Long-term continuous monitoring of arrhythmias in pigs with insertable cardiac monitors.

Arrhythmia detection is essential when assessing the safety of novel drugs and therapies in preclinical studies. Many short-term arrhythmia monitoring methods exist, including non-invasive ECG and Holter. However, there are no reliable, long-term, non-invasive, or minimally invasive methods for cardiac arrhythmia follow-up in large animals that allows free movement with littermates. A long follow-up time is needed when estimating the impact of long-lasting drugs or therapies, such as gene therapy. We evaluated the feasibility and performance of insertable cardiac monitors (ICMs) in pigs for minimally invasive, long-term monitoring of cardiac arrhythmias that allows free movement and species-specific behavior. Multiple implantation sites were tested to assess signal quality. ICMs recognized reliably many different arrhythmias but failed to detect single extrasystoles. They also over-diagnosed T-waves, resulting in oversensing. Muscle activity and natural startles of the animals caused noise, leading to a heterogeneous signal requiring post-recording evaluation. In spite of these shortcomings, the ICMs showed to be very useful for minimally invasive long-term monitoring of cardiac rhythm in pigs.

Supermarket/hypermarket opportunistic screening for atrial fibrillation (SHOPS-AF) using sensors embedded in the handles of supermarket trolleys: A feasibility study.

BACKGROUND: Atrial fibrillation (AF) increases the risk of death, stroke, heart failure, cognitive decline, and healthcare costs but is often asymptomatic and undiagnosed. There is currently no national screening program for AF. The advent of validated hand-held devices allows AF to be detected in non-healthcare settings, enabling screening to be undertaken within the community. METHOD AND RESULTS: In this novel observational study, we embedded a MyDiagnostick single lead ECG sensor into the handles of shopping trolleys in four supermarkets in the Northwest of England: 2155 participants were recruited. Of these, 231 participants either activated the sensor or had an irregular pulse, suggesting AF. Some participants agreed to use the sensor but refused to provide their contact details, or consent to pulse assessment. In addition, some data were missing, resulting in 203 participants being included in the final analyses. Fifty-nine participants (mean age 73.6 years, 43% female) were confirmed or suspected of having AF; 20 were known to have AF and 39 were previously undiagnosed. There was no evidence of AF in 115 participants and the remaining 46 recordings were non-diagnostic, mainly due to artefact. Men and older participants were significantly more likely to have newly diagnosed AF. Due to the number of non-diagnostic ECGs (n = 46), we completed three levels of analyses, excluding all non-diagnostic ECGs, assuming all non-diagnostic ECGs were masking AF, and assuming all non-diagnostic ECGs were not AF. Based on the results of the three analyses, the sensor's sensitivity (95% CI) ranged from 0.70 to 0.93; specificity from 0.15 to 0.97; positive predictive values (PPV) and negative predictive values (NPV) ranged from 0.24 to 0.56 and 0.55 to 1.00, respectively. These values should be interpreted with caution, as the ideal reference standard on 1934 participants was imperfect. CONCLUSION: The study demonstrates that the public will engage with AF screening undertaken as part of their daily routines using hand-held devices. Sensors can play a key role in identifying asymptomatic patients in this way, but the technology must be further developed to reduce the quantity of non-diagnostic ECGs.

Evaluation of 12-lead electrocardiogram at 0.55T for improved cardiac monitoring in magnetic resonance imaging.

