Detection Of Atrial Fibrillation Research Articles

Timing of detection of atrial fibrillation recurrence in patients with transient new-onset atrial fibrillation detected during hospitalization for noncardiac surgery or medical illness

Abstract Background Approximately one third of patients who experience transient new-onset atrial fibrillation (AF) during a hospitalization for noncardiac surgery or medical illness will have recurrent AF within 1 year. Purpose To estimate the expected rate of detection of AF recurrence using different durations of continuous ECG monitoring during follow-up of patients who have had AF occurring transiently during physiologic stress. Methods We used continuous ECG data from participants enrolled in the AFOTS Follow-up Study. Participants had transient new-onset AF while hospitalized for noncardiac surgery or medical illness and were discharged from hospital in sinus rhythm. We calculated the median time to detection of the first recurrent AF episode and the proportion of recurrent AF events that would be captured with an ECG monitor worn for 24 h, 48 h, 7 days and 14 days, and with a repeated 14-day monitor worn approximately 6 months after the first monitor. Results A total of 139 participants (41.0% female, median CHA2DS2-VaSC Score 3.0 (Interquartile Range [IQR] 2.0- 4.0)) wore at least one ECG monitor for a median of 13.5 days (IQR 11.7-13.8). A second ECG monitor was worn by 83 (59.7%) participants for a median of 12.9 days (IQR 10.0-13.9); the second monitor was worn a median of 175.0 days (IQR 153.0-216.0) after the first monitor. Among the study participants in whom recurrent AF was detected (n = 40, 28.8%), the median time to AF lasting at least 30 seconds was 128.0 hours (IQR 32.5-232.5). The proportion of AF recurrences detected after more than 24 hours of monitoring was 75.0% (n = 30). The yield of AF detection increased with longer monitoring times (Figure 1). AF was detected in 5.0% of participants at 24 hours, 5.8% at 48 hours, 6.5% at 72 hours, 12.2% at 7 days, 21.6% at 14 days and in 28.8% of participants by the end of the second 14-day monitor. Conclusions In patients who have experienced transient new-onset AF during a hospitalization for noncardiac surgery or medical illness, the rate of capture of AF recurrence in follow-up increases with longer monitoring durations. 14-day monitors capture nearly 4 times as many AF events as 48 h monitors.

Non-invasive detection of cardiac arrhythmias using photoplethysmography signals

Abstract Cardiac arrhythmias (CAs) are associated with critical heart-related complications such as stroke or heart failure. Because of the intermittent and asymptomatic presentation of some CAs in their early stages, the screening remains limited using traditional methods based on ECG. Recent advances in photoplethysmography (PPG) have revealed substantial potential for wearable devices to detect CAs within large populations. Although PPG has demonstrated efficiency in discriminating atrial fibrillation (AF) from normal sinus rhythm, it remains unclear whether AF detection methods remains effective in the presence of CAs other than AF. In this study, we applied a recurrent neural network on a dataset containing eight different types of CAs (Table 1). The classifier processes sequences of interbeat intervals (IBIs) as input and discriminates between normal and abnormal rhythms. The algorithm was evaluated on 64 patients (45 males and 19 females , with a mean age of 55.9 ± 16.0) undergoing a diagnostic or therapeutic electrophysiological procedure. This dataset includes simultaneous recordings of PPG signals from a wrist-bracelet and 12-lead ECG, with the latter used as the gold-standard for annotating cardiac rhythms. The classifier achieved 84% accuracy, 77% sensitivity and 88% specificity in detecting abnormal rhythms. Table 1 shows that AF (99.6%), atrial tachycardia (100%) and AVRT (96.4%) were well classified as abnormal rhythm, whereas the reliability of the detection decreased for atrial flutter (65.4%), atrial or ventricular bigeminy (72.4%) and ventricular tachycardia (80.2%). Undetected abnormal rhythms were often characterized by a rather regular rhythm (illustrated by VT in Figure 1) and CAs for which PPG heartbeat detection was initially not optimal (Bigeminy in Figure 1). In conclusion, this study shows the capability of PPG-based technology to detect various CAs extending beyond AF. It highlights the merits and limitations of IBI-based detection of abnormal rhythms, highlighting the need for a better comprehension of the peripheral hemodynamic signature of CAs.

