Detection Of Fibrillation Research Articles

MRMEBP: a unified framework for online detection of atrial fibrillation utilizing deep learning

mRMEBP: a unified framework for online detection of atrial fibrillation utilizing deep learning

Accuracy of the Apple Watch in Detecting Atrial Fibrillation Among Patients Undergoing 24-Hour Holter Monitoring: A Prospective, Pragmatic Study

BACKGROUND: As smartwatches with atrial fibrillation detection features gain popularity, it is important to assess the accuracy of these devices to guide decision-making. OBJECTIVES: Our study aimed to assess the sensitivity and specificity of the irregular rhythm notification and the electrocardiogram (ECG)–based detection features of a commonly used smart wearable device (Apple Watch) in detecting atrial fibrillation. METHODS: This was a prospective, pragmatic study conducted in Perpetual Succour Hospital–Cebu Heart Institute from August 2023 to January 2024. To assess the irregular rhythm notification feature, participants were asked to wear an Apple Watch alongside a 24-hour Holter monitor to verify notifications. For the ECG-based detection feature, participants had to tap the crown of the Apple Watch for 30 seconds to get a single-lead ECG similar to a lead I ECG tracing. They were instructed to get manual ECGs hourly, or more often while awake. Irregular rhythm notifications and ECG readings were then compared with that of the 24-hour Holter monitor. Sensitivity and specificity were then computed. RESULTS: A total of 140 participants consented to join after full study disclosure. The irregular rhythm notification feature of the Apple Watch exhibited a low sensitivity of 21.4% but achieved a high specificity of 100% in detecting atrial fibrillation. Meanwhile, the ECG-based detection feature, analyzed from 1295 manually taken ECGs with interpretable sinus rhythm or atrial fibrillation, demonstrated a high level of agreement with the Holter monitor, with a sensitivity of 100% and a specificity of 99.1%. CONCLUSION: The low sensitivity of the irregular rhythm notification feature of the Apple Watch in detecting atrial fibrillation cautions against relying on it as a primary screening tool. However, the high concordance of manually taken Apple Watch ECGs positions the device as a robust tool for detecting atrial fibrillation through manual ECG detection. KEYWORDS: Apple Watch, atrial fibrillation, smartwatches

Consumer Wearables-Advancing Atrial Fibrillation Care or Too Much Information?

This Viewpoint describes the diffusion of direct-to-consumer wearable devices capable of early atrial fibrillation detection and the unclear implications for initiation of anticoagulation therapy based on such findings.

Ventricular Arrhythmia Classification Using Similarity Maps and Hierarchical Multi-Stream Deep Learning.

Ventricular arrhythmias are the primary arrhythmias that cause sudden cardiac death. We address the problem of classification between ventricular tachycardia (VT), ventricular fibrillation (VF) and non-ventricular rhythms (NVR). To address the challenging problem of the discrimination between VT and VF, we develop similarity maps - a novel set of features designed to capture regularity within an ECG trace. These similarity maps are combined with features extracted through learnable Parzen band-pass filters and derivative features to discriminate between VT, VF, and NVR. To combine the benefits of these different features, we propose a hierarchical multi-stream ResNet34 architecture. Our empirical results demonstrate that the similarity maps significantly improve the accuracy of distinguishing between VT and VF. Overall, the proposed approach achieves an average class sensitivity of 89.68%, and individual class sensitivities of 81.46% for VT, 89.29% for VF, and 98.28% for NVR. The proposed method achieves a high accuracy of ventricular arrhythmia detection and classification. Correct detection and classification of ventricular fibrillation and ventricular tachycardia are essential for effective intervention and for the development of new therapies and translational medicine.