BACKGROUND: The 12-lead electrocardiogram (ECG) is a standard diagnostic tool for monitoring cardiac ischemia and heart rhythm during cardiac interventional procedures and stress testing. These procedures can benefit from magnetic resonance imaging (MRI) information; however, the MRI scanner magnetic field leads to ECG distortion that limits ECG interpretation. This study evaluated the potential for improved ECG interpretation in a "low field" 0.55T MRI scanner. METHODS: The 12-lead ECGs were recorded inside 0.55T, 1.5T, and 3T MRI scanners, as well as at scanner table "home" position in the fringe field and outside the scanner room (seven pigs). To assess interpretation of ischemic ECG changes in a 0.55T MRI scanner, ECGs were recorded before and after coronary artery occlusion (seven pigs). ECGs was also recorded for five healthy human volunteers in the 0.55T scanner. ECG error and variation were assessed over 2-minute recordings for ECG features relevant to clinical interpretation: the PR interval, QRS interval, J point, and ST segment. RESULTS: ECG error was lower at 0.55T compared to higher field scanners. Only at 0.55T table home position, did the error approach the guideline recommended 0.025 mV ceiling for ECG distortion (median 0.03 mV). At scanner isocenter, only in the 0.55T scanner did J point error fall within the 0.1 mV threshold for detecting myocardial ischemia (median 0.03 mV in pigs and 0.06 mV in healthy volunteers). Correlation of J point deviation inside versus outside the 0.55T scanner following coronary artery occlusion was excellent at scanner table home position (r2 = 0.97), and strong at scanner isocenter (r2 = 0.92). CONCLUSION: ECG distortion is improved in 0.55T compared to 1.5T and 3T MRI scanners. At scanner home position, ECG distortion at 0.55T is low enough that clinical interpretation appears feasible without need for more cumbersome patient repositioning. At 0.55T scanner isocenter, ST segment changes during coronary artery occlusion appear detectable but distortion is enough to obscure subtle ST segment changes that could be clinically relevant. Reduced ECG distortion in 0.55T scanners may simplify the problem of suppressing residual distortion by ECG cable positioning, averaging, and filtering and could reduce current restrictions on ECG monitoring during interventional MRI procedures.

Reliability of single-lead electrocardiogram interpretation to detect atrial fibrillation: insights from the SAFER feasibility study.

AIMS: Single-lead electrocardiograms (ECGs) can be recorded using widely available devices such as smartwatches and handheld ECG recorders. Such devices have been approved for atrial fibrillation (AF) detection. However, little evidence exists on the reliability of single-lead ECG interpretation. We aimed to assess the level of agreement on detection of AF by independent cardiologists interpreting single-lead ECGs and to identify factors influencing agreement. METHODS AND RESULTS: In a population-based AF screening study, adults aged ≥65 years old recorded four single-lead ECGs per day for 1-4 weeks using a handheld ECG recorder. Electrocardiograms showing signs of possible AF were identified by a nurse, aided by an automated algorithm. These were reviewed by two independent cardiologists who assigned participant- and ECG-level diagnoses. Inter-rater reliability of AF diagnosis was calculated using linear weighted Cohen's kappa (κw). Out of 2141 participants and 162 515 ECGs, only 1843 ECGs from 185 participants were reviewed by both cardiologists. Agreement was moderate: κw = 0.48 (95% confidence interval, 0.37-0.58) at participant level and κw = 0.58 (0.53-0.62) at ECG level. At participant level, agreement was associated with the number of adequate-quality ECGs recorded, with higher agreement in participants who recorded at least 67 adequate-quality ECGs. At ECG level, agreement was associated with ECG quality and whether ECGs exhibited algorithm-identified possible AF. CONCLUSION: Inter-rater reliability of AF diagnosis from single-lead ECGs was found to be moderate in older adults. Strategies to improve reliability might include participant and cardiologist training and designing AF detection programmes to obtain sufficient ECGs for reliable diagnoses.

Validation of smartwatch electrocardiogram intervals in children compared to standard 12 lead electrocardiograms.

Lay people are now able to obtain one-lead electrocardiograms (ECG) using smartwatches, which facilitates documentation of arrhythmias. The accuracy of smartwatch derived ECG intervals has not been validated in children though. Home-based monitoring of ECG intervals using a smartwatch could improve monitoring of children, e.g. when taking QTc prolonging medications. The aim of this study was to validate the ECG intervals measured by smartwatch in comparison to standard 12-lead ECGs in children and adolescents. Prospective study of children (age 5-17 years) at the outpatient clinic of a national pediatric heart center. Patients underwent a smartwatch ECG (ScanWatch, Withings) and a simultaneous standard 12-lead ECG. ECG intervals were measured both automatically and manually from the smartwatch ECG and the 12-lead ECG. Intraclass correlation coefficients and Bland-Altman plots were performed. 100 patients (54% male, median age 12.9 (IQR 8.7-15.6) were enrolled. The ICC calculated from the automated smartwatch and automated 12-lead ECG were excellent for heart rate (ICC 0.97, p < 0.001), good for the PR and QT intervals (ICC 0.86 and 0.8, p < 0.001), and moderate for the QRS duration and QTc interval (ICC 0.7 and 0.53, p < 0.001). When using manual measurements for the smartwatch ECG, validity was improved for the PR interval (ICC 0.93, p < 0.001), QRS duration (ICC 0.92, p < 0.001), QT (ICC 0.95, p < 0.001) and QTc interval (ICC 0.84, p < 0.001). CONCLUSION: Automated smartwatch intervals are most reliable measuring the heart rate. The automated smartwatch QTc intervals are less reliable, but this may be improved by manual measurements. WHAT IS KNOWN: In adults, smartwatch derived ECG intervals measured manually have previously been shown to be accurate, though agreement for automated QTc may be fair. WHAT IS NEW: In children, automated smartwatch QTc intervals are less reliable than RR, PR, QRS and uncorrected QT interval. Accuracy of the QTc can be improved by peroforming manual measurements.