Post-operative atrial fibrillation after noncardiac surgery: diagnostic role of ambulatory rhythm monitoring for recurrent arrhythmia after hospital discharge

Abstract Introduction Postoperative atrial fibrillation (POAF) after noncardiac surgery constitutes 10-15% of all cases of incident atrial fibrillation (AF) in the community and has been associated with an increased risk of stroke. The risk of subsequent AF among patients with POAF is not well defined. Purpose To evaluate the role of systematic 30-day electrocardiographic (ECG) monitoring in the detection of AF recurrence after POAF. Methods In a prospective interventional study, patients who underwent major noncardiac surgeries and had new AF documented by ECG in the post-operative period between May 2022 and April 2023 were identified. For these patients, a 30-day ambulatory ECG monitor was recommended upon discharge, with final ordering deferred to the primary inpatient teams. Rates of recurrent AF of any duration detected by 30-day monitoring plus routine care versus routine care alone after the surgery were compared. Results Eighty-five patients with new-onset POAF were included (mean age 72 years, male 67%). Of these, 35 (41%) patients underwent up to 30 days [median 23 (IQR 6-27) days] of continuous ECG monitoring after hospital discharge. There were no significant differences in baseline characteristics between patients who did or did not undergo monitoring (Table 1). During median 281 (IQR 197-385) days of follow-up, recurrent AF was documented in 14/35 (40%) patients in the monitoring group as opposed to 7/50 (14%) patients in the usual care group (log-rank p=0.003, Figure 1). The median time from day of surgery until documented AF recurrence was 25 (IQR 6-53) days in the monitoring group and 66 (IQR 25-145) in the usual care group. Overall, 81% of recurrent AF events in both groups were detected in the first 3 months after surgery. The median burden of AF in the cardiac monitoring group was 7% (IQR 1-15), with the longest AF event lasting a median of 6 (IQR 2-18) hours. Anticoagulation for AF was prescribed in 24/64 (37%) patients with POAF without recurrence and in 16/21 (76%) patients with recurrent AF. Conclusion Recurrent AF following POAF is common and detected most frequently in the first 3 months after noncardiac surgery. Systematic ambulatory rhythm monitoring is associated with a higher rate of AF detection following hospital discharge and may identify a patient subgroup who might benefit from anticoagulation therapy.Table 1Figure 1

The impact of cardiac rehabilitation on atrial fibrillation recurrence after pulmonary vein isolation: results of a retrospective study

Abstract Background Although pulmonary vein isolation (PVI) is an extensively utilized atrial fibrillation (AF) treatment, many patients still experience AF recurrences. Multimorbidity care, including physical activity, may reduce recurrences. In Belgium, patients undergoing an ablation are entitled to a cardiac rehabilitation (CR) program after PVI, but not all do participate. More evidence that such (standardized) CR programs for AF patients after ablation can positively impact rhythm control is desirable. Purpose This study retrospectively investigated the impact of CR on AF recurrence after PVI. Methods We analysed an extensive database of a large Belgian tertiary care center with patients who underwent a primary PVI between 2007 and 2020 and who had a follow-up of at least 1 year. General cardiology follow-up included a 24h Holter monitoring 3 and 12 months after PVI. Patients were retrospectively divided into an intervention and control group based on their choice to participate in a standardized CR program. The Kaplan-Meier method was used to estimate the time-to-recurrence and event-free survival curves after the primary ablation. Differences between curves were tested with the log-rank test. Mann-Whitney U test was used to analyze continuous variables and the Chi-square test for categorical variables. Results In total, 1767 patients were included, of which 589 participated to CR and 1178 served as controls (median 64 (56-70) y, 68.9% male, median BMI 27.5 (24.7-30.7) kg/m2). There was no significant difference between CR and controls in median age (64y vs. 63y respectively; p=0.203), the proportion of male (57.9% vs. 47.0% respectively; p=0.111) and the proportion of paroxysmal AF (66.4% vs. 70.6% respectively; p=0.210). The proportion with recurrent AF at the end of study follow-up (with a three-month initial blanking period) in the CR and control group was similar (32.6% vs. 35.1% respectively; p=0.288), and also survival analysis demonstrated no significant difference (p=0.827; Figure 1). We noted a cross-over of the recurrence curves after about 1500 days, but the rate of AF recurrence was not different among CR and controls within the first year post-ablation (p=0.136) nor when analysed from day 365 until the end of the study (p=0.341) (Figure 2). There was no significant difference in AF recurrences between CR and controls for either paroxysmal AF (p= 0.551) or persistent AF (p=0.181) based on the survival analysis. Conclusions This retrospective analysis could not demonstrate reduced AF recurrence in post-PVI patients following a structured CR program. The unexpected crossover of curves could point to earlier AF detection in CR patients, while there might be long-term benefit of physical activity. Multivariable analysis will have to evaluate whether impact of CR is related to certain clinical patient characteristics, which might also have contributed to inclusion bias.Figure 1Figure 2

