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| ID | Type | Description | Link |
|---|---|---|---|
| UW 24-763 | Other Identifier | Institutional Review Board of The University of Hong Kong/Hospital Authority Hong Kong West Cluster |
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| Name | Class |
|---|---|
| Laboratory of Data Discovery for Health | INDIV |
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The purpose of this 4-in-1 observational study is to test the performance of artificial intelligences (AIs) in distinguishing irregularly irregular heart rhythm called atrial fibrillation (AF) from normal heart rhythm using physiological signals collected by smartphones' built-in hardware and/or external accessories.
Participants will:
The researchers will also create a database containing the physiological signals collected in this study along with the participants' medically relevant information to help train and test future AIs for medical applications.
4 observational studies have been combined into 1 observational study to share the same pool of participants. These 4 studies are designated as AUSC-AF, ECG-AF, AUSC+ECG-AF, and rPPG-AF corresponding to the signal modality/modalities used for AF detection (see outcome measures) and designated as AUSC/ECG/rPPG-AF when combined as 1 study.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Atrial Fibrillation |
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| Sinus Rhythm |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Computer algorithms | Diagnostic Test | Computer algorithms that are designed to perform heart sound, electrocardiography (ECG), and/or facial photoplethysmography (rPPG) analysis on data collected from smartphone's internal hardware and/or external accessories. |
| Measure | Description | Time Frame |
|---|---|---|
| Differentiation of Atrial Fibrillation from Sinus Rhythm in Heart Sound Recordings | AUSC-AF: Identification of atrial fibrillation (AF) from sinus rhythm in recorded heart sounds (phonocardiogram [PCG]) as verified by the gold standard 12-lead electrocardiography (ECG) interpretation, measured in the form of sensitivity and specificity. | Day 0 |
| Measure | Description | Time Frame |
|---|---|---|
| Differentiation of Atrial Fibrillation from Sinus Rhythm in Heart Sound Recordings | AUSC-AF: Identification of atrial fibrillation from sinus rhythm in recorded heart sounds (phonocardiogram [PCG]) as verified by the gold standard 12-lead ECG interpretation, measured in the form of positive and negative predictive values, and accuracy. | Day 0 |
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Inclusion Criteria:
Exclusion Criteria:
Any of the following:
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Hospital cardiology patients
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Queen Mary Hospital | Hong Kong | China |
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| ID | Term |
|---|---|
| D001281 | Atrial Fibrillation |
| ID | Term |
|---|---|
| D001145 | Arrhythmias, Cardiac |
| D006331 | Heart Diseases |
| D002318 | Cardiovascular Diseases |
| D010335 | Pathologic Processes |
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| Differentiation of Atrial Fibrillation from Sinus Rhythm in 1-Lead ECG Signals | ECG-AF: Identification of atrial fibrillation from sinus rhythm in recorded 1-lead ECG signals as verified by the gold standard 12-lead ECG interpretation, measured in the form of sensitivity, specificity, positive and negative predictive values, and accuracy. | Day 0 |
| Differentiation of Atrial Fibrillation from Sinus Rhythm in PCG and 1-Lead ECG Signals | AUSC+ECG-AF: Identification of atrial fibrillation from sinus rhythm in PCG and 1-lead ECG signals as verified by the gold standard 12-lead ECG interpretation, measured in the form of sensitivity, specificity, positive and negative predictive values, and accuracy. | Day 0 |
| Differentiation of Atrial Fibrillation from Sinus Rhythm in Facial Photoplethysmography Signals | rPPG-AF: Identification of atrial fibrillation from sinus rhythm in facial photoplethysmography signals (also known as remote photoplethysmography [rPPG]) as verified by the gold standard 12-lead ECG interpretation, measured in the form of sensitivity, specificity, positive and negative predictive values, and accuracy. | Day 0 |
| D013568 |
| Pathological Conditions, Signs and Symptoms |