Arrhythmia Detection Research Articles

Catch-AF—Early Diagnosis of Symptomatic Arrythmias in the Waiting Period Prior to Seeing a Cardiologist in Victoria, British Columbia

BackgroundAtrial fibrillation (AF) is the most common cardiac arrhythmia. Given its often-paroxysmal nature, screening at a single time point, using a 12-lead electrocardiogram (ECG) or a Holter monitor, has limited benefit. The AliveCor KardiaMobile device is a validated ECG recorder that can be used for patient-directed arrhythmia diagnosis and symptom–rhythm correlation. The aim of this study was to evaluate whether using the KardiaMobile device could reduce the time-to-diagnosis, for AF as well as other arrhythmias. We hypothesized that providing patients with a KardiaMobile device during their waiting period for specialist care could reduce the length of time that passes before ECG detection of arrhythmia. MethodsPatients were randomized 1:1 to receive either standard monitoring (ECG and a Holter monitor) or enhanced monitoring (ECG, a Holter monitor, and a KardiaMobile device). Patients were instructed to upload ECG recordings if they had cardiac symptoms, so that symptom–rhythm correlation could be achieved. The primary outcome was the time-to-diagnosis for AF. The secondary endpoint was the time-to-diagnosis for any arrhythmias. ResultsFrom October 2018 to October 2022, a total of 69 patients were enrolled, and they were followed up to 12 months. Overall, 6 of the 7 patients diagnosed with AF were in the enhanced-monitoring group (P = 0.106). The time-to-diagnosis was not significantly different in the 2 groups (P = 0.053). Overall arrhythmias were diagnosed in 10 patients (29%) in the standard-monitoring arm, compared to 22 patients (63%) in the enhanced-monitoring arm (P = 0.008). The time-to-diagnosis was reduced in the enhanced-monitoring arm (P = 0.010). ConclusionsThe time-to-diagnosis of any arrhythmia was reduced significantly in patients randomized to receive KardiaMobile device monitoring. Providing patients with a KardiaMobile device may expedite the diagnosis of arrhythmias during the waiting period for specialist care. Clinical Trial RegistrationNCT04302311.

Temporal attention network for CNNE model of variable-length ECG signals in early arrhythmia detection

<p>Cardiac arrhythmia identification and categorization are crucial for prompt treatment and better patient outcomes. Arrhythmia identification is the main focus of this study's temporal attention network (TAN)-based multiclass categorization of varied-length electrocardiogram (ECG) data. The suggested TAN is designed to handle variable-duration ECG signals, making it ideal for real-time monitoring. The TAN uses a dynamic snippet extraction approach to choose meaningful ECG segments to ensure the model captures essential properties despite the constraints of processing such heterogeneous data. Training and assessment use a large dataset of atrial fibrillation, ventricular, and supraventricular arrhythmias. The TAN outperforms current approaches in multiclass early arrhythmia classification and is very accurate. Concatenating EfficientNet with CNN layer helped overcome different data and variable-length signals. High accuracy: 98% of normal, 97.1% of atrial fibrillation (AF), 98% of other, and 98% of noisy using the proposed CEEC model. Early arrhythmia diagnosis has improved due to the TAN's ability to effectively identify varied-length ECG data and give interpretability. It enables quicker interventions, personalised treatment plans, and improved arrhythmia control, which can greatly benefit patient care.</p>

Sensor-Enhanced Deep Learning for ECG Arrhythmia Detection: Integrating Multiscale Convolutional and Self-Attentive GRU Networks

Sensor-Enhanced Deep Learning for ECG Arrhythmia Detection: Integrating Multiscale Convolutional and Self-Attentive GRU Networks

GAN-SkipNet: A Solution for Data Imbalance in Cardiac Arrhythmia Detection Using Electrocardiogram Signals from a Benchmark Dataset