Accuracy of the OMRON HEM-7361T blood pressure monitor in the differentiation between atrial fibrillation and sinus rhythm.

We investigated the accuracy of the OMRON HEM-7361T automated oscillometric blood pressure (BP) monitor in the differentiation between atrial fibrillation and sinus rhythm. An approximately equal number of patients with persistent atrial fibrillation and individuals with sinus rhythm were recruited from outpatients and inpatients of Ruijin Hospital, Shanghai, China. BP was measured three times consecutively with a 30-s interval with the OMRON HEM-7361T automatic electronic BP monitor for atrial fibrillation detection. A hand-held single lead electrocardiogram device was used for simultaneous electrocardiogram recordings. The device accurately identified atrial fibrillation in 100 (99.0%) of the 101 patients, with only 1 patient incorrectly classified as non-atrial fibrillation. The device correctly identified 99 (95.2%) of the 104 participants with sinus rhythm as non-atrial fibrillation, with five participants incorrectly classified as atrial fibrillation. The device had a positive predictive value of 95.2%, negative predictive value of 99.0%, and overall accuracy of 97.1%. Among the six misclassified participants, one with atrial fibrillation had a heart rate of 65 beats/min, and four of the five participants with sinus rhythm had cardiac arrhythmias (atrial or ventricular premature beat in one participants, sinus tachycardia in one participant, and both arrhythmias in one participant). The OMRON HEM-7361T BP monitor is accurate in the differentiation between atrial fibrillation and sinus rhythm. Whether the device is sufficiently accurate in the differentiation between atrial fibrillation and other cardiac arrhythmias remains under investigation.

MANAGEMENT OF REMOTE MONITORING FOR IMPLANTABLE DEVICES CONTROLS: SINGLE CENTER EXPERIENCE

Abstract Background Nowadays the benefits of remote monitoring (RM)of cardiac implantable electronic devices (CIEDs) are Known: an improved both device surveillance and patient clinical management and a decrease in office follow up. So RM is used as supplement in clinic follow up for patients with CIED.However despite decades to RM utilization there are challenges in managing patients with RM including large number of trasmissions and lack of trained staff. There are many different model of care to RM management. Methods We describe our model of care to management CIED recipients with RM considering data collected from January 2021 to November 2023. Results Our model of care consists of 4 electrophysiologists Physicians and 4 Tecnicians. RM enrollment is performed at scheduled in office visit after ten days from hospital discharge after device implantation. implantable cardiac device(ICD)recipients with RM are annualy followed in office and biennal for pacemaker (PM) recipients and they send every three months transmissions. Trained tecnicians gives home monitor, take care of patients education and collected and screen transmissions. Tecnicians inform immediately physicians in case of urgent alert (shock or antitachicardia pacing therapy (ATP), sustained ventricular tachycardia(TV), ventricular fibrillation (Fv) indicator of elective replacement (ERI), lead releted alert or noise). Physician contact patients the same day (exept Saturday and Sunday). In addiction Physicians twice a week review less urgent and scheduled transmissions and call the patients by phone for visit in office for patients with low rate of biventricular pacing, detection of atrial fibrillation. We have 1000 patients with RM: 746 ICD recipients (103 Biotronik, 150 Abbott, 200 Boston scientific, 293 medtronic), 50 implantable cardiac monitor (ICM) recipients and 204 PM recipients. We analyze 12721 transmissions.Considering ICD recipient we detect 96 episode of ATP and 31 of shock. Shock of ICD was registered in 52 patients (6,9%)We received 1982 atrial fibrillation (AF) detection, with 35 for new detectable AF.The most of transmisisone were for low bIV pacing. We contacted these patients by phone about clinical status and in some case we called them in office. Conclusions Our model of care allows to control a large number of trasmissions with fast management of urgent alert trasmissions.

