Ambulatory atrial fibrillation detection and quantification by wristworn AI device compared to standard holter monitoring

Abstract

Timely detection of atrial fibrillation (AF) is crucial for the prevention of serious consequences such as stroke and heart failure, yet it remains challenging due to its often asymptomatic or paroxysmal nature. Wearable devices with artificial intelligence algorithms offer promising solutions. AF detection by the CardioWatch 287-2 (CW2), a wrist-worn photoplethysmography (PPG) and single-lead ECG device, was compared to 24-h Holter. Patient compliance, AF prevalence and AF burden were evaluated for 27 additional days. Data from 150 participants (mean age 64 ± 12 SD; 41% female) were analysed. The CW2's PPG and single-lead ECG algorithms achieved a specificity ≥98% and sensitivity ≥95% for AF detection, and 99% correlation for AF burden, compared to 24-h Holter. AF prevalence increased from 14.7% (24-h Holter) to 26.7% (28-day CW2). Thus, the wrist-worn device showed promising performance in detecting AF and determining AF burden. The trial was registered on ClinicalTrials.gov (NCT05899959) on June 2, 2023.

Fig. 1. Example of gathered data across 28 days in three patients with (paroxysmal) atrial fibrillation (Afib).

Each panel repesents the data of a single patient. AF was detected in all three patients according to CardioWatch 287-2’s photoplethysmography and single-lead-ECG algorithms, but only twice (top two) according to 24-h Holter. The 24-h Holter period is marked by a red box. The red/blue line represents the photoplethysmography (PPG)-based categorisation based on the Happitech algorithm, where red represents normal sinus rhythm (NSR), and blue represents Afib. Black dots represent the incidences where an ECG was performed. The blue/red dots below the PPG-based diagnosis represent the categorisation made by the Cardiolyse Analytics platform using the single-lead ECG, again red represents NSD, and blue represents Afib.

Fig. 2. Correlation and Bland-Altman plot comparing photoplethysmography-based and holter-based atrial fibrillation burden.

Comparison between photoplethysmography (PPG)-based and Holter-based atrial fibrillation (AF) burden across 24 h. Demonstrating (a) a correlation of R²: 0.9937 between the PPG-based and Holter-based AF burden across 24-h, and (b) a Bland-Altman plot showing a mean difference in AF burden between PPG and Holter of 0.17% with lower and upper limits of −4.19% (SD: −1.96) and 4.53% (SD: 1.96), respectively.

Fig. 3. Demonstration of the CardioWatch 287-2 photoplethysmography (PPG)-based atrial fibrillation detection method.

This method includes the algorithm developed by Happitech (Rotterdam, the Netherlands) which, here, is applied on a 100-s time window and shows the detection of an irregular heart rythm. In the upper pannel a poncoire plot is included, demonstrating the instability of RR-intervals. An RR Tachogram follows, showing the variation in RR-intervals over a time period of 100-s. Finally, the raw PPG data of the 100-s window is shown.

Fig. 4. Demonstration of CardioWatch 287-2 atrial fibrillation detection method.

This demonstation includes (a) the use chain showing the steps from health care provider, through patient, arrhythmia detection, data transfer, to the eventual data report, and (b) the results of a single-lead ECG for a patient with atrial fibrillation within the Corsano cloud-based portal showing irregular RR-intervals (red highlights) and classification as atrial fibrillation.