Key result
Wearable PPG-accelerometer algorithm detects abnormal rhythms with ~96% accuracy versus ambulatory ECG.
Why the study?
Cardiac arrhythmias are frequently undiagnosed due to their intermittent or asymptomatic nature, and continuous wearable PPG combined with accelerometer data offers a potential approach for detecting abnormal rhythms in ambulatory conditions.
Does a new algorithm using wearable PPG and accelerometer signals accurately detect abnormal cardiac rhythms compared to cardiologist-validated ECG monitoring in individuals referred for ambulatory ECG monitoring?
Cohort (n=445)
Does a new algorithm using wearable PPG and accelerometer signals accurately detect abnormal cardiac rhythms compared to cardiologist-validated ECG monitoring in individuals referred for ambulatory ECG monitoring?
An algorithm combining wearable PPG and accelerometer data accurately detects cardiac arrhythmias—primarily atrial fibrillation—in real-world ambulatory settings, showing promise as a scalable screening tool.
Supports wearable PPG-accelerometer arrhythmia screening in ambulatory referrals; extends validation data but leaves open prospective outcome trials before adoption.
Introduction Cardiac arrhythmias contribute significantly to cardiovascular illness and mortality, yet they are frequently undiagnosed due to their intermittent or asymptomatic nature. Photoplethysmography (PPG) offers strong promise as a continuous, long-duration modality for identifying irregular heart rhythms. In this work, we introduce a new algorithm designed to detect abnormal rhythms using PPG combined with accelerometer data collected in everyday, ambulatory conditions. Materials and Methods ECG, PPG, and accelerometer recordings were obtained from 445 individuals referred for ambulatory ECG monitoring, divided into two study cohorts: a 24-hour cohort (45 participants) and a 7-day cohort (400 participants). Cardiac rhythms were automatically labelled using ECG-based software, and a cardiologist validated these annotations. The considered heart rhythms were normal sinus rhythm, sinus bradycardia, (sinus and supraventricular) tachycardia, and abnormal rhythms (atrial fibrillation and ventricular bigeminy). We developed an algorithm that classifies rhythms into five classes based solely on PPG and accelerometer signals: normal, bradycardia, tachycardia, abnormal, and undecidable (representing low signal quality, motion artifacts, or uncertainty). The algorithm first extracts interbeat intervals and then classifies these interbeat intervals over 2-minute episodes into one of the classes mentioned above. Performance was assessed by comparing the predicted class of each episode to the corresponding ECG annotation. Results Table 1 summarizes the performance obtained in both study cohorts. For the 24-hour cohort, the algorithm reached an accuracy of 94.1% for abnormal rhythm detection, which further improved to 96.4% in the 7-day cohort. Atrial fibrillation accounted for most abnormal episodes (92.4% in the 24-hour cohort and 99.0% in the 7-day cohort). Both sensitivity and specificity were high and consistent across the two cohorts. Conclusion We present an algorithm capable of accurately identifying arrhythmias from PPG and accelerometer data in real-world ambulatory environments, achieving excellent specificity. This method shows strong potential for scalable implementation as a screening tool in populations with elevated arrhythmia risk.
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Zaen et al. (2026) conducted a cohort in Cardiac arrhythmias (sinus bradycardia, sinus and supraventricular tachycardia, atrial fibrillation, ventricular bigeminy) (n=445). PPG and accelerometer-based rhythm classification algorithm vs. Cardiologist-validated ambulatory ECG annotations was evaluated on Accuracy for abnormal rhythm detection. The PPG and accelerometer-based algorithm achieved 94.1% accuracy for abnormal rhythm detection in the 24-hour cohort and 96.4% in the 7-day cohort compared with ECG annotations.
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