Wearable Devices for Ambulatory Cardiac Monitoring: JACC State-of-the-Art Review

Accuracy of a Single-Lead ECG Device for Diagnosis of Cardiac Arrhythmias Compared Against Cardiac Electrophysiology Study

BACKGROUND

Single-lead electrocardiogram (ECG) devices may allow detection and diagnosis of cardiac rhythms. However, data on their accuracy for detecting cardiac arrhythmias beyond atrial fibrillation are limited. We aimed to determine the accuracy of the AliveCor KardiaMobile (AC) (AliveCor Inc, Mountain View, CA, USA) for the diagnosis of arrhythmias against gold standard cardiac electrophysiology study (EPS).

METHOD

Patients undergoing clinically indicated EPS underwent simultaneous rhythm recording with an AC, standard 12-lead ECG, and EP catheters for intracardiac electrograms. Rhythms recorded during EPS were classified based on electrogram, 12-lead ECG, and clinical findings. Blinded reviewers provided differential diagnoses for the single-lead AC tracings; a separate reviewer compared diagnoses made between the AC tracings and EPS findings.

RESULTS

In 49 patients, 843 cardiac rhythms were captured during 502 AC recordings. Analysis of tracings containing sinus rhythm (n=273) returned an overall accuracy of 92%, with sensitivity and specificity values of 93% and 92%, respectively. Accuracy for tracings per rhythm was atrial fibrillation 91% (n=51); supraventricular tachycardia accuracy was 89% (n=191), ventricular tachycardia 91% (n=198), ventricular fibrillation 98% (n=11), and asystole 100% (n=5). Accuracy for supraventricular ectopy was 93% (n=28) and for premature ventricular complexes was 91% (n=86). Overall accuracy was 94% for solitary rhythms and 93% in tracings from patients with baseline bundle branch block.

CONCLUSIONS

When compared against the gold standard EPS diagnosis, the interpretation of arrhythmias recorded by an AliveCor single-lead ECG device had reasonable diagnostic accuracy.

Overnight Sleep Staging Using Chest-Worn Accelerometry

Overnight sleep staging is an important part of the diagnosis of various sleep disorders. Polysomnography is the gold standard for sleep staging, but less-obtrusive sensing modalities are of emerging interest. Here, we developed and validated an algorithm to perform "proxy" sleep staging using cardiac and respiratory signals derived from a chest-worn accelerometer. We collected data in two sleep centers, using a chest-worn accelerometer in combination with full PSG. A total of 323 participants were analyzed, aged 13-83 years, with BMI 18-47 kg/m2. We derived cardiac and respiratory features from the accelerometer and then applied a previously developed method for automatic cardio-respiratory sleep staging. We compared the estimated sleep stages against those derived from PSG and determined performance. Epoch-by-epoch agreement with four-class scoring (Wake, REM, N1+N2, N3) reached a Cohen's kappa coefficient of agreement of 0.68 and an accuracy of 80.8%. For Wake vs. Sleep classification, an accuracy of 93.3% was obtained, with a sensitivity of 78.7% and a specificity of 96.6%. We showed that cardiorespiratory signals obtained from a chest-worn accelerometer can be used to estimate sleep stages among a population that is diverse in age, BMI, and prevalence of sleep disorders. This opens up the path towards various clinical applications in sleep medicine.

A Crowdsourced AI Framework for Atrial Fibrillation Detection in Apple Watch and Kardia Mobile ECGs

Background: Atrial fibrillation (AFib) detection via mobile ECG devices is promising, but algorithms often struggle to generalize across diverse datasets and platforms, limiting their real-world applicability. Objective: This study aims to develop a robust, generalizable AFib detection approach for mobile ECG devices using crowdsourced algorithms. Methods: We developed a voting algorithm using random forest, integrating six open-source AFib detection algorithms from the PhysioNet Challenge. The algorithm was trained on an AliveCor dataset and tested on two disjoint AliveCor datasets and one Apple Watch dataset. Results: The voting algorithm outperformed the base algorithms across all metrics: the average of sensitivity (0.884), specificity (0.988), PPV (0.917), NPV (0.985), and F1-score (0.943) on all datasets. It also demonstrated the least variability among datasets, signifying its highest robustness and effectiveness in diverse data environments. Moreover, it surpassed Apple's algorithm on all metrics and showed higher specificity but lower sensitivity than AliveCor's Kardia algorithm. Conclusions: This study demonstrates the potential of crowdsourced, multi-algorithmic strategies in enhancing AFib detection. Our approach shows robust cross-platform performance, addressing key generalization challenges in AI-enabled cardiac monitoring and underlining the potential for collaborative algorithms in wearable monitoring devices.

The Spectrum of Coronary Artery Disease in Elite Endurance Athletes-A Long-Standing Debate: State-of-the-Art Review

Physical activity is recommended for the prevention of primary and secondary cardiovascular (CV) disease as it is linked to a number of health benefits, especially CV. However, recent research suggests that high-volume, long-term endurance exercise may hasten rather than slow the coronary atherosclerosis progression. This contentious theory has generated a great discussion and is still a major source of doubt when it comes to the clinical treatment of coronary artery disease (CAD) in athletes. CAD is the primary cause of sudden cardiac death in athletes over 35 years. Thus, recent studies evaluated the prevalence of CAD in athletes and its clinical and prognostic implications. Indeed, many studies have shown a relationship between endurance sports and higher volumes of coronary calcified plaque as determined by computed tomography. However, the precise pathogenetic substrate for the existence of an increased coronary calcification burden among endurance athletes remains unclear. Moreover, the idea that coronary plaques in elite athletes present a benign morphology has been cast into doubt by some recent studies showing potential association with adverse cardiovascular events. This review aims to analyze the association between physical activity and CAD, explaining possible underlying mechanisms of atherosclerotic progression and non-ischemic coronary lesions, focusing primarily on clinical and prognostic implications, multimodal evaluation, and management of CAD in endurance athletes.

Feasibility of a using chest strap and dry electrode system for longer term cardiac arrhythmia monitoring: Results from a pilot observational study

BACKGROUND AND AIM

Cardiac arrhythmia diagnostic yield improves with increased duration of monitoring. We investigated patient comfort, diagnostic quality of ECG, and arrhythmia diagnostic yield using a single lead longer term external cardiac monitor (ECM).

METHODS

The observational ECM feasibility study enrolled patients with increased risk of cardiac arrhythmia. The ECM investigational prototype was designed using a chest strap with dry electrodes connected to module capable of triggered loop recording of ECG, and automatic detection of arrhythmia. In group-A of study (24-h inpatient), patients wore ECM and Holter that recorded ECG from the ECM and adhesive electrodes. In group-B of study (12-weeks ambulatory), at monthly follow-ups patients filled out a comfort survey and device stored arrhythmia episodes were reviewed.

RESULTS

The study enrolled 34 patients (38 % females, average age 57.5 years, 65 % had palpitations, 12 % had syncope). Diagnostic quality ECG was recorded on 76.5 % of the monitoring duration in 12 of 20 patients with reviewable data in group-A, with motion artifacts causing loss in ECG signal for 18.7 % of the time. In 14 patients in group-B, 94.9 % of the survey responses indicated that ECM was comfortable to wear. Cardiac arrhythmia was observed in 4 of 17 patients (24 %) in group-A and 9 of 14 patients (64 %) in group-B in device recorded episodes. All ECM detected pause and tachycardia were inappropriate detections due to motion artifacts and temporary device removal.

CONCLUSION

The chest strap-based ECM device was mostly comfortable to wear and recorded diagnostic quality ECG in three-fourth of monitoring period. Cardiac arrhythmia was observed in 64 % of patients over 3-month monitoring along with large number of motion artifact induced inappropriate detections.

