis of various health problems. In this review, we are interested in the possible health disorders that can be detected using PPG signals.

METHODS

We applied PRISMA guidelines to systematically search various journal databases and identified 43 PPG studies that fit the criteria of this review.

RESULTS

Twenty-five health issues were identified from these studies that were classified into six categories: cardiac, blood pressure, sleep health, mental health, diabetes, and miscellaneous. Various routes were employed in these PPG studies to perform the diagnosis: machine learning, deep learning, and statistical routes. The studies were reviewed and summarized.

CONCLUSIONS

We identified limitations such as poor standardization of sampling frequencies and lack of publicly available PPG databases. We urge that future work should consider creating more publicly available databases so that a wide spectrum of health problems can be covered. We also want to promote the use of PPG signals as a potential precision medicine tool in both ambulatory and hospital settings.

Remote Cardiac Rhythm Monitoring in the Era of Smart Wearables: Present Assets and Future Perspectives

Remote monitoring and control of heart function are of primary importance for patient evaluation and management, especially in the modern era of precision medicine and personalized approach. Breaking technological developments have brought to the frontline a variety of smart wearable devices, such as smartwatches, chest patches/straps, or sensors integrated into clothing and footwear, which allow continuous and real-time recording of heart rate, facilitating the detection of cardiac arrhythmias. However, there is great diversity and significant differences in the type and quality of the information they provide, thus impairing their integration into daily clinical practice and the relevant familiarization of practicing physicians. This review will summarize the different types and dominant functions of cardiac smart wearables available in the market. Furthermore, we report the devices certified by official American and/or European authorities and the respective sources of evidence. Finally, we comment pertinent limitations and caveats as well as the potential answers that flow from the latest technological achievements and future perspectives.

Current Advancement in Diagnosing Atrial Fibrillation by Utilizing Wearable Devices and Artificial Intelligence: A Review Study

Atrial fibrillation (AF) is a common arrhythmia affecting 8–10% of the population older than 80 years old. The importance of early diagnosis of atrial fibrillation has been broadly recognized since arrhythmias significantly increase the risk of stroke, heart failure and tachycardia-induced cardiomyopathy with reduced cardiac function. However, the prevalence of atrial fibrillation is often underestimated due to the high frequency of clinically silent atrial fibrillation as well as paroxysmal atrial fibrillation, both of which are hard to catch by routine physical examination or 12-lead electrocardiogram (ECG). The development of wearable devices has provided a reliable way for healthcare providers to uncover undiagnosed atrial fibrillation in the population, especially those most at risk. Furthermore, with the advancement of artificial intelligence and machine learning, the technology is now able to utilize the database in assisting detection of arrhythmias from the data collected by the devices. In this review study, we compare the different wearable devices available on the market and review the current advancement in artificial intelligence in diagnosing atrial fibrillation. We believe that with the aid of the progressive development of technologies, the diagnosis of atrial fibrillation shall be made more effectively and accurately in the near future.

Continuous 24-h Photoplethysmogram Monitoring Enables Detection of Atrial Fibrillation

Aim: Atrial fibrillation (AF) detection is challenging because it is often asymptomatic and paroxysmal. We evaluated continuous photoplethysmogram (PPG) for signal quality and detection of AF.

Methods: PPGs were recorded using a wrist-band device in 173 patients (76 AF, 97 sinus rhythm, SR) for 24 h. Simultaneously recorded 3-lead ambulatory ECG served as control. The recordings were split into 10-, 20-, 30-, and 60-min time-frames. The sensitivity, specificity, and F1-score of AF detection were evaluated for each time-frame. AF alarms were generated to simulate continuous AF monitoring. Sensitivities, specificities, and positive predictive values (PPVs) of the alarms were evaluated. User experiences of PPG and ECG recordings were assessed. The study was registered in the Clinical Trials database (NCT03507335).

Results: The quality of PPG signal was better during night-time than in daytime (67.3 ± 22.4% vs. 30.5 ± 19.4%, p < 0.001). The 30-min time-frame yielded the highest F1-score (0.9536), identifying AF correctly in 72/76 AF patients (sensitivity 94.7%), only 3/97 SR patients receiving a false AF diagnosis (specificity 96.9%). The sensitivity and PPV of the simulated AF alarms were 78.2 and 97.2% at night, and 49.3 and 97.0% during the daytime. 82% of patients were willing to use the device at home.

Conclusion: PPG wrist-band provided reliable AF identification both during daytime and night-time. The PPG data’s quality was better at night. The positive user experience suggests that wearable PPG devices could be feasible for continuous rhythm monitoring.

Photoplethysmography-Based Machine Learning Approaches for Atrial Fibrillation Prediction

Background

Current wearable devices enable the detection of atrial fibrillation (AF), but a machine learning (ML)–based approach may facilitate accurate prediction of AF onset.

Objectives

The present study aimed to develop, optimize, and validate an ML-based model for real-time prediction of AF onset in a population at high risk of incident AF.

