Characterization of Atrial Fibrillation Episode Patterns: A Comparative Study.

OBJECTIVE: The episode patterns of paroxysmal atrial fibrillation (AF) may carry important information on disease progression and complication risk. However, existing studies offer very little insight into to what extent a quantitative characterization of AF patterns can be trusted given the errors in AF detection and various types of shutdown, i.e., poor signal quality and non-wear. This study explores the performance of AF pattern characterizing parameters in the presence of such errors. METHODS: To evaluate the performance of the parameters AF aggregation and AF density, both previously proposed to characterize AF patterns, the two measures mean normalized difference and the intraclass correlation coefficient are used to describe agreement and reliability, respectively. The parameters are studied on two PhysioNet databases with annotated AF episodes, also accounting for shutdowns due to poor signal quality. RESULTS: The agreement is similar for both parameters when computed for detector-based and annotated patterns, which is 0.80 for AF aggregation and 0.85 for AF density. On the other hand, the reliability differs substantially, with 0.96 for AF aggregation but only 0.29 for AF density. This finding suggests that AF aggregation is considerably less sensitive to detection errors. The results from comparing three strategies to handle shutdowns vary considerably, with the strategy that disregards the shutdown from the annotated pattern showing the best agreement and reliability. CONCLUSIONS: Due to its better robustness to detection errors, AF aggregation should be preferred. To further improve performance, future research should put more emphasis on AF pattern characterization.

Six-lead electrocardiography compared to single-lead electrocardiography and photoplethysmography of a wrist-worn device for atrial fibrillation detection controlled by premature atrial or ventricular contractions: six is smarter than one.

Background: Smartwatches are commonly capable to record a lead-I-like electrocardiogram (ECG) and perform a photoplethysmography (PPG)-based atrial fibrillation (AF) detection. Wearable technologies repeatedly face the challenge of frequent premature beats, particularly in target populations for screening of AF. Objective: To investigate the potential diagnostic benefit of six-lead ECG compared to single-lead ECG and PPG-based algorithm for AF detection of the wrist-worn device. Methods and results: From the database of DoubleCheck-AF 249 adults were enrolled in AF group (n = 121) or control group of SR with frequent premature ventricular (PVCs) or atrial (PACs) contractions (n = 128). Cardiac rhythm was monitored using a wrist-worn device capable of recording continuous PPG and simultaneous intermittent six-lead standard-limb-like ECG. To display a single-lead ECG, the six-lead ECGs were trimmed to lead-I-like ECGs. Two diagnosis-blinded cardiologists evaluated reference, six-lead and single-lead ECGs as "AF", "SR", or "Cannot be concluded". AF detection based on six-lead ECG, single-lead ECG, and PPG yielded a sensitivity of 99.2%, 95.7%, and 94.2%, respectively. The higher number of premature beats per minute was associated with false positive outcomes of single-lead ECG (18.80 vs. 5.40 beats/min, P < 0.01), six-lead ECG (64.3 vs. 5.8 beats/min, P = 0.018), and PPG-based detector (13.20 vs. 5.60 beats/min, P = 0.05). Single-lead ECG required 3.4 times fewer extrasystoles than six-lead ECG to result in a false positive outcome. In a control subgroup of PACs, the specificity of six-lead ECG, single-lead ECG, and PPG dropped to 95%, 83.8%, and 90%, respectively. The diagnostic value of single-lead ECG (AUC 0.898) was inferior to six-lead ECG (AUC 0.971) and PPG-based detector (AUC 0.921). In a control subgroup of PVCs, the specificity of six-lead ECG, single-lead ECG, and PPG was 100%, 96.4%, and 96.6%, respectively. The diagnostic value of single-lead ECG (AUC 0.961) was inferior to six-lead ECG (AUC 0.996) and non-inferior to PPG-based detector (AUC 0.954). Conclusions: A six-lead wearable-recorded ECG demonstrated the superior diagnostic value of AF detection compared to a single-lead ECG and PPG-based AF detection. The risk of type I error due to the widespread use of smartwatch-enabled single-lead ECGs in populations with frequent premature beats is significant.

Wearable-Based Assessment of Frailty Trajectories During Cardiac Rehabilitation After Open-Heart Surgery.

Frailty in patients after open-heart surgery influences the type and intensity of a cardiac rehabilitation program. The response to tailored exercise training can be different, requiring convenient tools to assess the effectiveness of a training program routinely. The study aims to investigate whether kinematic measures extracted from the acceleration signals can provide information about frailty trajectories during rehabilitation. One hundred patients after open-heart surgery, assigned to the equal-sized intervention and control groups, participated in exercise training during inpatient rehabilitation. After rehabilitation, the intervention group continued exercise training at home, whereas the control group was asked to maintain the usual physical activity regimen. Stride time, cadence, movement vigor, gait asymmetry, Lissajous index, and postural sway were estimated during the clinical walk and stair-climbing tests before and after inpatient rehabilitation as well as after home-based exercise training. Frailty was assessed using the Edmonton frail scale. Most kinematic measures estimated during walking improved after rehabilitation along with the improvement in frailty status, i.e., stride time, cadence, postural sway, and movement vigor improved in 71%, 77%, 81%, and 83% of patients, respectively. Meanwhile, kinematic measures during stair-climbing improved to a lesser extent compared to walking. Home-based exercise training did not result in a notable change in kinematic measures which agrees well with only a negligible deterioration in frailty status. The study demonstrates the feasibility to follow frailty trajectories during inpatient rehabilitation after open-heart surgery based on kinematic measures extracted using a single wearable sensor.

