Develop and Validate Machine-Learning Algorithm to Detect Atrial Fibrillation With Wearable Devices

Summary

Atrial Fibrillation (AF) is an abnormal heart rhythm. Because AF is often asymptomatic, it often remains undiagnosed in the early stages. Anticoagulant therapy greatly reduces the risks of stroke in patients diagnosed with AF. However, diagnosis of AF requires long-term ambulatory monitoring procedures that are burdensome and/or expensive. Smart devices (such as Apple or Fitbit) use light sensors (called "photoplethysmography" or PPG) and motion sensors (called "accelerometers") to continuously record biometric data, including heart rhythm. Smart devices are already widely adopted. This study seeks to validate an investigational machine-learning software (also called "algorithms") for the long-term monitoring and detection of abnormal cardiac rhythms using biometric data collected from consumer smart devices. The research team aims to enroll 500 subjects who are being followed after a stroke event of uncertain cause at the Emory Stroke Center. Subjects will undergo standard long-term cardiac monitoring (ECG), using FDA-approved wearable devices fitted with skin electrodes or implantable continuous recorders, and backed by FDA-approved software for abnormal rhythm detection. Patients will wear a study-provided consumer wrist device at home, for the 30 days of ECG monitoring, 23 hours a day. At the end of the 30 days, the device data will be uploaded to a secure cloud server and will be analyzed offline using proprietary software (called "algorithms") and artificial intelligence strategies. Detection of AF events using the investigational algorithms will be compared to the results from the standard monitoring to assess their reliability. Attention will be paid to recorded motion artifacts that can affect the quality and reliability of recorded signals. The ultimate aim is to establish that smart devices can potentially be used for monitoring purposes when used with specialized algorithms. Smart devices could offer an affordable alternative to standard-of-care cardiac monitoring.

Description

Eligibility

Age range

55+ years

Sex

All

Healthy volunteers

No

Interventions

• Device
  wearable wristband model

  MOTO 360 smartwatch: is a specific consumer wearable wristband model (Motorola: MOTO 360), fitted with proprietary firmware (LifeQ) to collect continuous biometric signals, including PPG signals and 3-axis accelerometers in an ambulatory setting. The device is not a medical or diagnostic device, but rather a photoplethysmography (PPG) data collection device. PPG is a non-invasive technology that uses light to measure the change in the volume of blood beneath the skin that occurs as the heart beats. LifeQ has developed software that enables the collection of vital signs data from PPG technology.

• Other
  Samsung Galaxy Watch 6

  The Samsung Galaxy Watch6 will collect study data on physiological signals with a compatible Samsung Galaxy phone S21. The Samsung Galaxy Watch6 will include various models, the difference being the size of the watch face or the analog front end of the device. The software device is installed on the Samsung Galaxy Watch. The app on the watch continuously records PPG and/or ECG data and transmits it. The phone app allows study staff to enter the subject ID, initiate data collection, and stop data collection sessions on the watch. It also receives and stores PPG and ECG data from the paired watch. The PPG app used in the study does not trigger irregular rhythm notifications or display rhythm classification. The data collected using the PPG app will support algorithm development.

• Device
  Standard of care extended ECG monitoring

  Participants enrolled in the study are prescribed ambulatory ECG monitoring (Mobile Cardiac Outpatient Telemetry, Biotel e-Patch, or LINQ insertable cardiac monitor). If the patient is negative for Afib during their time wearing an ECG monitoring patch, then patients may proceed with LINQ insertable cardiac monitor, as part of their standard of care. These are standard-of-care FDA-approved devices and detection software. Researchers will rely on the final ECG report to identify arrhythmic events to use as a golden standard to evaluate the algorithm findings. Specifically, the raw data will be used for establishing and getting an accurate ground truth for the algorithm.

Location