Testing the Performance of Smartphones and Their Accessories in Detecting Irregularly Irregular Heart Rhythm

Condition(s) studied

Investigational drug(s) / intervention(s)

Computer algorithms: 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.

Study summary

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.

Eligibility

Primary outcome measure(s)

• Differentiation of Atrial Fibrillation from Sinus Rhythm in Heart Sound Recordings — Day 0
  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.

Trial sites (1)

More The University of Hong Kong trials in China

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