Accuracy of continuous photoplethysmography-based 1 min mean heart rate assessment during atrial fibrillation

Intelligent Detection Method of Atrial Fibrillation by CEPNCC-BiLSTM Based on Long-Term Photoplethysmography Data

Atrial fibrillation (AF) is the most prevalent arrhythmia characterized by intermittent and asymptomatic episodes. However, traditional detection methods often fail to capture the sporadic and intricate nature of AF, resulting in an increased risk of false-positive diagnoses. To address these challenges, this study proposes an intelligent AF detection and diagnosis method that integrates Complementary Ensemble Empirical Mode Decomposition, Power-Normalized Cepstral Coefficients, Bi-directional Long Short-term Memory (CEPNCC-BiLSTM), and photoelectric volumetric pulse wave technology to enhance accuracy in detecting AF. Compared to other approaches, the proposed method demonstrates faster preprocessing efficiency and higher sensitivity in detecting AF while effectively filtering out false alarms from photoplethysmography (PPG) recordings of non-AF patients. Considering the limitations of conventional AF detection evaluation systems that lack a comprehensive assessment of efficiency and accuracy, this study proposes the ET-score evaluation system based on F-measurement, which incorporates both computational speed and accuracy to provide a holistic assessment of overall performance. Evaluated with the ET-score, the CEPNCC-BiLSTM method outperforms EEMD-based improved Power-Normalized Cepstral Coefficients and Bi-directional Long Short-term Memory (EPNCC-BiLSTM), Support Vector Machine (SVM), EPNCC-SVM, and CEPNCC-SVM methods. Notably, this approach achieves an outstanding accuracy rate of up to 99.2% while processing PPG recordings within 5 s, highlighting its potential for long-term AF monitoring.

Remote rhythm monitoring using a photoplethysmography smartphone application after cardioversion for atrial fibrillation

Aims

Direct current cardioversion (DCCV) is a commonly utilized rhythm control technique for atrial fibrillation. Follow-up typically comprises a hospital visit for 12-lead electrocardiogram (ECG) two weeks post-DCCV. We report the feasibility, costs, and environmental benefit of remote photoplethysmography (PPG) monitoring as an alternative.

Methods and results

We retrospectively analysed DCCV cases at our centre from May 2020 to October 2022. Patients were stratified into those with remote PPG follow-up and those with traditional 12-lead ECG follow-up. Monitoring type was decided by the specialist nurse performing the DCCV at the time of the procedure after discussing with the patient and offering them both options if appropriate. Outcomes included the proportion of patients who underwent PPG monitoring, patient compliance and experience, and cost, travel, and environmental impact. Four hundred sixteen patients underwent 461 acutely successful DCCV procedures. Two hundred forty-six underwent PPG follow-up whilst 214 underwent ECG follow-up. Patient compliance was high (PPG 89.4% vs. ECG 89.8%; P > 0.999) and the majority of PPG users (90%) found the app easy to use. Sinus rhythm was maintained in 71.1% (PPG) and 64.7% (ECG) of patients (P = 0.161). Twenty-nine (11.8%) PPG patients subsequently required an ECG either due to non-compliance, technical failure, or inconclusive PPG readings. Despite this, mean healthcare costs (£47.91 vs. £135 per patient; P < 0.001) and median cost to the patient (£0 vs. £5.97; P < 0.001) were lower with PPG. Median travel time per patient (0 vs. 44 min; P < 0.001) and CO2 emissions (0 vs. 3.59 kg; P < 0.001) were also lower with PPG. No safety issues were identified.

Conclusion

Remote PPG monitoring is a viable method of assessing for arrhythmia recurrence post-DCCV. This approach may save patients significant travel time, reduce environmental CO2 emission, and be cost saving in a publicly-funded healthcare system.

[Characterization of atrial fibrillation burden using wearables]

The characterization of atrial fibrillation (AF) according to current guidelines categorically refers to the differentiation between paroxysmal, persistent, and permanent AF. A more precise characterization of AF, including the evaluation of AF burden, is playing an increasingly significant role in both scientific research and clinical practice. Digital devices, especially those with the capability of passive (semi-)continuous recording, can contribute to a more accurate quantification of AF burden. Particularly in patients with an already established diagnosis of AF, the evaluation of AF burden can be used to monitor the success of antiarrhythmic therapy including antiarrhythmic drugs or pulmonary vein isolation. However, important questions remain unanswered: In addition to a uniform, evidence-based definition of AF burden, clinically relevant cut-offs for AF burden and resulting therapeutic consequences (e.g., subclinical AF) need to be elaborated. Furthermore, the establishment and evaluation of care structures for assessing and integrating AF burden in clinical care, especially by incorporating data from wearable medical devices, should take place.

