Detection and management of atrial fibrillation using remote monitoring

Hickey, Kathleen T.; Riga, Teresa C.; Mitha, Shazia; Reading, Meghan

doi:10.1097/01.npr.0000530214.17031.45

Cited by 10 publications

(10 citation statements)

References 42 publications

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“…These include implantable cardiac monitors, medical-grade wearables, direct-to-consumer devices, and free mobile applications (apps). 3 This study focused on evaluating free mobile apps because health care access and cost are not barriers to their adoption and use. Therefore, they are accessible to a far greater proportion of the world population, including underserved adults, 4 creating the potential to transform AF screening in at-risk populations.…”

Section: Introductionmentioning

confidence: 99%

Review of mobile applications for the detection and management of atrial fibrillation

et al. 2020

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BACKGROUND Free mobile applications (apps) that use photoplethysmography (PPG) waveforms may extend atrial fibrillation (AF) detection to underserved populations, but they have not been rigorously evaluated.OBJECTIVE The purpose of this study was to systematically review and evaluate the quality, functionality, and adherence to selfmanagement behaviors of existing mobile apps for AF.METHODS We systematically searched 3 app stores for apps that were free, available in English, and intended for use by patients to detect and manage AF. A minimum of 2 reviewers evaluated (1) app quality, using the Mobile Application Rating Scale (MARS); (2) functionality using published criteria; and (3) features that support 4 self-management behaviors (including PPG waveform monitoring) identified using evidence-based guidelines. Interrater reliability between the reviewers was calculated. RESULTSOf 12 included apps, 5 (42%) scored above average for quality (MARS score 3.0). App quality was highest for their ease of use, navigation, layout, and visual appeal (eg, functionality and aesthetics) and lowest for their behavioral change support and subjective impressions of quality. The most common app functionalities were capturing and graphically displaying user-entered data (n 5 9 [75%]). Nearly all apps (n 5 11 [92%]) supported PPG waveform monitoring, but only 2 (17%) supported all 4 selfmanagement behaviors. Interrater reliability was high (0.75-0.83).CONCLUSION The reviewed apps had wide variability in quality, functionality, and adherence to self-management behaviors. Given the accessibility of these apps to underserved populations and the tremendous potential they hold for improving AF detection and management, high priority should be given to improving app quality and functionality.

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Section: Introductionmentioning

confidence: 99%

Review of mobile applications for the detection and management of atrial fibrillation

et al. 2020

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“…Many AF patients fail to recognize and distinguish symptoms associated with AF, which may hinder timely treatment and put the patient at risk for complications . Additionally, AF can be missed through conventional monitoring approaches, such as 12‐lead ECGs (electrocardiograms) and Holter studies, which only capture a finite period of time …”

Section: Introductionmentioning

confidence: 99%

“…18 Additionally, AF can be missed through conventional monitoring approaches, such as 12-lead ECGs (electrocardiograms) and Holter studies, which only capture a finite period of time. 19,20 Currently available mobile health tools that aim to promote earlier detection and treatment of AF and atrial flutter (AFL), adherence to cardiovascular regimens, and improvement in selfmanagement behaviors have not been systematically evaluated. The aim of this randomized, prospective study, iPhone Helping Evaluate Atrial fibrillation Rhythm through Technology (iHEART), was to evaluate the impact of frequent cardiac rhythm monitoring by selfrecordings on outcomes in post-RFA and -DCCV populations with at least one known risk factor for AF.…”

Section: Introductionmentioning

confidence: 99%

Recurrent atrial fibrillation/flutter detection after ablation or cardioversion using the AliveCor KardiaMobile device: iHEART results