Atrial fibrillation burden and clinical outcomes following BTK inhibitor initiation

Bruton's tyrosine kinase inhibitors (BTKi) have dramatic efficacy against B-cell malignancies, but link with cardiotoxicity, including atrial fibrillation (AF). Burden, severity, and implications of BTKi-related AF are unknown. Leveraging a large-cohort of consecutive B-cell malignancy patients initiated on BTKi from 2009-2020, we identified patients with extended ambulatory rhythm monitoring. The primary outcome was AF burden after BTKi-initiation. Secondary outcomes included ventricular arrhythmia burden and other arrhythmias. Observed incident-AF rates and burden with next-generation BTKi's were compared to ibrutinib. Multivariable regression defined association between rhythm measures and major adverse cardiac events (MACE), and mortality. There were 98 BTKi-treated patients [38.8% next-generation BTKi's, 14.3% prior-AF], with 28,224 h of monitoring. Median duration BTKi-use was 34 months. Over mean duration 12 days monitoring, 72.4% developed arrhythmias (16.3% incident-AF, 31.6% other SVTs, 14.3% ventricular tachycardia). 14.3% had high AF-burden. AF-burden was similar between ibrutinib and next-generation BTKi's. No single antiarrhythmic-therapy prevented BTKi-related AF. However, antiarrhythmic initiation associated with reduction in arrhythmic burden (P = 0.009). In a multivariable model accounting for traditional cardiovascular risk factors, prior-AF associated with increased post-BTKi AF-burden. In follow-up, high AF burden associated with MACE (HR 3.12, P = 0.005) and mortality (HR 2.97, P = 0.007). Among BTKi-treated patients, high AF burden prognosticates future MACE and mortality risk.

On the design and development of a handheld electrocardiogram device in a clinical setting

Cardiovascular diseases (CVDs) are a global burden that requires attention. For the detection and diagnosis of CVDs, the 12-lead ECG is a key tool. With technological advancements, ECG devices are becoming smaller and available for home use. Most of these devices contain a limited number of leads and are aimed to detect atrial fibrillation (AF). To investigate whether a four-electrode arrangement could provide enough information to diagnose other CVDs, further research is necessary. At the University Medical Center Utrecht in a multidisciplinary team, we developed the miniECG, a four-electrode ECG handheld system for scientific research in clinical environments (TRL6). This paper describes the process followed during the development of the miniECG. From assembling a multidisciplinary team, which includes engineers, cardiologists, and clinical physicians to the contribution of team members in the design input, design, and testing for safety and functionality of the device. Finally, we detail how the development process was composed by iterative design steps based on user input and intended use evolution. The miniECG is a device compliant for scientific research with patients within Dutch Medical Centers. We believe that hospital-based development led to a streamlined process, which could be applied for the design and development of other technologies used for scientific research in clinical environments.

Arterial stiffness assessment by pulse wave velocity in postmenopausal women: comparison between noninvasive devices

OBJECTIVE

This study aimed to ascertain the accuracy of measure arterial stiffness using the HUAWEI GT 3 Pro smartwatch and pOpmètre device against the SphygmoCor (algorithms: intersect tangent and maximum of the second derivate).

METHODS

Twenty-three physically active postmenopausal women (age: 58.9 ± 3.2 years; body mass index: 26.3 ± 4.8 kg/m 2 ) were recruited. Carotid-femoral pulse wave velocity, finger-toe pulse wave velocity, and wrist-finger pulse wave velocity were obtained using SphygmoCor, pOpmètre and HUAWEI GT 3 Pro devices in a randomized order. Additionally, the pulse mean carotid-femoral and finger-toe pulse transit time was registered for SphygmoCor and pOpmètre, respectively.

RESULTS

Lower values of pulse wave velocity were recorded by HUAWEI in comparison with SphygmoCor with both algorithms, whereas no significant differences were detected between SphygmoCor and pOpmètre results. Pulse wave velocity values from SphygmoCor were positively correlated with pOpmètre results ( r = 0.464 and r = 0.451 using intersect tangent and second derivative algorithms), whereas this was not the case with those obtained from HUAWEI. Coefficients of bias of Lin's concordance coefficients close to 1 (0.832 and 0.831 for intersect tangent and second derivative algorithm, respectively) and mean bias close to 0 from Bland-Altman analysis suggested an acceptable agreement between pulse wave velocity obtained from SphygmoCor and pOpmètre.

CONCLUSIONS

Our results suggest an acceptable concordance of pulse wave velocity values recoded by SphygmoCor and pOpmètre, whereas this was not the case for data obtained from HUAWEI GT 3 Pro smartwatch. Therefore, the pOpmètre may be a viable alternative for assessing arterial stiffness, but measurement via the smartwatch device cannot be recommended.

Approaches of wearable and implantable biosensor towards of developing in precision medicine

In the relentless pursuit of precision medicine, the intersection of cutting-edge technology and healthcare has given rise to a transformative era. At the forefront of this revolution stands the burgeoning field of wearable and implantable biosensors, promising a paradigm shift in how we monitor, analyze, and tailor medical interventions. As these miniature marvels seamlessly integrate with the human body, they weave a tapestry of real-time health data, offering unprecedented insights into individual physiological landscapes. This log embarks on a journey into the realm of wearable and implantable biosensors, where the convergence of biology and technology heralds a new dawn in personalized healthcare. Here, we explore the intricate web of innovations, challenges, and the immense potential these bioelectronics sentinels hold in sculpting the future of precision medicine.

Enhancing Spinal Cord Injury Care: Using Wearable Technologies for Physical Activity, Sleep, and Cardiovascular Health

Wearable devices have the potential to advance health care by enabling real-time monitoring of biobehavioral data and facilitating the management of an individual's health conditions. Individuals living with spinal cord injury (SCI) have impaired motor function, which results in deconditioning and worsening cardiovascular health outcomes. Wearable devices may promote physical activity and allow the monitoring of secondary complications associated with SCI, potentially improving motor function, sleep, and cardiovascular health. However, several challenges remain to optimize the application of wearable technologies within this population. One is striking a balance between research-grade and consumer-grade devices in terms of cost, accessibility, and validity. Additionally, limited literature supports the validity and use of wearable technology in monitoring cardio-autonomic and sleep outcomes for individuals with SCI. Future directions include conducting performance evaluations of wearable devices to precisely capture the additional variation in movement and physiological parameters seen in those with SCI. Moreover, efforts to make the devices small, lightweight, and inexpensive for consumer ease of use may affect those with severe motor impairments. Overcoming these challenges holds the potential for wearable devices to help individuals living with SCI receive timely feedback to manage their health conditions and help clinicians gather comprehensive patient health information to aid in diagnosis and treatment.

Consumer wearable devices for evaluation of heart rate control using digoxin versus beta-blockers: the RATE-AF randomized trial

Consumer-grade wearable technology has the potential to support clinical research and patient management. Here, we report results from the RATE-AF trial wearables study, which was designed to compare heart rate in older, multimorbid patients with permanent atrial fibrillation and heart failure who were randomized to treatment with either digoxin or beta-blockers. Heart rate (n = 143,379,796) and physical activity (n = 23,704,307) intervals were obtained from 53 participants (mean age 75.6 years (s.d. 8.4), 40% women) using a wrist-worn wearable linked to a smartphone for 20 weeks. Heart rates in participants treated with digoxin versus beta-blockers were not significantly different (regression coefficient 1.22 (95% confidence interval (CI) -2.82 to 5.27; P = 0.55); adjusted 0.66 (95% CI -3.45 to 4.77; P = 0.75)). No difference in heart rate was observed between the two groups of patients after accounting for physical activity (P = 0.74) or patients with high activity levels (≥30,000 steps per week; P = 0.97). Using a convolutional neural network designed to account for missing data, we found that wearable device data could predict New York Heart Association functional class 5 months after baseline assessment similarly to standard clinical measures of electrocardiographic heart rate and 6-minute walk test (F1 score 0.56 (95% CI 0.41 to 0.70) versus 0.55 (95% CI 0.41 to 0.68); P = 0.88 for comparison). The results of this study indicate that digoxin and beta-blockers have equivalent effects on heart rate in atrial fibrillation at rest and on exertion, and suggest that dynamic monitoring of individuals with arrhythmia using wearable technology could be an alternative to in-person assessment. ClinicalTrials.gov identifier: NCT02391337 .

Tachyarrhythmias in congenital heart disease

The prevalence of congenital heart disease (CHD) in adult patients has risen with advances in diagnostic and surgical techniques. Surgical modifications and hemodynamic changes increase the susceptibility to arrhythmias, impacting morbidity and mortality rates, with arrhythmias being the leading cause of hospitalizations and sudden deaths. Patients with CHD commonly experience both supraventricular and ventricular arrhythmias, with each CHD type associated with different arrhythmia patterns. Macroreentrant atrial tachycardias, particularly cavotricuspid isthmus-dependent flutter, are frequently reported. Ventricular arrhythmias, including monomorphic ventricular tachycardia, are prevalent, especially in patients with surgical scars. Pharmacological therapy involves antiarrhythmic and anticoagulant drugs, though data are limited with potential adverse effects. Catheter ablation is preferred, demanding meticulous procedural planning due to anatomical complexity and vascular access challenges. Combining imaging techniques with electroanatomic navigation enhances outcomes. However, risk stratification for sudden death remains challenging due to anatomical variability. This article practically reviews the most common tachyarrhythmias, treatment options, and clinical management strategies for these patients.