An explainable AI for trustworthy detection of atrial fibrillation on reduced lead ECGs in mobile applications

Abstract Introduction Artificial intelligence (AI), particularly deep learning (DL), has demonstrated high performance in various diagnostic problems, including the detection of paroxysmal atrial fibrillation (AF) from electrocardiograms (ECGs). In mobile applications, these approaches can help to detect AF at an early stage and thus prevent possible secondary diseases. However, due to their black-box nature, DL approaches lack explainability and interpretability. Furthermore, 12-lead ECGs are the clinical standard, but mobile devices usually only provide 1–3 leads, which are similar but not identical to Einthoven leads and differ in morphology. Purpose We apply a recently introduced explainable ECG analysis architecture (xECGArch) to single-lead ECGs to verify its performance in mobile applications. xECGArch can learn rhythmic and morphological characteristics using two parallel convolutional neural networks of different dimensions (Fig. 1). When combined with methods from explainable AI, the classification can be traced back to rhythmic and morphological characteristics. Methods We used a subset of 9 854 ECGs (n(AF) = n(non-AF) = 4 927) from 4 public databases (Chapman-Shaoxing, CPSC2018, Georgia, and PTB-XL). Noise was removed using a cascade of high-pass filtering at 0.3 Hz and discrete wavelet transform. For each lead, we trained xECGArch on single-lead ECGs of 10 s length to classify AF vs. non-AF using 90% of the ECGs with 5-fold cross-validation and tested it on the remaining randomly selected ECGs (n(AF) = 507, n(non-AF) = 479). Consistent data splitting was ensured for all leads. Model explanations were generated using deep Taylor decomposition. Results Both models achieved minimum 91.1% accuracy for each ECG lead (Fig. 1). On lead I, the morphology model outperformed the rhythm model with 92.8% vs. 91.5% accuracy, while the rhythm model slightly outperformed the morphology model on leads II and III with 94.2%–95.0% accuracy vs. 91.1%–94.0%. Both models achieved maximum accuracy of 95.0% (rhythm) and 94.0% (morphology) on lead II. By combining both models, xECGArch reached 93.7%–95.1% accuracy. The model explanations confirm that the rhythm model primarily considers QRS complexes as relevant, while the morphology model focuses on fibrillatory waves (Fig. 1). Conclusions Both rhythm and morphology models achieved reliable results across all leads, competing with the state-of-the-art for automatic AF detection from single-lead ECGs. The combination of both models in xECGArch further increased the accuracy. xECGArch is based on the medical reading of ECGs by considering rhythm and morphology characteristics and is therefore interpretable by design. The model explanations are consistent with the diagnostic criteria for AF (Fibrillatory waves and absolute arrhythmia) across leads. With high accuracy on different single-lead ECGs and improved interpretability, xECGArch provides a trustworthy method for ECG analysis in mobile applications.

Validation of an artificial intelligence system for holter ECG analysis

Abstract Introduction Holter ECG is a commonly used tool to identify heart rhythm disorders. As physician-based analysis of such Holter ECG is time consuming, various approaches using computerized interpretation have been developed. However, accuracy of these tools remains inferior to physician-based analysis. Thus, this study assesses the diagnostic performance of an artificial intelligence (AI)-powered Holter ECG service in comparison to results from reference databases as well as with physician-based evaluation. Methods 231 ECG recordings, covering 2,261 hours of ECG with annotations for 10,024,315 heartbeats from the following reference databases were used: MIT-BIH Arrhythmia (MITDB), American Heart Association ECG (AHDB), MIT-BIH Atrial Fibrillation (AFDB), Long Term Atrial Fibrillation (LTAFDB). Those recordings were originally collected from a mixed population of inpatients and outpatients between 1975 and 2008. Analysis of these recordings regarding the detection of QRS complexes, QRS classification and detection of Atrial Fibrillation (AF) was performed using a novel service for AI-based ECG analysis. Results were compared to the diagnosis stated within the database. QRS complex detection accuracy was performed on recordings from MITDB and AHADB by checking if time positions are within 150 ms to reference annotations. The QRS classification in three classes (normal, supraventricular, ventricular) was then compared for each detected heartbeat. Mismatches were adjudicated manually by three experts. Episodes of Atrial Fibrillation were detected in recordings from MITDB, AFDB and LTAFDB and compared to the reference episodes from the databases. The accuracy was evaluated based on the overlap with the reference annotations in the time-domain. Results Sensitivity and Precision of heartbeat detection compared to the reference databases exceeded 99% Figure 1 displays the results of heartbeat classification. Normal heartbeats were classified with a sensitivity of 99.6%, precision of 99.9% and specificity of 99.6%. For ventricular heartbeats, a sensitivity of 99.58 %, precision of 99.88 and specificity of 99.99% were achieved. Premature supraventricular contractions were recognized with a sensitivity of 99.58 %, precision of 100% and specificity of 100%. When comparing the 248,187 QRS complex classifications of MITDB and AHADB to the AI-powered analysis, 128 discrepancies were revealed. 43 were due to a discrepancy of convention as these beats were in sections that were also marked as AF episodes by the databases and the AI-powered analysis marks all beats in AF episodes as normal. Of the 85 remaining discrepancies, more than 87% were adjudicated in favor of the AI-powered analysis by the experts. Figure 2 shows the comparison of AF detection results showing Sensitivity and Precision above 99%. Conclusion AI-powered Holter ECG analysis compared to reference ECG stripes exceed 99% of sensitivity, specificity and precision.QRS complex detectionAF/ AFib detection

