Aims Atrial fibrillation (AF) is the most common tachyarrhythmia and a significant cause of cardioembolic strokes. Atrial fibrillation is often intermittent and asymptomatic making detection a major clinical challenge. We evaluated a photoplethysmography (PPG) wrist band in individual pulse detection in patients with AF and tested the reliability of two commonly used algorithms for AF detection. Methods and results A 5-min PPG was recorded from patients with AF or sinus rhythm (SR) with a wrist band and analysed with two AF detection algorithms; AFEvidence and COSEn. Simultaneously registered electrocardiogram served as the golden standard for rhythm analysis and was interpreted by two cardiologists. The study population consisted of 213 (106 AF, 107 SR) patients. The wrist band PPG achieved individual pulse detection with a sensitivity of 91.7 ± 11.2% and a positive predictive value (PPV) of 97.5 ± 4.6% for AF, with a sensitivity of 99.4 ± 1.5% [7.7% (95% confidence interval, 95% CI 5.5% to 9.9%); P < 0.001] and PPV of 98.1 ± 4.1% [0.6% (95% CI −0.6% to 1.7%); P = 0.350] for SR. The pulse detection sensitivity was lower 86.7 ± 13.9% with recent-onset AF (AF duration <48 h, n = 43, 40.6%) as compared to late AF (≥48 h, n = 63, 59.4%) with 95.1 ± 7.2% [−8.3% (95% CI −12.9% to −3.7%); P = 0.001]. For the detection of AF from the wrist band PPG, the sensitivities were 96.2%/95.3% and specificity 98.1% with two algorithms. Conclusion The wrist band PPG enabled accurate algorithm-based detection of AF with two AF detection algorithms and high individual pulse detection. Algorithms allowed accurate detection of AF from the PPG. A PPG wrist band provides an easy solution for AF screening.

Atrial fibrillation (AF) is a significant cause of cardioembolic strokes. AF is often symptomless and intermittent, making its detection challenging. The aim of this study was to assess the possibility to use a chest strap (Suunto Movesense) to detect AF both by cardiologists and automated algorithms.A single channel electrocardiogram (ECG) from a chest strap of 220 patients (107 AF and 111 sinus rhythm SR with 2 inconclusive rhythms) were analyzed by two cardiologists (Doc1, Doc2) and two different algorithms (COSEn, AFEvidence). A 3-lead Holter served as the gold standard ECG for rhythm analysis. Both cardiologists evaluated the quality of the chest strap ECG to be superior to the quality of the Holter ECG; p<0.05/p<0.001 (Doc1 / Doc 2). Accurate automated algorithmbased AF detection was achieved with sensitivity of 95,3%/96.3% and specificity of 95,5/98.2% with two AF detection algorithms from chest strap and 93.5%/97.2 % and 98.2%/95.5% from Holter, respectively. P-waves were detectable in 93.7% (Doc1) and 94.6% (Doc2) of the cases from the chest strap ECG with sinus rhythm and 98.2% (Doc1) and 95.5% (Doc2) from the Holter (p=n.s). In conclusion, the ECGs from both methods enabled AF detection by a cardiologist and by automated algorithms. Both methods studied enabled P-wave detection in sinus rhythm.

Background Atrial fibrillation (AF) is the most common tachyarrhythmia and associated with a risk of stroke. The detection and diagnosis of AF represent a major clinical challenge due to AF’s asymptomatic and intermittent nature. Novel consumer-grade mobile health (mHealth) products with automatic arrhythmia detection could be an option for long-term electrocardiogram (ECG)-based rhythm monitoring and AF detection. Objective We evaluated the feasibility and accuracy of a wearable automated mHealth arrhythmia monitoring system, including a consumer-grade, single-lead heart rate belt ECG device (heart belt), a mobile phone application, and a cloud service with an artificial intelligence (AI) arrhythmia detection algorithm for AF detection. The specific aim of this proof-of-concept study was to test the feasibility of the entire sequence of operations from ECG recording to AI arrhythmia analysis and ultimately to final AF detection. Methods Patients (n=159) with an AF (n=73) or sinus rhythm (n=86) were recruited from the emergency department. A single-lead heart belt ECG was recorded for 24 hours. Simultaneously registered 3-lead ECGs (Holter) served as the gold standard for the final rhythm diagnostics and as a reference device in a user experience survey with patients over 65 years of age (high-risk group). Results The heart belt provided a high-quality ECG recording for visual interpretation resulting in 100% accuracy, sensitivity, and specificity of AF detection. The accuracy of AF detection with the automatic AI arrhythmia detection from the heart belt ECG recording was also high (97.5%), and the sensitivity and specificity were 100% and 95.4%, respectively. The correlation between the automatic estimated AF burden and the true AF burden from Holter recording was >0.99 with a mean burden error of 0.05 (SD 0.26) hours. The heart belt demonstrated good user experience and did not significantly interfere with the patient’s daily activities. The patients preferred the heart belt over Holter ECG for rhythm monitoring (85/110, 77% heart belt vs 77/109, 71% Holter, P=.049). Conclusions A consumer-grade, single-lead ECG heart belt provided good-quality ECG for rhythm diagnosis. The mHealth arrhythmia monitoring system, consisting of heart-belt ECG, a mobile phone application, and an automated AF detection achieved AF detection with high accuracy, sensitivity, and specificity. In addition, the mHealth arrhythmia monitoring system showed good user experience. Trial Registration ClinicalTrials.gov NCT03507335; https://clinicaltrials.gov/ct2/show/NCT03507335

Atrial fibrillation is often asymptomatic and intermittent making its detection challenging. A photoplethysmography (PPG) provides a promising option for atrial fibrillation detection. However, the shapes of pulse waves vary in atrial fibrillation decreasing pulse and atrial fibrillation detection accuracy. This study evaluated ten robust photoplethysmography features for detection of atrial fibrillation. The study was a national multi-center clinical study in Finland and the data were combined from two broader research projects (NCT03721601, URL: https://clinicaltrials.gov/ct2/show/NCT03721601 and NCT03753139, URL: https://clinicaltrials.gov/ct2/show/NCT03753139). A photoplethysmography signal was recorded with a wrist band. Five pulse interval variability, four amplitude features and a novel autocorrelation-based morphology feature were calculated and evaluated independently as predictors of atrial fibrillation. A multivariate predictor model including only the most significant features was established. The models were 10-fold cross-validated. 359 patients were included in the study (atrial fibrillation n = 169, sinus rhythm n = 190). The autocorrelation univariate predictor model detected atrial fibrillation with the highest area under receiver operating characteristic curve (AUC) value of 0.982 (sensitivity 95.1%, specificity 93.7%). Autocorrelation was also the most significant individual feature (p &lt; 0.00001) in the multivariate predictor model, detecting atrial fibrillation with AUC of 0.993 (sensitivity 96.4%, specificity 96.3%). Our results demonstrated that the autocorrelation independently detects atrial fibrillation reliably without the need of pulse detection. Combining pulse wave morphology-based features such as autocorrelation with information from pulse-interval variability it is possible to detect atrial fibrillation with high accuracy with a commercial wrist band. Photoplethysmography wrist bands accompanied with atrial fibrillation detection algorithms utilizing autocorrelation could provide a computationally very effective and reliable wearable monitoring method in screening of atrial fibrillation.