Machine Learning Modeling to Predict Atrial Fibrillation Detection in Embolic Stroke of Undetermined Source Patients.

In patients with embolic stroke of undetermined source (ESUS), occult atrial fibrillation (AF) has been implicated as a key source of cardioembolism. However, only a minority acquire implantable cardiac loop recorders (ILRs) to detect occult paroxysmal AF, partly due to financial cost and procedural inconvenience. Without the initiation of appropriate anticoagulation, these patients are at risk of increased ischemic stroke recurrence. Hence, cost-effective and accurate methods of predicting AF in ESUS patients are highly sought after. We aimed to incorporate clinical and echocardiography data into machine learning (ML) algorithms for AF prediction on ILRs in ESUS. This was a single-center cohort study that included 157 consecutive patients diagnosed with ESUS from October 2014 to October 2017 who had ILR evaluation. We developed four ML models, with hyperparameters tuned, to predict AF detection on an ILR. The median age of the cohort was 67 (IQR 59-74) years old and the median monitoring duration was 1051 (IQR 478-1287) days. Of the 157 patients, 32 (20.4%) had occult AF detected on the ILR. Support vector machine predicted for AF with a 95% confidence interval area under the receiver operating characteristic curve (AUC) of 0.736-0.737, multilayer perceptron with an AUC of 0.697-0.708, XGBoost with an AUC of 0.697-0.697, and random forest with an AUC of 0.663-0.674. ML feature importance found that age, HDL-C, and admitting heart rate were important non-echocardiography variables, while peak mitral A-wave velocity and left atrial volume were important echocardiography parameters aiding this prediction. Machine learning modeling incorporating clinical and echocardiographic variables predicted AF in ESUS patients with moderate accuracy.