Performance of apple watch for detecting atrial fibrillation among a known atrial fibrillation population

Abstract Background Photoplethysmography (PPG) based consumer accessible devices with algorithms for irregular rhythm detection are gaining popularity as an opportunistic screening tool for atrial arrhythmias, and are FDA cleared for use in patients without a history of atrial fibrillation (AF). However, these devices are increasingly used for the management of AF, a purpose for which some algorithms are not intended. Purpose To compare the performance of the Apple Watch Irregular Rhythm Notification (IRN) with an implantable cardiac monitor (ICM) for the detection of atrial fibrillation (AF) in patients with known AF from the DEFINE AFib study. Methods Patients with known AF, a LINQ or LINQ II ICM, and an Apple Watch with PPG-based IRN activated were included in the analysis. ICM detected AF episodes served as the standard to which Apple Watch IRNs were compared. All AF episodes detected by the ICM device were manually adjudicated by two trained, independent reviewers with a third independent reviewer in situations of disagreement. Only AF episodes ≥60 minutes during a 75-minute window in which the Apple Watch was monitoring were included in accordance with the known Apple Watch algorithm detection parameters. To eliminate oversampling of ICM detected episodes compared to Apple Watch IRNs, 1,000 simulations of one randomly selected episode per subject was done to avoid single-subject bias. Results In total, 97 subjects were analyzed of which 42 had an AF episode ≥60 minutes. Applying the 75-minute window of Apple Watch monitoring to episodes ≥60 minutes, 20 subjects had qualifying, adjudicated ICM detected AF. These 20 subjects accounted for 481 qualifying AF episodes. Of the ICM detected episodes, 191 (40%) occurred while an Apple Watch was not being worn. In total, 11/20 (55%) subjects had an IRN generated by the Apple Watch. Adjudication of the ICM episodes resulted in 290 true positive AF episodes ≥60 minutes during a 75-minute window while the watch was actively monitoring, with 74 (26%) of those episodes triggering an IRN resulting in a per episode sensitivity of 24 ± 7% and PPV of 91 ± 4% (Figure 1A). The efficacy of Apple Watch to detect AF episodes of differing lengths was assessed for episodes between 1-6 hours, 6-12 hours, 12-24 hours, and > 24 hours with sensitivities of 19 ± 19%, 38 ± 8%, 32 ± 10%, and 31 ± 8%, respectively (Figure 1B). The Apple Watch generated an annualized false positive rate of 2.25 IRNs for every patient year of monitoring. At the subject level, Apple Watch had a sensitivity of 48%, specificity of 100%, PPV of 100%, and NPV of 71% (Figure 1A). Conclusions In patients with a history of AF, the Apple Watch IRN was most sensitive at the individual subject level while watch performance was lower by episode. The current IRN algorithm favors longer episode durations. The limited sensitivity, dependence on user wear-time, and reliance on long-durations of AF limit the use of Apple Watch for AF management.

Smartphone motion sensor based detection of atrial fibrillation in populations referred to cardiology

