Effect of Photoplethysmogram Artifacts on Uncertainty in Atrial Fibrillation Detection

Abstract

Detection of atrial fibrillation (AF) from photoplethysmogram (PPG) is highly sensitive to artifacts, yet their effect on uncertainty of different AF detectors remains poorly understood. This work aims to quantify how different PPG artifact types affect the uncertainty of AF detectors. Two machine learning approaches to AF detection were explored: one using 25-s PPG signals as input ($\mathit{D}_r$) and another using AF-related rhythm irregularity features ($\mathit{D}_f$). The detectors were trained on wrist PPG signals acquired during cardiac rehabilitation and then systematically evaluated on 260,000 PPG signals containing controlled artifact types and durations. Uncertainty was quantified using a threshold-based error rate, conformal prediction, and Monte Carlo dropout. Using artifact-free PPG signals, $\mathit{D}_f$ outperforms $\mathit{D}_r$ with sensitivity/specificity of 0.94/0.94 versus 0.92/0.86. Relative to artifact-free performance, sensitivity/specificity drops by 0.52/0.03, 0.25/0.03, 0.16/0.02, and 0.07/0.02 using $\mathit{D}_r$ for 12-s artifacts of device displacement, forearm motion, hand motion, and poor contact respectively. For $\mathit{D}_f$, the respective drops are 0.31/0.07, 0.19/0.11, 0.14/0.15, and 0.13/0.21 for the same artifacts. $\mathit{D}_f$ is more robust to short artifacts but exhibits increasing uncertainty with longer artifact durations, whereas $\mathit{D}_r$ shows an abrupt performance drop when artifacts occur but is less sensitive to artifact duration. Applying conformal prediction with 90$\%$ coverage reduces the false-positive rate by up to 12$\%$ for $\mathit{D}_r$ and up to 64$\%$ for $\mathit{D}_f$. Artifact type and duration have detector-specific effects on AF detection uncertainty. Device displacement causes the largest increase in uncertainty.