Key result
SDOF-TF method achieves 100% accuracy detecting AF from resting PPG signals using respiration and HRV.
Why the study?
Current signal-processing techniques using at-rest PPG signals do not achieve perfect accuracy for detecting AF, even under low-motion artifact conditions.
Does the SDOF-TF method improve the accuracy of atrial fibrillation detection from at-rest PPG signals in subjects from the MIMIC PERform AF dataset?
Does the SDOF-TF method improve the accuracy of atrial fibrillation detection from at-rest PPG signals in subjects from the MIMIC PERform AF dataset?
The SDOF-TF method demonstrated 100% accuracy in detecting atrial fibrillation from at-rest PPG signals by effectively modeling and removing motion artifacts.
May enhance controlled PPG-based AF detection; leaves open clinical translation pending prospective validation.
At-rest PPG signals have been explored for detecting atrial fibrillation (AF), yet current signal-processing techniques do not achieve perfect accuracy even under low-motion artifact (MA) conditions. This study evaluates the effectiveness of a single-degree-of-freedom time–frequency (SDOF-TF) method in analyzing at-rest PPG signals for AF detection. The method leverages the influence of MA on the instant parameters of each harmonic, which is identified using an SDOF model in which the tissue–contact–sensor (TCS) stack is treated as an SDOF system. In this model, MA induces baseline drift and time-varying system parameters. The SDOF-TF method enables the quantification and removal of MA and noise, allowing for the accurate extraction of the arterial pulse waveform, heart rate (HR), heart rate variability (HRV), respiration rate (RR), and respiration modulation (RM). Using data from the MIMIC PERform AF dataset, the method achieved 100% accuracy in distinguishing AF from non-AF cases based on three features: (1) RM, (2) HRV derived from instant frequency and instant initial phase, and (3) standard deviation of HR across harmonics. Compared with non-AF, the RM for each harmonic was increased by AF. RM exhibited an increasing trend with harmonic order in non-AF subjects, whereas this trend was diminished in AF subjects.
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Hasan et al. (2026) studied this question. The SDOF-TF method achieved 100% accuracy in detecting atrial fibrillation from at-rest PPG signals using features like respiration modulation and heart rate variability.
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