Abstract Introduction The motion sensors of a common smartphone when placed on chest can be used to detect vibrations caused by cardiac motion. The concept has been shown to detect atrial fibrillation (AFib) in clinical setting with 96% accuracy. Purpose We sought to examine how the smartphone motion sensor based AFib detection performs in various populations referred to cardiology pooled from 3 different studies addressing arrhythmias, heart failure or suspected coronary artery disease. Methods From a total of 1 524 patient recordings 748 recordings were from men (49%). The mean age of the patients was 64 years (range 20-94 years) and mean BMI was 28.9kg/m2 (range 15.2-63.7kg/m2). The signals were collected by placing a smartphone on chest of the patients in supine position and then analyzed with the CardioSignal solution. From the recordings two 60 second strips were analyzed. A simultaneous 5-lead or 1-lead ECG data was collected for reference. The ECG signals were analyzed for quality and rhythm diagnostics by 2 physicians blinded for the motion sensor AFib detection result. Finally, a head-to-head comparison was conducted for the motion sensor AFib detection result and the ECG data. Results From the 1524 patient recordings the final analysis set included 237 individuals with AFib, whereas the rest had sinus rhythm. The sensitivity to detect AFib was 91.9% and specificity 98.7%. The positive predictive value was 91.0% (88.3% to 94.2%) and the negative predictive value 98.3% (95.2% to 99.1%). The accuracy was 97.0% (94.7% to 97.7%). Conclusions A smartphone-based assessment of AFib using the embedded motion sensors seem to yield very good detection results, especially specificity. The accuracy of the results is comparable to available ECG and PPG based AFib detection solutions.

Validation of diagnostic wearable ECG data from high-risk patients and integration into the atrial fibrillation catheter ablation pathway

Abstract Background Wearables like the Apple Watch (AW) offer non-invasive, long-term monitoring and patient-led, single-lead ECG recordings. Although validated for Atrial Fibrillation (AF) detection in the general population, the accuracy and clinical utility of the single-lead ECG recording feature for the diagnosis of AF has not been evaluated in patients at high risk of atrial arrhythmias.(1) An evaluation of the accuracy of the Wearable-derived labels is important to determine the feasibility of integrating AW-based remote monitoring into clinical pathways. Objective To evaluate the accuracy and data burden from AW-based, patient-led ECG monitoring in patients undergoing AF catheter ablation. Methods Patients referred for first-time AF catheter ablation to the Study Institution between January 2022 and March 2023 were invited to enrol in the AF Follow-up with Apple Watch (AFFU-AW) Trial. Patients randomised to the active arm of the study received 12 months of post-ablation follow-up using a loaned Apple Watch. Participants underwent an education session and were asked to perform daily ECGs; sending through any labelled as AF, inconclusive, or in the context of symptoms. The accuracy of the automated ECG labelling was determined against blinded, independent labelling by two experienced Electrophysiologists. Results 1093 remote transmissions were emailed in for evaluation from 52 patients to the Study team. This was a median of 6 [2, 17] per patient and a receiver burden of 2 [1, 4] ECGs per day. 475 were categorised by the AW as AF with a sensitivity of 0.71 and specificity of 0.96 compared to expert labelling. 342 (31%) were not categorised by the wearable, with an ‘inconclusive’ interpretation (161 [47%]) or ‘high heart rate’ (108 [32%]) as the most cited reasons. 178 (55%) of the uncategorised ECGs were subsequently overread as AF. Accompanying symptoms were reported with 324 (30%) ECGs, most commonly palpitations. Conclusion This is the first reported evaluation of the AF diagnostic feature based on AW ECG recordings as part of a remote patient rhythm surveillance pathway after catheter ablation. The high positive predictive value of ‘Sinus Rhythm’ and ‘AF’ labels and the high specificity of the ‘AF’ label are comparable to diagnostic algorithms in implantable loop recorders.(2) The source code to automate data curation and structured databasing of wearable ECGs has been made available for care providers to integrate the service into device monitoring workflows.(3)

Atrial fibrillation detection on implantable loop recorders in embolic stroke of undetermined source