Data Interoperability for Ambulatory Monitoring of Cardiovascular Disease: A Scientific Statement From the American Heart Association

Wearable devices are increasingly used by a growing portion of the population to track health and illnesses. The data emerging from these devices can potentially transform health care. This requires an interoperability framework that enables the deployment of platforms, sensors, devices, and software applications within diverse health systems, aiming to facilitate innovation in preventing and treating cardiovascular disease. However, the current data ecosystem includes several noninteroperable systems that inhibit such objectives. The design of clinically meaningful systems for accessing and incorporating these data into clinical workflows requires strategies to ensure the quality of data and clinical content and patient and caregiver accessibility. This scientific statement aims to address the best practices, gaps, and challenges pertaining to data interoperability in this area, with considerations for (1) data integration and the scope of measures, (2) application of these data into clinical approaches/strategies, and (3) regulatory/ethical/legal issues.

Long-term incidence of arrhythmias in extracardiac conduit Fontan and comparison between systemic left and right ventricle

AIMS

The extracardiac conduit-Fontan (ECC) has become the preferred technique for univentricular heart palliation, but there are currently no data on the incidence of long-term arrhythmias. This study investigated the incidence of arrhythmias and relation to single ventricle morphology in the long-term follow-up (FU) in ECC.

METHODS AND RESULTS

All patients with ECC performed in our Centre between 1987 and 2017 were included (minimum FU 5 years). Of 353 consecutive patients, 303 [57.8% males, aging 8-50 (median 20) years at last FU] were considered and divided into two groups depending on left (194 in Group 1) or right (109 in Group 2) ventricular morphology. Eighty-five (28%) experienced ≥1 arrhythmic complications, with early and late arrhythmias in 17 (5.6%) and 73 (24.1%) patients, respectively. Notably, late bradyarrhythmias occurred after 6 years in 21 (11%) patients in Group 1, and in 15 (13.8%) in Group 2 [P = 0.48]. Late tachyarrhythmias occurred in 55 (18.2%) patients after 12 years: 33 (17%) in Group 1 and 22 (20.2%) patients in Group 2 [P  = 0.5]. Ventricular tachycardias (VT) were documented after 12.5 years in 14 (7.2%) patients of Group 1 and 15 (13.8%) of Group 2 [P = 0.06] with a higher incidence in Group 2 during the FU [P = 0.005].

CONCLUSION

Extracardiac conduit is related to a significant arrhythmic risk in the long-term FU, higher than previously reported. Bradyarrhythmias occur earlier but are less frequent than tachyarrhythmias. Interestingly, patients with systemic right ventricle have a significantly higher incidence of VT, especially in a very long FU.

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.

Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association

A major focus of academia, industry, and global governmental agencies is to develop and apply artificial intelligence and other advanced analytical tools to transform health care delivery. The American Heart Association supports the creation of tools and services that would further the science and practice of precision medicine by enabling more precise approaches to cardiovascular and stroke research, prevention, and care of individuals and populations. Nevertheless, several challenges exist, and few artificial intelligence tools have been shown to improve cardiovascular and stroke care sufficiently to be widely adopted. This scientific statement outlines the current state of the art on the use of artificial intelligence algorithms and data science in the diagnosis, classification, and treatment of cardiovascular disease. It also sets out to advance this mission, focusing on how digital tools and, in particular, artificial intelligence may provide clinical and mechanistic insights, address bias in clinical studies, and facilitate education and implementation science to improve cardiovascular and stroke outcomes. Last, a key objective of this scientific statement is to further the field by identifying best practices, gaps, and challenges for interested stakeholders.

An Arrhythmia classification approach via deep learning using single-lead ECG without QRS wave detection

Arrhythmia, a frequently encountered and life-threatening cardiac disorder, can manifest as a transient or isolated event. Traditional automatic arrhythmia detection methods have predominantly relied on QRS-wave signal detection. Contemporary research has focused on the utilization of wearable devices for continuous monitoring of heart rates and rhythms through single-lead electrocardiogram (ECG), which holds the potential to promptly detect arrhythmias. However, in this study, we employed a convolutional neural network (CNN) to classify distinct arrhythmias without QRS wave detection step. The ECG data utilized in this study were sourced from the publicly accessible PhysioNet databases. Taking into account the impact of the duration of ECG signal on accuracy, this study trained one-dimensional CNN models with 5-s and 10-s segments, respectively, and compared their results. In the results, the CNN model exhibited the capability to differentiate between Normal Sinus Rhythm (NSR) and various arrhythmias, including Atrial Fibrillation (AFIB), Atrial Flutter (AFL), Wolff-Parkinson-White syndrome (WPW), Ventricular Fibrillation (VF), Ventricular Tachycardia (VT), Ventricular Flutter (VFL), Mobitz II AV Block (MII), and Sinus Bradycardia (SB). Both 10-s and 5-s ECG segments exhibited comparable results, with an average classification accuracy of 97.31%. It reveals the feasibility of utilizing even shorter 5-s recordings for detecting arrhythmias in everyday scenarios. Detecting arrhythmias with a single lead aligns well with the practicality of wearable devices for daily use, and shorter detection times also align with their clinical utility in emergency situations.

Identification of Atrial Fibrillation With Single-Lead Mobile ECG During Normal Sinus Rhythm Using Deep Learning

BACKGROUND

The acquisition of single-lead electrocardiogram (ECG) from mobile devices offers a more practical approach to arrhythmia detection. Using artificial intelligence for atrial fibrillation (AF) identification enhances screening efficiency. However, the potential of single-lead ECG for AF identification during normal sinus rhythm (NSR) remains under-explored. This study introduces a method to identify AF using single-lead mobile ECG during NSR.

METHODS

We employed three deep learning models: recurrent neural network (RNN), long short-term memory (LSTM), and residual neural networks (ResNet50). From a dataset comprising 13,509 ECGs from 6,719 patients, 10,287 NSR ECGs from 5,170 patients were selected. Single-lead mobile ECGs underwent noise filtering and segmentation into 10-second intervals. A random under-sampling was applied to reduce bias from data imbalance. The final analysis involved 31,767 ECG segments, including 15,157 labeled as masked AF and 16,610 as Healthy.

RESULTS

ResNet50 outperformed the other models, achieving a recall of 79.3%, precision of 65.8%, F1-score of 71.9%, accuracy of 70.5%, and an area under the receiver operating characteristic curve (AUC) of 0.79 in identifying AF from NSR ECGs. Comparative performance scores for RNN and LSTM were 0.75 and 0.74, respectively. In an external validation set, ResNet50 attained an F1-score of 64.1%, recall of 68.9%, precision of 60.0%, accuracy of 63.4%, and AUC of 0.68.

CONCLUSION

The deep learning model using single-lead mobile ECG during NSR effectively identified AF at risk in future. However, further research is needed to enhance the performance of deep learning models for clinical application.

Hydrogels for the Treatment of Myocardial Infarction: Design and Therapeutic Strategies

Cardiovascular diseases (CVDs) have become the leading global burden of diseases in recent years and are the primary cause of human mortality and loss of healthy life expectancy. Myocardial infarction (MI) is the top cause of CVDs-related deaths, and its incidence is increasing worldwide every year. Recently, hydrogels have garnered great interest from researchers as a promising therapeutic option for cardiac tissue repair after MI. This is due to their excellent properties, including biocompatibility, mechanical properties, injectable properties, anti-inflammatory properties, antioxidant properties, angiogenic properties, and conductive properties. This review discusses the advantages of hydrogels as a novel treatment for cardiac tissue repair after MI. The design strategies of various hydrogels in MI treatment are then summarized, and the latest research progress in the field is classified. Finally, the future perspectives of this booming field are also discussed at the end of this review.