Methods

A primary ML-based prediction model of AF onset (M1) was developed on the basis of the Huawei Heart Study, a general-population AF screening study using photoplethysmography (PPG)–based smart devices. After optimization in 554 individuals with 469,267 PPG data sets, the optimized ML-based model (M2) was further prospectively validated in 50 individuals with paroxysmal AF at high risk of AF onset, and compared with 72-hour Holter electrocardiographic (ECG) monitoring, a criterion standard, from September 1, 2019, to November 5, 2019.

Results

Among 50 patients with paroxysmal AF (mean age 67 ± 12 years, 40% women), there were 2,808 AF events from a total of 14,847,356 ECGs over 72 hours and 6,860 PPGs (45.83 ± 13.9 per subject per day). The best performance of M1 for AF onset prediction was achieved 4 hours before AF onset (area under the receiver operating characteristic curve: 0.94; 95% confidence interval: 0.93-0.94). M2 sensitivity, specificity, positive predictive value, negative predictive value, and accuracy (at 0 to 4 hours before AF onset) were 81.9%, 96.6%, 96.4%, 83.1%, and 88.9%, respectively, compared with 72-hour Holter ECG.

Conclusions

The PPG- based ML model demonstrated good ability for AF prediction in advance. (Mobile Health [mHealth] technology for improved screening, patient involvement and optimizing integrated care in atrial fibrillation; ChiCTR-OOC-17014138)

Comparative Clinical Effectiveness of Population-Based Atrial Fibrillation Screening Using Contemporary Modalities: A Decision-Analytic Model

Background Atrial fibrillation (AF) screening is endorsed by certain guidelines for individuals aged ≥65 years. Yet many AF screening strategies exist, including the use of wrist-worn wearable devices, and their comparative effectiveness is not well-understood. Methods and Results We developed a decision-analytic model simulating 50 million individuals with an age, sex, and comorbidity profile matching the United States population aged ≥65 years (ie, with a guideline-based AF screening indication). We modeled no screening, in addition to 45 distinct AF screening strategies (comprising different modalities and screening intervals), each initiated at a clinical encounter. The primary effectiveness measure was quality-adjusted life-years, with incident stroke and major bleeding as secondary measures. We defined continuous or nearly continuous modalities as those capable of monitoring beyond a single time-point (eg, patch monitor), and discrete modalities as those capable of only instantaneous AF detection (eg, 12-lead ECG). In total, 10 AF screening strategies were effective compared with no screening (300-1500 quality-adjusted life-years gained/100 000 individuals screened). Nine (90%) effective strategies involved use of a continuous or nearly continuous modality such as patch monitor or wrist-worn wearable device, whereas 1 (10%) relied on discrete modalities alone. Effective strategies reduced stroke incidence (number needed to screen to prevent a stroke: 3087-4445) but increased major bleeding (number needed to screen to cause a major bleed: 1815-4049) and intracranial hemorrhage (number needed to screen to cause intracranial hemorrhage: 7693-16 950). The test specificity was a highly influential model parameter on screening effectiveness. Conclusions When modeled from a clinician-directed perspective, the comparative effectiveness of population-based AF screening varies substantially upon the specific strategy used. Future screening interventions and guidelines should consider the relative effectiveness of specific AF screening strategies.

Mobile health solutions for atrial fibrillation detection and management: a systematic review

Aim

We aimed to systematically review the available literature on mobile Health (mHealth) solutions, including handheld and wearable devices, implantable loop recorders (ILRs), as well as mobile platforms and support systems in atrial fibrillation (AF) detection and management.

Methods

This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. The electronic databases PubMed (NCBI), Embase (Ovid), and Cochrane were searched for articles published until 10 February 2021, inclusive. Given that the included studies varied widely in their design, interventions, comparators, and outcomes, no synthesis was undertaken, and we undertook a narrative review.

Results

We found 208 studies, which were deemed potentially relevant. Of these studies included, 82, 46, and 49 studies aimed at validating handheld devices, wearables, and ILRs for AF detection and/or management, respectively, while 34 studies assessed mobile platforms/support systems. The diagnostic accuracy of mHealth solutions differs with respect to the type (handheld devices vs wearables vs ILRs) and technology used (electrocardiography vs photoplethysmography), as well as application setting (intermittent vs continuous, spot vs longitudinal assessment), and study population.

Conclusion

While the use of mHealth solutions in the detection and management of AF is becoming increasingly popular, its clinical implications merit further investigation and several barriers to widespread mHealth adaption in healthcare systems need to be overcome.

Graphic abstract

Mobile health solutions for atrial fibrillation detection and management: a systematic review.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00392-021-01941-9.

Artificial intelligence in the diagnosis and management of arrhythmias

The field of cardiac electrophysiology (EP) had adopted simple artificial intelligence (AI) methodologies for decades. Recent renewed interest in deep learning techniques has opened new frontiers in electrocardiography analysis including signature identification of diseased states. Artificial intelligence advances coupled with simultaneous rapid growth in computational power, sensor technology, and availability of web-based platforms have seen the rapid growth of AI-aided applications and big data research. Changing lifestyles with an expansion of the concept of internet of things and advancements in telecommunication technology have opened doors to population-based detection of atrial fibrillation in ways, which were previously unimaginable. Artificial intelligence-aided advances in 3D cardiac imaging heralded the concept of virtual hearts and the simulation of cardiac arrhythmias. Robotics, completely non-invasive ablation therapy, and the concept of extended realities show promise to revolutionize the future of EP. In this review, we discuss the impact of AI and recent technological advances in all aspects of arrhythmia care.

