scholarly journals Diagnostic performance of a wearing dynamic ECG recorder for atrial fibrillation screening: the HUAMI heart study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Wenxia Fu ◽  
Ruogu Li
Keyword(s):  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
Diagnostic Accuracy ◽  
Sensitivity And Specificity ◽  
Normal Sinus Rhythm ◽  
Screening Method ◽  
Upright Position ◽  
Great Promise ◽  
High Morbidity ◽  
Normal Sinus

Abstract Background Atrial fibrillation (AF) is the most prevalent cardiac dysrhythmia with high morbidity and mortality rate. Evidence shows that in every three patients with AF, one is asymptomatic. The asymptomatic and paroxysmal nature of AF is the reason for unsatisfactory and delayed detection using traditional instruments. Research indicates that wearing a dynamic electrocardiogram (ECG) recorder can guide accurate and safe analysis, interpretation, and distinction of AF from normal sinus rhythm. This is also achievable in an upright position and after exercises, assisted by an artificial intelligence (AI) algorithm. Methods This study enrolled 114 participants from the outpatient registry of our institution from June 24, 2020 to July 24, 2020. Participants were tested with a wearable dynamic ECG recorder and 12-lead ECG in a supine, an upright position and after exercises for 60 s. Results Of the 114 subjects enrolled in the study, 61 had normal sinus rhythm and 53 had AF. The number of cases that could not be determined by the wristband of dynamic ECG recorder was two, one and one respectively. Case results that were not clinically objective were defined as false-negative or false-positive. Results for diagnostic accuracy, sensitivity, and specificity tested by wearable dynamic ECG recorders in a supine position were 94.74% (95% CI% 88.76–97.80%), 88.68% (95% CI 77.06–95.07%), and 100% (95% CI 92.91–100%), respectively. Meanwhile, the diagnostic accuracy, sensitivity and specificity in an upright position were 97.37% (95% CI 92.21–99.44%), 94.34% (95% CI 84.03–98.65%), and 100% (95% CI 92.91–100%), respectively. Similar results as those of the upright position were obtained after exercise. Conclusion The widely accessible wearable dynamic ECG recorder integrated with an AI algorithm can efficiently detect AF in different postures and after exercises. As such, this tool holds great promise as a useful and user-friendly screening method for timely AF diagnosis in at-risk individuals.

scholarly journals Diagnostic performance of a wearing dynamic ECG recorder for Atrial Fibrillation screening: The HUAMI heart study

2021 ◽  
Author(s):  
Fu Wenxia ◽  
Li Ruogu
Keyword(s):  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
Diagnostic Accuracy ◽  
Sensitivity And Specificity ◽  
Normal Sinus Rhythm ◽  
False Negative ◽  
Upright Position ◽  
Normal Sinus ◽  
Heart Study ◽  
User Friendly

Abstract Background: Atrial fibrillation (AF) is the most prevalent cardiac dysrhythmia with a significant morbidity and mortality rate. Notably, one out of three patients with AF is asymptomatic. Given the asymptomatic and paroxysmal nature of AF, AF's timely detection with traditional instruments is somewhat unsatisfactory and delayed. Thus, wearing a dynamic electrocardiogram (ECG) recorder can help analyze, interpret, and distinguish AF from normal sinus rhythm accurately and safely, even in an upright position and after exercises, using an artificial intelligence (AI) algorithm.Methods: A total of 114 participants in the outpatient registry of our institution from June 24, 2020 to July 24, 2020, were enrolled. Participants were tested with a wearable dynamic ECG recorder and 12-lead ECG in a supine, an upright position and after exercises for 60seconds. Results: A total of 114 subjects (sixty-one with normal sinus rhythm, fifty-three with AF) were enrolled in the study. The number of cases unable to be determined by the dynamic ECG recorder wristband was two, one in each group. Case results not clinically objective were defined as false-negative or false-positive. The diagnostic accuracy, sensitivity and specificity using wearable dynamic ECG recorders in a supine position were 94.74% (95% CI% 88.76%-97.80%), 88.68% (95% CI 77.06%-95.07%) and100% (95% CI 92.91%-100%), respectively. Meanwhile, the diagnostic accuracy, sensitivity and specificity in an upright position were 97.37% (95% CI% 92.21%-99.44%), 94.34% (95% CI 84.03%-98.65%), and 100% (95% CI 92.91%-100%), respectively. The result after exercise was the same as the result of the upright position.Conclusion: AF can be detected using the widely accessible wearable dynamic ECG recorder with an AI algorithm after different postures and exercises. It may provide a useful and user-friendly screening tool, diagnosing AF early in at-risk individuals.