Smartphone-based six-lead ECG: A new device for electrocardiographic recording in dogs

Smartphone-based technology for electrocardiographic recording is now part of the new concept of mobile health in both human and veterinary medicine. Although smartphone-based ECG for electrocardiographic screening in dogs is reliable, one-lead ECG devices have mainly been evaluated. This prospective study assessed the feasibility and the diagnostic reliability of a new smartphone-based six-lead electrocardiograph (smECG) in dogs, in comparison to a standard six-lead electrocardiograph (stECG). All ECG tracings were blindly reviewed by an expert operator, who judged whether tracings were acceptable for interpretation, performed the electrocardiographic measurements, and assigned a diagnosis. The agreement in the electrocardiographic interpretation and diagnosis between smECG and stECG was assessed using the Bland-Altman test and Cohen's k test. The study included 108 client-owned dogs. The tracings obtained by the smECG were interpretable in 100 % of cases. No clinically relevant differences between smECG and stECG were found in the assessment of heart rate, interval duration, and QRS mean electrical axis. The smECG tended to underestimate the amplitude of the P and R waves. Perfect agreement was found in the detection of sinus rhythm, atrial fibrillation, ventricular arrhythmias, atrioventricular blocks, and bundle branch blocks. Our study suggests that the tested smartphone-based six-lead ECG is a clinically reliable device for the assessment of heart rate and heart rhythm in dogs, and thus could be used in a clinical setting in dogs and for telemedicine.

Adaptive convolutional dictionary learning for denoising seismocardiogram to enhance the classification performance of aortic stenosis

BACKGROUND

Aortic stenosis (AS) is the most prevalent type of valvular heart disease (VHD), traditionally diagnosed using echocardiogram or phonocardiogram. Seismocardiogram (SCG), an emerging wearable cardiac monitoring modality, is proved to be feasible in non-invasive and cost-effective AS diagnosis. However, SCG waveforms acquired from patients with heart diseases are typically weak, making them more susceptible to noise contamination. While most related researches focus on motion artifacts, sensor noise and quantization noise have been mostly overlooked. These noises pose additional challenges for extracting features from the SCG, especially impeding accurate AS classification.

METHOD

To address this challenge, we present a convolutional dictionary learning-based method. Based on sparse modeling of SCG, the proposed method generates a personalized adaptive-size dictionary from noisy measurements. The dictionary is used for sparse coding of the noisy SCG into a transform domain. Reconstruction from the domain removes the noise while preserving the individual waveform pattern of SCG.

RESULTS

Using two self-collected SCG datasets, we established optimal dictionary learning parameters and validated the denoising performance. Subsequently, the proposed method denoised SCG from 50 subjects (25 AS and 25 non-AS). Leave-one-subject-out cross-validation (LOOCV) was applied to 5 machine learning classifiers. Among the classifiers, a bi-layer neural network achieved a moderate accuracy of 90.2%, with an improvement of 13.8% from the denoising.

CONCLUSIONS

The proposed sparsity-based denoising technique effectively removes stochastic sensor noise and quantization noise from SCG, consequently improving AS classification performance. This approach shows promise for overcoming instrumentation constraints of SCG-based diagnosis.

Utility of smart watches for identifying arrhythmias in children

BACKGROUND

Arrhythmia symptoms are frequent complaints in children and often require a pediatric cardiology evaluation. Data regarding the clinical utility of wearable technologies are limited in children. We hypothesize that an Apple Watch can capture arrhythmias in children.

METHODS

We present an analysis of patients ≤18 years-of-age who had signs of an arrhythmia documented by an Apple Watch. We include patients evaluated at our center over a 4-year-period and highlight those receiving a formal arrhythmia diagnosis. We evaluate the role of the Apple Watch in arrhythmia diagnosis, the results of other ambulatory cardiac monitoring studies, and findings of any EP studies.

RESULTS

We identify 145 electronic-medical-record identifications of Apple Watch, and find arrhythmias confirmed in 41 patients (28%) [mean age 13.8 ± 3.2 years]. The arrythmias include: 36 SVT (88%), 3 VT (7%), 1 heart block (2.5%) and wide 1 complex tachycardia (2.5%). We show that invasive EP study confirmed diagnosis in 34 of the 36 patients (94%) with SVT (2 non-inducible). We find that the Apple Watch helped prompt a workup resulting in a new arrhythmia diagnosis for 29 patients (71%). We note traditional ambulatory cardiac monitors were worn by 35 patients (85%), which did not detect arrhythmias in 10 patients (29%). In 73 patients who used an Apple Watch for recreational or self-directed heart rate monitoring, 18 (25%) sought care due to device findings without any arrhythmias identified.

CONCLUSION

We demonstrate that the Apple Watch can record arrhythmia events in children, including events not identified on traditionally used ambulatory monitors.

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.

Electrocardiogram-based deep learning model to screen peripartum cardiomyopathy

BACKGROUND

Peripartum cardiomyopathy, one of the most fatal conditions during delivery, results in heart failure secondary to left ventricular systolic dysfunction. Left ventricular dysfunction can result in abnormalities in electrocardiography. However, the usefulness of electrocardiography in the identification of peripartum cardiomyopathy in pregnant women remains unclear.

OBJECTIVE

This study aimed to evaluate the effectiveness of a 12-lead electrocardiography-based artificial intelligence/machine learning-based software as a medical device for screening peripartum cardiomyopathy.

STUDY DESIGN

This retrospective cohort study included pregnant women who underwent transthoracic echocardiography between a month before and 5 months after delivery and underwent 12-lead electrocardiography within 30 days of echocardiography between December 2011 and May 2022 at Seoul National University Hospital. The performance of 12-lead electrocardiography-based artificial intelligence/machine learning analysis (AiTiALVSD software; version 1.00.00, which was developed to screen for left ventricular systolic dysfunction in the general population) was evaluated for the identification of peripartum cardiomyopathy. In addition, the performance of another artificial intelligence/machine learning algorithm using only 1-lead electrocardiography to detect left ventricular systolic dysfunction was evaluated in identifying peripartum cardiomyopathy. The results were obtained under a 95% confidence interval and considered significant when P<.05.

RESULTS

Among the 14,557 women who delivered during the study period, 204 (1.4%) underwent transthoracic echocardiography a month before and 5 months after delivery. Among them, 12 (5.8%) were diagnosed with peripartum cardiomyopathy. The results showed that AiTiALVSD for 12-lead electrocardiography was highly effective in detecting peripartum cardiomyopathy, with an area under the receiver operating characteristic of 0.979 (95% confidence interval, 0.953-1.000), an area under the precision-recall curve of 0.715 (95% confidence interval, 0.499-0.951), a sensitivity of 0.917 (95% confidence interval, 0.760-1.000), a specificity of 0.927 (95% confidence interval, 0.890-0.964), a positive predictive value of 0.440 (95% confidence interval, 0.245-0.635), and a negative predictive value of 0.994 (95% confidence interval, 0.983-1.000). In addition, a 1-lead (lead I) artificial intelligence/machine learning algorithm showed excellent performance; the area under the receiver operating characteristic, area under the precision-recall curve, sensitivity, specificity, positive predictive value, and negative predictive value were 0.944 (95% confidence interval, 0.895-0.993), 0.520 (95% confidence interval, 0.319-0.801), 0.833 (95% confidence interval, 0.622-1.000), 0.880 (95% confidence interval, 0.834-0.926), 0.303 (95% confidence interval, 0.146-0.460), and 0.988 (95% confidence interval, 0.972-1.000), respectively.

CONCLUSION

The 12-lead electrocardiography-based artificial intelligence/machine learning-based software as a medical device (AiTiALVSD) and 1-lead algorithm are noninvasive and effective ways of identifying cardiomyopathies occurring during the peripartum period, and they could potentially be used as highly sensitive screening tools for peripartum cardiomyopathy.

Noncontact remote sensing of abnormal blood pressure using a deep neural network: a novel approach for hypertension screening

Background

As the global burden of hypertension continues to increase, early diagnosis and treatment play an increasingly important role in improving the prognosis of patients. In this study, we developed and evaluated a method for predicting abnormally high blood pressure (HBP) from infrared (upper body) remote thermograms using a deep learning (DL) model.

Methods

The data used in this cross-sectional study were drawn from a coronavirus disease 2019 (COVID-19) pilot cohort study comprising data from 252 volunteers recruited from 22 July to 4 September 2020. Original video files were cropped at 5 frame intervals to 3,800 frames per slice. Blood pressure (BP) information was measured using a Welch Allyn 71WT monitor prior to infrared imaging, and an abnormal increase in BP was defined as a systolic blood pressure (SBP) ≥140 mmHg and/or diastolic blood pressure (DBP) ≥90 mmHg. The PanycNet DL model was developed using a deep neural network to predict abnormal BP based on infrared thermograms.

Results

A total of 252 participants were included, of which 62.70% were male and 37.30% were female. The rate of abnormally high HBP was 29.20% of the total number. In the validation group (upper body), precision, recall, and area under the receiver operating characteristic curve (AUC) values were 0.930, 0.930, and 0.983 [95% confidence interval (CI): 0.904-1.000], respectively, and the head showed the strongest predictive ability with an AUC of 0.868 (95% CI: 0.603-0.994).