First noncontact millimeter-wave radar measurement of heart rate in great apes: Validation in chimpanzees (Pan troglodytes).

Heart rate is a crucial vital sign and a valuable indicator for assessing the physical and psychological condition of a target animal. Heart rate contributes to (1) fundamental information for cognitive research, (2) an indicator of psychological and physical stress, and (3) improving the animal welfare of captive animals, especially in nonhuman primate studies. Heart rate has been measured using a contact-type device; however, the device burdens the target animals and that there are risks associated with anesthesia during installation. This study explores the application of heartbeat measurement techniques using millimeter-wave radar, primarily developed for humans, as a remote and noninvasive method for measuring the heart rate of nonhuman primates. Through a measurement test conducted on two chimpanzees, we observed a remarkable correspondence between the peak frequency spectrum of heart rate estimated using millimeter-wave radar and the mean value obtained from electrocardiograph data, thereby validating the accuracy of the method. To the best of our knowledge, this is the first demonstration of the precise measurement of great apes' heart rate using millimeter-wave radar technology. Compared to heart rate measurement using video analysis, the method using millimeter-wave radar has the advantage that it is less susceptible to weather and lighting conditions and that measurement techniques for multiple individuals have been developed for human subjects, while its disadvantage is that validation of measurement from long distances has not been completed. Another disadvantage common to both methods is that measurement becomes difficult when the movement of the target individual is large. The possibility of noncontact measurement of heart rate in wild and captive primates will undoubtedly open up a new research area while taking animal welfare into consideration.

Palpitations: Evaluation, Management, and Wearable Smart Devices.

Palpitations are a common symptom described by patients as a feeling of a racing or fluttering heart, a pounding chest, irregular or skipped heartbeats, or a pounding sensation in the neck. They are associated with a low mortality rate; however, recurrent palpitations have been shown to impair quality of life and increase health care use. Common triggers are cardiac disorders, endocrine and metabolic disorders, medication or illicit drug use, or psychosomatic disorders. A detailed history, physical examination, directed laboratory studies, and 12-lead electrocardiography are often sufficient to identify the etiology of palpitations. Additional testing may be indicated to include echocardiography, cardiac stress testing, electrocardiogram monitoring, or electrophysiologic studies to distinguish whether symptoms correlate with cardiac arrhythmia or structural or ischemic heart disease. Management of palpitations is based on the suspected etiology. In most cases of cardiac-induced palpitations, the treatment can include reassurance, education, trigger avoidance, or use of atrioventricular nodal blockers. Tachyarrhythmias may require cardiac ablation. Patients who have palpitations with no arrhythmia causality and no cardiac disease should be reassured; however, screening for psychosomatic disorders should be considered. Wearable smart devices with ambulatory electrocardiogram monitoring technologies are currently available to consumers; these tools have shown diagnostic accuracy for detection of arrhythmias, allowing patients to have greater participation in their health care. Am Fam Physician. 2024; 110(3):259-269.

Early detection of cardiorespiratory complications and training monitoring using wearable ECG sensors and CNN.

