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|Accuracy of continuous photoplethysmography-based heart rate assessment during atrial fibrillation||2023||
Although mobile health tools using photoplethysmography (PPG) technology have been validated for the detection of atrial fibrillation (AF), their utility for heart rate assessment during AF remains unclear. Therefore, we aimed to evaluate the accuracy of continuous PPG-based heart rate assessment during AF.
During persistent AF, continuous PPG-based heart rate assessment is feasible and shows high accuracy compared to Holter ECG. Motion and recording quality among other PPG-derived covariates did not introduce a systematic and correctable bias in the heart rate assessment.
|2023||The aim of this study was to assess the accuracy of a well-known standalone PPG-AF detection algorithm added to a popular wristband and smartwatch, with regard to discriminating AF and sinus rhythm, in a group of patients with AF before and after cardioversion (CV).||This study demonstrates that the addition of a well-known standalone PPG-AF detection algorithm to a popular PPG smartwatch and wristband without integrated algorithm yields a high accuracy for the detection of AF, with an acceptable unclassifiable rate, in a semicontrolled environment.|
|Accuracy of continuous photoplethysmography-based 1 min mean heart rate assessment during atrial fibrillation||2023||To evaluate the accuracy of continuous PPG-based 1 min mean heart rate assessment during AF compared with Holter ECG monitoring as a reference and establish predictors for an accurate PPG-based 1 min mean heart rate assessment in patients with persistent AF||
Continuous PPG-based 1 min mean heart rate assessment during AF seems feasible to guide a lenient rate control and shows good accuracy compared with Holter ECG as a reference
Future studies need to be performed to evaluate how to integrate PPG-derived heart rate information into clinical decision-making processes to guide rate control in patients with AF
Motion and recording quality among other PPG-derived covariates did not introduce a systematic and correctable bias in the 1 min mean heart rate assessment
Chronic heart failure was associated with lower accuracy of the PPG-based 1 min mean heart rate assessment
|Assessment of heart rate agreement on continuous photoplethysmography monitoring using a smartwatch versus beat-to-beat synchronized ECG monitoring||2021||To assess the agreement between continuous PPG monitoring using a smartwatch and continuous ECG Holter monitoring in the identification of heartbeats and calculation of the HR||The AI algorithm analyzing continuous out-of-hospital PPG tracings can annotate heartbeats and assess HR without a clinically significant bias compared to continuous ECG monitoring, both during AF and non-AF rhythms in a heterogeneous patient population|
|Evaluation of the device independent nature of a photoplethysmography-deriving smartphone app||2021||To study the device independency of AF detection performance by a PPG-based smartphone application||The sensitivity and specificity of the AF detection algorithm ranged from 90.9% to 100.0% and 94.5% to 100.0%, respectively.
Head-to-head comparisons of the results did not reveal significant differences in sensitivity or specificity of the proprietary AF detection algorithm among the different devices
|Head-to-head comparison of proprietary PPG and single-lead ECG algorithms for atrial fibrillation detection||2021||To evaluate and compare the diagnostic performance of a PPG-deriving smartphone app and a single-lead ECG-deriving hand-held device for AF detection||Results demonstrated a 96.4% accuracy for PPG and 94.1% for single-lead ECG.
No significant differences in sensitivity (P = 0.453) or specificity (P = 0.219) between the proprietary PPG and single-lead ECG algorithms were found.
This study demonstrated equivalent diagnostic performance of PPG and single-lead ECG proprietary AF detection algorithms in smartphone apps
|Performance of an artificial intelligence algorithm to detect atrial fibrillation on a 24-hour continuous photoplethysmography recording using a smartwatch: ACURATE study||2021||To determine the diagnostic performance of an artificial intelligence algorithm to detect AF using photoplethysmography acquired by a smartwatch.||Continuous out-of-hospital PPG monitoring using a smart-watch in combination with an AI algorithm can accurately discriminate between AF and non-AF rhythms in a heterogeneous patient population.