Conclusions

This is the first technique that can perform screening for hypertension without contact using existing equipment and data. It is anticipated that this technique will be suitable for mass screening of the population for abnormal BP in public places and home BP monitoring.

Implantable Triboelectric Nanogenerators for Self-Powered Cardiovascular Healthcare

Triboelectric nanogenerators (TENGs) have gained significant traction in recent years in the bioengineering community. With the potential for expansive applications for biomedical use, many individuals and research groups have furthered their studies on the topic, in order to gain an understanding of how TENGs can contribute to healthcare. More specifically, there have been a number of recent studies focusing on implantable triboelectric nanogenerators (I-TENGs) toward self-powered cardiac systems healthcare. In this review, the progression of implantable TENGs for self-powered cardiovascular healthcare, including self-powered cardiac monitoring devices, self-powered therapeutic devices, and power sources for cardiac pacemakers, will be systematically reviewed. Long-term expectations of these implantable TENG devices through their biocompatibility and other utilization strategies will also be discussed.

Smart Technologies used as Smart Tools in the Management of Cardiovascular Disease and their Future Perspective

Cardiovascular disease (CVD) is a leading cause of morbidity and mortality worldwide. The advent of smart technologies has significantly impacted the management of CVD, offering innovative tools and solutions to improve patient outcomes. Smart technologies have revolutionized and transformed the management of CVD, providing innovative tools to improve patient care, enhance diagnostics, and enable more personalized treatment approaches. These smart tools encompass a wide range of technologies, including wearable devices, mobile applications,3D printing technologies, artificial intelligence (AI), remote monitoring systems, and electronic health records (EHR). They offer numerous advantages, such as real-time monitoring, early detection of abnormalities, remote patient management, and data-driven decision-making. However, they also come with certain limitations and challenges, including data privacy concerns, technical issues, and the need for regulatory frameworks. In this review, despite these challenges, the future of smart technologies in CVD management looks promising, with advancements in AI algorithms, telemedicine platforms, and bio fabrication techniques opening new possibilities for personalized and efficient care. In this article, we also explore the role of smart technologies in CVD management, their advantages and disadvantages, limitations, current applications, and their smart future.

Exploring Disparities in Healthcare Wearable Use among Cardiovascular Patients: Findings from a National Survey

Background

Use of healthcare wearable devices holds significant potential for improving the prevention and management of cardiovascular diseases (CVD). However, we have limited knowledge on the actual use of wearable devices by CVD patients and the key factors associated with their use. This study aims to assess wearable device use and willingness to share health data among CVD patients, while identifying socio-demographic, health, and technology-related factors associated with wearable technology use.

Methods

Using a national survey of 933 CVD patients, we assess use of wearable healthcare devices (use, frequency of use and willingness to share health data from wearable with a provider), and a set of socio-demographic factors (age, gender, race, education and household income), health-related variables (general health, presence of comorbid conditions: diabetes and high blood pressure, attitude towards exercise) and technology self-efficacy using logistic regression.

Results

Of the 933 CVD patients, 18.34% reported using a healthcare wearable device in the prior 12 months. Of those, 41.92% indicated using it every day and another 19.76% indicated using it 'almost every day'. 83.54% of wearable users indicated their willingness to share health data with their healthcare providers. Female CVD patients are more likely to use wearables compared to men (odds ratio (OR) = 1.65, 95% confidence interval (CI) = 1.04-2.63). The odds decrease with age, and are significantly high in patients with higher income levels. In comparison with non-Hispanic White, Hispanic (OR = 0.14, 95% CI = 0.03-0.70) and African Americans (OR = 0.17, 95% CI = 0.04-0.86) are less likely to use healthcare wearables. CVD patients who perceive their general health to be better (OR = 1.45, 95% CI = 1.11-1.89) and those who enjoy exercising (OR = 1.76, 95% CI = 1.22-2.55) are more likely to use wearables. CVD patients who use the internet for searching for medical information (OR = 2.10, 95% CI = 1.17-3.77) and those who use electronic means to make appointments with their providers (OR = 2.35, 95% CI = 1.48-3.74) are more inclined to use wearables.

Conclusions

Addressing low wearable device usage among CVD patients requires targeted policy interventions to ensure equitable access. Variations in gender, age, race/ethnicity, and income levels emphasize the need for tailored strategies. Technological self-efficacy, positive health perceptions, and exercise enjoyment play significant roles in promoting wearable use. These insights should guide healthcare leaders in designing effective strategies for integrating wearables into cardiovascular care.

Main Mechanisms of Remote Monitoring Programs for Cardiac Rehabilitation and Secondary Prevention: A SYSTEMATIC REVIEW

PURPOSE

The objective of this report was to identify the main mechanisms of home-based remote monitoring programs for cardiac rehabilitation (RM CR) and examine how these mechanisms vary by context.

METHODS

This was a systematic review using realist synthesis. To be included, articles had to be published in English between 2010 and November 2020 and contain specific data related to mechanisms of effect of programs. MEDLINE All (1946-) via Ovid, Embase (1974-) via Ovid, APA PsycINFO (1806-), CINAHL via EBSCO, Scopus databases, and gray literature were searched.

RESULTS

From 13 747 citations, 91 focused on cardiac conditions, with 23 reports including patients in CR. Effective RM CR programs more successfully adapted to different patient home settings and broader lives, incorporated individualized patient health data, and had content designed specifically for patients in cardiac rehabilitation. Relatively minor but common technical issues could significantly reduce perceived benefits. Patients and families were highly receptive to the programs and viewed themselves as fortunate to receive such services. The RM CR programs could be improved via incorporating more connectivity to other patients. No clear negative effects on perceived utility or outcomes occurred by patient age, ethnicity, or sex. Overall, the programs were seen to best suit highly motivated patients and consolidated rather than harmed existing relationships with health care professionals and teams.

CONCLUSIONS

Remote monitoring CR programs are perceived by patients to be beneficial and attractive. Future RM CR programs should consider adaptability to different home settings, incorporate individualized health data, and contain content specific to patient needs.

Mississippi church leaders' perceptions of challenges and barriers to the use of consumer wearables among community members

Background

Wearables have begun to play a transformative role in health management and disease prevention.

Objective

This study examined the use of wearable devices in African American communities in Mississippi, USA, through the lens of church leaders.

Methods

We conducted focus groups with church leaders to record their perceptions about the use of wearables of their community members. We conducted six focus groups with a total of 89 church leaders from across the state of Mississippi. The focus groups were designed to contextualize and explain the socio-cognitive processes that provided an understanding of wearable device adoption practices among community members. Participants were male and female church leaders who were recruited from the three Mississippi Districts. The church leaders' perceptions of barriers and challenges to the adoption of consumer wearables in their communities were thoroughly analyzed using thematic analysis.

Results

There was great apprehension on the part of community members about the security of the information they entered into the wearable devices and about how that information could be used by other parties. Many community members who understood the value of proactive health behaviors could not afford the high cost of purchasing wearable devices, while others displayed a low level of comfort with technology, believing that wearable use was for younger people.

Conclusion

More expansive adoption of wearable devices in Mississippi will depend on the ability of the public health professionals, policy-makers and manufacturers to address the barriers that were identified by this study, thereby enabling the community to have full access to the potential benefits of these technologies.

Contemporary wearable and handheld technology for the diagnosis of cardiac arrhythmias in Singapore

Twelve-lead electrocardiography (ECG) remains the gold standard for the diagnosis of cardiac arrhythmias. It provides a snapshot of the cardiac electrical activity while the leads are attached to the patient. As medical training is required to use the ECG machine, its use remains restricted to the clinic and hospital settings. These aspects limit the usefulness of 12-lead ECG in the diagnosis of cardiac arrhythmias, especially in individuals with short-lasting and infrequent paroxysmal symptoms. The introduction of ECG recording features in wearable and handheld smart devices has changed the paradigm of cardiac arrhythmia diagnosis, empowering patients to record their ECG as and when symptoms occur. This review describes contemporary ambulatory heart rhythm monitors commonly available in Singapore and their expanding role in the diagnosis of cardiac rhythm abnormalities.

One-Week Suicide Risk Prediction Using Real-Time Smartphone Monitoring: Prospective Cohort Study

BACKGROUND

Suicide is a major global public health issue that is becoming increasingly common despite preventive efforts. Though current methods for predicting suicide risk are not sufficiently accurate, technological advances provide invaluable tools with which we may evolve toward a personalized, predictive approach.