Goldenthal

Sciacca

Riga

et al. 2019

Cardiovasc electrophysiol

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Objective: This study evaluated the impact of daily ECG self-recordings on time to documented recurrent atrial fibrillation (AF) or atrial flutter (AFL) and time to treatment of recurrent arrhythmia in patients undergoing catheter radiofrequency ablation (RFA) or direct current cardioversion (DCCV) for AF/AFL. Background: AF recurrence rates after RFA and DCCV are 20–45% and 60–80% respectively. Randomized trials comparing mobile ECG devices to standard of care have not been performed in an AF/AFL population after treatment. Methods: Of 262 patients consented, 238 were randomized to either standard of care (123) or to receive the iHEART intervention (115). Patients in the intervention group were provided with and trained to use an AliveCor® KardiaMobile ECG monitor, and were instructed to take and transmit daily ECG recordings. Data were collected from transmitted ECG recordings and patients’ electronic health records. Results: In a multivariate Cox model, the likelihood of recurrence detection was greater in the intervention group (hazard ratio = 1.56, 95% CI: 1.06–2.30, p = .024). Hazard ratios did not differ significantly for RFA and DCCV procedures. Recurrence during the first month after ablation strongly predicted later recurrence (hazard ratio = 4.53, 95% CI: 2.05–10.00, p = .0006). Time to from detection to treatment was shorter for the control group. (hazard ratio = 0.33, 95% CI: 0.57–2.92, p <.0001) Conclusions: The use of mobile ECG self-recording devices allows for earlier detection of AF/AFL recurrence and may empower patients to engage in shared health decision making.

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“…However, they turn out to not be the perfect solution for arrhythmia diagnosis for many patients [ 5 , 6 , 7 ]. Most of the ECG monitoring devices are single- to three-lead and may not reliably depict important components for the correct diagnosis of the record, particularly the low-amplitude ‘P’-waves of normal atrial depolarisation in sinus rhythm from the ‘saw-like’ waves of atrial excitation in atrial flutter or the fine deviations of the isoelectric line (’f’-waves) in AF [ 5 , 6 , 7 , 8 ]. Differentiation between atrial fibrillation and flutter may become even more difficult if the unidentifiable waves of atrial excitation are combined with alternating atrio–ventricular conductance.…”

Section: Introductionmentioning

confidence: 99%

“…Differentiation between atrial fibrillation and flutter may become even more difficult if the unidentifiable waves of atrial excitation are combined with alternating atrio–ventricular conductance. On the other hand, no detected irregular rhythm means AF [ 5 , 6 , 7 , 8 , 9 ]. In addition, Holter ECG monitoring is performed during patients’ daily activities, and the signal is highly affected by noise and artifacts: noise could be a matter of major concern because its presence may result in false diagnoses [ 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Dense Neural Networks for Detection of Atrial Fibrillation and Ranking of Augmented ECG Feature Set

Krasteva

Christov

Naydenov

et al. 2021

Sensors

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Considering the significant burden to patients and healthcare systems globally related to atrial fibrillation (AF) complications, the early AF diagnosis is of crucial importance. In the view of prominent perspectives for fast and accurate point-of-care arrhythmia detection, our study optimizes an artificial neural network (NN) classifier and ranks the importance of enhanced 137 diagnostic ECG features computed from time and frequency ECG signal representations of short single-lead strips available in 2017 Physionet/CinC Challenge database. Based on hyperparameters’ grid search of densely connected NN layers, we derive the optimal topology with three layers and 128, 32, 4 neurons per layer (DenseNet-3@128-32-4), which presents maximal F1-scores for classification of Normal rhythms (0.883, 5076 strips), AF (0.825, 758 strips), Other rhythms (0.705, 2415 strips), Noise (0.618, 279 strips) and total F1 relevant to the CinC Challenge of 0.804, derived by five-fold cross-validation. DenseNet-3@128-32-4 performs equally well with 137 to 32 features and presents tolerable reduction by about 0.03 to 0.06 points for limited input sets, including 8 and 16 features, respectively. The feature reduction is linked to effective application of a comprehensive method for computation of the feature map importance based on the weights of the activated neurons through the total path from input to specific output in DenseNet. The detailed analysis of 20 top-ranked ECG features with greatest importance to the detection of each rhythm and overall of all rhythms reveals DenseNet decision-making process, noticeably corresponding to the cardiologists’ diagnostic point of view.

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Detection and management of atrial fibrillation using remote monitoring

Cited by 10 publications

References 42 publications

Review of mobile applications for the detection and management of atrial fibrillation

Review of mobile applications for the detection and management of atrial fibrillation

Recurrent atrial fibrillation/flutter detection after ablation or cardioversion using the AliveCor KardiaMobile device: iHEART results

Application of Dense Neural Networks for Detection of Atrial Fibrillation and Ranking of Augmented ECG Feature Set

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