Smart wearable devices in cardiovascular care: where we are and how to move forward

Technological innovations reach deeply into our daily lives and an emerging trend supports the use of commercial smart wearable devices to manage health. In the era of remote, decentralized and increasingly personalized patient care, catalysed by the COVID-19 pandemic, the cardiovascular community must familiarize itself with the wearable technologies on the market and their wide range of clinical applications. In this Review, we highlight the basic engineering principles of common wearable sensors and where they can be error-prone. We also examine the role of these devices in the remote screening and diagnosis of common cardiovascular diseases, such as arrhythmias, and in the management of patients with established cardiovascular conditions, for example, heart failure. To date, challenges such as device accuracy, clinical validity, a lack of standardized regulatory policies and concerns for patient privacy are still hindering the widespread adoption of smart wearable technologies in clinical practice. We present several recommendations to navigate these challenges and propose a simple and practical 'ABCD' guide for clinicians, personalized to their specific practice needs, to accelerate the integration of these devices into the clinical workflow for optimal patient care.

The Challenges and Pitfalls of Detecting Sleep Hypopnea Using a Wearable Optical Sensor: Comparative Study

Background

Obstructive sleep apnea (OSA) is the most prevalent respiratory sleep disorder occurring in 9% to 38% of the general population. About 90% of patients with suspected OSA remain undiagnosed due to the lack of sleep laboratories or specialists and the high cost of gold-standard in-lab polysomnography diagnosis, leading to a decreased quality of life and increased health care burden in cardio- and cerebrovascular diseases. Wearable sleep trackers like smartwatches and armbands are booming, creating a hope for cost-efficient at-home OSA diagnosis and assessment of treatment (eg, continuous positive airway pressure [CPAP] therapy) effectiveness. However, such wearables are currently still not available and cannot be used to detect sleep hypopnea. Sleep hypopnea is defined by ≥30% drop in breathing and an at least 3% drop in peripheral capillary oxygen saturation (Spo₂) measured at the fingertip. Whether the conventional measures of oxygen desaturation (OD) at the fingertip and at the arm or wrist are identical is essentially unknown.

Objective

We aimed to compare event-by-event arm OD (arm_OD) with fingertip OD (finger_OD) in sleep hypopneas during both naïve sleep and CPAP therapy.

Methods

Thirty patients with OSA underwent an incremental, stepwise CPAP titration protocol during all-night in-lab video-polysomnography monitoring (ie, 1-h baseline sleep without CPAP followed by stepwise increments of 1 cmH₂O pressure per hour starting from 5 to 8 cmH₂O depending on the individual). Arm_OD of the left biceps muscle and finger_OD of the left index fingertip in sleep hypopneas were simultaneously measured by frequency-domain near-infrared spectroscopy and video-polysomnography photoplethysmography, respectively. Bland-Altman plots were used to illustrate the agreements between arm_OD and finger_OD during baseline sleep and under CPAP. We used t tests to determine whether these measurements significantly differed.

Results

In total, 534 obstructive apneas and 2185 hypopneas were recorded. Of the 2185 hypopneas, 668 (30.57%) were collected during baseline sleep and 1517 (69.43%), during CPAP sleep. The mean difference between finger_OD and arm_OD was 2.86% (95% CI 2.67%-3.06%, t₆₆₇=28.28; P<.001; 95% limits of agreement [LoA] –2.27%, 8.00%) during baseline sleep and 1.83% (95% CI 1.72%-1.94%, t₁₅₁₆=31.99; P<.001; 95% LoA –2.54%, 6.19%) during CPAP. Using the standard criterion of 3% saturation drop, arm_OD only recognized 16.32% (109/668) and 14.90% (226/1517) of hypopneas at baseline and during CPAP, respectively.

Conclusions

arm_OD is 2% to 3% lower than standard finger_OD in sleep hypopnea, probably because the measured arm_OD originates physiologically from arterioles, venules, and capillaries; thus, the venous blood adversely affects its value. Our findings demonstrate that the standard criterion of ≥3% OD drop at the arm or wrist is not suitable to define hypopnea because it could provide large false-negative results in diagnosing OSA and assessing CPAP treatment effectiveness.

Mobile health applications for the detection of atrial fibrillation: a systematic review

AIMS

Atrial fibrillation (AF) is the most common sustained arrhythmia and an important risk factor for stroke and heart failure. We aimed to conduct a systematic review of the literature and summarize the performance of mobile health (mHealth) devices in diagnosing and screening for AF.

