Estimation of respiratory rate from ECG, photoplethysmogram, and piezoelectric pulse transducer signals: a comparative study of timefrequency methods (original) (raw)
Related papers
Estimation of Respiratory Rate From Photoplethysmogram Data Using Time–Frequency Spectral Estimation
IEEE Transactions on Biomedical Engineering, 2000
We present a new method that uses the pulse oximeter signal to estimate the respiratory rate. The method uses a recently developed time-frequency spectral estimation method, variablefrequency complex demodulation (VFCDM), to identify frequency modulation (FM) of the photoplethysmogram waveform. This FM has a measurable periodicity, which provides an estimate of the respiration period. We compared the performance of VFCDM to the continuous wavelet transform (CWT) and autoregressive (AR) model approaches. The CWT method also utilizes the respiratory sinus arrhythmia effect as represented by either FM or AM to estimate respiratory rates. Both CWT and AR model methods have been previously shown to provide reasonably good estimates of breathing rates that are in the normal range (12-26 breaths/min). However, to our knowledge, breathing rates higher than 26 breaths/min and the real-time performance of these algorithms are yet to be tested. Our analysis based on 15 healthy subjects reveals that the VFCDM method provides the best results in terms of accuracy (smaller median error), consistency (smaller interquartile range of the median value), and computational efficiency (less than 0.3 s on 1 min of data using a MATLAB implementation) to extract breathing rates that varied from 12-36 breaths/min. Index Terms-FM, pulse oximeter, respiratory sinus arrhythmia, time-frequency analysis.
Toward a Robust Estimation of Respiratory Rate From Pulse Oximeters
IEEE Transactions on Biomedical Engineering, 2017
Goal: Current methods for estimating respiratory rate (RR) from the photoplethysmogram (PPG) typically fail to distinguish between periods of high-and low-quality input data, and fail to perform well on independent "validation" datasets. The lack of robustness of existing methods directly results in a lack of penetration of such systems into clinical practice. The present work proposes an alternative method to improve the robustness of the estimation of RR from the PPG. Methods: The proposed algorithm is based on the use of multiple autoregressive models of different orders for determining the dominant respiratory frequency in the three respiratory-induced variations (frequency, amplitude, and intensity) derived from the PPG. The algorithm was tested on two different datasets comprising 95 eight-minute PPG recordings (in total) acquired from both children and adults in different clinical settings, and its performance using two window sizes (32 and 64 seconds) was compared with that of existing methods in the literature. Results: The proposed method achieved comparable accuracy to existing methods in the literature, with mean absolute errors (median, 25th-75th percentiles for a window size of 32 seconds) of 1.5 (0.3-3.3) and 4.0 (1.8-5.5) breaths per minute (for each dataset respectively), whilst providing RR estimates for a greater proportion of windows (over 90% of the input data are kept). Conclusion: Increased robustness of RR estimation by the proposed method was demonstrated. Significance: This work demonstrates that the use of large publicly available
Towards a Robust Estimation of Respiratory Rate from Pulse Oximeters
IEEE transactions on bio-medical engineering, 2016
Current methods for estimating respiratory rate (RR) from the photoplethysmogram (PPG) typically fail to distinguish between periods of high- and low-quality input data, and fail to perform well on independent "validation" datasets. The lack of robustness of existing methods directly results in a lack of penetration of such systems into clinical practice. The present work proposes an alternative method to improve the robustness of the estimation of RR from the PPG. The proposed algorithm is based on the use of multiple autoregressive models of different orders for determining the dominant respiratory frequency in the three respiratory-induced variations (frequency, amplitude and intensity) derived from the PPG. The algorithm was tested on two different datasets comprising 95 8-minute PPG recordings (in total) acquired from both children and adults in different clinical settings, and its performance using two window sizes (32 and 64 seconds) was compared with that of existi...
Validation of Instantaneous Respiratory Rate Using Reflectance PPG from Different Body Positions
Sensors, 2018
Respiratory rate (RR) is a key parameter used in healthcare for monitoring and predicting patient deterioration. However, continuous and automatic estimation of this parameter from wearable sensors is still a challenging task. Various methods have been proposed to estimate RR from wearable sensors using windowed segments of the data; e.g., often using a minimum of 32 s. Little research has been reported in the literature concerning the instantaneous detection of respiratory rate from such sources. In this paper, we develop and evaluate a method to estimate instantaneous respiratory rate (IRR) from body-worn reflectance photoplethysmography (PPG) sensors. The proposed method relies on a nonlinear time-frequency representation, termed the wavelet synchrosqueezed transform (WSST). We apply the latter to derived modulations of the PPG that arise from the act of breathing.We validate the proposed algorithm using (i) a custom device with a PPG probe placed on various body positions and (i...
