PPG-basis: A python-based toolbox for fast photoplethysmogram decomposition and reconstruction.

BACKGROUND &

objectivePhotoplethysmogram (PPG) data are commonly used for critical biometric extractions, such as heart rate and blood oxygenation. However, PPG data contains valuable information on other metrics, such as vascular stiffness, within the morphology of the data itself, rather than individual points. As such, a growing body of literature is focused on reliably decomposing PPG data morphology for subsequent biometric extraction. Separately, the accurate reconstruction of noisy PPG data for downstream model training is necessary for developing noise-resistant models. However, to our knowledge, there is no unified toolbox that enables the testing of multiple decomposition methods and subsequent reconstruction.

methodsWe reformulate the PPG generation problem in the phase, rather than time, domain and use a look-up table to avoid recompilation. We then provide three distinct kernels to represent PPG data and develop a two-stage decomposition pipeline to accurately reconstruct PPG data.

resultsWe demonstrate an order-of-magnitude decrease in computational time using our phase-domain approach while retaining high accuracy when compared to existing methods. We further demonstrate the utility of this work through the decomposition and reconstruction of experimental data with injected noise, as well as the extraction of complex biometrics in N = 50 subjects from the MIMIC-III clinical database.

conclusionOn clean input, ppg-basis recovers morphological features - such as dicrotic notch depth and augmentation index - with substantially lower error than bandpass filtering alone, despite higher global waveform MSE. Under noise injection, the framework maintains stable morphological feature detection across SNR levels, whereas filter-based approaches produce artefactual detections that inflate with increasing noise. Furthermore, we anticipate combining morphologically accurate PPGs with noise-injection frameworks could be used for developing noise-resistant models and testing the robustness of existing models focused on PPG analysis.