PPG quality is more often affected than ECG quality during daily life activities
|Assessment of a standalone photoplethysmography (PPG) algorithm for detection of atrial fibrillation on wristband-derived data||2020||The use of the Fibricheck algorithm on wristband derived-data has never been studied. Present study aims to evaluate the standalone Fibricheck algorithm using wristband-derived data with regard to diagnosing AF in small real-world cohort of mostly elderly people.||AF detection by the Fibricheck standalone algorithm is feasible and has a high sensitivity and specificity in a small real-world cohort. It shows comparable results to the use of a one-lead ECG wristband.
The results of this study encourage further development of PPG-AF algorithms in combination with wristbands by demonstrating high diagnostic accuracy and acceptable unclassifiable quality rate.
|Mobile Phone–Based Use of the Photoplethysmography Technique to Detect Atrial Fibrillation in Primary Care- Diagnostic Accuracy Study of the FibriCheck App||2019||To test the diagnostic accuracy of such an approach using the FibriCheck mobile phone app (Qompium) in comparison with the gold standard method of AF diagnosis, the 12-lead ECG||Cardiac rhythm analysis through a mobile phone–based PPG signal with the FibriCheck AF algorithm had very good sensitivity and specificity to detect AF.
False-positive results were mainly because of the presence of extrasystoles.
The FibriCheck AF algorithm accurately diagnosed AF on the basis of a single-lead ECG, with a similar sensitivity and specificity compared with the PPG signal.
Beat-to-beat analysis showed a strong agreement between the PPG and the single-lead ECG signal.
The patient does not require any experience or medical education and can be easily trained to use the app.
Physicians can remotely review the transferred data, which enables optimal patient follow-up in a less time-consuming manner.
|2019||This study evaluated the diagnostic accuracy of a PPG-based pulse-deriving smartphone application with respect to handheld single-lead ECG and 12-lead ECG. In addition, the device dependent nature and robustness of the performance of the application was assessed.||The diagnostic accuracy of the pulse-deriving smartphone application and the handheld single-lead ECG device was strongly influenced by the presence of regular atrial flutters, stressing the importance of further thorough validation. For the pulse-deriving smartphone application, there was no significant influence from device type in terms of diagnostic accuracy for the detection of AF. Insufficient quality measurements were more frequently performed on Android devices.|
|Evaluation of screening technologies and assessments in a voluntary screening programme in the general belgian population||2018||Screening for AF has been proposed as a way of reducing the burden of the disease by offering the possibility to timely initiate anticoagulation therapy. This study was organised to assess the efficacy and feasibility of a nationwide voluntary screening program in the Belgian general population over 40 years of age.||A dual assessment was made in this study: aiming for maximum sensitivity (AF + other arrhythmias) versus maximum specificity (normal+ other arrhythmia).
AF was present in 0.5% of the participants.
All AF-patients had an increased stroke risk.
The present study shows that a voluntary screening programme using high accuracy PPG-based and single-lead ECG tools was able to detect an important number of patients with previously undetected AF.
|Using smartphone enabled technologies for detection atrial fibrillation- is there a difference in signal quality between ECG and PPG||2018||This work focuses on comparing the performance between photoplethysmography (PPG) and single lead ECG based smartphone applications during a national incentivized screening initiative and evaluate the quality related issues between these technologies.||Detection of pulse intervals based on PPG is a sensitive and accurate screening tool for the detection of atrial fibrillation and has a high level of agreement with the results obtained using the single lead ECG.
Despite the quality challenges of PPG signals, there is no correlation found in the cause nor the agreement between both technologies indicating that for the general population the quality parameters are properly tuned to prevent misdiagnosis as much as possible.
These quality parameters will be a fundamental requirement to further leverage PPG signals as a suitable signal for heart rhythm analysis.
|Clinical Validation of Heart Rate Apps- Mixed-Methods Evaluation Study||2017||To investigate and describe the necessary elements in validating and comparing HR apps versus standard technology. To investigate the correct method that should be used to clinically validate smartphone apps that measure HR, the smartphone app FibriCheck was used as a test case.||The most suitable method for the validation of an HR app is a simultaneous measurement of the HR by the smartphone app and an ECG system and comparing the obtained intervals.