METHODS AND RESULTS

We conducted a systematic search of MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials. Forty-three studies met the inclusion criteria and were divided into two groups: 28 studies aimed at validating smart devices for AF diagnosis, and 15 studies used smart devices to screen for AF. Evaluated technologies included smartphones, with photoplethysmographic (PPG) pulse waveform measurement or accelerometer sensors, smartbands, external electrodes that can provide a smartphone single-lead electrocardiogram (iECG), such as AliveCor, Zenicor and MyDiagnostick, and earlobe monitor. The accuracy of these devices depended on the technology and the population, AliveCor and smartphone PPG sensors being the most frequent systems analysed. The iECG provided by AliveCor demonstrated a sensitivity and specificity between 66.7% and 98.5% and 99.4% and 99.0%, respectively. The PPG sensors detected AF with a sensitivity of 85.0-100% and a specificity of 93.5-99.0%. The incidence of newly diagnosed arrhythmia ranged from 0.12% in a healthy population to 8% among hospitalized patients.

CONCLUSION

Although the evidence for clinical effectiveness is limited, these devices may be useful in detecting AF. While mHealth is growing in popularity, its clinical, economic, and policy implications merit further investigation. More head-to-head comparisons between mHealth and medical devices are needed to establish their comparative effectiveness.

New Technologies for Detection and Management of Atrial Fibrillation

Atrial fibrillation (AF) is a common and prevalent form of arrhythmia. It is associated with various morbidities with stroke being the major hazard. Since AF is often reported to be asymptomatic, many individuals remain unaware of their condition and may not receive the requisite treatment. Hence, screening for AF has gained substantial attention recently. Growing advancement in technology has paved way for numerous approaches for AF screening using medical-prescribed devices as well as consumer electronic devices. However, there still lies scope for large-scale randomized trials which would explore additional aspects associated with AF. This review very concisely summarizes AF, screening, present technology, current literature and clinical studies associated with it.

Wearables in cardiology: Here to stay

The adoption of wearables in medicine has rapidly expanded worldwide. New generations of wearables are emerging, driven by consumers' demand to monitor their own health. With the ongoing development of new features capable of assessing real-time biometric data, the impact of wearables on cardiovascular management has become inevitable. Smartwatches, among other wearable devices, offer a user-friendly noninvasive approach to continuously monitor for health parameters. With advancements in artificial intelligence, the photoplethysmography-generated pulse waveform has the potential to accurately detect episodes of atrial fibrillation and one day could replace conventional diagnostic and long-term monitoring methods. Clinical benefits that could arise from the use of such devices include refining stroke prevention strategies, personalizing AF management, and optimizing the patient-physician relationship. Wearables are changing not only the way clinicians conduct research but also the future of cardiovascular preventive and therapeutic care. As such, wearables are here to stay.

Wrist Band Photoplethysmography Autocorrelation Analysis Enables Detection of Atrial Fibrillation Without Pulse Detection

Atrial fibrillation is often asymptomatic and intermittent making its detection challenging. A photoplethysmography (PPG) provides a promising option for atrial fibrillation detection. However, the shapes of pulse waves vary in atrial fibrillation decreasing pulse and atrial fibrillation detection accuracy. This study evaluated ten robust photoplethysmography features for detection of atrial fibrillation. The study was a national multi-center clinical study in Finland and the data were combined from two broader research projects (NCT03721601, URL: https://clinicaltrials.gov/ct2/show/NCT03721601 and NCT03753139, URL: https://clinicaltrials.gov/ct2/show/NCT03753139). A photoplethysmography signal was recorded with a wrist band. Five pulse interval variability, four amplitude features and a novel autocorrelation-based morphology feature were calculated and evaluated independently as predictors of atrial fibrillation. A multivariate predictor model including only the most significant features was established. The models were 10-fold cross-validated. 359 patients were included in the study (atrial fibrillation n = 169, sinus rhythm n = 190). The autocorrelation univariate predictor model detected atrial fibrillation with the highest area under receiver operating characteristic curve (AUC) value of 0.982 (sensitivity 95.1%, specificity 93.7%). Autocorrelation was also the most significant individual feature (p < 0.00001) in the multivariate predictor model, detecting atrial fibrillation with AUC of 0.993 (sensitivity 96.4%, specificity 96.3%). Our results demonstrated that the autocorrelation independently detects atrial fibrillation reliably without the need of pulse detection. Combining pulse wave morphology-based features such as autocorrelation with information from pulse-interval variability it is possible to detect atrial fibrillation with high accuracy with a commercial wrist band. Photoplethysmography wrist bands accompanied with atrial fibrillation detection algorithms utilizing autocorrelation could provide a computationally very effective and reliable wearable monitoring method in screening of atrial fibrillation.

Diagnostic Accuracy of Ambulatory Devices in Detecting Atrial Fibrillation: Systematic Review and Meta-analysis

Background

Atrial fibrillation (AF) is the most common cardiac arrhythmia worldwide. Early diagnosis of AF is crucial for preventing AF-related morbidity, mortality, and economic burden, yet the detection of the disease remains challenging. The 12-lead electrocardiogram (ECG) is the gold standard for the diagnosis of AF. Because of technological advances, ambulatory devices may serve as convenient screening tools for AF.

