Cardiovascular Digital Health Journal – Performance of an Automated Photoplethysmography-Based Algorithm

Cardiovascular Digital Health Journal – Performance of an Automated Photoplethysmography-Based Algorithm

6 Nov, 2020

Clinical study conducted in the Amsterdam OLVG Hospital over a period of 3 years has shown a sensitivity and specificity of 98.1% in addition:

  • The shallow neural network showed excellent performance for peak detection.
  • High sensitivity and specificity for detection of atrial fibrillation were obtained using a new automated plethysmography (PPG) algorithm.
  • Predefined exclusion of recordings with low confidence boosted the diagnostic performance of the algorithm, resulting in 1.8% increase in sensitivity and 4.6% increase in specificity.

Atrial fibrillation (AF) is the most common sustained arrhythmia. Symptoms may include palpitations, dyspnea, limited exercise tolerance, and fatigue. However, AF is asymptomatic in 12%–25% of cases and remains undetected in 1.4% of patients aged >65 years.1,2 Early detection and adequate treatment of silent AF is essential, especially in patients who are at increased risk for stroke.

Photoplethysmography (PPG) using the camera of smartphones and smartwatches is a promising technology for heart rate and rhythm assessment. To retrieve PPG recordings, a photoemitter is connected to a photoreceiver. The amount of light absorbed or reflected by the blood is related to the cardiac cycle. Although PPG technology in smartphones or smartwatches is easy to use and publicly accessible, reliable PPG recording requires good signal quality, which may be affected by many factors, including poorly perfused tissue, tremors, ambient light, camera characteristics, and correct placement of the PPG sensor.

Recently, an artificial intelligence smartphone-based PPG algorithm for detection of AF was developed by Happitech (Amsterdam, The Netherlands). The algorithm was trained using 2560 selected recordings retrieved from a worldwide online data donation campaign (Heart for Heart) and consists of 3 main components: (1) peak detection to measure R-R intervals; (2) quality; and (3) rhythm classification. The most critical part of rhythm classification is peak detection.

Full publication can be read here