This approach could lead to almost exact accuracy in the clinical setting.
|Evaluating smartphone based photoplethysmography as a screening solution for atrial fibrillation: a digital tool to detect AF?||2017||This work focuses on comparing the feasibility, sensitivity and accuracy of photoplethysmography (PPG) and single lead-ECG based smartphone applications for the diagnosis of AF during a national incentivized screening campaign in the community.||The obtained results indicate that detection of pulse intervals based on PPG is a feasible, sensitive and accurate screening tool for the detection of AF with a high level of agreement when compared to the results obtained using the single lead ECG.
The use of a smartphone-only application could unlock the potential of digital screening and support case finding in selected populations at risk for AF.
|PPG versus single lead ECG for the diagnose of Atrial Fibrillation||2017||Diagnosis of AF, based on the visual interpretation of a PPG-signal results in a high clinical accuracy compared to single-lead ECGs and the current gold 12-lead ECG-standard||The use of a smartphone application for AF patients results in a good accuracy for diagnosis.
Possible problems could arise concerning education and training for cardiologists.
After enabling data-analysis, sensitivity and specificity rates increase to a very high accuracy corresponding to the 12-lead gold standard ECG.
Algorithms could be important to process PPG measurements to adjust the quality of the data.
|Validation of a new smartphone application (“FibriCheck”) for the diagnosis of atrial fibrillation in primary care||2017||To investigate the diagnostic accuracy of the FibriCheck app in a convenience sample of patients aged 65 and older in general practice.||The FibriCheck algorithm was able to accurately diagnose AF based on the obtained single-lead ECG with a high sensitivity and specificity.
The application scored an equally high sensitivity, but a slightly lower specificity when measuring and interpreting the PPG signal.
The high sensitivity of the application reflects the good capacity of the algorithm to rule out AF. False positive results were mainly due to the presence of extra systoles and low signal quality that remained undetected by the filter.
The main advantages of the app are that it is a quick, cheap and practical measurement method without the need for special infrastructure or any external hardware.
A disadvantage was the number of false positive results ,due to atrial or ventricular extrasystoles, a known AF screening issue using RR-interval variability analysis.
|Screening for atrial fibrillation using only a smartphone application - a new tool to unlock digital screening||2016||The performance evaluation of using a smartphone application FibriCheckas a screening tool for Atrial Fibrillation, for identifying cases of untreated, frequently asymptomatic AF.||The FibriCheck application had a sensitivity of 100% and a specificity of 95.8% for the detection of atrial fibrillation.
No correlation was found between the cases with bad quality measurements for both measurement techniques.
The obtained results indicate that detection of pulse intervals based on PPG is a sensitive and accurate screening tool for the detection of atrial fibrillation and has a high level of agreement with the results obtained using the single lead ECG.
The use of a smartphone-only application could unlock the potential of digital screening and support case finding of atrial fibrillation in selected populations at risk for atrial fibrillation.
|“Smart” solutions for paroxysmal atrial fibrillation||2016||A 66-year-old female patient who was implanted with an implant-able loop recorder (ILR) due to a history of unexplained syncope and symptoms of palpitations. After the procedure, the patient received a smartwatch device to measure the PPG signal at the wrist||After synchronizing the data streams between the ILR, smartwatch, and smartphone, all AF events that occurred while wearing or using one of the smart devices were picked-up and identified as AF by the in-house-developed algorithms.
Persuasive technologies such as smartphones and smartwatches can provide a new potential in the detection and management of patients with AF.
|Validation of a smartphone based photoplethysmographic beat detection algorithm for normal and ectopic complexes||2014||A smartphone based acquisition and processing algorithm was developed to collect PPG data in a controlled hospital environment. The aim is to identify the presence of premature, atrial ectopic beats using only a smartphone.||It is possible to do more with the PPG collected with a smartphone camera than currently being done by popular heart rate apps.
Beside the heart rate measurement of a sinus rhythm, the presence of premature atrial ectopic beats can also be detected based on the observation of the resulting compensatory pause.
The designed algorithm for this research was sufficient for the most datasets but needs more work to be made robust for future studies and field testing