Key result
Wrist photoplethysmogram waveform analysis using 25 heartbeats accurately detected atrial fibrillation, achieving a significantly higher AUC than 10 heartbeats (0.9676 vs 0.9453; P<2.2e-16).
Why the study?
ECG-based AF screening has limitations, prompting investigation into whether wrist PPG waveform analysis can distinguish AF from sinus rhythm and what data length optimizes detection.
Does wrist photoplethysmography accurately detect atrial fibrillation compared to continuous ECG in patients undergoing catheter ablation or electrical cardioversion?
Observational (n=116)
Does wrist photoplethysmography accurately detect atrial fibrillation compared to continuous ECG in patients undergoing catheter ablation or electrical cardioversion?
Absolute Event Rate: 0.9676% vs 0.9453%
p-value: p=<2.2e-16
Wrist photoplethysmography using a 25-heartbeat data length can accurately distinguish atrial fibrillation from sinus rhythm, supporting the use of wearable devices for AF detection.
25-interval PPG yields high AF detection accuracy in periprocedural patients; hypothesis-generating for wearables, needs prospective validation before broader use.
BACKGROUND Atrial fibrillation (AF) is associated with an increased risk of stroke, heart failure, and all-cause mortality. The electrocardiogram (ECG)–based strategy of screening for AF has some limitations. Photoplethysmography (PPG) is used in AF detection algorithms and allows passive and continuous monitoring by modern wearable devices. OBJECTIVE The objective of this study was to investigate the following: (1) whether quantitatively analyzing wrist PPG waveforms can clearly distinguish AF from sinus rhythm and (2) to determine the appropriate data length of the PPG for feature extraction to optimize the PPG analytics program for AF detection. METHODS Continuous waveforms of ECG through an electrophysiology recording system and PPG signals through a wrist–worn smartwatch were simultaneously collected from patients undergoing catheter ablation or electrical cardioversion for AF. The PPG features (temporal, spectral, or morphological) were extracted from 10, 25, 40, or 80 heartbeats of split segments. Machine learning with a support vector machine (SVM) approach was used for detecting AF. The receiver operating characteristic (ROC) curves were determined to evaluate the diagnostic accuracy. RESULTS A total of 116 patients were evaluated. The mean age was 59.6±11.4 years and 32.8% were women. We collected and annotated more than 117 hours of PPG waveforms. A total of 6478 and 3957 segments of 25-beat pulse-to-pulse interval (PPI) were annotated as AF and sinus rhythm, respectively. A total of eight features were extracted to distinguish AF, including the PPI standard deviation (SD), PPI root-mean-squared standard deviation (RMSSD), Shannon Entropy with bin = 10, 100, 1000 (SE10, SE100, SE1000), moving average of 3 PPI SD, moving SD of 3 PPI RMSSD, and moving SD of maximum FFT frequency in 3 PPI. The accuracy of all the eight PPG features extracted from the 25 PPI achieved a test AUC (area under the receiver operating characteristic curve) which was significantly better than that from the 10 PPI (the AUC was 0.9676 versus 0.9453, respectively; P<2.2e-16). CONCLUSIONS This study demonstrated that quantitatively analyzing PPG waveforms can clearly discriminate the signals of AF from those of sinus rhythm. The appropriate data length of the PPG to optimize the PPG analytics program was 25 heartbeats. CLINICALTRIAL N/A
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Liao et al. (2020) conducted an observational in Atrial fibrillation (n=116). Wrist photoplethysmogram (PPG) waveform analysis (25 heartbeats) vs. 10 heartbeats was evaluated on Test AUC for detecting atrial fibrillation (p=<2.2e-16). Wrist photoplethysmogram waveform analysis using 25 heartbeats accurately detected atrial fibrillation, achieving a significantly higher AUC than 10 heartbeats (0.9676 vs 0.9453; P<2.2e-16).
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