This research study demonstrates an efficient scheme for early detection of cardiorespiratory complications in pandemics by Utilizing Wearable Electrocardiogram (ECG) sensors for pattern generation and Convolution Neural Networks (CNN) for decision analytics. In health-related outbreaks, timely and early diagnosis of such complications is conclusive in reducing mortality rates and alleviating the burden on healthcare facilities. Existing methods rely on clinical assessments, medical history reviews, and hospital-based monitoring, which are valuable but have limitations in terms of accessibility, scalability, and timeliness, particularly during pandemics. The proposed scheme commences by deploying wearable ECG sensors on the patient's body. These sensors collect data by continuously monitoring the cardiac activity and respiratory patterns of the patient. The collected raw data is then transmitted securely in a wireless manner to a centralized server and stored in a database. Subsequently, the stored data is assessed using a preprocessing process which extracts relevant and important features like heart rate variability and respiratory rate. The preprocessed data is then used as input into the CNN model for the classification of normal and abnormal cardiorespiratory patterns. To achieve high accuracy in abnormality detection the CNN model is trained on labeled data with optimized parameters. The performance of the proposed scheme is evaluated and gauged using different scenarios, which shows a robust performance in detecting abnormal cardiorespiratory patterns with a sensitivity of 95% and specificity of 92%. Prominent observations, which highlight the potential for early interventions include subtle changes in heart rate variability and preceding respiratory distress. These findings show the significance of wearable ECG technology in improving pandemic management strategies and informing public health policies, which enhances preparedness and resilience in the face of emerging health threats.

Wearable Sensors, Data Processing, and Artificial Intelligence in Pregnancy Monitoring: A Review.

Pregnancy monitoring is always essential for pregnant women and fetuses. According to the report of WHO (World Health Organization), there were an estimated 287,000 maternal deaths worldwide in 2020. Regular hospital check-ups, although well established, are a burden for pregnant women because of frequent travelling or hospitalization. Therefore, home-based, long-term, non-invasive health monitoring is one of the hot research areas. In recent years, with the development of wearable sensors and related data-processing technologies, pregnancy monitoring has become increasingly convenient. This article presents a review on recent research in wearable sensors, physiological data processing, and artificial intelligence (AI) for pregnancy monitoring. The wearable sensors mainly focus on physiological signals such as electrocardiogram (ECG), uterine contraction (UC), fetal movement (FM), and multimodal pregnancy-monitoring systems. The data processing involves data transmission, pre-processing, and application of threshold-based and AI-based algorithms. AI proves to be a powerful tool in early detection, smart diagnosis, and lifelong well-being in pregnancy monitoring. In this review, some improvements are proposed for future health monitoring of pregnant women. The rollout of smart wearables and the introduction of AI have shown remarkable potential in pregnancy monitoring despite some challenges in accuracy, data privacy, and user compliance.

Design, Fabrication, and Evaluation of 3D Biopotential Electrodes and Intelligent Garment System for Sports Monitoring.

This study presents the development and evaluation of an innovative intelligent garment system, incorporating 3D knitted silver biopotential electrodes, designed for long-term sports monitoring. By integrating advanced textile engineering with wearable monitoring technologies, we introduce a novel approach to real-time physiological signal acquisition, focusing on enhancing athletic performance analysis and fatigue detection. Utilizing low-resistance silver fibers, our electrodes demonstrate significantly reduced skin-to-electrode impedance, facilitating improved signal quality and reliability, especially during physical activities. The garment system, embedded with these electrodes, offers a non-invasive, comfortable solution for continuous ECG and EMG monitoring, addressing the limitations of traditional Ag/AgCl electrodes, such as skin irritation and signal degradation over time. Through various experimentation, including impedance measurements and biosignal acquisition during cycling activities, we validate the system's effectiveness in capturing high-quality physiological data. Our findings illustrate the electrodes' superior performance in both dry and wet conditions. This study not only advances the field of intelligent garments and biopotential monitoring, but also provides valuable insights for the application of intelligent sports wearables in the future.

Wearable ECG Device and Machine Learning for Heart Monitoring.

With cardiovascular diseases (CVD) remaining a leading cause of mortality, wearable devices for monitoring cardiac activity have gained significant, renewed interest among the medical community. This paper introduces an innovative ECG monitoring system based on a single-lead ECG machine, enhanced using machine learning methods. The system only processes and analyzes ECG data, but it can also be used to predict potential heart disease at an early stage. The wearable device was built on the ADS1298 and a microcontroller STM32L151xD. A server module based on the architecture style of the REST API was designed to facilitate interaction with the web-based segment of the system. The module is responsible for receiving data in real time from the microcontroller and delivering this data to the web-based segment of the module. Algorithms for analyzing ECG signals have been developed, including band filter artifact removal, K-means clustering for signal segmentation, and PQRST analysis. Machine learning methods, such as isolation forests, have been employed for ECG anomaly detection. Moreover, a comparative analysis with various machine learning methods, including logistic regression, random forest, SVM, XGBoost, decision forest, and CNNs, was conducted to predict the incidence of cardiovascular diseases. Convoluted neural networks (CNN) showed an accuracy of 0.926, proving their high effectiveness for ECG data processing.