PLoS ONE, 2014
The photoplethysmogram (PPG) obtained from pulse oximetry measures local variations of blood volume in tissues, reflecting the peripheral pulse modulated by heart activity, respiration and other physiological effects. We propose an algorithm based on the correntropy spectral density (CSD) as a novel way to estimate respiratory rate (RR) and heart rate (HR) from the PPG. Time-varying CSD, a technique particularly well-suited for modulated signal patterns, is applied to the PPG. The respiratory and cardiac frequency peaks detected at extended respiratory (8 to 60 breaths/min) and cardiac (30 to 180 beats/min) frequency bands provide RR and HR estimations. The CSD-based algorithm was tested against the Capnobase benchmark dataset, a dataset from 42 subjects containing PPG and capnometric signals and expert labeled reference RR and HR. The RR and HR estimation accuracy was assessed using the unnormalized root mean square (RMS) error. We investigated two window sizes (60 and 120 s) on the Capnobase calibration dataset to explore the time resolution of the CSD-based algorithm. A longer window decreases the RR error, for 120-s windows, the median RMS error (quartiles) obtained for RR was 0.95 (0.27, 6.20) breaths/min and for HR was 0.76 (0.34, 1.45) beats/min. Our experiments show that in addition to a high degree of accuracy and robustness, the CSD facilitates simultaneous and efficient estimation of RR and HR. Providing RR every minute, expands the functionality of pulse oximeters and provides additional diagnostic power to this non-invasive monitoring tool.
Respiratory rate extraction from pulse oximeter and electrocardiographic recordings
Physiological Measurement, 2011
We present an algorithm of respiratory rate extraction using particle filter (PF), which is applicable to both photoplethysmogram (PPG) and electrocardiogram (ECG) signals. For the respiratory rate estimation, 1 min data are analyzed with combination of a PF method and an autoregressive model where among the resultant coefficients, the corresponding pole angle with the highest magnitude is searched since this reflects the closest approximation of the true breathing rate. The PPG data were collected from 15 subjects with the metronome breathing rate ranging from 24 to 36 breaths per minute in the supine and upright positions. The ECG data were collected from 11 subjects with spontaneous breathing ranging from 36 to 60 breaths per minute during treadmill exercises. Our method was able to accurately extract respiratory rates for both metronome and spontaneous breathing even during strenuous exercises. More importantly, despite slow increases in breathing rates concomitant with greater exercise vigor with time, our method was able to accurately track these progressive increases in respiratory rates. We quantified the accuracy of our method by using the mean, standard deviation and interquartile range of the error rates which all reflected high accuracy in estimating the true breathing rates. We are not aware of any other algorithms that are able to provide accurate respiratory rates directly from either ECG signals or PPG signals with spontaneous breathing during strenuous exercises. Our method is near real-time realizable because the computational time on 1 min data segment takes only 10 ms on a 2.66 GHz Intel Core2 microprocessor; the data are subsequently shifted every 10 s to obtain near-continuous breathing rates. This is an attractive feature
A Signal Processing Method for Respiratory Rate Estimation through Photoplethysmography
International Journal of Signal Processing, Image Processing and Pattern Recognition, 2018
Monitoring of respiration is crucial for determining a patient´s health status, specially previously and after an operation. However, many conventional methods are difficult to use in a spontaneously ventilating patient. This paper presents a method for estimating respiratory rate from the signal of a photoplethysmograph. This is a non-invasive sensor that can be used to obtain an estimation of beats per minute of a given patient by measuring light reflection on the patient's blood vessel and counting changes in blood flow. The PPG signal also offers information about respiration, so respiratory rate can be obtained through signal processing. The proposed method based on digital filtering was implemented in a wearable device and tested on 30 volunteers, and the results were compared with the ones measured by traditional ways. The results show that there is no statistically significant difference between the data measured by the device and the traditional method.
Sensors
Respiration rate (RR) provides useful information for assessing the status of a patient. We propose RR estimation based on photoplethysmography (PPG) because the blood perfusion dynamics are known to carry information on breathing, as respiration-induced modulations in the PPG signal. We studied the use of amplitude variability of transmittance mode finger PPG signal in RR estimation by comparing four time-frequency (TF) representation methods of the signal cascaded with a particle filter. The TF methods compared were short-time Fourier transform (STFT) and three types of synchrosqueezing methods. The public VORTAL database was used in this study. The results indicate that the advanced frequency reallocation methods based on synchrosqueezing approach may present improvement over linear methods, such as STFT. The best results were achieved using wavelet synchrosqueezing transform, having a mean absolute error and median error of 2.33 and 1.15 breaths per minute, respectively. Synchrosqueezing methods were generally more accurate than STFT on most of the subjects when particle filtering was applied. While TF analysis combined with particle filtering is a promising alternative for real-time estimation of RR, artefacts and non-respiration-related frequency components remain problematic and impose requirements for further studies in the areas of signal processing algorithms an PPG instrumentation.