Key result
Principal Component Analysis and Empirical Mode Decomposition efficiently extracted respiratory information from photoplethysmographic signals, demonstrating high similarity to reference respiratory rates.
Principal Component Analysis and Empirical Mode Decomposition can successfully extract respiratory rates from non-invasive PPG signals, potentially eliminating the need for separate respiratory sensors.
May enable PPG-based respiratory monitoring without dedicated sensors; hypothesis-generating pending clinical validation.
Photoplethysmography is a non-invasive electro-optic method developed by Hertzman, which provides information on the blood volume flowing at a particular test site on the body close to the skin. PPG waveform contains two components; one, attributable to the pulsatile component in the vessels, i.e. the arterial pulse, which is caused by the heartbeat, and gives a rapidly alternating signal (AC component). The second one is due to the blood volume and its change in the skin which gives a steady signal that changes very slowly (DC component). PPG signal consists of not only the heart-beat information but also a respiratory signal.
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Bojjireddy Prathyusha (2012) studied Healthy volunteers and sleep apnea (MIMIC database) (n=18). Principal Component Analysis (PCA) and Empirical Mode Decomposition (EMD) vs. Reference respiratory signals (e.g., nasal thermistor) was evaluated on Similarity of estimated respiratory rates to reference respiratory rates in time and frequency domains. Principal Component Analysis and Empirical Mode Decomposition efficiently extracted respiratory information from photoplethysmographic signals, demonstrating high similarity to reference respiratory rates.
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