Background: We developed a video-based monitoring technology to detect AF and PR without the need for a patient to adopt a dedicated wearable device. It minimizes requirements for patient compliance with recording procedures since it passively monitors patients while they use their personal computer for other purposes.
Methods: We validated our algorithms that have already been trained on the Samsung Galaxy S10 device on a personal computer running Windows 10 and utilizing a Logitech HD 1090p webcam. We collected validation data from 22 paroxysmal and persistent AF patients who did not participate in the training of the algorithms. Subjects were spread across the entire skin-tone Fitzpatrick scale. Measurements per subject were collected under 4 indoor illumination levels (50, 100, 200, and 500 lux) for both LED and incandescent light sources. For reference, we used a single-lead EKG. PR accuracy is assessed using Bland-Altman (BA) analysis, and AF detection performance is assessed using RoC performance curve of sensitivity vs. specificity.
Results: We collected 1,061 recordings from 22 AF patients (age: 67±10 years). 173 recordings were automatically discarded by our technology quality filtering algorithms due to subject motion and face obstruction. The remaining 888 recordings (84% of all recordings) were used as the validation data set. The BA plot shows unbiased estimation with strong agreement to ECG. 95% of estimated PR values have no more than 5 BPM deviation from the ECG HR measure. The AF detection ROC curve has an Area Under the Curve (AUC) of 0.9. Setting operating parameters similar to the S10 device results in (sensitivity,specificity) of (0.88,0.8).
Conclusion: This monitoring technology operates across platforms (Android Galaxy S10 to Windows webcam), thereby enabling accurate measurement of HR and detection of AF on personal computers.