Respiratory rate detection algorithms by photoplethysmography signal processing

Area of Science:

• Biomedical Engineering
• Physiological Monitoring

Background:

• Photoplethysmography (PPG) is a non-invasive optical technique used to measure blood volume changes, providing clinically relevant physiological parameters.
• PPG signals can potentially be utilized for respiratory rate estimation, offering a convenient alternative to traditional methods.

Purpose of the Study:

• To evaluate the efficacy of three distinct algorithms (Min-Max, Peak-to-Peak, Pulse Shape) for respiratory signal detection using raw PPG data.
• To compare the performance of these PPG-based algorithms against a reference nasal sensor signal.

Main Methods:

• Three respiratory detection algorithms were developed and applied to raw PPG data from a commercial sensor.
• Two moving average filtering techniques were employed to process the PPG signals.
• Laboratory experiments involved 6 subjects breathing at controlled and arbitrary rates (10, 15 breaths/min).

Main Results:

• The Min-Max and Peak-to-Peak algorithms demonstrated superior performance in detecting respiratory rate compared to the Pulse Shape algorithm.
• The Pulse Shape algorithm exhibited accuracy only for one subject (subject 4).

Conclusions:

• Min-Max and Peak-to-Peak algorithms are viable for respiratory rate detection using PPG.
• Further research and more extensive experimental data are needed to enhance the accuracy and reliability of PPG-based respiratory monitoring, particularly for the Pulse Shape algorithm.