OBJECTIVE

We aim to predict the short-term (1-week) risk of suicide by identifying changes in behavioral patterns characterized through real-time smartphone monitoring in a cohort of patients with suicidal ideation.

METHODS

We recruited 225 patients between February 2018 and March 2020 with a history of suicidal thoughts and behavior as part of the multicenter SmartCrisis study. Throughout 6 months of follow-up, we collected information on the risk of suicide or mental health crises. All participants underwent voluntary passive monitoring using data generated by their own smartphones, including distance walked and steps taken, time spent at home, and app usage. The algorithm constructs daily activity profiles for each patient according to these data and detects changes in the distribution of these profiles over time. Such changes are considered critical periods, and their relationship with suicide-risk events was tested.

RESULTS

During follow-up, 18 (8%) participants attempted suicide, and 14 (6.2%) presented to the emergency department for psychiatric care. The behavioral changes identified by the algorithm predicted suicide risk in a time frame of 1 week with an area under the curve of 0.78, indicating good accuracy.

CONCLUSIONS

We describe an innovative method to identify mental health crises based on passively collected information from patients' smartphones. This technology could be applied to homogeneous groups of patients to identify different types of crises.

Recognition of Supraventricular Arrhythmias in Holter ECG Recordings by ECHOView Color Map: A Case Series Study

Ambulatory 24-72 h Holter ECG monitoring is recommended for patients with suspected arrhythmias, which are often transitory and might remain unseen in resting standard 12-lead ECG. Holter manufacturers provide software diagnostic tools to assist clinicians in evaluating these large amounts of data. Nevertheless, the identification of short arrhythmia events and differentiation of the arrhythmia type might be a problem in limited Holter ECG leads. This observational clinical study aims to explore a novel and weakly investigated ECG modality integrated into a commercial diagnostic tool ECHOView (medilog DARWIN 2, Schiller AG, Switzerland), while used for the interpretation of long-term Holter-ECG records by a cardiologist. The ECHOView transformation maps the beat waveform amplitude to a color-coded bar. One ECHOView page integrates stacked color bars of about 1740 sequential beats aligned by R-peak in a window (R ± 750 ms). The collected 3-lead Holter ECG recordings from 86 patients had a valid duration of 21 h 20 min (19 h 30 min-22 h 45 min), median (quartile range). The ECG rhythm was reviewed with 3491 (3192-3723) standard-grid ECG pages and a substantially few number of 51 (44-59) ECHOView pages that validated the ECHOView compression ratio of 67 (59-74) times. Comments on the ECG rhythm and ECHOView characteristic patterns are provided for 14 examples representative of the most common rhythm disorders seen in our population, including supraventricular arrhythmias (supraventricular extrasystoles, paroxysmal supraventricular arrhythmia, sinus tachycardia, supraventricular tachycardia, atrial fibrillation, and flutter) and ventricular arrhythmias (ventricular extrasystoles, non-sustained ventricular tachycardia). In summary, the ECHOView color map transforms the ECG modality into a novel diagnostic image of the patient's rhythm that is comprehensively interpreted by a cardiologist. ECHOView has the potential to facilitate the manual overview of Holter ECG recordings, to visually identify short-term arrhythmia episodes, and to refine the diagnosis, especially in high-rate arrhythmias.

Electrocardiogram Devices for Home Use: Technological and Clinical Scoping Review

BACKGROUND

Electrocardiograms (ECGs) are used by physicians to record, monitor, and diagnose the electrical activity of the heart. Recent technological advances have allowed ECG devices to move out of the clinic and into the home environment. There is a great variety of mobile ECG devices with the capabilities to be used in home environments.

OBJECTIVE

This scoping review aimed to provide a comprehensive overview of the current landscape of mobile ECG devices, including the technology used, intended clinical use, and available clinical evidence.

METHODS

We conducted a scoping review to identify studies concerning mobile ECG devices in the electronic database PubMed. Secondarily, an internet search was performed to identify other ECG devices available in the market. We summarized the devices' technical information and usability characteristics based on manufacturer data such as datasheets and user manuals. For each device, we searched for clinical evidence on the capabilities to record heart disorders by performing individual searches in PubMed and ClinicalTrials.gov, as well as the Food and Drug Administration (FDA) 510(k) Premarket Notification and De Novo databases.

RESULTS

From the PubMed database and internet search, we identified 58 ECG devices with available manufacturer information. Technical characteristics such as shape, number of electrodes, and signal processing influence the capabilities of the devices to record cardiac disorders. Of the 58 devices, only 26 (45%) had clinical evidence available regarding their ability to detect heart disorders such as rhythm disorders, more specifically atrial fibrillation.

CONCLUSIONS

ECG devices available in the market are mainly intended to be used for the detection of arrhythmias. No devices are intended to be used for the detection of other cardiac disorders. Technical and design characteristics influence the intended use of the devices and use environments. For mobile ECG devices to be intended to detect other cardiac disorders, challenges regarding signal processing and sensor characteristics should be solved to increase their detection capabilities. Devices recently released include the use of other sensors on ECG devices to increase their detection capabilities.

Application of artificial intelligence in the diagnosis of sleep apnea

STUDY OBJECTIVES

Machine learning (ML) models have been employed in the setting of sleep disorders. This review aims to summarize the existing data about the role of ML techniques in the diagnosis, classification, and treatment of sleep-related breathing disorders.

METHODS

A systematic search in Medline, EMBASE, and Cochrane databases through January 2022 was performed.

RESULTS

Our search strategy revealed 132 studies that were included in the systematic review. Existing data show that ML models have been successfully used for diagnostic purposes. Specifically, ML models showed good performance in diagnosing sleep apnea using easily obtained features from the electrocardiogram, pulse oximetry, and sound signals. Similarly, ML showed good performance for the classification of sleep apnea into obstructive and central categories, as well as predicting apnea severity. Existing data show promising results for the ML-based guided treatment of sleep apnea. Specifically, the prediction of outcomes following surgical treatment and optimization of continuous positive airway pressure therapy can be guided by ML models.

CONCLUSIONS

The adoption and implementation of ML in the field of sleep-related breathing disorders is promising. Advancements in wearable sensor technology and ML models can help clinicians predict, diagnose, and classify sleep apnea more accurately and efficiently.

CITATION

Bazoukis G, Bollepalli SC, Chung CT, et al. Application of artificial intelligence in the diagnosis of sleep apnea. J Clin Sleep Med. 2023;19(7):1337-1363.

Photoplethysmography-based derivation of physiological information using the BioPoint

The BioPoint is a new wireless and wearable device, targeting both the ambulatory and on-site monitoring of biosignals. It is described as being capable of streaming and recording the i) electromyography, ii) electrocardiography, iii) electrodermal activity, iv) photoplethysmography, v) skin temperature and vi) actigraphy simultaneously, while making the raw signals recorded by the sensors readily available. However, an in-depth assessment of the biophysical signals recorded by this device, as well as its ability to derive vital signs and other health metrics, remains to be carried out. Consequently, this work proposes a preliminary study to evaluate the quality of the signals that can be acquired by this wearable with a focus on the derivation of heart rate and peripheral blood oxygenation via photoplethysmography. The device is quantitatively compared to the medical-grade pulse oximeter NoninConnect 3245, by Nonin inc. This study was performed with participants wearing the BioPoint at different positions on the body (finger, wrist, forearm, biceps and plantar arch), while the NoninConnect was worn on the fingertip and used as the ground truth. The results show that the BioPoint can accurately determine both heart rate and oxygen saturation from various locations on the body. However, as the BioPoint's photoplethysmograph is not calibrated it cannot be used for medical purposes (non-medical-grade).

Smartphone-Based Remote Monitoring in Heart Failure With Reduced Ejection Fraction: Retrospective Cohort Study of Secondary Care Use and Costs

BACKGROUND

Despite effective therapies, the economic burden of heart failure with reduced ejection fraction (HFrEF) is driven by frequent hospitalizations. Treatment optimization and admission avoidance rely on frequent symptom reviews and monitoring of vital signs. Remote monitoring (RM) aims to prevent admissions by facilitating early intervention, but the impact of noninvasive, smartphone-based RM of vital signs on secondary health care use and costs in the months after a new diagnosis of HFrEF is unknown.

OBJECTIVE

The purpose of this study is to conduct a secondary care health use and health-economic evaluation for patients with HFrEF using smartphone-based noninvasive RM and compare it with matched controls receiving usual care without RM.