Cardiac arrhythmias in patients with type 2 diabetes mellitus

Introduction. The contribution of type 2 diabetes mellitus (type 2 diabetes) and cardiovascular diseases (CVD) to the increase in disability and mortality rates worldwide continues to grow steadily. Currently, the main focus is on the problem of atherosclerotic cardiovascular diseases (ACCD) in DM, while the manifestations of cardiac arrhythmias (LDC) in conditions of carbohydrate metabolism disorders have not been sufficiently studied.The purpose of the study. To analyze and compare laboratory, instrumental and objective indicators in patients with type 2 diabetes mellitus (type 2 diabetes mellitus) and without, who have a history of cardiac arrhythmias (LDC).Materials and methods. 103 patients with LDC (52 men and 51 women) were examined. 2 groups were formed: the main group – patients with LDC and type 2 diabetes (n=51) (group 1), the control group – patients with LDC without diabetes (n=52) (group 2). A comparative analysis of laboratory-instrumental and clinical-anamnestic data in the groups was performed. Statistical data processing was performed using the statistical software package "Excel" ("Microsoft"), the program "Statistica 10" ("Statsoft Inc"). The value of p <0.05 was taken as the significance level. The reliability of the differences between the groups was determined using the Mann-Whitney U-test, and the correlation analysis was carried out using the Spearman criterion.Results. The average age of the general group was 67[18;97] years, patients with type 2 diabetes 69 [53;93] years, patients without diabetes 65 [18;97] years. According to the performed regression analysis, it was found that DM in patients was associated with the detection of atrial fibrillation (OR 2.69 (95% CI 1.076;6.71), p=0.034), in particular, its constant form (p=0.025, coefficient. Kramer's V = 0.33), as well as with the presence of supraventricular extrasystole (NJES) (OR 0.235 (95%CI 0.09;0.59), p=0.002, coefficient. Kramer's V = 0.311). At the same time, there was no significant association of DM with the presence of paroxysmal and persistent forms of atrial fibrillation, atrial flutter (TP), BLNPG, AV block, CA block, ventricular extrasystole, supraventricular tachycardia (NVT) and SSSI.When evaluating the functional parameters of the myocardium according to Echo-K G data, it was found that in the main group, compared with the control group, significant (34% vs. 27%) and pronounced (18% vs. 6%) pulmonary hypertension were more common, but the difference was not statistically significant (p>0.05).When assessing the structural and geometric parameters of the myocardium, it was found that concentric myocardial hypertrophy was more common in the main group (22% vs. 15%), and in the control group – concentric myocardial remodeling (14% vs. 25%), (p>0.05).Conclusions. DM is a significant risk factor for a more severe course of LDC, in particular atrial fibrillation and supraventricular ectopias. In this regard, it is of fundamental importance to control carbohydrate metabolism and prevent risk factors for cardiovascular diseases in order to preserve the basic functions of the myocardium and improve the quality of life of patients with diabetes mellitus.

Machine Learning Approaches in Atrial Fibrillation Detection

Atrial fibrillation (AF) characterized by rapid and irregular electrical activity in the atria represents a prevalent form of cardiac arrhythmia that significantly challenges healthcare systems due to its links to heightened mortality and morbidity rates. Early detection of AF is critical for accurate and effective management and treatment. In response to this pressing need, numerous researchers have used machine learning (ML) to enhance the precision and efficiency of AF detection. By analyzing available datasets, signal lengths, preprocessing techniques, and a diverse array of ML approaches, this paper aims to cover methodologies of AF detection using electrocardiogram (ECG) data and ML.

Risk of Atrial Fibrillation in Patients with Different Cancer Types in Taiwan.

Atrial fibrillation (AF) commonly occurs in approximately 2% of cancer patients, and the incidence of AF among cancer patients is greater than in the general population. This observational study presented the incidence risk of AF among cancer patients, including specific cancer types, using a population database. The Taiwan Cancer Registry was used to identify cancer patients between 2008 and 2017. The diagnosis of AF was based on the International Classification of Diseases codes (ICD-9-CM: 427.31 or ICD-10-CM: I48.0, I48.1, I48.2, and I48.91) in Taiwan national health insurance research datasets. The incidence of developing AF in the cancer population was calculated as the number of new-onset AF cases per person-year of follow-up during the study period. The overall incidence of AF among cancer patients was 50.99 per 100,000 person-years. Patients aged older than 65 years and males had higher AF incidence rates. Lung cancer males and esophageal cancer females showed the highest AF incidence risk (185.02 and 150.30 per 100,000 person-years, respectively). Our findings identified esophageal, lung, and gallbladder cancers as the top three cancers associated with a higher incidence of AF. Careful monitoring and management of patients with these cancers are crucial for early detection and intervention of AF.

Screening and diagnosis of atrial fibrillation using wearable devices.

In recent years, the development and use of various devices for the screening of atrial fibrillation (AF) have significantly increased. Such devices include 12-lead electrocardiogram (ECG), photoplethysmography systems, and single-lead ECG and ECG patches. This review outlines several studies that have focused on the feasibility and efficacy of such devices for AF screening, and summarizes the risks and benefits involved in the initiation of anticoagulant therapy after early detection of AF. We also describe several ongoing trials on unresolved issues associated with AF screening. Overall, this review provides a comprehensive summary of the current state of AF screening and its implications for patient care.