Objective

The objective of this review was to investigate the diagnostic accuracy of 2 relatively new technologies used in ambulatory devices, non-12-lead ECG and photoplethysmography (PPG), in detecting AF. We performed a meta-analysis to evaluate the diagnostic accuracy of non-12-lead ECG and PPG compared to the reference standard, 12-lead ECG. We also conducted a subgroup analysis to assess the impact of study design and participant recruitment on diagnostic accuracy.

Methods

This systematic review and meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. MEDLINE and EMBASE were systematically searched for articles published from January 1, 2015 to January 23, 2021. A bivariate model was used to pool estimates of sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and area under the summary receiver operating curve (SROC) as the main diagnostic measures. Study quality was evaluated using the quality assessment of diagnostic accuracy studies (QUADAS-2) tool.

Results

Our search resulted in 16 studies using either non-12-lead ECG or PPG for detecting AF, comprising 3217 participants and 7623 assessments. The pooled estimates of sensitivity, specificity, PLR, NLR, and diagnostic odds ratio for the detection of AF were 89.7% (95% CI 83.2%-93.9%), 95.7% (95% CI 92.0%-97.7%), 20.64 (95% CI 10.10-42.15), 0.11 (95% CI 0.06-0.19), and 224.75 (95% CI 70.10-720.56), respectively, for the automatic interpretation of non-12-lead ECG measurements and 94.7% (95% CI 93.3%-95.8%), 97.6% (95% CI 94.5%-99.0%), 35.51 (95% CI 18.19-69.31), 0.05 (95% CI 0.04-0.07), and 730.79 (95% CI 309.33-1726.49), respectively, for the automatic interpretation of PPG measurements.

Conclusions

Both non-12-lead ECG and PPG offered high diagnostic accuracies for AF. Detection employing automatic analysis techniques may serve as a useful preliminary screening tool before administering a gold standard test, which generally requires competent physician analyses. Subgroup analysis indicated variations of sensitivity and specificity between studies that recruited low-risk and high-risk populations, warranting future validity tests in the general population.

Trial Registration

PROSPERO International Prospective Register of Systematic Reviews CRD42020179937; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=179937

Vital Signs During the COVID-19 Outbreak: A Retrospective Analysis of 19,960 Participants in Wuhan and Four Nearby Capital Cities in China

Background

The implications of city lockdown on vital signs during the COVID-19 outbreak are unknown.

Objective

We longitudinally tracked vital signs using data from wearable sensors and determined associations with anxiety and depression.

Methods

We selected all participants in the HUAWEI Heart Study from Wuhan and four nearby large provincial capital cities (Guangzhou, Chongqing, Hangzhou, Zhengzhou) and extracted all data from 26 December 2019 (one month before city lockdown) to 21 February 2020. Sleep duration and quality, daily steps, oxygen saturation and heart rate were collected on a daily basis. We compared the vital signs before and after the lockdown using segmented regression analysis of the interrupted time series. The depression and anxiety cases were defined as scores ≥8 on the Hospital Anxiety and Depression Scale depression and anxiety subscales [HADS-D and HADS-A] in 727 participants who finished the survey.

Results

We included 19,960 participants (mean age 36 yrs, 90% men). Compared with pre-lockdown, resting heart rate dropped immediately by 1.1 bpm after city lockdown (95% confidence interval [CI]: -1.8, -0.4). Sleep duration increased by 0.5 hour (95% CI: 0.3, 0.8) but deep sleep ratio decreased by 0.9% (95% CI: -1.2, -0.6). Daily steps decreased by 3352 steps (95% CI: -4333, -2370). Anxiety and depression existed in 26% and 17% among 727 available participants, respectively, and associated with longer sleep duration (0.2 and 0.1 hour, both p < 0.001).

Conclusions

Lockdown of Wuhan in China was associated with an adverse vital signs profile (reduced physical activity, heart rate, and sleep quality, but increased sleep duration). Wearable devices in combination with mobile-based apps may be useful to monitor both physical and mental health.

Clinical trial registration

The trial is registered at Chinese Clinical Trial Registry (ChiCTR) website (ChiCTR-OOC-17014138).

[Telemedicine and ECG monitoring : Technical prerequisites and clinical workflow]

Classic telemonitoring for the detection of arrhythmias is well established. The advent of wearable ECG devices is associated with great potential and significant challenges. New data collection pathways have to be integrated into clinical workflows. Preliminary studies indicate that positive effects are to be expected from this new form of telemonitoring.

Atrial fibrillation monitoring with wrist-worn photoplethysmography-based wearables: State-of-the-art review

Early detection and diagnosis of atrial fibrillation (AF) is essential in order to prevent stroke and other severe health consequences. The challenges in diagnosing AF arise from its intermittent and asymptomatic nature. Wrist-worn devices that use monitoring based on photoplethysmography have been proposed recently as a possible solution because of their ability to monitor heart rate and rhythm for long periods of time at low cost. Long-term continuous monitoring with implantable devices has been shown to increase the percentage of detected AF episodes, but the additional value of wrist-worn devices has yet to be determined. In this review, we present the state of the art in AF detection with wrist-worn devices, discuss the potential of the technology and current knowledge gaps, and propose directions for future research. The state-of-the-art methods show excellent accuracy for AF detection. However, most of the studies were conducted in hospital settings, and more studies showing the accuracy of the technology for ambulatory long-term monitoring are needed. Objective comparison of results and methodologies among different studies currently is difficult due to the lack of adequate public datasets.

