Key result
Autoregressive moving average models removed respiratory sinus arrhythmia from raw pulse-to-pulse interval signals, yielding mean absolute errors of 132.5 ms (individual) and 137.8 ms (general).
Why the study?
PPG-derived pulse-to-pulse interval signals are affected by motion artifacts and respiratory sinus arrhythmias, impairing pulse rate variability accuracy for evaluating autonomic nervous system function.
Does an ARMA model improve the accuracy of pulse-to-pulse interval signals by removing respiratory sinus arrhythmia in healthy adults?
Does an ARMA model improve the accuracy of pulse-to-pulse interval signals by removing respiratory sinus arrhythmia in healthy adults?
Absolute Event Rate: 132.5% vs 137.8%
p-value: p=ns
An ARMA model can effectively remove respiratory sinus arrhythmia energy coupled in raw PPI signals extracted from PPGs.
May aid research denoising of PPG pulse intervals; leaves open whether it enhances clinical arrhythmia detection.
Background: Pulse rate variability (PRV), a critical biomarker of autonomic nervous system (ANS) function, is typically evaluated using the pulse-to-pulse interval (PPI) signal extracted from a photoplethysmogram (PPG). Although PPGs have been widely used in wearable devices, the PPI signal is easily affected by motion artifacts or respiratory sinus arrhythmias (RSAs). These disturbances affect the accuracy of PRV for evaluating ANS function. The aim of this study was to remove the respiratory signals from raw PPI signals with an autoregressive moving average (ARMA) model. Methods: An R-wave to R-wave interval (RRI) sequence was extracted from the electrocardiogram (ECG). A self-made measurement system was used to record PPG, ECG, and respiratory signals. Nineteen healthy adults were recruited and requested to breathe with a spontaneous breathing rate (SBR) and control breathing rates (CBRs) (6, 18, and 30 breathing rate per minute, BRPM). Their ECG, PPG, and breathing signals were recorded for 6 min under different CBRs. The measurement was performed twice, i.e., eight measurements were performed. The raw RRI(t) and PPI(t) signals of 4 Hz were segmented into samples of one minute and shifted by 30 s. Thus, a subject had 80 samples, and there were 10 samples for each BRPM. RSA-free RRI signals were generated by a spectral method to filter RSA from raw RRI(t) to produce the target RRI(t). We proposed the individual subject ARMA models trained by samples with the maximum mean absolute errors between the target RRI(t) and raw PPI(t) (MAERAWs) of each subject, and the general model trained by samples with all maximum MAERAWs of all 19 subjects. Results: The mean absolute errors between the target RRI(t) and predicted by the individual subject ARMA models (MAESubject-Models) and general ARMA model (MAEGeneral-Model) were used to evaluate the performance of the two models. The results for the MAESubject-Models and MAEGeneral-Model were 132.5 ± 59.1 ms and 137.8 ± 67.8 ms, respectively, with no significant difference. MAESubject-Models and MAEGeneral-Model were compared with MAERAWs, whose attenuations (ATTs) were 28.5 ± 13.1% and 27.8 ± 12.6%, respectively. Conclusions: The two proposed models are capable of removing the RSA energy coupled in the raw PPI signals.
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Liu et al. (2026) studied Healthy adults (n=19). Autoregressive moving average (ARMA) model vs. General ARMA model vs Individual ARMA model was evaluated on Mean absolute error between target RRI(t) and predicted by ARMA models (p=ns). Autoregressive moving average models removed respiratory sinus arrhythmia from raw pulse-to-pulse interval signals, yielding mean absolute errors of 132.5 ms (individual) and 137.8 ms (general).
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