High Specificity Wearable Device With Photoplethysmography and Six-Lead Electrocardiography for Atrial Fibrillation Detection Challenged by Frequent Premature Contractions: DoubleCheck-AF.

Background: Consumer smartwatches have gained attention as mobile health (mHealth) tools able to detect atrial fibrillation (AF) using photoplethysmography (PPG) or a short strip of electrocardiogram (ECG). PPG has limited accuracy due to the movement artifacts, whereas ECG cannot be used continuously, is usually displayed as a single-lead signal and is limited in asymptomatic cases. Objective: DoubleCheck-AF is a validation study of a wrist-worn device dedicated to providing both continuous PPG-based rhythm monitoring and instant 6-lead ECG with no wires. We evaluated its ability to differentiate between AF and sinus rhythm (SR) with particular emphasis on the challenge of frequent premature beats. Methods and Results: We performed a prospective, non-randomized study of 344 participants including 121 patients in AF. To challenge the specificity of the device two control groups were selected: 95 patients in stable SR and 128 patients in SR with frequent premature ventricular or atrial contractions (PVCs/PACs). All ECG tracings were labeled by two independent diagnosis-blinded cardiologists as "AF," "SR" or "Cannot be concluded." In case of disagreement, a third cardiologist was consulted. A simultaneously recorded ECG of Holter monitor served as a reference. It revealed a high burden of ectopy in the corresponding control group: 6.2 PVCs/PACs per minute, bigeminy/trigeminy episodes in 24.2% (31/128) and runs of ≥3 beats in 9.4% (12/128) of patients. AF detection with PPG-based algorithm, ECG of the wearable and combination of both yielded sensitivity and specificity of 94.2 and 96.9%; 99.2 and 99.1%; 94.2 and 99.6%, respectively. All seven false-positive PPG-based cases were from the frequent PVCs/PACs group compared to none from the stable SR group (P < 0.001). In the majority of these cases (6/7) cardiologists were able to correct the diagnosis to SR with the help of the ECG of the device (P = 0.012). Conclusions: This is the first wearable combining PPG-based AF detection algorithm for screening of AF together with an instant 6-lead ECG with no wires for manual rhythm confirmation. The system maintained high specificity despite a remarkable amount of frequent single or multiple premature contractions.

Considerations on Performance Evaluation of Atrial Fibrillation Detectors.

OBJECTIVE: A large number of atrial fibrillation (AF) detectors have been published in recent years, signifying that the comparison of detector performance plays a central role, though not always consistent. The aim of this study is to shed needed light on aspects crucial to the evaluation of detection performance. METHODS: Three types of AF detector, using either information on rhythm, rhythm and morphology, or segments of ECG samples, are implemented and studied on both real and simulated ECG signals. The properties of different performance measures are investigated, for example, in relation to dataset imbalance. RESULTS: The results show that performance can differ considerably depending on the way detector output is compared to database annotations, i.e., beat-to-beat, segment-to-segment, or episode-to-episode comparison. Moreover, depending on the type of detector, the results substantiate that physiological and technical factors, e.g., changes in ECG morphology, rate of atrial premature beats, and noise level, can have a considerable influence on performance. CONCLUSION: The present study demonstrates overall strengths and weaknesses of different types of detector, highlights challenges in AF detection, and proposes five recommendations on how to handle data and characterize performance.

Modeling and Estimation of Temporal Episode Patterns in Paroxysmal Atrial Fibrillation.

OBJECTIVE: The present study proposes a model-based, statistical approach to characterizing episode patterns in paroxysmal atrial fibrillation (AF). Thanks to the rapid advancement of noninvasive monitoring technology, the proposed approach should become increasingly relevant in clinical practice. METHODS: History-dependent point process modeling is employed to characterize AF episode patterns, using a novel alternating, bivariate Hawkes self-exciting model. In addition, a modified version of a recently proposed statistical model to simulate AF progression throughout a lifetime is considered, involving non-Markovian rhythm switching and survival functions. For each model, the maximum likelihood estimator is derived and used to find the model parameters from observed data. RESULTS: Using three databases with a total of 59 long-term ECG recordings, the goodness-of-fit analysis demonstrates that the proposed alternating, bivariate Hawkes model fits SR-to-AF transitions in 40 recordings and AF-to-SR transitions in 51; the corresponding numbers for the AF model with non-Markovian rhythm switching are 40 and 11, respectively. Moreover, the results indicate that the model parameters related to AF episode clustering, i.e., aggregation of temporal AF episodes, provide information complementary to the well-known clinical parameter AF burden. CONCLUSION: Point process modeling provides a detailed characterization of the occurrence pattern of AF episodes that may improve the understanding of arrhythmia progression.