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: * Have their weight, height, resting heart rate and blood pressures measured * Have 12-lead electrocardiogram (ECG) of their heart electrical activities recorded * Have their heart sounds and 1-lead ECG recorded from their chest, and optical-based blood flow data (photoplethysmography or PPG) and 1-lead ECG recorded from their fingers using smartphones' built-in microphone, camera, and/or external accessories * Optionally have their optical-based blood flow data recorded from their face using smartphones' built-in camera (remote PPG or rPPG). 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.
Study Type
OBSERVATIONAL
Enrollment
209
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.
Queen Mary Hospital
Hong Kong, China
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.
Time frame: Day 0
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.
Time frame: Day 0
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.
Time frame: 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.
Time frame: 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.
Time frame: Day 0
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