Electrocardiography (ECG) plays a pivotal role in monitoring cardiac health, yet the manual analysis of ECG signals is challenging due to the complex task of identifying and categorizing various waveforms and morphologies within the data. Additionally, ECG datasets often suffer from a significant class imbalance issue, which can lead to inaccuracies in detecting minority class samples. To address these challenges and enhance the effectiveness and efficiency of cardiac arrhythmia detection from imbalanced ECG datasets, this study proposes a novel approach. This research leverages the MIT-BIH arrhythmia dataset, encompassing a total of 109,446 ECG beats distributed across five classes following the Association for the Advancement of Medical Instrumentation (AAMI) standard. Given the dataset’s inherent class imbalance, a 1D generative adversarial network (GAN) model is introduced, incorporating the Bi-LSTM model to synthetically generate the two minority signal classes, which represent a mere 0.73% fusion (F) and 2.54% supraventricular (S) of the data. The generated signals are rigorously evaluated for similarity to real ECG data using three key metrics: mean squared error (MSE), structural similarity index (SSIM), and Pearson correlation coefficient (r). In addition to addressing data imbalance, the work presents three deep learning models tailored for ECG classification: SkipCNN (a convolutional neural network with skip connections), SkipCNN+LSTM, and SkipCNN+LSTM+Attention mechanisms. To further enhance efficiency and accuracy, the test dataset is rigorously assessed using an ensemble model, which consistently outperforms the individual models. The performance evaluation employs standard metrics such as precision, recall, and F1-score, along with their average, macro average, and weighted average counterparts. Notably, the SkipCNN+LSTM model emerges as the most promising, achieving remarkable precision, recall, and F1-scores of 99.3%, which were further elevated to an impressive 99.60% through ensemble techniques. Consequently, with this innovative combination of data balancing techniques, the GAN-SkipNet model not only resolves the challenges posed by imbalanced data but also provides a robust and reliable solution for cardiac arrhythmia detection. This model stands poised for clinical applications, offering the potential to be deployed in hospitals for real-time cardiac arrhythmia detection, thereby benefiting patients and healthcare practitioners alike.

ECG classification via integration of adaptive beat segmentation and relative heart rate with deep learning networks

We propose a state-of-the-art deep learning approach for accurate electrocardiogram (ECG) signal analysis, addressing both waveform delineation and beat type classification tasks. For beat type classification, we integrated two novel schemes into the deep learning model, significantly enhancing its performance. The first scheme is an adaptive beat segmentation method that determines the optimal duration for each heartbeat based on RR-intervals, mitigating segmenting errors from conventional fixed-period segmentation. The second scheme incorporates relative heart rate information of the target beat compared to neighboring beats, improving the model’s ability to accurately detect premature atrial contractions (PACs) that are easily confused with normal beats due to similar morphology. Extensive evaluations on the PhysioNet QT Database, MIT-BIH Arrhythmia Database, and real-world wearable device data demonstrated the proposed approach’s superior capabilities over existing methods in both tasks. The proposed approach achieved sensitivities of 99.81% for normal beats, 99.08% for premature ventricular contractions, and 97.83% for PACs in beat type classification. For waveform delineation, we achieved F1-scores of 0.9842 for non-waveform, 0.9798 for P-waves, 0.9749 for QRS complexes, and 0.9848 for T-waves. It significantly outperforms existing methods in PAC detection while maintaining high performance across both tasks. The integration of aforementioned two schemes into the deep learning model improved the accuracy of normal sinus rhythms and arrhythmia detection.

An Enhancement for Wireless Body Area Network Using Adaptive Algorithms

Wireless Body Area Networks (WBANs) are one of the most critical technologies for maintaining constant monitoring of patient’s health and diagnosing diseases. They consist of small, wearable wireless sensors transmitting signals. Within this vision, WBANs are not without unique difficulties, for instance, high energy consumption, heat from the sensor, and impaired data accuracy. This paper introduces adaptive algorithms combining Convolutional Neural Networks (CNNs) and dynamic threshold mechanisms to enhance the performance and energy efficiency of Wireless Body Area Networks. The study utilizes the MIB-BIH Arrhythmias dataset to improve the detection of arrhythmias. The results show a 10.53% improvement in battery life and a 5.62-fold enhancement in temperature management when sleep mode technology is applied. As a result, the model reached the average accuracy of ECG classification of 98% and a high level of selectivity and sensitivity to a normal type of heartbeat and quite satisfactory results in the classification of arrhythmia type of heartbeat.

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

Background and aimCardiac 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). MethodsThe 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. ResultsThe 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. ConclusionThe 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.

