Heart-rate tuned comb filters for processing photoplethysmogram (PPG) signals in pulse oximetry

Calculation of peripheral capillary oxygen saturation \({\text{(SpO}}_{{\text{2}}} {\text{)}}\) levels in humans is often made with a pulse oximeter, using photoplethysmography (PPG) waveforms. However, measurements of PPG waveforms are susceptible to motion noise due to subject and sensor movements. In this study, we compare two \({\text{SpO}}_{{\text{2}}}\)-level calculation techniques, and measure the effect of pre-filtering by a heart-rate tuned comb peak filter on their performance. These techniques are: (1) “Red over Infrared,” calculating the ratios of AC and DC components of the red and infrared PPG signals,\(\frac{(AC/DC)_{red}}{(AC/DC)_{infrared}}\), followed by the use of a calibration curve to determine the \({\text{SpO}}_{{\text{2}}}\) level Webster (in: Design of pulse oximeters, CRC Press, Boca Raton, 1997); and (2) a motion-resistant algorithm which uses the Discrete Saturation Transform (DST) (Goldman in J Clin Monit Comput 16:475–83, 2000). The DST algorithm isolates individual “saturation components” in the optical pathway, which allows separation of components corresponding to the \({\text{SpO}}_{{\text{2}}}\) level from components corresponding to noise and interference, including motion artifacts. The comparison we provide here (employing the two techniques with and without pre-filtering) addresses two aspects: (1) accuracy of the \({\text{SpO}}_{{\text{2}}}\) calculations; and (2) computational complexity. We used both synthetic data and experimental data collected from human subjects. The human subjects were tested at rest and while exercising; while exercising, their measurements were subject to the impacts of motion. Our main conclusion is that if an uninterrupted high-quality heart rate measurement is available, then the “Red over Infrared” approach preceded by a heart-rate tuned comb filter provides the preferred trade-off between \({\text{SpO}}_{{\text{2}}}\)-level accuracy and computational complexity. A modest improvement in \({\text{SpO}}_{{\text{2}}}\) estimate accuracy at very low SNR environments may be achieved by switching to the pre-filtered DST-based algorithm (up to 6% improvement in \({\text{SpO}}_{{\text{2}}}\) level accuracy at −10 dB over unfiltered DST algorithm and the filtered “Red over Infrared” approach). However, this improvement comes at a significant computational cost.