Remote electrocardiogram monitoring in cancer patients for detecting cardiotoxicity. Case report

Cardiotoxicity is the direct adverse effect of anticancer agents on the function or structure of the heart, occurring both during treatment and many years later. The manifestations of cardiotoxicity can be very different: heart failure, coronary heart disease, cardiomyopathy, myocarditis, pericarditis, and arrhythmias, including atrial fibrillation (AF). The incidence of AF in the cancer patient population during therapy is 20–30%. Current options of remote electrocardiogram monitoring ensure the timely detection and treatment of AF in cancer patients, including the administration of anticoagulants. The article presents a clinical case of a 59-year-old patient with squamous cell carcinoma of the lower third of the esophagus after the first course of the TPF polychemotherapy regimen (docetaxel, cisplatin, 5-fluorouracil) with AF detected using a single-channel electrocardiogram monitor in a smartphone case.

AUTOMATED ATRIAL FIBRILLATION DETECTION DURING ROUTINE BLOOD PRESSURE MEASUREMENT: DIAGNOSTIC ACCURACY OF THE OSCILLOMETRIC BLOOD PRESSURE MONITOR MICROLIFE BP B3 AFIB ADVANCED

Objective: To evaluate the diagnostic accuracy of the oscillometric upper-arm cuff home blood pressure (BP) monitor Microlife BP B3 AFIB Advanced in detecting atrial fibrillation (AF) during automated BP measurement. Design and method: Participants with permanent AF aged >60 years (AF group) and others with sinus rhythm (SR) matched for age and sex (control group) were recruited. Electrocardiography (ECG) recording and blood pressure (BP) measurements were simultaneously conducted twice for each participant. Using an AF specific diagnostic algorithm of the BP monitor, the rhythmicity of heart rate was evaluated three times during each automated BP measurement. When AF is detected in at least 2 of the 3 assessments, the device reports the presence of AF detection. A single wide-range cuff of the test device was used for arm circumference 22-42 cm. Results: A total of 108 individuals were analyzed, (50%/50% with AF/SR, 59%/59% men, mean age [SD] 76±10.5/73±9.5 years for AF/SR, systolic BP 123.4±19.6/131.5±17.9 mmHg [p<0.01], diastolic BP 74.2±11.7/73.4±10.6 mmHg [p=NS]). False negative and false positive AF detection in AF and control group respectively are shown in Table. The sensitivity of the test device in detecting AF was 87%, specificity 100%, positive predictive value 100% and negative predictive value 88.5%. Conclusions: These findings indicate that the Microlife BP B3 AFIB Advanced automated BP monitor equipped with an AF detecting algorithm with triplicate assessment of the cardiac rhythm during each automated BP measurement, has an exceptional diagnostic accuracy. Thus, this novel technology can be employed as a reliable screening tool for detecting asymptomatic AF in elderly adults during routine screening or long-term monitoring of treated hypertension.

Diagnostic yield of implantable loop recorders: results from the hellenic registry

ObjectiveImplantable loop recorders (ILRs) are increasingly being used for long-term cardiac monitoring in different clinical settings. The aim of this study was to investigate the real-world performance of ILRs—including the time to diagnosis—in unselected patients with different ILR indications. Methods and ResultsIn this multicenter, observational study, 871 patients with an indication of pre-syncope/syncope (61.9%), unexplained palpitations (10.4%), and atrial fibrillation (AF) detection with a history of cryptogenic stroke (CS) (27.7%) underwent ILR implantation. The median follow-up was 28.8 ± 12.9 months. In the presyncope/syncope group, 167 (31%) received a diagnosis established by the device. Kaplan-Meier estimates indicated that 16.9% of patients had a diagnosis at 6 months, and the proportion increased to 22.5% at 1 year. Of 91 patients with palpitations, 20 (22%) received a diagnosis based on the device. The diagnosis was established in 12.2% of patients at 6 months, and the proportion increased to 13.3% at 1 year. Among 241 patients with CS, 47 (19.5%) were diagnosed with AF. The diagnostic yield of the device was 10.4% at 6 months and 12.4% at 1 year. In all cases, oral anticoagulation was initiated. Overall, ILR diagnosis altered the therapeutic strategy in 26.1% of the presyncope/syncope group, 2.2% of the palpitations group, and 3.7% of the CS group in addition to oral anticoagulation initiation. ConclusionIn this real-world patient population, ILR determines diagnosis and initiates new therapeutic management for nearly one-fourth of patients. ILR implantation is valuable in the evaluation of patients with unexplained presyncope/syncope, CS, and palpitations.