A novel method for detecting cardiac arrhythmias in electrocardiogram signals using the ANFIS classifier

In general, ventricular repolarization instability with Premature Activations (PA) plays a significant role in the detection of arrhythmia using an electrocardiogram (ECG) signal. However, clinical detection of this instability is challenging. In this work, a unique technique for analyzing QT Interval (QTI) dynamics unreliability and other abnormalities in the ECG signal are utilized to detect ventricular tachycardia based on the identified anomalies. Further, a feature extraction method based on Chopped Displacement Gaussian (CDGa) is adopted and the features are retrieved from the CDGa signal and its size is reduced using Fisher Discrimination Analysis (FDA). Also, the results of the FDA are fed into the Adaptive Neuro-Fuzzy interference (ANFIS) classifier. The performance of the ANFIS classifier is enhanced in this study by integrating the Grasshopper optimization algorithm for optimizing network parameters. As a result, the values in the signals are predicted using an adaptive Least Mean Square (LMS)-based prediction algorithm, which is utilized to forecast the future status of VT-affected patients. Results indicate that the proposed work has an overall accuracy of 96 % which is higher than any other conventional methods. Also, the various additional performance criteria are tested, and it is demonstrated that the CDGa with adaptive LMS outperforms all previous approaches.

Implementation of electrical tomography in a medical measurement system for innovative imaging and area monitoring

LETS (Lung Electrical Tomography System) is an innovative medical system enabling comprehensive heart and lung function monitoring. It utilizes advanced techniques such as electrical impedance tomography (EIT), body surface potential mapping (BSPM), and electrical capacitance tomography (ECT) to provide accurate information about physiological parameters. The LETS system allows one-time examinations and continuous monitoring 24 hours a day. Remotely measuring and monitoring parameters such as the electrical activity of the heart muscle, blood flow, blood pressure, pulse, and lung aeration enables rapid identification and intervention in case of abnormalities. The system also allows for the detection of cardiac arrhythmias and other heart disorders, as well as the assessment of lung respiratory capacity and optimization of ventilation strategies. However, it's important to note that the LETS system has some limitations. For example, it may not be suitable for patients with certain pacemakers or other implanted devices. Overall, LETS represents a breakthrough in medical imaging, opening up new possibilities in diagnosing and monitoring heart and lung diseases. Its integration may contribute to improving patient care and increasing the effectiveness of medical interventions.

Role of new generation implantable loop recorders in managing undiagnosed pediatric cardiac symptoms

Pediatric cardiac symptoms such as palpitations, syncope, or seizure-like episodes pose diagnostic challenges for general pediatricians. These symptoms, though often benign, may reveal underlying arrhythmias or inherited cardiac conditions (ICCs), affecting the quality of life and limiting activity participation. The purpose of this study is to determine the effectiveness and safety of implantable loop recorders (ILRs) in diagnosing and managing arrhythmias in pediatric patients. A retrospective cohort study conducted over an 8-year period from January 2016 to December 2023 in a single pediatric cardiology center. A cohort of 155 pediatric patients (median age 11.4 years) who underwent ILR implantation were selected based on symptoms such as palpitations, chest pain, or syncope, and those with previously recorded arrhythmias or high-risk ICCs. The primary outcomes were the diagnostic yield of ILRs for arrhythmias and subsequent changes in patient management. Diagnostic yield was defined as the detection of relevant arrhythmias, such as pauses of 3 s or longer, high-degree AV block, sinus node dysfunction, supraventricular tachycardia, ventricular tachycardia, or inappropriate sinus tachycardia. The median follow-up period was 2.3 years (845 days). Diagnostic arrhythmias were recorded in 60% of patients with symptom-activated transmissions and 80% of device-activated transmissions. Sinus pauses (37.5%) and VT (30%) were the most common arrhythmias detected. In patients with syncope (n = 76), 30% had relevant arrhythmias. In the palpitations group (n = 20), 35% had relevant arrhythmias. Approximately 80% of patients with ILR-diagnosed arrhythmias underwent targeted management, including medication changes and additional procedures. No significant complications were observed; minor complications occurred in 2.5% of patients.Conclusions: New generation ILRs are effective and safe for diagnosing and managing pediatric arrhythmias, providing significant reassurance to patients and families. Further studies are needed to evaluate the impact of ILRs on quality of life and sports participation in high-risk young patients.What is Known:• Pediatric Cardiac Symptoms: Symptoms like palpitations, syncope, and seizure-like episodes in pediatric patients pose significant diagnostic challenges.• Diagnostic Tools: Traditional diagnostic tools such as ECG, Holter monitors, and external loop recorders often have low diagnostic yields due to the sporadic nature of symptoms.• Implantable Loop Recorders (ILRs): ILRs have been shown to improve the diagnostic yield for arrhythmias in adults and provide long-term rhythm monitoring.What is New:• Effectiveness in Pediatrics: This study demonstrates that new generation ILRs significantly enhance the diagnostic yield for arrhythmias in pediatric patients with undiagnosed cardiac symptoms.• Safety and Efficacy: The study confirms that ILRs are both safe and effective in the pediatric population, with minimal complications and high diagnostic accuracy.• Targeted Management: Findings show that ILR-diagnosed arrhythmias lead to targeted management changes in approximately 80% of cases, including medication adjustments and additional procedures.• Long-term Monitoring: ILRs provide continuous long-term monitoring, offering reassurance to patients and families, and potentially improving quality of life and participation in activities for high-risk young patients.