Abstract Introduction Embolic stroke of undetermined source (ESUS) represents 17% of all ischaemic strokes, and has a 5% annual stroke recurrence risk. Recent large randomised control trials failed to demonstrate a benefit of empirical anticoagulation for all ESUS patients, with sub-analyses supporting the need for a tailored approach to those patients at greatest risk of atrial fibrillation (AF) development. Understanding how the increased incidence of ILR-detected AF in the ESUS cohort, compares with ILR-detected AF in other cohorts is an essential step in understanding the significance of this finding. Purpose To compare the incidence of ILR-detected AF following ESUS against patients undergoing ILR implantation for non-stroke indications for example syncope and palpitations. Methods We retrospectively studied all patients without known AF who were referred to our institution for ILR implantation following ESUS, syncope or palpitations from 2009 to 2019. The primary endpoint was any detection of AF or atrial flutter (AFL) by ILR. Kaplan-Meier and multivariable Cox-regression analyses were utilised to account for time-to-event. Results A total of 750 patients were included and followed up for a mean of 731 days (SD 443). An ILR was implanted following ESUS in 323 patients and for another reason in 427 patients (figure 1). The incidence of AF was significantly higher among patients with ESUS compared to the non-ESUS group; 48.6% versus 13.8% (for any duration of AF) and 32.2% versus 12.4% (for AF lasting > 30 seconds) both p <0.001. Kaplan-Meier analysis (figure 2), showed significantly higher incidence of AF for ESUS for any duration of AF. This was driven predominantly by AF duration <6 minutes and between 5.5 hours and 24 hours. Importantly, in a multivariable Cox regression analysis, ESUS independently conferred an almost 5-fold increase in the hazard for any duration of AF (Hazard Ratio 4.948, 95% confidence interval 3.587 – 6.825, p<0.001). Conclusion The incidence of AF is significantly higher amongst ESUS versus non-ESUS patients monitored constantly by an ILR. A high number of ESUS survivors have short duration AF episodes. Further work is needed to determine the optimal treatment strategy of these AF episodes in ESUS considering AF duration and overall burden.

Accuracy of the Apple Watch in detecting atrial fibrillation among patients undergoing 24-hour Holter monitoring

Abstract Background As direct-to-consumer wearable devices like the Apple Watch become more common, an increasing number of patients arrive at clinics with reports of irregular rhythms captured on their devices. Purpose This study sought to assess the accuracy of a commonly used smart wearable device (Apple Watch) in detecting atrial fibrillation. Methods We enrolled 140 patients referred for 24-hour Holter monitoring in a Cardiovascular Center. Following thorough disclosure, participants were instructed to wear an Apple Watch provided by the researchers in addition to the Holter monitor to facilitate comparison. The Apple Watch was selected due to its widespread usage among patients. Participants were instructed to record an ECG using the Apple Watch hourly, or more frequently while awake. The objective of the study was to evaluate the sensitivity and specificity of the irregular rhythm notification and ECG-based detection features of the Apple Watch in comparison to the conventional 24-hour Holter monitoring for atrial fibrillation detection. Results The irregular rhythm notification feature of the Apple Watch exhibited a low sensitivity of 21.4% but achieved a high specificity of 100% in detecting atrial fibrillation. Meanwhile, the ECG-based detection feature of the Apple Watch analyzed from 1,295 ECGs captured by the participants when compared in real-time to their ECG rhythms recorded by the Holter monitor, revealed a high level of agreement with a sensitivity of 100% and a specificity of 99.1%. Conclusion The low sensitivity of the irregular notification feature of the Apple watch in detecting atrial fibrillation cautions against relying solely on it to rule out atrial fibrillation as the cause of a patient’s symptoms. Nevertheless, the high concordance of manually taken Apple watch ECGs positions the device as a reliable tool for detecting atrial fibrillation through its ECG-based detection feature.

Reimagining Cryptogenic Stroke Care: Collaborative Care and Inpatient Insertable Cardiac Monitors for Detection of Atrial Fibrillation

BACKGROUND Atrial fibrillation (AF) is a known risk factor of ischemic stroke, and AF‐related stroke is twice more likely to be fatal. Long‐term cardiac rhythm monitoring using insertable cardiac monitors (ICMs) has greater diagnostic yield compared with conventional monitoring in detecting AF, and the clinical utility of ICMs is established in cryptogenic stroke, strokes attributable to large‐artery atherosclerosis, and strokes attributable to small‐vessel disease. A registry‐based study was conducted to evaluate the inpatient implantation of ICMs and the feasibility of vascular and interventional neurologists as implanters using novel collaborative clinical care pathway for cryptogenic stroke. METHODS Multiyear data from a hospital‐based registry at a comprehensive stroke center were reviewed to evaluate inpatient ICM implantation and test feasibility of vascular and interventional neurologists as implanters of ICMs together with cardiology using a novel collaborative care pathway. Reviewed data included the number of ICMs, implantation trend, inpatient versus outpatient setting, time to ICM implantation, inpatient workflow, including defined roles of team members, and AF detection rate. RESULTS A total of 290 ICMs for cryptogenic stroke were implanted when patients were in the hospital and 78 as outpatients after discharge during the study period of 3 years. Most inpatient ICM implants were performed by vascular and interventional neurology (n = 181), and ICM use for cryptogenic stroke increased by 130%. The average time to inpatient ICM implant was 4.1 days, with 77% in 5 days and 95.5% within 10 days poststroke. The average time to out‐patient ICM placement was 57 days. AF detection rate of 36.5% was noted at 24 months with a collaborative care pathway. CONCLUSION Inpatient implantation of ICMs is feasible and was performed safely and efficiently by vascular and interventional neurology together with cardiology using a collaborative care pathway. An increase in use of ICMs and higher AF detection rates were noted. Findings support innovative efforts to improve access and close the gaps in the delivery of cryptogenic stroke care to ultimately reduce the secondary stroke burden.