Population-Based Screening or Targeted Screening Based on Initial Clinical Risk Assessment for Atrial Fibrillation: A Report from the Huawei Heart Study

Background: A general-population approach has been advocated to improve the screening of patients with atrial fibrillation (AF). A more pragmatic alternative may be targeted screening of patients at high risk of developing AF. We assess the value of a simple clinical risk score, C₂HEST (C2, coronary artery disease/chronic obstructive pulmonary disease; COPD (1 point each); H, hypertension; E, elderly (age ≥75, doubled); S, systolic heart failure; HF (doubled); T, hyperthyroidism)); to facilitate population screening and detection of incident AF in the general population, in a prespecified ancillary analysis of the Huawei Heart Study. Methods: The Huawei Heart Study investigated general population screening for AF, identified using photoplethysmography (PPG)-based HUAWEI smart devices. We compared the value of a general population approach to a target screening approach between 26 October 2018 and 20 November 2019. Results: There were 644,124 individuals (mean age ± standard deviation, SD 34 ± 11; female 15.9%) who monitored their pulse rhythm using smart devices, among which 209,274 individuals (mean age 34 years, SD11; 10.6% female) completed the questionnaire on cardiovascular risk factors, with 739 detecting AF. Of these, 31.4% (n = 65,810) subjects reported palpitations. The median (interquartile range, IQR) duration to first detected AF was 11 (1–46), 6 (1–49), and 4 (1–24) in the population with low, intermediate, and high C₂HEST score category, respectively (p = 0.03). Detected AF events rates increased with increasing C₂HEST score points, stratified by age (p for trend, p < 0.001). Hazard ratios of the components of the C₂HEST score for detected AF were between 1.31 and 2.75. A combination of symptomatic palpitations and C₂HEST score increased prediction of AF detection, compared to using C₂HEST score alone (c-indexes 0.72 vs. 0.76, Delong test, p < 0.001). Conclusions: The C₂HEST score, especially when combined with symptoms, could facilitate a targeted population-based screening and preventive strategy for AF.

Screening for atrial fibrillation: a call for evidence

Atrial fibrillation (AF) is the most common cardiac arrhythmia and prevalence is predicted to double over the next 30 years due to changing demographics and the rise in prevalence of risk factors such as hypertension and diabetes. Atrial fibrillation is associated with a five-fold increased stroke risk, but anticoagulation in eligible patients can reduce this risk by around 65%. Many people with AF currently go undetected and therefore untreated, either because they are asymptomatic or because they have paroxysmal AF. Screening has been suggested as one approach to increase AF detection rates and reduce the incidence of ischaemic stroke by earlier initiation of anticoagulation therapy. However, international taskforces currently recommend against screening, citing the cost implications and uncertainty over the benefits of a systematic screening programme compared to usual care. A number of large randomized controlled trials have commenced to determine the cost-effectiveness and clinical benefit of screening using a range of devices and across different populations. The recent AppleWatch study demonstrates how advances in technology are providing the public with self-screening devices that are increasingly affordable and accessible. Health care professionals should be aware of the implications of these emerging data for diagnostic pathways and treatment. This review provides an overview of the gaps in the current evidence and a summary of the arguments for and against screening.

Use of a Smart Watch for Early Detection of Paroxysmal Atrial Fibrillation: Validation Study

Background

Wearable devices with photoplethysmography (PPG) technology can be useful for detecting paroxysmal atrial fibrillation (AF), which often goes uncaptured despite being a leading cause of stroke.

Objective

This study is the first part of a 2-phase study that aimed at developing a method for immediate detection of paroxysmal AF using PPG-integrated wearable devices. In this study, the diagnostic performance of 2 major smart watches, Apple Watch Series 3 and Fitbit (FBT) Charge HR Wireless Activity Wristband, each equipped with a PPG sensor, was compared, and the pulse rate data outputted from those devices were analyzed for precision and accuracy in reference to the heart rate data from electrocardiography (ECG) during AF.

Methods

A total of 40 subjects from patients who underwent cardiac surgery at a single center between September 2017 and March 2018 were monitored for postoperative AF using telemetric ECG and PPG devices. AF was diagnosed using a 12-lead ECG by qualified physicians. Each subject was given a pair of smart watches, Apple Watch and FBT, for simultaneous pulse rate monitoring. The heart rate of all subjects was also recorded on the telemetry system. Time series pulse rate trends and heart rate trends were created and analyzed for trend pattern similarities. Those trend data were then used to determine the accuracy of PPG-based pulse rate measurements in reference to ECG-based heart rate measurements during AF.

