From peripheral finger-derived pulse waveforms to aortic pressure waveform features: an application of a generalized...

Area of Science:

• Cardiovascular physiology
• Biomedical engineering
• Wearable health technology

Background:

• Aortic (central) pressure features are linked to cardiovascular complications.
• These features can be derived from peripheral arterial waveforms using algorithms.
• Conventionally, pressure waveforms (tonometry, oscillometry) are used, not optical sensors like photoplethysmography (PPG) common in wearables.

Purpose of the Study:

• To investigate the extraction of aortic features from peripheral photoplethysmography (PPG) waveforms.
• To compare aortic features derived from different peripheral arterial waveform sensor modalities using an identical transfer function.

Main Methods:

• Measured radial tonometry (reference), finger volume-clamped PPG (Peňáz), and fingertip PPG waveforms in 29 participants.
• Converted acquired waveforms into aortic pressure waveforms using a transfer function.
• Extracted waveform features and compared them using correlation plots and Bland-Altman analysis.

Main Results:

• Aortic pressure features derived from the Peňáz PPG technique were comparable to radial tonometry features.
• Aortic features extracted from fingertip PPG waveforms exhibited greater variability compared to radial tonometry.

Conclusions:

• Aortic pressure waveform features offer valuable hemodynamic information and are suitable for wearable health devices.
• Further development and optimization of a unique transfer function are necessary for PPG-based wearable devices to accurately represent aortic pressure for cardiovascular assessment.