Global Results of Implantable Loop Recorder for Detection of Atrial Fibrillation After Stroke: Reveal LINQ Registry.

We aimed to quantify the incidence of atrial fibrillation (AF) in patients with cryptogenic stroke globally, as well as separately in patients in and outside of Japan, using an implantable loop recorder from a prospective, observational, Reveal LINQ Registry. Patients developing cryptogenic stroke and monitored by implantable loop recorder for searching AF were studied. The primary end point was incidence of AF within 36 months after insertion. Secondary end points were recurrent ischemic stroke/transient ischemic attack and AF-related treatment strategies. A total of 271 patients (61.6±14.3 years, 170 men, 60 from Japan) were enrolled from 12 countries. AF was detected in 28.2% at 36 months. The median time from enrollment to AF detection was 7.9 months. During the first 12 months, the AF detection rate slope was relatively steeper in the Japanese subgroup versus non-Japanese patients. However, by 3 years, the cumulative incidence of AF detection did not differ between groups. Age was the only variable associated with AF detection (hazard ratio, 1.05 [95% CI, 1.02-1.07] per year), trending higher in older age groups. Of the 271 patients, 11 (4.1%) developed recurrent ischemic stroke/transient ischemic attack; AF was detected by implantable loop recorder in only 1 of these patients. Patients with detected AF were more commonly taking oral anticoagulation than those without AF at the last follow-up (64.7% versus 25.3%, P<0.001). The rate of AF detection was similar to other studies in stroke populations monitored by implantable loop recorders, including CRYSTAL-AF (Cryptogenic Stroke and Underlying Atrial Fibrillation), STROKE-AF (Stroke of Unknown Cause and Underlying Atrial Fibrillation) and PER-DIEM (Post-Embolic Rhythm Detection With Implantable Versus External Monitoring). Patients with detected AF more commonly initiated anticoagulation than those without AF.

Clinical implication of smartphone‐based atrial fibrillation detection system

Clinical implication of smartphone‐based atrial fibrillation detection system

Left Ventricular Diastolic Dysfunction with Elevated Filling Pressures Is Associated with Embolic Stroke of Undetermined Source and Atrial Fibrillation.

Background/Objectives: Left ventricular diastolic dysfunction (LVDD) and elevated left ventricular filling pressure (LVFP) are strong predictors of clinical outcomes across various populations. However, their diagnostic utility in embolic stroke of undetermined source (ESUS) remains unclear. We hypothesized that LVDD with elevated LVFP (based on echocardiography) was more likely to be prevalent in ESUS compared to non-cardioembolic stroke (NCE) and to be associated with atrial fibrillation (AF) on follow-up monitoring. Methods: This is a single-center retrospective study that included adult patients with a diagnosis of acute ischemic stroke between January 2016 and June 2017. LV function was assessed by inpatient transthoracic echocardiogram (TTE), and stroke etiology was adjudicated by the neurologist per the consensus criteria. Patients with cardioembolic stroke and those with indeterminate diastolic function on TTE were excluded. Baseline patient characteristics and clinical variables were compared among patients with and without LVDD and elevated LVFP. Multivariable regression models were used to assess the associations between diastolic dysfunction, ESUS, and AF detection in ESUS patients. Results: We identified 509 patients with ESUS and NCE stroke who had reported diastolic function. The mean age was 64.19 years, 45.19% were female, and 146 had LVDD with available LVFP data. LVDD was not associated with ESUS (adjusted OR: 1.43, 95% CI: 0.90-2.27, p = 0.130) or atrial fibrillation (AF) detection on cardiac monitoring (adjusted OR: 1.88, 95% CI: 0.75-4.72, p = 0.179). However, LVDD with elevated LVFP was borderline associated with ESUS (adjusted OR: 2.17, 95% CI: 0.99-4.77, p = 0.054) and significantly associated with AF detection (adjusted OR: 3.59, 95% CI: 1.07-12.06, p = 0.038). Conclusions: Our data suggest that LVDD with elevated LVFP is borderline associated with ESUS and significantly associated with AF detection on follow-up cardiac monitoring. Therefore, the presence of LVDD with an increased probability of elevated LVFP may help identify a subset of stroke patients more likely to have ESUS, potentially due to atrial cardiopathy with underlying occult AF. Further studies are needed to confirm our findings and to evaluate the safety and efficacy of anticoagulation in patients with ESUS and LVDD with elevated LVFP.