Results

Of the 20 AF events in group FBT, 6 (30%) showed a moderate or higher correlation (cross-correlation function>0.40) between pulse rate trend patterns and heart rate trend patterns. Of the 16 AF events in group Apple Watch (workout [W] mode), 12 (75%) showed a moderate or higher correlation between the 2 trend patterns. Linear regression analyses also showed a significant correlation between the pulse rates and the heart rates during AF in the subjects with Apple Watch. This correlation was not observed with FBT. The regression formula for Apple Watch W mode and FBT was X=14.203 + 0.841Y and X=58.225 + 0.228Y, respectively (where X denotes the mean of all average pulse rates during AF and Y denotes the mean of all corresponding average heart rates during AF), and the coefficient of determination (R²) was 0.685 and 0.057, respectively (P<.001 and .29, respectively).

Conclusions

In this validation study, the detection precision of AF and measurement accuracy during AF were both better with Apple Watch W mode than with FBT.

Photoplethysmography based atrial fibrillation detection: a review

Atrial fibrillation (AF) is a cardiac rhythm disorder associated with increased morbidity and mortality. It is the leading risk factor for cardioembolic stroke and its early detection is crucial in both primary and secondary stroke prevention. Continuous monitoring of cardiac rhythm is today possible thanks to consumer-grade wearable devices, enabling transformative diagnostic and patient management tools. Such monitoring is possible using low-cost easy-to-implement optical sensors that today equip the majority of wearables. These sensors record blood volume variations-a technology known as photoplethysmography (PPG)-from which the heart rate and other physiological parameters can be extracted to inform about user activity, fitness, sleep, and health. Recently, new wearable devices were introduced as being capable of AF detection, evidenced by large prospective trials in some cases. Such devices would allow for early screening of AF and initiation of therapy to prevent stroke. This review is a summary of a body of work on AF detection using PPG. A thorough account of the signal processing, machine learning, and deep learning approaches used in these studies is presented, followed by a discussion of their limitations and challenges towards clinical applications.

Validation of Single Centre Pre-Mobile Atrial Fibrillation Apps for Continuous Monitoring of Atrial Fibrillation in a Real-World Setting: Pilot Cohort Study

Background

Atrial fibrillation is the most common recurrent arrhythmia in clinical practice, with most clinical events occurring outside the hospital. Low detection and nonadherence to guidelines are the primary obstacles to atrial fibrillation management. Photoplethysmography is a novel technology developed for atrial fibrillation screening. However, there has been limited validation of photoplethysmography-based smart devices for the detection of atrial fibrillation and its underlying clinical factors impacting detection.

Objective

This study aimed to explore the feasibility of photoplethysmography-based smart devices for the detection of atrial fibrillation in real-world settings.

Methods

Subjects aged ≥18 years (n=361) were recruited from September 14 to October 16, 2018, for screening of atrial fibrillation with active measurement, initiated by the users, using photoplethysmography-based smart wearable devices (ie, a smart band or smart watches). Of these, 200 subjects were also automatically and periodically monitored for 14 days with a smart band. The baseline diagnosis of “suspected” atrial fibrillation was confirmed by electrocardiogram and physical examination. The sensitivity and accuracy of photoplethysmography-based smart devices for monitoring atrial fibrillation were evaluated.

Results

A total of 2353 active measurement signals and 23,864 periodic measurement signals were recorded. Eleven subjects were confirmed to have persistent atrial fibrillation, and 20 were confirmed to have paroxysmal atrial fibrillation. Smart devices demonstrated >91% predictive ability for atrial fibrillation. The sensitivity and specificity of devices in detecting atrial fibrillation among active recording of the 361 subjects were 100% and about 99%, respectively. For subjects with persistent atrial fibrillation, 127 (97.0%) active measurements and 2240 (99.2%) periodic measurements were identified as atrial fibrillation by the algorithm. For subjects with paroxysmal atrial fibrillation, 36 (17%) active measurements and 717 (19.8%) periodic measurements were identified as atrial fibrillation by the algorithm. All persistent atrial fibrillation cases could be detected as “atrial fibrillation episodes” by the photoplethysmography algorithm on the first monitoring day, while 14 (70%) patients with paroxysmal atrial fibrillation demonstrated “atrial fibrillation episodes” within the first 6 days. The average time to detect paroxysmal atrial fibrillation was 2 days (interquartile range: 1.25-5.75) by active measurement and 1 day (interquartile range: 1.00-2.00) by periodic measurement (P=.10). The first detection time of atrial fibrillation burden of <50% per 24 hours was 4 days by active measurement and 2 days by periodic measurementThe first detection time of atrial fibrillation burden of >50% per 24 hours was 1 day for both active and periodic measurements (active measurement: P=.02, periodic measurement: P=.03).

Conclusions

Photoplethysmography-based smart devices demonstrated good atrial fibrillation predictive ability in both active and periodic measurements. However, atrial fibrillation type could impact detection, resulting in increased monitoring time.

Trial Registration

Chinese Clinical Trial Registry of the International Clinical Trials Registry Platform of the World Health Organization ChiCTR-OOC-17014138; http://www.chictr.org.cn/showprojen.aspx?proj=24191.

Detecting Atrial Fibrillation and Atrial Flutter in Daily Life Using Photoplethysmography Data

OBJECTIVE

Photoplethysmography (PPG) enables unobtrusive heart rate monitoring, which can be used in wrist-worn applications. Its potential for detecting atrial fibrillation (AF) has been recently presented. Besides AF, another cardiac arrhythmia increasing stroke risk and requiring treatment is atrial flutter (AFL). Currently, the knowledge about AFL detection with PPG is limited. The objective of our study was to develop a model that classifies AF, AFL, and sinus rhythm with or without premature beats from PPG and acceleration data measured at the wrist in daily life.