METHODS

We conducted a retrospective study of 2 cohorts of newly diagnosed HFrEF patients, matched 1:1 for demographics, socioeconomic status, comorbidities, and HFrEF severity. They are (1) the RM group, with patients using the RM platform for >3 months and (2) the control group, with patients referred before RM was available who received usual heart failure care without RM. Emergency department (ED) attendance, hospital admissions, outpatient use, and the associated costs of this secondary care activity were extracted from the Discover data set for a 3-month period after diagnosis. Platform costs were added for the RM group. Secondary health care use and costs were analyzed using Kaplan-Meier event analysis and Cox proportional hazards modeling.

RESULTS

A total of 146 patients (mean age 63 years; 42/146, 29% female) were included (73 in each group). The groups were well-matched for all baseline characteristics except hypertension (P=.03). RM was associated with a lower hazard of ED attendance (hazard ratio [HR] 0.43; P=.02) and unplanned admissions (HR 0.26; P=.02). There were no differences in elective admissions (HR 1.03, P=.96) or outpatient use (HR 1.40; P=.18) between the 2 groups. These differences were sustained by a univariate model controlling for hypertension. Over a 3-month period, secondary health care costs were approximately 4-fold lower in the RM group than the control group, despite the additional cost of RM itself (mean cost per patient GBP £465, US $581 vs GBP £1850, US $2313, respectively; P=.04).

CONCLUSIONS

This retrospective cohort study shows that smartphone-based RM of vital signs is feasible for HFrEF. This type of RM was associated with an approximately 2-fold reduction in ED attendance and a 4-fold reduction in emergency admissions over just 3 months after a new diagnosis with HFrEF. Costs were significantly lower in the RM group without increasing outpatient demand. This type of RM could be adjunctive to standard care to reduce admissions, enabling other resources to help patients unable to use RM.

Role of wearable devices in cardiac telerehabilitation: A scoping review

BACKGROUND

Cardiac rehabilitation (CR) is an evidence-based comprehensive program that includes exercise training, health education, physical activity promotion, and extensive counseling for the management of cardiovascular risk factors. Wearable devices monitor certain physiological functions, providing biometric data such as heart rate, movement, sleep, ECG analysis, blood pressure, energy expenditure, and numerous other parameters. Recent evidence supports wearable devices as a likely relevant component in cardiovascular risk assessment and disease prevention. The purpose of this scoping review is to better understand the role of wearable devices in home-based CR (HBCR) and to characterize the evidence regarding the incorporation of wearable devices in HBCR programs and cardiovascular outcomes.

METHODS & FINDINGS

We created a search strategy for multiple databases, including PubMed, Embase (Elsevier), CINAHL (Ebsco), Cochrane CENTRAL (Wiley), and Scopus (Elsevier). Studies were included if the patients were eligible for CR per Medicare guidelines and >18 years of age and if some type of wearable device was utilized during HBCR. Our search yielded 57 studies meeting all criteria. The studies were classified into 4 groups: patients with coronary heart disease (CHD) without heart failure (HF); patients with HF; patients with heart valve repair or replacement; and patients with exposure to center-based CR. In three groups, there was an upward trend toward improvement in quality of life (QOL) and peak VO2, less sedentary time, and an increase in daily step count in the intervention groups compared to control groups.

CONCLUSIONS

HBCR using wearable devices can be a comparable alternative or adjunct to center-based CR for patients with CHD and HF. More studies are needed to draw conclusions about the comparability of HBCR to center-based CR in patients with heart valve repair or replacement.

Research on a Precision Calibration Model of a Flexible Strain Sensor Based on a Variable Section Cantilever Beam

The flexible strain sensor's measuring range is usually over 5000 με, while the conventional variable section cantilever calibration model has a measuring range within 1000 με. In order to satisfy the calibration requirements of flexible strain sensors, a new measurement model was proposed to solve the inaccurate calculation problem of the theoretical strain value when the linear model of a variable section cantilever beam was applied to a large range. The nonlinear relationship between deflection and strain was established. The finite element analysis of a variable section cantilever beam with ANSYS shows that the linear model's relative deviation is as high as 6% at 5000 με, while the relative deviation of the nonlinear model is only 0.2%. The relative expansion uncertainty of the flexible resistance strain sensor is 0.365% (k = 2). Simulation and experimental results show that this method solves the imprecision of the theoretical model effectively and realizes the accurate calibration of a large range of strain sensors. The research results enrich the measurement models and calibration models for flexible strain sensors and contribute to the development of strain metering.

Rapid Roll Out of a Pacemaker Home Monitoring Programme: A Patient Perspective

OBJECTIVE

To assess the safety, efficacy, and patient acceptability of a pacemaker home monitoring (HM) service.

METHODS

All patients receiving a new Biotronik (Biotronik, Berlin, Germany) pacemaker between March 2020 and February 2021 were contacted for participation. Participants were surveyed on their experience of pacemaker HM. HM alerts and remote wound monitoring rates were also assessed.

RESULTS

Of the patients contacted, 77% responded, with a mean age of 80.6±9.9 years. Of these, 95.8% agreed that the home monitoring (HM) has been beneficial. Two thirds preferred HM to face-to-face follow-up and two thirds felt safe with HM. Three themes were identified from the comments: reassurance, technology and data security. Forty-one percent (41%) of respondents would like more reassurance that their HM is working, 18% mentioned technology with mixed responses, and 4.7% cited cybersecurity or the use of their personal data as a concern. The average one-way patient journey saved was 24.3±16.7 km (15.1±10.4 miles). One in three HM alerts required action but only 3.4% were urgent. Remote wound review was successful in 59%.

CONCLUSIONS

The majority of patients prefer HM and almost all think it has been beneficial. It saves significant travel time and provides actionable alerts. The patient experience could be improved by reassuring patients that their device is being monitored.

Data Quality Degradation on Prediction Models Generated From Continuous Activity and Heart Rate Monitoring: Exploratory Analysis Using Simulation

BACKGROUND

Limited data accuracy is often cited as a reason for caution in the integration of physiological data obtained from consumer-oriented wearable devices in care management pathways. The effect of decreasing accuracy on predictive models generated from these data has not been previously investigated.

OBJECTIVE

The aim of this study is to simulate the effect of data degradation on the reliability of prediction models generated from those data and thus determine the extent to which lower device accuracy might or might not limit their use in clinical settings.

METHODS

Using the Multilevel Monitoring of Activity and Sleep in Healthy People data set, which includes continuous free-living step count and heart rate data from 21 healthy volunteers, we trained a random forest model to predict cardiac competence. Model performance in 75 perturbed data sets with increasing missingness, noisiness, bias, and a combination of all 3 perturbations was compared to model performance for the unperturbed data set.

RESULTS

The unperturbed data set achieved a mean root mean square error (RMSE) of 0.079 (SD 0.001) in predicting cardiac competence index. For all types of perturbations, RMSE remained stable up to 20%-30% perturbation. Above this level, RMSE started increasing and reached the point at which the model was no longer predictive at 80% for noise, 50% for missingness, and 35% for the combination of all perturbations. Introducing systematic bias in the underlying data had no effect on RMSE.

CONCLUSIONS

In this proof-of-concept study, the performance of predictive models for cardiac competence generated from continuously acquired physiological data was relatively stable with declining quality of the source data. As such, lower accuracy of consumer-oriented wearable devices might not be an absolute contraindication for their use in clinical prediction models.

A Sliding Scale Signal Quality Metric of Photoplethysmography Applicable to Measuring Heart Rate across Clinical Contexts with Chest Mounting as a Case Study

Photoplethysmography (PPG) signal quality as a proxy for accuracy in heart rate (HR) measurement is useful in various public health contexts, ranging from short-term clinical diagnostics to free-living health behavior surveillance studies that inform public health policy. Each context has a different tolerance for acceptable signal quality, and it is reductive to expect a single threshold to meet the needs across all contexts. In this study, we propose two different metrics as sliding scales of PPG signal quality and assess their association with accuracy of HR measures compared to a ground truth electrocardiogram (ECG) measurement.

METHODS

We used two publicly available PPG datasets (BUT PPG and Troika) to test if our signal quality metrics could identify poor signal quality compared to gold standard visual inspection. To aid interpretation of the sliding scale metrics, we used ROC curves and Kappa values to calculate guideline cut points and evaluate agreement, respectively. We then used the Troika dataset and an original dataset of PPG data collected from the chest to examine the association between continuous metrics of signal quality and HR accuracy. PPG-based HR estimates were compared with reference HR estimates using the mean absolute error (MAE) and the root-mean-square error (RMSE). Point biserial correlations were used to examine the association between binary signal quality and HR error metrics (MAE and RMSE).