Self-supervised inter–intra period-aware ECG representation learning for detecting atrial fibrillation

Atrial fibrillation is a commonly encountered clinical arrhythmia associated with stroke and increased mortality. Since professional medical knowledge is required for annotation, exploiting a large corpus of ECGs to develop accurate supervised learning-based atrial fibrillation algorithms remains challenging. Self-supervised learning (SSL) is a promising recipe for generalized ECG representation learning, eliminating the dependence on expensive labeling. However, without well-designed incorporations of knowledge related to atrial fibrillation, existing SSL approaches typically suffer from unsatisfactory capture of robust ECG representations. In this paper, we propose an inter–intra period-aware ECG representation learning approach. Considering ECGs of atrial fibrillation patients exhibit the irregularity in RR intervals and the absence of P-waves, we develop specific pre-training tasks for interperiod and intraperiod representations, aiming to learn the single-period stable morphology representation while retaining crucial interperiod features. After further fine-tuning, our approach demonstrates remarkable AUC performances on the BTCH dataset, i.e., 0.953/0.996 for paroxysmal/persistent atrial fibrillation detection. On commonly used benchmarks of CinC2017 and CPSC2021, the generalization capability and effectiveness of our methodology are substantiated with competitive results.

Detection of atrial fibrillation after stroke due to large or small vessel disease: Systematic review and meta-analysis.

Emerging evidence indicates a frequent occurrence of atrial fibrillation (AF) detection among patients with established causes of ischemic stroke unrelated to AF. This systematic review and meta-analysis aimed to evaluate AF detection rates in stroke patients with large or small vessel disease, considering the AF detection modality and duration of cardiac rhythm monitoring. We conducted a comprehensive search of PubMed and Scopus databases up to 2 March 2024, to identify randomized controlled trials, non-randomized prospective studies, and retrospective studies assessing the frequency of AF detection in stroke patients with large or small vessel disease. The primary outcome of interest was the rate of AF detection. We utilized inverse-variance weights to produce the pooled prevalence (effect size (ES)) and 95% confidence interval (CI) of patients diagnosed with post-stroke AF. In the analysis of 14 eligible studies encompassing 4334 patients, AF was identified in 154 out of 2082 patients with strokes attributed to small or large vessel disease, yielding a pooled prevalence of 6.27% (ES; 95% confidence interval (CI): 3.18-10.17, I2 = 87.83%). Among patients with large vessel disease strokes, AF was diagnosed in 79 out of 1042 patients, accounting for a pooled prevalence of 5.07% (ES; 95% CI: 1.30-10.33, I2 = 77.05%). Similarly, among those with small vessel disease strokes, AF was detected in 75 out of 1040 patients, with a pooled prevalence of 5.03% (ES; 95% CI: 1.96-9.06, I2 = 78.05%). AF is often found in ischemic stroke patients with large or small vessel disease. Detection rates increase with longer cardiac rhythm monitoring. The safety and benefits of oral anticoagulation for these AF episodes are uncertain.

