Smartphone detection of atrial fibrillation using photoplethysmography: a systematic review and meta-analysis

OBJECTIVES: Timely diagnosis of atrial fibrillation (AF) is essential to reduce complications from this increasingly common condition. We sought to assess the diagnostic accuracy of smartphone camera photoplethysmography (PPG) compared with conventional electrocardiogram (ECG) for AF detection. METHODS: This is a systematic review of MEDLINE, EMBASE and Cochrane (1980-December 2020), including any study or abstract, where smartphone PPG was compared with a reference ECG (1, 3 or 12-lead). Random effects meta-analysis was performed to pool sensitivity/specificity and identify publication bias, with study quality assessed using the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2) risk of bias tool. RESULTS: 28 studies were included (10 full-text publications and 18 abstracts), providing 31 comparisons of smartphone PPG versus ECG for AF detection. 11 404 participants were included (2950 in AF), with most studies being small and based in secondary care. Sensitivity and specificity for AF detection were high, ranging from 81% to 100%, and from 85% to 100%, respectively. 20 comparisons from 17 studies were meta-analysed, including 6891 participants (2299 with AF); the pooled sensitivity was 94% (95% CI 92% to 95%) and specificity 97% (96%-98%), with substantial heterogeneity (p<0.01). Studies were of poor quality overall and none met all the QUADAS-2 criteria, with particular issues regarding selection bias and the potential for publication bias. CONCLUSION: PPG provides a non-invasive, patient-led screening tool for AF. However, current evidence is limited to small, biased, low-quality studies with unrealistically high sensitivity and specificity. Further studies are needed, preferably independent from manufacturers, in order to advise clinicians on the true value of PPG technology for AF detection

Artificial neural network for atrial fibrillation identification in portable devices

none6siAtrial fibrillation (AF) is a common cardiac disorder that can cause severe complications. AF diagnosis is typically based on the electrocardiogram (ECG) evaluation in hospitals or in clinical facilities. The aim of the present work is to propose a new artificial neural network for reliable AF identification in ECGs acquired through portable devices. A supervised fully connected artificial neural network (RSL_ANN), receiving 19 ECG features (11 morphological, 4 on F waves and 4 on heart-rate variability (HRV)) in input and discriminating between AF and non-AF classes in output, was created using the repeated structuring and learning (RSL) procedure. RSL_ANN was created and tested on 8028 (training: 4493; validation: 1125; testing: 2410) annotated ECGs belonging to the “AF Classification from a Short Single Lead ECG Recording” database and acquired with the portable KARDIA device by AliveCor. RSL_ANN performance was evaluated in terms of area under the curve (AUC) and confidence intervals (CIs) of the received operating characteristic. RSL_ANN performance was very good and very similar in training, validation and testing datasets. AUC was 91.1% (CI: 89.1%–93.0%), 90.2% (CI: 86.2%–94.3%) and 90.8% (CI: 88.1%–93.5%) for the training, validation and testing datasets, respectively. Thus, RSL_ANN is a promising tool for reliable identification of AF in ECGs acquired by portable devices.openMarinucci D.; Sbrollini A.; Marcantoni I.; Morettini M.; Swenne C.A.; Burattini L.Marinucci, D.; Sbrollini, A.; Marcantoni, I.; Morettini, M.; Swenne, C. A.; Burattini, L

Evolutionary Optimization of Atrial Fibrillation Diagnostic Algorithms

The goal of this research is to introduce an improved method for detecting atrial fibrillation (AF). The foundation of our algorithm is the irregularity of the RR intervals in the electrocardiogram (ECG) signal, and their correlation with AF. Three statistical techniques, including root mean squares of successive differences (RMSSD), turning points ratio (TPR), and Shannon entropy (SE), are used to detect RR interval irregularity. We use the Massachusetts Institution of Technology / Beth Israel Hospital (MIT-BIH) atrial fibrillation databases and their annotations to tune the parameters of the statistical methods by biogeography-based optimization (BBO), which is an evolutionary optimization algorithm. We trained each statistical method to diagnose AF on each database. Then each trained method was tested on the rest of the databases. We were able to obtain accuracy levels as high as 99 for the detection of AF in the trained databases. We obtained accuracy levels of up to 75 in the tested database

Detection of Beat-to-Beat Intervals from Wrist Photoplethysmography in Patients with Sinus Rhythm and Atrial Fibrillation after Surgery

Wrist photoplethysmography (PPG) allows unobtrusive monitoring of the heart rate (HR). PPG is affected by the capillary blood perfusion and the pumping function of the heart, which generally deteriorate with age and due to presence of cardiac arrhythmia. The performance of wrist PPG in monitoring beat-to-beat HR in older patients with arrhythmia has not been reported earlier. We monitored PPG from wrist in 18 patients recovering from surgery in the post anesthesia care unit, and evaluated the inter-beat interval (IBI) detection accuracy against ECG based R-to-R intervals (RRI). Nine subjects had sinus rhythm (SR, 68.0y$\pm$10.2y, 6 males) and nine subjects had atrial fibrillation (AF, 71.3y$\pm$7.8y, 4 males) during the recording. For the SR group, 99.44% of the beats were correctly identified, 2.39% extra beats were detected, and the mean absolute error (MAE) was 7.34 ms. For the AF group, 97.49% of the heartbeats were correctly identified, 2.26% extra beats were detected, and the MAE was 14.31 ms. IBI from the PPG were hence in close agreement with the ECG reference in both groups. The results suggest that wrist PPG provides a comfortable alternative to ECG and can be used for long-term monitoring and screening of AF episodes.Comment: Submitted to the 2018 IEEE International Conference on Biomedical and Health Informatic

