Cross Time-Frequency Analysis for Combining Information of Several Sources: Application to Estimation of Spontaneous Respiratory Rate from Photoplethysmography

Abstract

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.

Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review

“…Several approaches have been used to fuse simultaneous BR estimates derived from different respiratory signals. First, BRs can be fused by averaging using the mean, median, or mode [8], [64], [176], optionally after exclusion of outliers [64], [113]. The quality of the final estimate can then be assessed from the standard deviation of the individual estimates [8].…”

Non-Invasive Detection of Mechanical Alternans Utilizing Photoplethysmography

“…The ability to detect MA with existing, widely available medical equipment, opens avenues for further clinical investigations into its prognostic utility to risk stratify patients who may benefit from an implantable cardioverter defibrillator or ventricular assist device. With the expansion of wearable devices and m-health, one could speculate that MA could be integrated into an adaptive multi-variate risk-prediction model based on data recorded through a PPG sensor continuously monitoring heart rate, heart rate variability [14], turbulence [21] and respiratory rate [15], [16]. The combination of predictors related to complementary pathophysiological mechanisms provides better risk profiling [38] and an opportunity that should be tested in future studies.…”

“…Multiwavelength analysis permits the calculation of oxygenated blood in the local tissue, an application which today is rarely absent from patient monitors. Beyond its conventional use in oxygen saturation monitoring, the PPG technology has advanced in recent years and is currently capable of detecting: pulse rate variability, as a surrogate of heart rate variability [14], respiratory rate [15], [16] sleep apnea [17]- [19], ectopic beats [20], heart rate turbulence [21] and atrial fibrillation [22], [23]. Estimation of blood pressure is another active area of research, however, achieving high accuracy remains challenging [24]- [26], particularly without patient-specific calibration.…”

Parasympathetic Characterization Guided by Respiration from Wrist Peripheral Venous Pressure Waveform

“…The second method centers the band in the same way, but adjusts the bandwidth to -3dB from the peak, instead of a fixed width. The last one is based on spectral coherence between PRV and STT [12]. Spectral coherence is a function of frequency with values between 0 and 1.…”