Atrial fibrillation (AFib) is a common type of cardiac arrhythmia in clinical practice, affecting millions of people worldwide. Early detection and treatment of atrial fibrillation are crucial in preventing serious complications such as stroke and heart failure. In recent years, with the flourishing development of wearable devices and mobile technology, electrocardiogram (ECG) measurement applications embedded in smartwatches have gradually become a non-invasive and convenient method for heart rate monitoring. However, the accuracy of these devices has not yet been fully determined. This study aims to verify the ECG measurement and atrial fibrillation detection function of the ASUS Blood Pressure Monitor/Oximeter/ECG Monitor. The accuracy of the ECG application in detecting atrial fibrillation and measuring ECG will be evaluated by comparison with standard 12-lead ECGs.
This study plans to recruit 602 adults over the age of 22. All participants will undergo heart rate measurements using both the smartwatch ECG application and the 12-lead ECG. The heart rate measurement using the ECG application will be operated by the participants themselves under the guidance of the testing personnel. The 12-lead ECG will be operated by trained medical professionals. The results of the heart rate measurement from both devices will be recorded synchronously, and the consistency of heart rate interpretation and ECG waveforms between the smartwatch ECG application and the 12-lead ECG measurement will be compared to verify the accuracy of the ECG application.
Study Type
OBSERVATIONAL
Enrollment
602
The ASUS Blood Pressure Monitor/Oximeter/ECG Monitor analyzes data collected by the integrated electrical sensors on a ASUS Vivowatch to generate an ECG waveform similar to a Lead I, calculate average heart rate, and provide a rhythm classification to the user for a given 30 second session. When a user opens the ECG App while wearing the ASUS VivoWatch on one wrist and places the finger of the opposite hand on the digital crown, they are completing the circuit across the heart which begins a recording session. Once the recording session is complete, the ECG App performs signal processing, feature extraction and rhythm classification to generate a session result.
Sensitivity and specificity of the ECG App algorithm
Sensitivity and specificity of the ECG App algorithm in detecting AFib compared with physician-adjudicated 12-lead ECG will be calculated.
Time frame: One day visit
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