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Published on: December 11, 2019
Diagnostic performance of a wearing dynamic ECG recorder for atrial fibrillation screening: the HUAMI heart study
1Department of Cardiac Function, Shanghai Chest Hospital, Shanghai Jiao Tong University, 241 Huaihai West Rd, Xuhui District, 200030, Shanghai, China.
Insights
A wearable dynamic electrocardiogram (ECG) recorder with AI accurately detects atrial fibrillation (AF) in various positions and after exercise. This tool offers a promising, user-friendly method for early AF diagnosis in at-risk patients.
Area of Science:
- Cardiology
- Medical Devices
- Artificial Intelligence
Background:
- Atrial fibrillation (AF) is a common heart rhythm disorder with significant health risks.
- Many AF cases are asymptomatic or paroxysmal, leading to delayed diagnosis with traditional methods.
- Wearable dynamic ECG recorders offer a potential solution for improved AF detection.
Purpose of the Study:
- To evaluate the diagnostic accuracy of a wearable dynamic ECG recorder for atrial fibrillation.
- To assess the device's performance in supine, upright, and post-exercise positions.
- To determine the effectiveness of an integrated AI algorithm in AF detection.
Main Methods:
- 114 participants were enrolled, including those with normal sinus rhythm and AF.
- A wearable dynamic ECG recorder and a 12-lead ECG were used for testing.
- Recordings were taken in supine, upright, and post-exercise positions for 60 seconds.
Main Results:
- The wearable ECG recorder demonstrated high diagnostic accuracy, sensitivity, and specificity.
- In the upright position, diagnostic accuracy was 97.37%, sensitivity 94.34%, and specificity 100%.
- Similar high performance was observed after exercise, with minimal undetermined cases.
Conclusions:
- A wearable dynamic ECG recorder with AI effectively detects AF in diverse conditions.
- This technology shows promise as a user-friendly screening tool for timely AF diagnosis.
- The device facilitates early detection of AF in individuals at risk.
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.
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