IoT-Based Heartbeat Rate-Monitoring Device Powered by Harvested Kinetic Energy.

Remote patient-monitoring systems are helpful since they can provide timely and effective healthcare facilities. Such online telemedicine is usually achieved with the help of sophisticated and advanced wearable sensor technologies. The modern type of wearable connected devices enable the monitoring of vital sign parameters such as: heart rate variability (HRV) also known as electrocardiogram (ECG), blood pressure (BLP), Respiratory rate and body temperature, blood pressure (BLP), respiratory rate, and body temperature. The ubiquitous problem of wearable devices is their power demand for signal transmission; such devices require frequent battery charging, which causes serious limitations to the continuous monitoring of vital data. To overcome this, the current study provides a primary report on collecting kinetic energy from daily human activities for monitoring vital human signs. The harvested energy is used to sustain the battery autonomy of wearable devices, which allows for a longer monitoring time of vital data. This study proposes a novel type of stress- or exercise-monitoring ECG device based on a microcontroller (PIC18F4550) and a Wi-Fi device (ESP8266), which is cost-effective and enables real-time monitoring of heart rate in the cloud during normal daily activities. In order to achieve both portability and maximum power, the harvester has a small structure and low friction. Neodymium magnets were chosen for their high magnetic strength, versatility, and compact size. Due to the non-linear magnetic force interaction of the magnets, the non-linear part of the dynamic equation has an inverse quadratic form. Electromechanical damping is considered in this study, and the quadratic non-linearity is approximated using MacLaurin expansion, which enables us to find the law of motion for general case studies using classical methods for dynamic equations and the suitable parameters for the harvester. The oscillations are enabled by applying an initial force, and there is a loss of energy due to the electromechanical damping. A typical numerical application is computed with Matlab 2015 software, and an ODE45 solver is used to verify the accuracy of the method.

Stretchable Ag/AgCl Nanowire Dry Electrodes for High-Quality Multimodal Bioelectronic Sensing.

Bioelectrical signal measurements play a crucial role in clinical diagnosis and continuous health monitoring. Conventional wet electrodes, however, present limitations as they are conductive gel for skin irritation and/or have inflexibility. Here, we developed a cost-effective and user-friendly stretchable dry electrode constructed with a flexible network of Ag/AgCl nanowires embedded in polydimethylsiloxane (PDMS). We compared the performance of the stretched Ag/AgCl nanowire electrode with commonly used commercial wet electrodes to measure electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG) signals. All the signal-to-noise ratios (SNRs) of the as-fabricated or stretched (50% tensile strain) Ag/AgCl nanowire electrodes are higher than that measured by commercial wet electrodes as well as other dry electrodes. The evaluation of ECG signal quality through waveform segmentation, the signal quality index (SQI), and heart rate variability (HRV) reveal that both the as-fabricated and stretched Ag/AgCl nanowire electrode produce high-quality signals similar to those obtained from commercial wet electrodes. The stretchable electrode exhibits high sensitivity and dependability in measuring EMG and EEG data, successfully capturing EMG signals associated with muscle activity and clearly recording α-waves in EEG signals during eye closure. Our stretchable dry electrode shows enhanced comfort, high sensitivity, and convenience for curved surface biosignal monitoring in clinical contexts.

A Narrowband IoT Personal Sensor for Long-Term Heart Rate Monitoring and Atrial Fibrillation Detection.

Long-term patient monitoring is required for detection of episodes of atrial fibrillation, one of the most widespread cardiac pathologies. Today, the most used non-invasive technique is Holter electrocardiographic (ECG) monitoring, which can often prove ineffective because of the short duration of recordings (e.g., one day). Other techniques such as photo-plethysmography are adopted by smartwatches for much longer duration monitoring, but this has the disadvantage of offering only intermittent measurements. This study proposes an Internet of Things (IoT) sensor that can provide a very long period of continuous monitoring. The sensor consists of an ECG-integrated Analog Front End (MAX30003), a microcontroller (STM32F401RE), and an IoT narrowband module (STEVAL-STMODLTE). The instantaneous heart rate is extracted from the ECG recording in real time. At intervals of two minutes, the sequence of inter-beat intervals is transmitted to an IoT cloud platform (ThingSpeak). Settled atrial fibrillation event recognition software runs on the cloud and generates alerts when it recognizes such arrhythmia. Performances of the proposed sensor were evaluated by generating analog ECG signals from a public dataset of ECG signals with atrial fibrillation episodes, the MIT-BIH Atrial Fibrillation Database, each recording lasting approximately 10 h. Software implementing the Lorentz algorithm, one of the best detectors of atrial fibrillation, was implemented on the cloud platform. The accuracy, sensitivity, and specificity in recognizing atrial fibrillation episodes of the proposed system was calculated by comparison with a cardiologist's reference data. Across all patients, the proposed method achieved an accuracy of 0.88, a sensitivity 0.71, and a specificity 0.99. The results obtained suggest that the developed system can continuously record and transmit heart rhythms effectively and efficiently and, in addition, offers considerable performance in recognizing atrial fibrillation episodes in real time.