HRIDM: Hybrid Residual/Inception-Based Deeper Model for Arrhythmia Detection from Large Sets of 12-Lead ECG Recordings

Heart diseases such as cardiovascular and myocardial infarction are the foremost reasons of death in the world. The timely, accurate, and effective prediction of heart diseases is crucial for saving lives. Electrocardiography (ECG) is a primary non-invasive method to identify cardiac abnormalities. However, manual interpretation of ECG recordings for heart disease diagnosis is a time-consuming and inaccurate process. For the accurate and efficient detection of heart diseases from the 12-lead ECG dataset, we have proposed a hybrid residual/inception-based deeper model (HRIDM). In this study, we have utilized ECG datasets from various sources, which are multi-institutional large ECG datasets. The proposed model is trained on 12-lead ECG data from over 10,000 patients. We have compared the proposed model with several state-of-the-art (SOTA) models, such as LeNet-5, AlexNet, VGG-16, ResNet-50, Inception, and LSTM, on the same training and test datasets. To show the effectiveness of the computational efficiency of the proposed model, we have only trained over 20 epochs without GPU support and we achieved an accuracy of 50.87% on the test dataset for 27 categories of heart abnormalities. We found that our proposed model outperformed the previous studies which participated in the official PhysioNet/CinC Challenge 2020 and achieved fourth place as compared with the 41 official ranking teams. The result of this study indicates that the proposed model is an implying new method for predicting heart diseases using 12-lead ECGs.

Detection of Arrhythmias Using Heart Rate Signals from Smartwatches

According to the World Health Organization (WHO), cardiovascular illnesses, including arrhythmia, are the primary cause of mortality globally, responsible for over 31% of all fatalities each year. To reduce mortality, early and precise diagnosis is essential. Although the analysis of electrocardiograms (ECGs) is the primary means of detecting arrhythmias, it depends significantly on the expertise and subjectivity of the health professional reading and interpreting the ECG, and errors may occur in detection. Artificial intelligence provides tools, techniques, and models that can support health professionals in detecting arrhythmias. However, these tools are based only on ECG data, of which the process of obtaining is an invasive, high-cost method requiring specialized equipment and personnel. Smartwatches feature sensors that can record real-time signals indicating the heart’s behavior, such as ECG signals and heart rate. Using this approach, we propose a machine learning- and deep learning-based approach for detecting arrhythmias using heart rate data obtained with smartwatches. Heart rate data were collected from 252 patients with and without arrhythmias who attended a clinic in Arequipa, Peru. Heart rates were also collected from 25 patients who wore smartwatches. Ten machine learning algorithms were implemented to generate the most effective arrhythmia recognition model, with the decision tree algorithm being the most suitable. The results were analyzed using accuracy, sensitivity, and specificity metrics. Using Holter data yielded values of 93.2%, 91.89%, and 94.59%, respectively. Using smartwatch data yielded values of 70.83%, 91.67%, and 50%, respectively. These results indicate that our model can effectively recognize arrhythmias from heart rate data. The high sensitivity score suggests that our model adequately recognizes true positives; that is, patients with arrhythmia. Likewise, its specificity suggests an adequate recognition of false positives.

Patient Driven EKG Device Performance in Adults with Fontan Palliation.