K-B2S+: A one-dimensional CNN model for AF detection with short single-lead ECG waves from wearable devices

Wearable signal analysis is an important technology for monitoring physiological signals without interfering with an individual's daily behavior. As detecting cardiovascular diseases can dramatically reduce mortality, arrhythmia recognition using ECG signals has attracted much attention. In this paper, we propose a single-channel convolutional neural network to detect Atrial Fibrillation (AF) based on ECG signals collected by wearable devices. It contains 3 primary modules. All recordings are firstly uniformly sized, normalized, and Butterworth low-pass filtered for noise removal. Then the preprocessed ECG signals are fed into convolutional layers for feature extraction. In the classification module, the preprocessed signals are fed into convolutional layers containing large kernels for feature extraction, and the fully connected layer provides probabilities. During the training process, the output of the previous pooling layer is concatenated with the vectors of the convolutional layer as a new feature map to reduce feature loss. Numerous comparison and ablation experiments are performed on the 2017 PhysioNet/CinC Challenge dataset, demonstrating the superiority of the proposed method.

Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection Using Eight Million Samples Labeled With Imprecise Arrhythmia Alarms.

Atrial fibrillation (AF) is a common cardiac arrhythmia with serious health consequences if not detected and treated early. Detecting AF using wearable devices with photoplethysmography (PPG) sensors and deep neural networks has demonstrated some success using proprietary algorithms in commercial solutions. However, to improve continuous AF detection in ambulatory settings towards a population-wide screening use case, we face several challenges, one of which is the lack of large-scale labeled training data. To address this challenge, we propose to leverage AF alarms from bedside patient monitors to label concurrent PPG signals, resulting in the largest PPG-AF dataset so far (8.5 M 30-second records from 24,100 patients) and demonstrating a practical approach to build large labeled PPG datasets. Furthermore, we recognize that the AF labels thus obtained contain errors because of false AF alarms generated from imperfect built-in algorithms from bedside monitors. Dealing with label noise with unknown distribution characteristics in this case requires advanced algorithms. We, therefore, introduce and open-source a novel loss design, the cluster membership consistency (CMC) loss, to mitigate label errors. By comparing CMC with state-of-the-art methods selected from a noisy label competition, we demonstrate its superiority in handling label noise in PPG data, resilience to poor-quality signals, and computational efficiency.

Residual-attention deep learning model for atrial fibrillation detection from Holter recordings

Residual-attention deep learning model for atrial fibrillation detection from Holter recordings

Diagnostic accuracy for detecting atrial fibrillation using a novel machine learning algorithm in a blood pressure monitor

BackgroundEarly detection of atrial fibrillation (AF) is key for preventing strokes. Blood pressure monitors (BPMs) with built-in AF screening features have the potential for early detection at home. Recently, 2 BPMs (HEM-7371T1-AZ and HEM-7372T1-AZAZ, Omron Healthcare Co., Ltd.) that share a novel AF screening feature have been developed. Their AF screening feature utilizes an algorithm that incorporates machine learning, with the potential to improve diagnostic accuracy. ObjectiveThe purpose of this study was to evaluate the performance of this AF screening feature in a multicenter, prospective clinical study at 5 sites in the United States. MethodsA total of 559 subjects were enrolled for this study: 267 in AF cohort and 292 in the non-AF cohort. AF screening was performed in all subjects by the 2 Omron BPMs and by 1 Microlife BPM (BP 3MX1-3, WatchBP Home A, Microlife Corp.), and a simultaneous 12-lead electrocardiogram (ECG) was recorded for comparison. All 12-lead ECGs were interpreted by a board-certified cardiologist who was blinded to the BPM results. Sensitivity, specificity, and accuracy for the diagnosis of AF were calculated. ResultsOmron HEM-7371T1-AZ BPM had sensitivity of 95.1% (95% confidence interval [CI] 91.8%–97.4%), specificity 98.6% (95% CI 96.6%–99.7%), and accuracy of 97.0% (95% CI 95.2%–98.2%). Equivalent results were obtained with the Omron HEM-7371T1-AZAZ BPM. This compared favorably to the Microlife BPM (sensitivity 78.5%, 95% CI 73.1%–83.3%; specificity 97.6%, 95% CI 95.1%–99.0%; accuracy 88.4%, 95% CI 85.5%–91.0%). ConclusionThese data support both home and professional use of these novel Omron BPMs for the detection of AF.

