Ambulatory atrial fibrillation detection and quantification by wristworn AI device compared to standard holter monitoring
Published: 25 March 2025
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 CardioWatch 287-2’s PPG and single-lead ECG algorithms achieved a specificity ≥98% and sensitivity ≥95% for
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.
Introduction
Atrial fibrillation (AF) is the most common sustained arrhythmia in adults, currently affecting more than 37.6 million persons worldwide, with the number increasing annually due to the growing prevalence of risk factors such as advanced age, obesity and hypertension.
AF is a major risk factor for ischaemic stroke (IS). Approximately 25% of strokes are attributed to previously undetected asymptomatic AF. Timely detection could have prevented many of these strokes through the initiation of anticoagulation therapy. However, as AF can be paroxysmal or completely asymptomatic at onset or occur when conventional monitoring is not nearby, its diagnosis remains challenging. While AF-associated stroke is currently the most actionable complication of AF, heart failure (HF) is twice as common following an AF diagnosis, with approximately one in five receiving their first HF diagnosis at the same time as their initial AF diagnosis.
The current clinical routine for AF detection often consists of Holter recording, which remains the gold standard for ambulatory ECG monitoring, with the likelihood of detecting AF in those with paroxysms dependent of the burden of AF and the duration of monitoring. Although Holter monitoring extends up to 14 days or more in some clinical settings, effectively enhancing paroxysmal AF detection, the sporadic nature of AF and variable access to long-duration monitors still lead to numerous undetected cases. An alternative approach is continuous long-term monitoring using implantable loop recorders (ILRs), overcoming the limitations of intermittent monitoring. However, their invasive and costly nature has spurred interest in exploring non-invasive alternatives for monitoring silent arrhythmias such as AF. Moreover, atrial fibrillation consequences and treatments may be influenced by the number, duration, severity and burden of atrial fibrillation, with longer durations of monitoring providing a clearer picture of all of these variables.
Over the last few years, various mobile devices and smartwatches equipped with artificial intelligence (AI) algorithms to detect AF were introduced. AI-based devices primarily employ two technologies for automatic AF detection: photoplethysmography (PPG) and single-lead ECG. PPG relies on light absorption and reflection by blood vessels, offering a non-invasive and reliable means to measure blood flow, typically at the skin’s surface. Single-lead ECG methods involve wearable devices recording a single-lead ECG. Individuals are instructed to maintain contact between specific body parts and the device for a predetermined duration. For both techniques, the recorded data are transmitted to an AI application, which classifies recordings such as “possible AF” and “no AF”.
These wearable technologies offer distinct advantages compared to the current gold standards as they enable long-term remote monitoring, are low in cost, are non-invasive, and allow simultaneous monitoring of various vital parameters. For paroxysmal AF, in particular, the prolonged monitoring times can increase the chance of AF detection. However, not all available devices and techniques have thoroughly been evaluated, whereas high sensitivity and specificity are required to limit false results. False positives have been shown to lead to stress among patients and increased healthcare burden and cost.
Considering these benefits and risks of wearable technologies for AF detection and quantification, this study compared the performance of a wrist-worn sensor device and the corresponding PPG- and single-lead ECG-based AF algorithms to conventional 24-h Holter recordings. First, we performed 24 h of head-to-head comparisons, and second, an extended 28-day evaluation.
Results
Informed consent was obtained from all 173 enroled patients. Among them, ten withdrew from the study due to reasons including a high study burden (n = 5), or an adverse event being either an allergic reaction to the bracelet strap (n = 4) or hospitalization (n = 1). Two patients with an adverse event, an allergic reaction (n = 1) and a concussion (n = 1), chose to remain in the study. Furthermore, insufficient quality PPG data was observed for thirteen patients. To adhere to the study protocol, participants who withdrew or had inadequate data were replaced, ensuring a total inclusion of 150 patients.