The aim of this study was to evaluate the accuracy of the KardiaMobile (KM) device in adults with a Fontan palliation, and to assess the KM function as a screening tool for atrial arrhythmias. While patient driven electrocardiogram (EKG) devices are becoming a validated way to evaluate cardiac arrhythmias, their role for patients with congenital heart disease is less clear. Patients with single ventricle Fontan palliation have a high prevalence of atrial arrhythmias and represent a unique cohort that could benefit from early detection of atrial arrhythmias. This single center prospective study enrolled adult patients with Fontan palliation to use the KM heart rhythm monitoring device for both symptomatic episodes and asymptomatic weekly screening over a 1-year period. Accuracy was assessed by comparing the automatic KM interpretation (KM-auto) to an electrophysiologist overread (KM-EP) and traditional EKG. Fifty patients were enrolled and 510 follow-up transmissions were received. The sensitivity and specificity of enrollment KM-auto compared to EKG was 65% and 100%, respectively. The sensitivity and specificity of enrollment KM-auto compared to the KM-EP was 75% and 96%, respectively. In the adult Fontan palliation, the accuracy of the KM device to detect a normal rhythm was reliable and best with a physician overread. Abnormal or uninterpretable KM-auto device interpretations, symptomatic transmissions, and any transmissions with a high heart rate compared to a patient's normal baseline should warrant further review.

Advancing cardiac diagnostics: Exceptional accuracy in abnormal ECG signal classification with cascading deep learning and explainability analysis

Arrhythmias, cardiac rhythm disorders, demand precise diagnosis for effective treatment planning, emphasizing the crucial role of electrocardiogram (ECG) signal interpretation. While deep learning excels in ECG classification, its interpretability remains a challenge. Addressing this issue, our research introduces an innovative approach employing image-based ECG representations and cascading deep neural networks (CDNNs) to enhance arrhythmia detection precision. Initiating with the transformation of 12-lead ECG time-series data, particularly focusing on Lead II, into image-based representations using relative positioning matrices (RPM), this novel conversion captures spatial and temporal information concurrently, enriching the depth of our data analysis. Subsequently, CDNNs play a pivotal role in feature extraction, designed to automatically extract impactful features from image-based ECG representations, providing profound insights into cardiac electrical activity. Extracted features serve as inputs for the subsequent deep neural network for classification. Experimental results on the Shaoxing–Chapman ECG database, encompassing over 10,000 recordings with an 80/10/10 split, 80/20 split, 60/40 split, and 10-fold cross-validation, showcase a remarkable 100% accuracy in distinguishing between seven and four different arrhythmia classes. Incorporating SHapley Additive exPlanations (SHAP) values, our methodology offers comprehensive insights into the model’s decision-making, ensuring interpretability. Additionally, Gradient-weighted Class Activation Mapping (Grad-CAM) aids feature visualization, elucidating key features driving predictions. By amalgamating image-based representation, CDNNs, and interpretability techniques, our research not only attains exceptional classification performance but also boosts model transparency and trustworthiness. These findings hold promise for advancing arrhythmia diagnosis and enhancing interpretability in deep learning-based medical applications.

Development and Validation of a Real-Time Service Model for Noise Removal and Arrhythmia Classification Using Electrocardiogram Signals.

Arrhythmias range from mild nuisances to potentially fatal conditions, detectable through electrocardiograms (ECGs). With advancements in wearable technology, ECGs can now be monitored on-the-go, although these devices often capture noisy data, complicating accurate arrhythmia detection. This study aims to create a new deep learning model that utilizes generative adversarial networks (GANs) for effective noise removal and ResNet for precise arrhythmia classification from wearable ECG data. We developed a deep learning model that cleans ECG measurements from wearable devices and detects arrhythmias using refined data. We pretrained our model using the MIT-BIH Arrhythmia and Noise databases. Least squares GANs were used for noise reduction, maintaining the integrity of the original ECG signal, while a residual network classified the type of arrhythmia. After initial training, we applied transfer learning with actual ECG data. Our noise removal model significantly enhanced data clarity, achieving over 30 dB in a signal-to-noise ratio. The arrhythmia detection model was highly accurate, with an F1-score of 99.10% for noise-free data. The developed model is capable of real-time, accurate arrhythmia detection using wearable ECG devices, allowing for immediate patient notification and facilitating timely medical response.

Comparison of Postoperative Continuous Wireless Cardiac Rhythm Monitoring with Traditional Telemetry in Cardiac Surgery Patients: the SMART-TEL Study.

