Finger and forehead PPG signal comparison for respiratory rate estimation.

OBJECTIVE: An evaluation of the location of the photoplethysmogram (PPG) sensor for respiratory rate estimation is performed. APPROACH: Finger PPG, forehead PPG, and respiratory signal were simultaneously recorded from 35 subjects while breathing spontaneously, and during controlled respiration experiments at a constant rate from 0.1 Hz to 0.6 Hz, in 0.1 Hz steps. Four PPG-derived respiratory (PDR) signals were extracted from each one of the recorded PPG signals: pulse rate variability (PRV), pulse width variability, pulse amplitude variability and the respiratory-induced intensity variability (RIIV). Respiratory rate was estimated from each one of the four PDR signals for both PPG sensor locations. In addition, different combinations of PDR signals, power distribution of the respiratory frequency range and differences of the morphological parameters extracted from both PPG signals have been analysed. MAIN RESULTS: Results show better performance in terms of successful estimation and relative error when: (i) PPG signal is recorded in the finger; (ii) the respiratory rate is less than 0.4 Hz; (iii) RIIV signal is not considered. Furthermore, lower spectral power around the respiratory rate in the PDR signals recorded from the forehead was observed. SIGNIFICANCE: These results suggest that respiratory rate estimation is better at lower rates (0.4 Hz and below) and that the finger is better than the forehead to estimate respiratory rate.

Cross time-frequency analysis for combining information of several sources: application to estimation of spontaneous respiratory rate from photoplethysmography.

A methodology that combines information from several nonstationary biological signals is presented. This methodology is based on time-frequency coherence, that quantifies the similarity of two signals in the time-frequency domain. A cross time-frequency analysis method, based on quadratic time-frequency distribution, has been used for combining information of several nonstationary biomedical signals. In order to evaluate this methodology, the respiratory rate from the photoplethysmographic (PPG) signal is estimated. The respiration provokes simultaneous changes in the pulse interval, amplitude, and width of the PPG signal. This suggests that the combination of information from these sources will improve the accuracy of the estimation of the respiratory rate. Another target of this paper is to implement an algorithm which provides a robust estimation. Therefore, respiratory rate was estimated only in those intervals where the features extracted from the PPG signals are linearly coupled. In 38 spontaneous breathing subjects, among which 7 were characterized by a respiratory rate lower than 0.15 Hz, this methodology provided accurate estimates, with the median error {0.00; 0.98} mHz ({0.00; 0.31}%) and the interquartile range error {4.88; 6.59} mHz ({1.60; 1.92}%). The estimation error of the presented methodology was largely lower than the estimation error obtained without combining different PPG features related to respiration.