METHODS

A dataset of 40 patients was collected by measuring PPG and accelerometer data, as well as electrocardiogram as a reference, during 24-hour monitoring. The dataset was split into 75%-25% for training and testing a Random Forest (RF) model, which combines features from PPG, inter-pulse intervals (IPI), and accelerometer data, to classify AF, AFL, and other rhythms. The performance was compared to an AF detection algorithm combining traditional IPI features for determining the robustness of the accuracy in presence of AFL.

RESULTS

The RF model classified AF/AFL/other with sensitivity and specificity of 97.6/84.5/98.1% and 98.2/99.7/92.8%, respectively. The results with the IPI-based AF classifier showed that the majority of false detections were caused by AFL.

CONCLUSION

The PPG signal contains information to classify AFL in the presence of AF, sinus rhythm, or sinus rhythm with premature contractions.

SIGNIFICANCE

PPG could indicate presence of AFL, not only AF.

Mobile Photoplethysmographic Technology to Detect Atrial Fibrillation

BACKGROUND

Low detection and nonadherence are major problems in current management approaches for patients with suspected atrial fibrillation (AF). Mobile health devices may enable earlier AF detection and improved AF management.

OBJECTIVES

This study sought to investigate the effectiveness of AF screening in a large population-based cohort using smart device-based photoplethysmography (PPG) technology, combined with a clinical care AF management pathway using a mobile health approach.

METHODS

AF screening was performed with smart devices using PPG technology, which were made available for the population ≥18 years of age across China. Monitoring for at least 14 days with a wristband (Honor Band 4) or wristwatch (Huawei Watch GT, Honor Watch, Huawei Technologies Co., Ltd., Shenzhen, China) was allowed. The patients with "possible AF" episodes using the PPG algorithm were further confirmed by health providers among the MAFA (mobile AF app) Telecare center and network hospitals, with clinical evaluation, electrocardiogram, or 24-h Holter monitoring.

RESULTS

There were 246,541 individuals who downloaded the PPG screening app, and 187,912 individuals used smart devices to monitor their pulse rhythm between October 26, 2018, and May 20, 2019. Among those with PPG monitoring (mean age 35 years, 86.7% male), 424 (of 187,912, 0.23%) (mean age 54 years, 87.0% male) received a "suspected AF" notification. Of those effectively followed up, 227 individuals (of 262, 87.0%) were confirmed as having AF, with the positive predictive value of PPG signals being 91.6% (95% confidential interval [CI]: 91.5% to 91.8%). Both suspected AF and identified AF markedly increased with age (p for trend <0.001), and individuals in Northeast China had the highest proportion of detected AF of 0.28% (95% CI: 0.20% to 0.39%). Of the individuals with identified AF, 216 (of 227, 95.1%) subsequently entered a program of integrated AF management using a mobile AF application; approximately 80% of high-risk patients were successfully anticoagulated.

CONCLUSIONS

Based on the present study, continuous home monitoring with smart device-based PPG technology could be a feasible approach for AF screening. This would help efforts at screening and detection of AF, as well as early interventions to reduce stroke and other AF-related complications. (Mobile Health [mHealth] Technology for Improved Screening, Patient Involvement and Optimizing Integrated Care in Atrial Fibrillation [MAFA II]; ChiCTR-OOC-17014138).

Accuracy of mHealth Devices for Atrial Fibrillation Screening: Systematic Review

BACKGROUND

Mobile health (mHealth) devices can be used for the diagnosis of atrial fibrillation. Early diagnosis allows better treatment and prevention of secondary diseases like stroke. Although there are many different mHealth devices to screen for atrial fibrillation, their accuracy varies due to different technological approaches.

OBJECTIVE

We aimed to systematically review available studies that assessed the accuracy of mHealth devices in screening for atrial fibrillation. The goal of this review was to provide a comprehensive overview of available technologies, specific characteristics, and accuracy of all relevant studies.

METHODS

PubMed and Web of Science databases were searched from January 2014 until January 2019. Our systematic review was performed according to the Preferred Reporting Items for Systematic Review and Meta-Analyses. We restricted the search by year of publication, language, noninvasive methods, and focus on diagnosis of atrial fibrillation. Articles not including information about the accuracy of devices were excluded.

RESULTS

We found 467 relevant studies. After removing duplicates and excluding ineligible records, 22 studies were included. The accuracy of mHealth devices varied among different technologies, their application settings, and study populations. We described and summarized the eligible studies.

CONCLUSIONS

Our systematic review identifies different technologies for screening for atrial fibrillation with mHealth devices. A specific technology's suitability depends on the underlying form of atrial fibrillation to be diagnosed. With the suitable use of mHealth, early diagnosis and treatment of atrial fibrillation are possible. Successful application of mHealth technologies could contribute to significantly reducing the cost of illness of atrial fibrillation.