Telemetry monitoring (conventional cardiac monitoring system [CCMS]) is a universal method for postoperative arrhythmia detection; however, the clinical challenge of alarm fatigue, primarily associated with noise or cable disconnections, persists. The introduction of wireless continuous cardiac monitoring (WCCM) represents a potential solution to enhance recording fidelity. Patients were simultaneously outfitted with both a monitoring device considered the standard of care and a novel adhesive wireless patch. A 48-h cardiac monitoring session with the two devices occurred after cardiac surgery in a unit equipped with a telemetry system. A total of 53 patients with a mean age of 60 ± 17 years were included in the trial. The number of events detected by the two systems was significantly different at 190 versus 174 for the CCMS and the WCCM system, respectively (P < .05). However, the percentage of agreement was not significantly different at 91% versus 88% (P = .37). Events were classified as follows: pause (2 events, 1%), atrial or premature ventricular contractions (18 events, 11%), atrial flutter or fibrillation (76 events, 45%), bradycardia (12 events, 7%), and tachycardia (61 events, 36%). False alarms were significantly more frequent with the CCMS (n = 21) than with the WCCM system (n = 5; P = .002). The study successfully demonstrated the feasibility and usability of wireless monitoring for patients requiring telemetry. The overall results are compelling, as the WCCM system performed satisfactorily, achieving results comparable to those obtained with the CCMS, even with significantly fewer false alarms.

QRS detection in single-lead, telehealth electrocardiogram signals: Benchmarking open-source algorithms.

A key step in electrocardiogram (ECG) analysis is the detection of QRS complexes, particularly for arrhythmia detection. Telehealth ECGs present a new challenge for automated analysis as they are noisier than traditional clinical ECGs. The aim of this study was to identify the best-performing open-source QRS detector for use with telehealth ECGs. The performance of 18 open-source QRS detectors was assessed on six datasets. These included four datasets of ECGs collected under supervision, and two datasets of telehealth ECGs collected without clinical supervision. The telehealth ECGs, consisting of single-lead ECGs recorded between the hands, included a novel dataset of 479 ECGs collected in the SAFER study of screening for atrial fibrillation (AF). Performance was assessed against manual annotations. A total of 12 QRS detectors performed well on ECGs collected under clinical supervision (F1 score ≥0.96). However, fewer performed well on telehealth ECGs: five performed well on the TELE ECG Database; six performed well on high-quality SAFER data; and performance was poorer on low-quality SAFER data (three QRS detectors achieved F1 of 0.78-0.84). The presence of AF had little impact on performance. The Neurokit and University of New South Wales QRS detectors performed best in this study. These performed sufficiently well on high-quality telehealth ECGs, but not on low-quality ECGs. This demonstrates the need to handle low-quality ECGs appropriately to ensure only ECGs which can be accurately analysed are used for clinical decision making.

An Untethered Heart Rhythm Monitoring System with Automated AI‐Based Arrhythmia Detection for Closed‐Loop Experimental Application

AbstractThe heart produces bioelectrical signals, which can be measured as an electrocardiogram (ECG) for the detection of rhythm disturbances. Rapid and precise detection of these arrhythmias is crucial for their termination by closed‐looped therapeutic interventions to counteract detrimental effects. However, there is a current lack of such systems tailored for experimental cardiovascular applications. This hampers not only in‐depth mechanistic studies but also translational testing of new therapeutic strategies, especially in an untethered manner in awake animal models. To break new ground, recent advances to develop a non‐invasive AI‐supported heart rhythm monitoring system for untethered automated arrhythmia detection in a continuous manner is combined. This system is housed in a lightweight jacket for mobile use and includes an on‐skin ECG sensor, a low‐power microprocessor unit, a massive data storage unit, and a power‐management system. By implementing a novel hybrid algorithm based on so‐called heart rate (R‐R) variability and a case‐specific AI model, 100% sensitivity and 95% specificity is achieved in detecting atrial arrhythmias within 2 s upon initiation in adult rats. Thereby, the novel system sets the stage for advanced mechanistic studies and therapeutic testing, including closed‐loop applications aiming for the termination of a broad range of atrial arrhythmias.

Keeping Pace with Wearables: A Living Umbrella Review of Systematic Reviews Evaluating the Accuracy of Consumer Wearable Technologies in Health Measurement.