Rate of atrial fibrillation by Holter-Stroke Risk Analysis in undetermined TIA/rapidly improving stroke symptoms patients.

Holter-SRA (Stroke Risk Analysis) is an automated analysis of ECG monitoring for Atrial Fibrillation (AF) detection. The aim of this study was to evaluate the rate of AF in undetermined TIA/Rapidly improving stroke symptoms (RISS) patients. Prospective study of undetermined TIA/RISS patients who presented to the emergency department. Early vascular studies (angio CT, transthoracic echocardiography and ECG) were performed in emergency department. The Holter-SRA device was placed for 2 h and the patients were classified into: confirmed AF, high risk of AF or low risk of AF. Prolonged ambulatory monitoring (7 days) was carried out every month for patients with a high-risk pattern. The results were evaluated until definitive detection of AF or low-risk pattern. The endpoints were rate of AF and vascular recurrence at 90 days. Over a period of 24 months, 83 undetermined TIA/RISS patients were enrolled. The mean age was 70 ± 10 years and 61% were men. The median ABCD2 score was 4 points (1-7). After 2 h of monitoring in the emergency department, AF was detected in one patient (1.2%), 51 patients with a low-risk pattern and 31 patients (37.3%) showed a high-risk pattern of AF. During the ambulatory monitoring, of the 31 patients high risk pattern patients, AF was diagnosed to 17 cases and of the 51 patients with a low-risk pattern, one case experienced a recurrent vascular due to undetected AF (1.9% false negative). Three patients (3.6%) suffered a vascular recurrence within the first 90 days, before AF diagnosis. In our study, AF was detected in 22.9% of the 83 patients with indeterminate TIA/RISS. Holter-SRA has allowed us to increase the detection of AF, especially those patients with a high-risk pattern in the first 3 months.

Automated interpretations of single-lead electrocardiograms predict incident atrial fibrillation: The VITAL-AF trial

BackgroundSingle-lead electrocardiograms (1L ECGs) are increasingly used for atrial fibrillation (AF) detection. Automated 1L ECG interpretation may have prognostic value for future AF in cases in which screening does not result in a short-term AF diagnosis. ObjectiveWe sought to investigate the association between automated 1L ECG interpretation and incident AF. MethodsVITAL-AF was a randomized controlled trial investigating the effectiveness of screening for AF by 1L ECGs. For this study, participants were divided into 4 groups based on automated classification of 1L ECGs. Patients with prevalent AF were excluded. Associations between groups and incident AF were assessed by Cox proportional hazards models adjusted for risk factors. The start of follow-up was defined as 60 days after the latest 1L ECG (as some individuals had numerous screening 1L ECGs). ResultsThe study sample included never screened (n = 16,306), normal (n = 10,914), other (n = 2675), and possible AF (n = 561). Possible AF had the highest AF incidence (5.91 per 100 person-years; 95% confidence interval [CI], 4.24–8.23). Possible AF was associated with greater hazard of incident AF compared with normal (adjusted hazard ratio, 2.48; 95% CI, 1.66–3.71). Other was associated with greater hazard of incident AF compared with normal (1.41; 95% CI, 1.04–1.90). ConclusionIn patients undergoing AF screening with 1L ECGs without prevalent AF or AF within 60 days of screening, presumptive positive and indeterminate 1L ECG interpretations were associated with future AF. Abnormal 1L ECG recordings may identify individuals at higher risk for future AF.