Real-world validation of smartphone-based photoplethysmography for rate and rhythm monitoring in atrial fibrillation

AIMS

Photoplethysmography- (PPG) based smartphone applications facilitate heart rate and rhythm monitoring in patients with paroxysmal and persistent atrial fibrillation (AF). Despite an endorsement from the European Heart Rhythm Association, validation studies in this setting are lacking. Therefore, we evaluated the accuracy of PPG-derived heart rate and rhythm classification in subjects with an established diagnosis of AF in unsupervised real-world conditions.

METHODS AND RESULTS

Fifty consecutive patients were enrolled, 4 weeks before undergoing AF ablation. Patients used a handheld single-lead electrocardiography (ECG) device and a fingertip PPG smartphone application to record 3907 heart rhythm measurements twice daily during 8 weeks. The ECG was performed immediately before and after each PPG recording and was given a diagnosis by the majority of three blinded cardiologists. A consistent ECG diagnosis was exhibited along with PPG data of sufficient quality in 3407 measurements. A single measurement exhibited good quality more often with ECG (93.2%) compared to PPG (89.5%; P < 0.001). However, PPG signal quality improved to 96.6% with repeated measurements. Photoplethysmography-based detection of AF demonstrated excellent sensitivity [98.3%; confidence interval (CI): 96.7-99.9%], specificity (99.9%; CI: 99.8-100.0%), positive predictive value (99.6%; CI: 99.1-100.0%), and negative predictive value (99.6%; CI: 99.0-100.0%). Photoplethysmography underestimated the heart rate in AF with 6.6 b.p.m. (95% CI: 5.8 b.p.m. to 7.4 b.p.m.). Bland-Altman analysis revealed increased underestimation in high heart rates. The root mean square error was 11.8 b.p.m.

CONCLUSION

Smartphone applications using PPG can be used to monitor patients with AF in unsupervised real-world conditions. The accuracy of AF detection algorithms in this setting is excellent, but PPG-derived heart rate may tend to underestimate higher heart rates.

Longer and better lives for patients with atrial fibrillation: the 9th AFNET/EHRA consensus conference

AIMS

Recent trial data demonstrate beneficial effects of active rhythm management in patients with atrial fibrillation (AF) and support the concept that a low arrhythmia burden is associated with a low risk of AF-related complications. The aim of this document is to summarize the key outcomes of the 9th AFNET/EHRA Consensus Conference of the Atrial Fibrillation NETwork (AFNET) and the European Heart Rhythm Association (EHRA).

METHODS AND RESULTS

Eighty-three international experts met in Münster for 2 days in September 2023. Key findings are as follows: (i) Active rhythm management should be part of the default initial treatment for all suitable patients with AF. (ii) Patients with device-detected AF have a low burden of AF and a low risk of stroke. Anticoagulation prevents some strokes and also increases major but non-lethal bleeding. (iii) More research is needed to improve stroke risk prediction in patients with AF, especially in those with a low AF burden. Biomolecules, genetics, and imaging can support this. (iv) The presence of AF should trigger systematic workup and comprehensive treatment of concomitant cardiovascular conditions. (v) Machine learning algorithms have been used to improve detection or likely development of AF. Cooperation between clinicians and data scientists is needed to leverage the potential of data science applications for patients with AF.

CONCLUSIONS

Patients with AF and a low arrhythmia burden have a lower risk of stroke and other cardiovascular events than those with a high arrhythmia burden. Combining active rhythm control, anticoagulation, rate control, and therapy of concomitant cardiovascular conditions can improve the lives of patients with AF.

Atrial fibrillation: epidemiology, screening and digital health

Atrial fibrillation (AF) is highly prevalent with a lifetime risk of about 1 in 3-5 individuals after the age of 45 years. Between 2010 and 2019, the global prevalence of AF has risen markedly from 33.5 million to 59 million individuals living with AF. Early detection of AF and implementation of appropriate treatment could reduce the frequency of complications associated with AF. International AF management guidelines recommend opportunistic and systematic screening for AF, but additional data are needed. Digital approaches and pathways have been proposed for early detection and for the transition to early AF management. Mobile health (mHealth) devices provide an opportunity for digital screening and should be part of novel models of care delivery based on integrated AF care pathways. For a broad implementation of mHealth-based, integrated care for patients with chronic diseases as AF, further high quality evidence is necessary. In this review, we present an overview of the present data on epidemiology, screening techniques, and the contribution of digital health solutions to the integrated management of AF. We also provide a systemic review on current data of digital and integrated AF management.