Consumer wearable technologies have become ubiquitous, with clinical and non-clinical populations leveraging a variety of devices to quantify various aspects of health and wellness. However, the accuracy with which these devices measure biometric outcomes such as heart rate, sleep and physical activity remains unclear. To conduct a 'living' (i.e. ongoing) evaluation of the accuracy of consumer wearable technologies in measuring various physiological outcomes. A systematic search of the literature was conducted in the following scientific databases: MEDLINE via PubMed, Embase, Cinahl and SPORTDiscus via EBSCO. The inclusion criteria required systematic reviews or meta-analyses that evaluated the validation of consumer wearable devices against accepted reference standards. In addition to publication details, review protocol, device specifics and a summary of the authors' results, we extracted data on mean absolute percentage error (MAPE), pooled absolute bias, intraclass correlation coefficients (ICCs) and mean absolute differences. Of 904 identified studies through the initial search, 24 systematic reviews met our inclusion criteria; these systematic reviews included 249 non-duplicate validation studies of consumer wearable devices involving 430,465 participants (43% female). Of the commercially available wearable devices released to date, approximately 11% have been validated for at least one biometric outcome. However, because a typical device can measure a multitude of biometric outcomes, the number of validation studies conducted represents just 3.5% of the total needed for a comprehensive evaluation of these devices. For heart rate, wearables showed a mean bias of ± 3%. In arrhythmia detection, wearables exhibited a pooled sensitivity and specificity of 100% and 95%, respectively. For aerobic capacity, wearables significantly overestimated VO2max by ± 15.24% during resting tests and ± 9.83% during exercise tests. Physical activity intensity measurements had a mean absolute error ranging from 29 to 80%, depending on the intensity of the activity being undertaken. Wearables mostly underestimated step counts (mean absolute percentage errors ranging from - 9 to 12%) and energy expenditure (mean bias = - 3kcal per minute, or - 3%, with error ranging from - 21.27 to 14.76%). For blood oxygen saturation, wearables showed a mean absolute difference of up to 2.0%. Sleep measurement showed a tendency to overestimate total sleep time (mean absolute percentage error typically > 10%). While consumer wearables show promise in health monitoring, a conclusive assessment of their accuracy is impeded by pervasive heterogeneity in research outcomes and methodologies. There is a need for standardised validation protocols and collaborative industry partnerships to enhance the reliability and practical applicability of wearable technology assessments. CRD42023402703.

Association of Atrial Fibrillation Burden and Mortality Among Patients With Cardiac Implantable Electronic Devices.

Current estimates of atrial fibrillation (AF)-associated mortality rely on claims- or clinical-derived diagnoses of AF, limit AF to a binary entity, or are confounded by comorbidities. The objective of the present study is to assess the association between device-recognized AF and mortality among patients with cardiac implantable electronic devices capable of sensitive and continuous atrial arrhythmia detection. Secondary outcomes include relative mortality among cohorts with no AF, paroxysmal AF, persistent AF, and permanent AF. Using the deidentified Optum Clinformatics US claims database (2015 to 2020) linked to the Medtronic CareLink database, we identified individuals with a cardiac implantable electronic device who transmitted data ≥6 months after implantation. AF burden was assessed during the first 6 months after implantation (baseline period). Subsequent mortality, assessed from claims data, was compared between patients with and without AF, with adjustment for age, geographic region, insurance type, Charlson Comorbidity Index, and implantation year. Of 21 391 patients (age, 72.9±10.9 years; 56.3% male) analyzed, 7798 (36.5%) had device-recognized AF. During a mean of 22.4±12.9 months (median, 20.1 [12.8-29.7] months) of follow-up, the overall incidence of mortality was 13.5%. Patients with AF had higher adjusted all-cause mortality than patients without AF (hazard ratio, 1.29 [95% CI, 1.20-1.39]; P<0.001). Among those with AF, patients with nonparoxysmal AF had the greatest risk of mortality (persistent AF versus paroxysmal AF: hazard ratio, 1.36 [95% CI, 1.18-1.58]; P<.001; permanent AF versus paroxysmal AF: hazard ratio, 1.23 [95% CI, 1.14-1.34]; P<.001). After adjustment for potential confounding factors, the presence of AF was associated with higher mortality in our cohort of patients with cardiac implantable electronic devices. Among those with AF, nonparoxysmal AF was associated with the greatest risk of mortality.