Multiparameter Respiratory Rate Estimation From the Photoplethysmogram

Abstract

We present a novel method for estimating respiratory rate in real time from the photoplethysmogram (PPG) obtained from pulse oximetry. Three respiratory-induced variations (frequency, intensity, and amplitude) are extracted from the PPG using the Incremental-Merge Segmentation algorithm. Frequency content of each respiratory-induced variation is analyzed using fast Fourier transforms. The proposed Smart Fusion method then combines the results of the three respiratory-induced variations using a transparent mean calculation. It automatically eliminates estimations considered to be unreliable because of detected presence of artifacts in the PPG or disagreement between the different individual respiratory rate estimations. The algorithm has been tested on data obtained from 29 children and 13 adults. Results show that it is important to combine the three respiratory-induced variations for robust estimation of respiratory rate. The Smart Fusion showed trends of improved estimation (mean root mean square error 3.0 breaths/min) compared to the individual estimation methods (5.8, 6.2, and 3.9 breaths/min). The Smart Fusion algorithm is being implemented in a mobile phone pulse oximeter device to facilitate the diagnosis of severe childhood pneumonia in remote areas.

Waveform Analysis to Estimate Respiratory Rate

“…In this case study we implemented exemplary algorithms (using RRest.m) which estimate RR by exploiting one of the three fundamental respiratory modulations, modelled on the approach described in [19]. RR algorithms generally consist of two compulsory components and two optional components.…”

“…RR estimation from the ECG and PPG was performed in both the frequency and time domain using the Fourier analysis and breathing cycle detection techniques used to estimate the reference RRs. An additional quality assessment and fusion step, the "Smart Fusion" method [19], was optionally performed in an attempt to increase the accuracy of RR estimates. The first step of "Smart Fusion" is to assess the quality of the RR estimates derived from the three modulations.…”

“…Two independent methods were used to estimate RR from each window in line with the methodology presented in [18]. Firstly, Fourier analysis was used to compute the power spectral density of the signal, as described in [19]. A first RR estimate was obtained as the frequency corresponding to the maximum power within the range of plausible respiratory frequencies (4-60 bpm).…”

Probabilistic Estimation of Respiratory Rate from Wearable Sensors

“…The simplest method of fusing multiple RR estimates is to calculate the average. Karlen et al used this approach, obtaining the final estimate as the mean of the individual estimates [11]. They also extended this approach by including a quality assessment check.…”

On how to extract breathing rate from PPG signal using wearable devices

“…In 2013, Karlen at al. [8] suggested a method, known as Smart Fusion, for estimating BR in real-time from the PPG signal. Three respiratory-induced variations (frequency, intensity, and amplitude) are extracted from PPG signal and the frequency content of each parameter is analysed in the frequency domain.…”