Evolutionary Optimization of Atrial Fibrillation Diagnostic Algorithms

The goal of this research is to introduce an improved method for detecting atrial fibrillation (AF). The foundation of our algorithm is the irregularity of the RR intervals in the electrocardiogram (ECG) signal, and their correlation with AF. Three statistical techniques, including root mean squares of successive differences (RMSSD), turning points ratio (TPR), and Shannon entropy (SE), are used to detect RR interval irregularity. We use the Massachusetts Institution of Technology / Beth Israel Hospital (MIT-BIH) atrial fibrillation databases and their annotations to tune the parameters of the statistical methods by biogeography-based optimization (BBO), which is an evolutionary optimization algorithm. We trained each statistical method to diagnose AF on each database. Then each trained method was tested on the rest of the databases. We were able to obtain accuracy levels as high as 99 for the detection of AF in the trained databases. We obtained accuracy levels of up to 75 in the tested database

Automated detection of atrial fibrillation using RR intervals and multivariate-based classification

Automated detection of AF from the electrocardiogram (ECG) still remains a challenge. In this study, we investigated two multivariate-based classification techniques, Random Forests (RF) and k-nearest neighbor (k-nn), for improved automated detection of AF from the ECG. We have compiled a new database from ECG data taken from existing sources. R-R intervals were then analyzed using four previously described R-R irregularity measurements: (1) the coefficient of sample entropy (CoSEn), (2) the coefficient of variance (CV), (3) root mean square of the successive differences (RMSSD), and (4) median absolute deviation (MAD). Using outputs from all four R-R irregularity measurements, RF and k-nn models were trained. RF classification improved AF detection over CoSEn with overall specificity of 80.1% vs. 98.3% and positive predictive value of 51.8% vs. 92.1% with a reduction in sensitivity, 97.6% vs. 92.8%. k-nn also improved specificity and PPV over CoSEn; however, the sensitivity of this approach was considerably reduced (68.0%)

Advances in screening for undiagnosed atrial fibrillation for stroke prevention and implications for patients with obstructive sleep apnoea: A literature review and research agenda

Atrial fibrillation (AF) is the most common type of sustained cardiac arrhythmia encountered in clinical practice, and its burden is expected to increase in most developed countries over the next few decades. Because AF can be silent, it is often not diagnosed until an AF-related complication occurs, such as stroke. AF is also associated with increased risk of heart failure, lower quality of life, and death. Anticoagulation has been shown to dramatically decrease embolic risk in the setting of atrial fibrillation, resulting in growing interest in early detection of previously undiagnosed AF. Newly developed monitoring devices have improved the detection of AF and have been recommended in guidelines for screening of AF in individuals aged 65 years and over. While screening is currently targeted to these older individuals, younger patients with obstructive sleep apnoea (OSA) are at higher risk of AF and stroke than the general population, indicating a need for targeted early detection of AF in this group. Compared to individuals without OSA, those with OSA are four times more likely to develop AF, and the risk of AF is strongly associated with OSA severity. The overall prevalence of AF among individuals with OSA remains unknown because of limitations related to study design and to the conventional methods previously used for AF detection. Recent and emerging technological advances may improve the detection of undiagnosed AF in high-risk population groups, such as those with OSA. In this clinical review, we discuss the methods of screening for AF and the applications of newer technologies for AF detection in patients with OSA. We conclude the review with a brief description of our research agenda in this area

Transformation of Medical Diagnostics with Machine Learning by Considering the Example of Atrial Fibrillation Identification

The paper addresses the problem of detecting one of the most common cardiac arrhythmias atrial fibrillation with artificial intelligence. The arrhythmia increases the risk of suffering from a stroke massively. Because of this, it is essential to detect atrial fibrillation early. As the arrhythmia occurs in short sequences, it is only possible to detect the disease in long-term measurements for example with electrocardiography. All common current detection techniques are calculating the R-R intervals with variations of the root mean square of successive differences. Because this approach is inflexible and expensive, a major hospital in Germany suggests the implementation of an artificial intelligence solution for atrial fibrillation detection. The aim of the paper is to study the feasibility of atrial fibrillation detection with artificial intelligence in the clinical setting of the hospital

Acceptability of a Novel Smartphone Application for Rhythm Evaluation in Patients with Atrial Fibrillation

Background: Investigators at UMass Medical School and WPI co-developed a novel smartphone application (app), PULSESMART, that detects atrial fibrillation (AF). AF is the world’s most common, serious heart rhythm problem. In its early stages, most cases of AF are paroxysmal (pAF), making them difficult to identify early in the course of disease. Long-term cardiac monitoring is frequently needed to diagnose and prevent complications from AF, such as stroke. Home monitoring for AF can be clinically impactful but existing technologies have cost or methodological limitations. Data are needed on the potential acceptability and usability of heart rhythm monitoring applications. Aim: Our aim was to examine patient acceptability of using a pAF detection app. Methods: 52 patients with pAF underwent rhythm assessment using the app and completed a standardized questionnaire. We looked specifically at responses to 3 questions: 1) how easy was it to use? 2) How important could it be for you? And 3) to what extent does it fit into your daily life? Results: The mean age was 68.5 years and 69% female. The majority of patients reported the app was easy to use (73%), could be important to them and their health (84%), and would fit into their daily lives (78%). Conclusions: After use of the pAF detection app, most patients reported positively. The results suggest that older persons with, or at risk for, pAF may benefit from smartphone-based arrhythmia detection platforms. Further work is needed to assess the feasibility of at-home or in-clinic app use