Wearable Technology for Monitoring Electrocardiograms (ECGs) in Adults: A Scoping Review.

In the rapidly evolving landscape of continuous electrocardiogram (ECG) monitoring systems, there is a heightened demand for non-invasive sensors capable of measuring ECGs and detecting heart rate variability (HRV) in diverse populations, ranging from cardiovascular patients to sports enthusiasts. Challenges like device accuracy, patient privacy, signal noise, and long-term safety impede the use of wearable devices in clinical practice. This scoping review aims to assess the performance and safety of novel multi-channel, sensor-based biopotential wearable devices in adults. A comprehensive search strategy was employed on four databases, resulting in 143 records and the inclusion of 12 relevant studies. Most studies focused on healthy adult subjects (n = 6), with some examining controlled groups with atrial fibrillation (AF) (n = 3), long QT syndrome (n = 1), and sleep apnea (n = 1). The investigated bio-sensor devices included chest-worn belts (n = 2), wrist bands (n = 2), adhesive chest strips (n = 2), and wearable textile smart clothes (n = 4). The primary objective of the included studies was to evaluate device performance in terms of accuracy, signal quality, comparability, and visual assessment of ECGs. Safety findings, reported in five articles, indicated no major side effects for long-term/continuous monitoring, with only minor instances of skin irritation. Looking forward, there are ample opportunities to enhance and test these technologies across various physical activity intensities and clinical conditions.

Validation of a Textile-Based Wearable Measuring Electrocardiogram and Breathing Frequency for Sleep Apnea Monitoring.

Sleep apnea (SA) is a prevalent disorder characterized by recurrent events of nocturnal apnea. Polysomnography (PSG) represents the gold standard for SA diagnosis. This laboratory-based procedure is complex and costly, and less cumbersome wearable devices have been proposed for SA detection and monitoring. A novel textile multi-sensor monitoring belt recording electrocardiogram (ECG) and breathing frequency (BF) measured by thorax excursion was developed and tested in a sleep laboratory for validation purposes. The aim of the current study was to evaluate the diagnostic performance of ECG-derived heart rate variability and BF-derived breathing rate variability and their combination for the detection of sleep apnea in a population of patients with a suspicion of SA. Fifty-one patients with a suspicion of SA were recruited in the sleep laboratory of the Cantonal Hospital St. Gallen. Patients were equipped with the monitoring belt and underwent a single overnight laboratory-based PSG. In addition, some patients further tested the monitoring belt at home. The ECG and BF signals from the belt were compared to PSG signals using the Bland-Altman methodology. Heart rate and breathing rate variability analyses were performed. Features derived from these analyses were used to build a support vector machine (SVM) classifier for the prediction of SA severity. Model performance was assessed using receiver operating characteristics (ROC) curves. Patients included 35 males and 16 females with a median age of 49 years (range: 21 to 65) and a median apnea-hypopnea index (AHI) of 33 (IQR: 16 to 58). Belt-derived data provided ECG and BF signals with a low bias and in good agreement with PSG-derived signals. The combined ECG and BF signals improved the classification accuracy for SA (area under the ROC curve: 0.98; sensitivity and specificity greater than 90%) compared to single parameter classification based on either ECG or BF alone. This novel wearable device combining ECG and BF provided accurate signals in good agreement with the gold standard PSG. Due to its unobtrusive nature, it is potentially interesting for multi-night assessments and home-based patient follow-up.

Efficacy of Wearable Single-Lead ECG Monitoring during Exercise Stress Testing: A Comparative Study.