RESULTS

ROC analysis from the BUT PPG data revealed that the AUC was 0.758 (95% CI 0.624 to 0.892) for signal quality metrics of STD-width and 0.741 (95% CI 0.589 to 0.883) for self-consistency. There was a significant correlation between criterion poor signal quality and signal quality metrics in both Troika and originally collected data. Signal quality was highly correlated with HR accuracy (MAE and RMSE, respectively) between PPG and ground truth ECG.

CONCLUSION

This proof-of-concept work demonstrates an effective approach for assessing signal quality and demonstrates the effect of poor signal quality on HR measurement. Our continuous signal quality metrics allow estimations of uncertainties in other emergent metrics, such as energy expenditure that relies on multiple independent biometrics. This open-source approach increases the availability and applicability of our work in public health settings.

Potential of electronic devices for detection of health problems in older adults at home: A systematic review and meta-analysis

OBJECTIVE

The aim of this review was to evaluate the overall diagnostic performance of e-devices for detection of health problems in older adults at home.

METHODS

A systematic review was conducted following the PRISMA-DTA guidelines.

RESULTS

31 studies were included with 24 studies included in meta-analysis. The included studies were divided into four categories according to the signals detected: physical activity (PA), vital signs (VS), electrocardiography (ECG) and other. The meta-analysis showed the pooled estimates of sensitivity and specificity were 0.94 and 0.98 respectively in the 'VS' group. The pooled sensitivity and specificity were 0.97 and 0.98 respectively in the 'ECG' group.

CONCLUSIONS

All kinds of e-devices perform well in diagnosing the common health problems. While ECG-based health problems detection system is more reliable than VS-based ones. For sole signal detection system has limitation in diagnosing specific health problems, more researches should focus on developing new systems combined of multiple signals.

Wearable Devices in Cardiovascular Medicine

Wearable devices, such as smartwatches and activity trackers, are commonly used by patients in their everyday lives to manage their health and well-being. These devices collect and analyze long-term continuous data on measures of behavioral or physiologic function, which may provide clinicians with a more comprehensive view of a patients' health compared with the traditional sporadic measures captured by office visits and hospitalizations. Wearable devices have a wide range of potential clinical applications ranging from arrhythmia screening of high-risk individuals to remote management of chronic conditions such as heart failure or peripheral artery disease. As the use of wearable devices continues to grow, we must adopt a multifaceted approach with collaboration among all key stakeholders to effectively and safely integrate these technologies into routine clinical practice. In this Review, we summarize the features of wearable devices and associated machine learning techniques. We describe key research studies that illustrate the role of wearable devices in the screening and management of cardiovascular conditions and identify directions for future research. Last, we highlight the challenges that are currently hindering the widespread use of wearable devices in cardiovascular medicine and provide short- and long-term solutions to promote increased use of wearable devices in clinical care.

Premature atrial and ventricular contractions detected on wearable-format electrocardiograms and prediction of cardiovascular events

Aims

Wearable devices are transforming the electrocardiogram (ECG) into a ubiquitous medical test. This study assesses the association between premature ventricular and atrial contractions (PVCs and PACs) detected on wearable-format ECGs (15 s single lead) and cardiovascular outcomes in individuals without cardiovascular disease (CVD).

Methods and results

Premature atrial contractions and PVCs were identified in 15 s single-lead ECGs from N = 54 016 UK Biobank participants (median age, interquartile range, age 58, 50-63 years, 54% female). Cox regression models adjusted for traditional risk factors were used to determine associations with atrial fibrillation (AF), heart failure (HF), myocardial infarction (MI), stroke, life-threatening ventricular arrhythmias (LTVAs), and mortality over a period of 11.5 (11.4-11.7) years. The strongest associations were found between PVCs (prevalence 2.2%) and HF (hazard ratio, HR, 95% confidence interval = 2.09, 1.58-2.78) and between PACs (prevalence 1.9%) and AF (HR = 2.52, 2.11-3.01), with shorter prematurity further increasing risk. Premature ventricular contractions and PACs were also associated with LTVA (P < 0.05). Associations with MI, stroke, and mortality were significant only in unadjusted models. In a separate UK Biobank sub-study sample [UKB-2, N = 29,324, age 64, 58-60 years, 54% female, follow-up 3.5 (2.6-4.8) years] used for independent validation, after adjusting for risk factors, PACs were associated with AF (HR = 1.80, 1.12-2.89) and PVCs with HF (HR = 2.32, 1.28-4.22).

Conclusion

In middle-aged individuals without CVD, premature contractions identified in 15 s single-lead ECGs are strongly associated with an increased risk of AF and HF. These data warrant further investigation to assess the role of wearable ECGs for early cardiovascular risk stratification.

Effectiveness of implantable loop recorder and Holter electrocardiographic monitoring for the detection of arrhythmias in patients with peripartum cardiomyopathy

BACKGROUND

Patients with peripartum cardiomyopathy (PPCM) are at increased risk of sudden cardiac death (SCD). However, the exact underlying mechanisms of SCD in PPCM remain unknown. By means of extended electrocardiographic monitoring, we aimed to systematically characterize the burden of arrhythmias occurring in patients with newly diagnosed PPCM.

METHODS AND RESULTS

Twenty-five consecutive women with PPCM were included in this single-centre, prospective clinical trial and randomised to receiving either 24 h-Holter ECG monitoring followed by implantable loop recorder implantation (ILR; REVEAL XT, Medtronic^®) or 24 h-Holter ECG monitoring alone. ILR + 24 h-Holter monitoring had a higher yield of arrhythmic events compared to 24 h-Holter monitoring alone (40% vs 6.7%, p = 0.041). Non-sustained ventricular tachycardia (NSVT) occurred in four patients (16%, in three patients detected by 24 h-Holter, and multiple episodes detected by ILR in one patient). One patient deceased from third-degree AV block with an escape rhythm that failed. All arrhythmic events occurred in patients with a severely impaired LV systolic function.

CONCLUSIONS

We found a high prevalence of potentially life-threatening arrhythmic events in patients with newly diagnosed PPCM. These included both brady- and tachyarrhythmias. Our results highlight the importance of extended electrocardiographic monitoring, especially in those with severely impaired LV systolic function. In this regard, ILR in addition to 24 h-Holter monitoring had a higher yield of VAs as compared to 24 h-Holter monitoring alone. In settings where WCDs are not readily available, ILR monitoring should be considered in patients with severely impaired LV systolic dysfunction, especially after uneventful 24 h-Holter monitoring.

TRIAL REGISTRATION

Pan African Clinical Trials Registry: PACTR202104866174807. Extended electrocardiographic monitoring for the detection of arrhythmias in PPCM. (CHB, complete heart block/third degree AV block; ECG, electrocardiogram; ILR, implantable loop recorder; NSVT, non-sustained ventricular tachycardia; PPCM, peripartum cardiomyopathy).

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

Background: The advancement of information and communication technologies and the growing power of artificial intelligence are successfully transforming a number of concepts that are important to our daily lives. Many sectors, including education, healthcare, industry, and others, are benefiting greatly from the use of such resources. The healthcare sector, for example, was an early adopter of smart wearables, which primarily serve as diagnostic tools. In this context, smart wearables have demonstrated their effectiveness in detecting and predicting cardiovascular diseases (CVDs), the leading cause of death worldwide. Objective: In this study, a systematic literature review of smart wearable applications for cardiovascular disease detection and prediction is presented. After conducting the required search, the documents that met the criteria were analyzed to extract key criteria such as the publication year, vital signs recorded, diseases studied, hardware used, smart models used, datasets used, and performance metrics. Methods: This study followed the PRISMA guidelines by searching IEEE, PubMed, and Scopus for publications published between 2010 and 2022. Once records were located, they were reviewed to determine which ones should be included in the analysis. Finally, the analysis was completed, and the relevant data were included in the review along with the relevant articles. Results: As a result of the comprehensive search procedures, 87 papers were deemed relevant for further review. In addition, the results are discussed to evaluate the development and use of smart wearable devices for cardiovascular disease management, and the results demonstrate the high efficiency of such wearable devices. Conclusions: The results clearly show that interest in this topic has increased. Although the results show that smart wearables are quite accurate in detecting, predicting, and even treating cardiovascular disease, further research is needed to improve their use.