ECG classification based on guided attention mechanism

Background and ObjectiveIntegrating domain knowledge into deep learning models can improve their effectiveness and increase explainability. This study aims to enhance the classification performance of electrocardiograms (ECGs) by customizing specific guided mechanisms based on the characteristics of different cardiac abnormalities. MethodsTwo novel guided attention mechanisms, Guided Spatial Attention (GSA) and CAM-based spatial guided attention mechanism (CGAM), were introduced. Different attention guidance labels were created based on clinical knowledge for four ECG abnormality classification tasks: ST change detection, premature contraction identification, Wolf-Parkinson-White syndrome (WPW) classification, and atrial fibrillation (AF) detection. The models were trained and evaluated separately for each classification task. Model explainability was quantified using Shapley values. ResultsGSA improved the F1 score of the model by 5.74%, 5%, 8.96%, and 3.91% for ST change detection, premature contraction identification, WPW classification, and AF detection, respectively. Similarly, CGAM exhibited improvements of 3.89%, 5.40%, 8.21%, and 1.80% for the respective tasks. The combined use of GSA and CGAM resulted in even higher improvements of 6.26%, 5.58%, 8.85%, and 4.03%, respectively. Moreover, when all four tasks were conducted simultaneously, a notable overall performance boost was achieved, demonstrating the broad adaptability of the proposed model. The quantified Shapley values demonstrated the effectiveness of the guided attention mechanisms in enhancing the model's explainability. ConclusionsThe guided attention mechanisms, utilizing domain knowledge, effectively directed the model's attention, leading to improved classification performance and explainability. These findings have significant implications in facilitating accurate automated ECG classification.

Evaluation of the performance of a machine learning based atrial fibrillation screening algorithm using an oscillometric blood pressure monitor

Blood pressure monitors (BPMs) with atrial fibrillation (AFib) detection function can be used to detect AFib early. However, conventional algorithms require multiple BP measurements. Here, the feasibility of a machine-learning-based approach for AFib detection through single BP measurement was evaluated. First, a custom AdaBoost-based software, which analyzes the pulse-to-pulse interval (PPI) pattern and classifies it based on AFib detection, was created. Then, its classification performance was validated. For the validation study, PPI and standard 12-lead electrocardiogram (ECG) datasets were collected from 79 and 92 Japanese participants with and without AFib, respectively. PPI data were obtained using two different BPMs. All ECG results were interpreted by cardiologists. The custom software output for the PPI dataset and ECG interpreted results was compared, and the sensitivity and specificity were calculated. A sensitivity and specificity for PPI from main device were 97.5% (95% confidence interval [CI] 91.2–99.3%) and 98.9 (95% CI 94.1–99.8), respectively. No significant differences in sensitivity and specificity were observed in the subgroup analysis between different devices, age groups, and arm size groups. These results reflect the high accuracy and robustness of this AFib algorithm using a single BP measurement and supports its use for widespread AFib screening.

Smart watch applications in atrial fibrillation detection: Current state and future directions.

Atrial fibrillation (Afib) is a prevalent chronic arrhythmia associated with severe complications, including stroke, heart failure, and increased mortality. This review explores the use of smartwatches for Afib detection, addressing the limitations of current monitoring methods and emphasizing the potential of wearable technology in revolutionizing healthcare. Current Afib detection methods, such as electrocardiography, have limitations in sensitivity and specificity. Smartwatches with advanced sensors offer continuous monitoring, improving the chances of detecting asymptomatic and paroxysmal Afib. The review meticulously examines major clinical trials studying Afib detection using smartwatches, including the landmark Apple Heart Study and ongoing trials such as the Heart Watch, Heartline, and Fitbit Heart Study. Detailed summaries of participant numbers, smartwatch devices used, and key findings are presented. It also comments on the cost-effectiveness and scalability of smartwatch-based screening, highlighting the potential to reduce healthcare costs and improve patient outcomes. The integration of wearable technology into healthcare can lead to earlier diagnosis, improved patient engagement, and enhanced cardiac health monitoring. Despite ethical considerations and disparities, the potential benefits outweigh the challenges. This review calls for increased awareness, collaboration with insurance companies, and ongoing research efforts to optimize smartwatch accuracy and encourage widespread adoption of Afib detection. With insights from major trials, this review serves as a comprehensive reference for healthcare professionals and policymakers, guiding future strategies in the early diagnosis and management of atrial fibrillation.