BACKGROUND AND OBJECTIVES: Few comparative studies have evaluated wearable single-lead electrocardiogram (ECG) devices and standard multi-lead ECG devices during exercise testing. This study aimed to validate the accuracy of a wearable single-lead ECG monitor for recording heart rate (HR) metrics during graded exercise tests (GXTs). METHODS: A cohort of 50 patients at a tertiary hospital underwent GXT while simultaneously being equipped with wearable single- and conventional multi-lead ECGs. The concordance between these modalities was quantified using the intraclass correlation coefficient and Bland-Altman plot analysis. RESULTS: The minimum and average HR readings between the devices were generally consistent. Parameters such as ventricular ectopic beats and supraventricular ectopic beats showed strong agreement. However, the agreement for the Total QRS and Maximum RR was not sufficient. HR measurements across different stages of the exercise test showed sufficient agreement. Although not statistically significant, the standard multi-lead ECG devices exhibited higher noise levels compared to the wearable single-lead ECG devices. CONCLUSIONS: Wearable single-lead ECG devices can reliably monitor HR and detect abnormal beats across a spectrum of exercise intensities, offering a viable alternative to traditional multi-lead systems.

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.

Comparison Between a Single-Lead ECG Garment Device and a Holter Monitor: A Signal Quality Assessment.

Wearable electronics are increasingly common and useful as health monitoring devices, many of which feature the ability to record a single-lead electrocardiogram (ECG). However, recording the ECG commonly requires the user to touch the device to complete the lead circuit, which prevents continuous data acquisition. An alternative approach to enable continuous monitoring without user initiation is to embed the leads in a garment. This study assessed ECG data obtained from the YouCare device (a novel sensorized garment) via comparison with a conventional Holter monitor. A cohort of thirty patients (age range: 20-82 years; 16 females and 14 males) were enrolled and monitored for twenty-four hours with both the YouCare device and a Holter monitor. ECG data from both devices were qualitatively assessed by a panel of three expert cardiologists and quantitatively analyzed using specialized software. Patients also responded to a survey about the comfort of the YouCare device as compared to the Holter monitor. The YouCare device was assessed to have 70% of its ECG signals as "Good", 12% as "Acceptable", and 18% as "Not Readable". The R-wave, independently recorded by the YouCare device and Holter monitor, were synchronized within measurement error during 99.4% of cardiac cycles. In addition, patients found the YouCare device more comfortable than the Holter monitor (comfortable 22 vs. 5 and uncomfortable 1 vs. 18, respectively). Therefore, the quality of ECG data collected from the garment-based device was comparable to a Holter monitor when the signal was sufficiently acquired, and the garment was also comfortable.

From lab to life: Evaluating the reliability and validity of psychophysiological data from wearable devices in laboratory and ambulatory settings.

Despite the increasing popularity of ambulatory assessment, the reliability and validity of psychophysiological signals from wearable devices is unproven in daily life settings. We evaluated the reliability and validity of physiological signals (electrocardiogram, ECG; photoplethysmography, PPG; electrodermal activity, EDA) collected from two wearable devices (Movisens EcgMove4 and Empatica E4) in the lab (N = 67) and daily life (N = 20) among adults aged 18-64 with Mindware as the laboratory gold standard. Results revealed that both wearable devices' valid data rates in daily life were lower than in the laboratory (Movisens ECG 82.94 vs. 93.10%, Empatica PPG 8.79 vs. 26.14%, and Empatica EDA 41.16 vs. 42.67%, respectively). The poor valid data rates of Empatica PPG signals in the laboratory could be partially attributed to participants' hand movements (r = - .27, p = .03). In laboratory settings, heart rate (HR) derived from both wearable devices exhibited higher concurrent validity than heart rate variability (HRV) metrics (ICCs 0.98-1.00 vs. 0.75-0.97). The number of skin conductance responses (SCRs) derived from Empatica showed higher concurrent validity than skin conductance level (SCL, ICCs 0.38 vs. 0.09). Movisens EcgMove4 provided more reliable and valid HRV measurements than Empatica E4 in both laboratory (split-half reliability: 0.95-0.99 vs. 0.85-0.98; concurrent validity: 0.95-1.00 vs. 0.75-0.98; valid data rate: 93.10 vs. 26.14%) and ambulatory settings (split-half reliability: 0.99-1.00 vs. 0.89-0.98; valid data rate: 82.94 vs. 8.79%). Although the reliability and validity of wearable devices are improving, findings suggest researchers should select devices that yield consistently robust and valid data for their measures of interest.