5105A deep neural network predicts atrial fibrillation from normal ECGs recorded on a smartphone-enabled device

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
C Galloway ◽  
D Treiman ◽  
J Shreibati ◽  
M Schram ◽  
Z Karbaschi ◽  
...  
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Deep Learning ◽  
Sinus Rhythm ◽  
Sensitivity And Specificity ◽  
Normal Sinus Rhythm ◽  
Learning Model ◽  
Normal Sinus ◽  
Normal Ecgs ◽  
Deep Learning Model

Abstract Background Electrocardiographic predictors of atrial fibrillation (AF) from a non-AF ECG–such as p wave abnormalities and supraventricular ectopy–have been extensively documented. However, risk prediction tools for AF utilize little if any of the wealth of information available from the ECG. Better AF prediction from the ECG may improve efficiency of screening and performance of AF risk tools. Deep learning methods have the potential to extract an unlimited number of features from the ECG to improve prediction of AF. Purpose We hypothesize that a deep learning model can identify patterns predictive of AF during normal sinus rhythm. To test the hypothesis, we trained and tested a neural network to predict AF from normal sinus rhythm ambulatory ECG data. Methods We trained a deep convolutional neural network to detect features of AF that are present in single-lead ECGs with normal sinus rhythm, recorded using a Food and Drug Administration (FDA)-cleared, smartphone-enabled device. A cohort of 27,526 patients with at least 50 ECGs recorded between January 7, 2013, and September, 19, 2018, and the FDA-cleared automated findings of Normal and Atrial Fibrillation associated with those ECGs, were used for model development. Specifically, we trained the deep learning model on 1,984,581 Normal ECGs from 19,267 patients with 1) only Normal ECG recordings, or 2) at least 30% ECGs with AF. Of the 27,526 patients, an internal set of 8,259 patients with 841,776 Normal ECGs was saved for testing (validation). Results Among 8,259 patients in the test set, 3,467 patients had at least 30% of their ECGs with an automated finding of AF. When the deep learning model was run on 841,776 Normal ECGs, it was able to predict whether the ECG was from a patient with no AF or with 30% or more AF, with an area under the curve (AUC) of 0.80. Using an operating point with equal sensitivity and specificity, the model's sensitivity and specificity were 73.1%. Using an operating point with high specificity (90.0%), the model's sensitivity was 48.0%. When the model was applied to a randomly-selected, broader cohort of 15,000 patients (at least 50 ECGs recorded, any amount of AF), a positive, non-linear relationship between neural network output and AF burden per patient was observed (Figure). Model Output vs AF Burden Per Patient Conclusions A deep learning model was able to predict AF from ECGs in normal sinus rhythm that were recorded on a smartphone-enabled device. The use of deep learning, if prospectively validated, may facilitate AF screening in patients with paroxysmal disease or warn patients who are at high risk for developing AF. Acknowledgement/Funding AliveCor

scholarly journals An Atrial Fibrillation with Normal Limits using Signal Processing- A Review

2018 ◽  
Vol 7 (2.24) ◽  
pp. 453
Author(s):  
S. Sathish ◽  
K Mohanasundaram
Keyword(s):  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
Normal Sinus Rhythm ◽  
P Wave ◽  
Wave Duration ◽  
Normal Limits ◽  
Normal Sinus ◽  
P Wave Duration ◽  
Standard Lead ◽  
Wave Morphology

Atrial fibrillation is an irregular heartbeat (arrhythmia) that can lead to the stroke, blood clots, heart failure and other heart related complications. This causes the symptoms like rapid and irregular heartbeat, fluttering, shortness of breath etc. In India for every around 4000 people eight of them are suffering from Atrial Fibrillation. P-wave Morphology.  Abnormality of P-wave (Atrial ECG components) seen during sinus rhythm are associated with Atrial fibrillation. P-wave duration is the best predictor of preoperative atrial fibrillation. but the small amplitudes of atrial ECG and its gradual increase from isometric line create difficulties in defining the onset of P wave in the Standard Lead Limb system (SLL).Studies shows that prolonged P-wave have duration in patients (PAF) In this Study, a Modified Lead Limb (MLL) which solves the practical difficulties in analyzing the P-ta interval for both in healthy subjects and Atrial Fibrillation patients. P-Ta wave interval and P-wave duration can be estimated with following proposed steps which is applicable for both filtered and unfiltered atrial ECG components which follows as the clinical database trials. For the same the p-wave fibrillated signals that escalates the diagnosis follows by providing minimal energy to recurrent into a normal sinus rhythm.  

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