Conference PaperPDF Available

A fast algorithm for extracting the breathing rate from PPG signal

Abstract In this contribution, an algorithm for breathing
rate extraction with low computational complexity is proposed.
The here presented approach is based on EMD method and it
proves to be robust and accurate, even in presence of noisy
epochs.
I. INTRODUCTION
Extraction of breathing rate (BR) from
photoplethysmographic (PPG) signals is a current topic in
the scientific community. Although several approaches
addressing PPG-derived BR have shown good performances
[1], algorithm complexity and computational requirements
have prevented real-time applications [1, 2]. Within this
context, the here presented algorithm was designed as a
trade-off between performances and computational cost.
II. METHODS
Madhav et al. [4] proposed a method based on the
Empirical Mode Decomposition (EMD) to estimate BR from
PPG signal. The key point was to identify the oscillatory
modes at different time scales, and then to decompose the
signal accordingly. When decomposing the PPG signal via
EMD, we have observed that two principal components can
fully describe the PPG signal dynamic, and the component
corresponding to the lowest frequency is an estimate of the
BR. Hence, in our approach the EMD is stopped at the first
step, reducing algorithm complexity, and thus the
computational burden. The respiratory component was used
to estimate BR taking the dominant frequency peak of its
power spectral density (PSD).
Recording sessions from Physiobank MIMIC II
Waveform Database archive [4] were selected for assessing
the accuracy of the proposed approach. All the sessions
include PPG and respiration signals. The latter is used as
“ground truth” in the validation of the algorithm. Sixty (60)
one-minute epochs, not affected by missing data nor signal
saturation artifacts, were selected from different sessions.
III. RESULTS AND DISCUSSIONS
This approach shows good performances in estimating BR
from PPG signal. The Mean Absolute Error (MAE) is
0.0044 Hz, corresponding to 0.26 breaths per minute; the
Spearman's correlation coefficient (ρs) is 0.991. Although
Madhav et al. method seems to outperform our results, they
analyzed only five (5) one-minute epochs by using a
complex and computationally heavy iterative procedure. By
contrast, ou simplified approach proves to be robust on a
more extended dataset even when processing noisy epochs or
analyzing recordings with abnormal BR (i.e., 0.7 Hz).
Figure 1 shows the Bland-Altman plot of the difference
between the groud truth and the EMD-based BR estimates; it
(1) DEIB, Politecnico di Milano, Milan, Italy; (2) Empatica Inc.
(*) davide3.locatelli@mail.polimi.it ; (†) alessandra1.fusco@mail.polimi.it
can be noted that the errors in the estimation are relatively
small and no bias nor offset is caused by the algorithm. This
is also confirmed by the high correlation between the ground
truth values and our algorithm estimates.
Figure 1 - Bland-Altman plot showing the distribution of the
difference between the BR estimated with the EMD-based method
and the ground truth.
I. CONCLUSIONS
In this late contribution we showed that it is possible to
track BR from PPG signal with high accuracy at a low
computational cost. Despite the relatively simple structure
of the algorithm, the results indicate a strong correlation with
the ground truth. Analysis of the PPG signal offers an
alternative way of monitoring BR; this indirect estimation
can be extremely useful when only PPG signal is available.
In particular, in the field of wearable devices, this kind of
fast and robust algorithms are crucial for real-time
applications.
IV. ACKNOWLEDGMENTS
This work was developed in and supported by Empatica Inc.
REFERENCES
[1] Fleming, S. G., and Lionel T. “A comparison of signal processing
techniques for the extraction of breathing rate from the
photoplethysmogram”. Int J Biol Med Sci 2(4), 232-6, 2007.
[2] Leonard, P. A., Douglas, J. G., Grubb, N. R., Clifton, D., Addison, P.
S., Watson, J. N. “A fully automated algorithm for the determination
of respiratory rate from the photoplethysmogram.” J Clin Monitor
Comp, 20(1), 33-36, 2006.
[3] Goldberger A. L., Amaral L. A. N., Glass L., Hausdorff J. M., Ivanov
P. Ch., Mark R. G., Mietus J. E., Moody G. B., Peng C.-K., Stanley
H. E. “PhysioBank, PhysioToolkit, and PhysioNet: Components of a
New Research Resource for Complex Physiologic Signals.
Circulation 101(23), e215-e220, 2000.
[4] Madhav, K. V., Ram, M. R., Krishna, E. H., Komalla, N. R., &
Reddy, K. A. “Estimation of respiration rate from ECG, BP and PPG
signals using empirical mode decomposition”. In: Instrumentation
and Measurement Technology Conference (I2MTC), 2011 IEEE.
IEEE, 1-4, 2011.
A fast algorithm for extracting the breathing rate from PPG signal
Davide Locatelli (1,*), Alessandra Fusco (1,†), Francesco Onorati (2) and Marco D. Santambrogio (1)
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Article
Full-text available
The photoplethysmogram (PPG) is the pulsatile wave-form produced by the pulse oximeter, which is widely used for monitoring arterial oxygen saturation in patients. Various methods for extracting the breathing rate from the PPG waveform have been compared using a consistent data set, and a novel technique using autoregressive modelling is presented. This novel technique is shown to outperform the existing techniques, with a mean error in breathing rate of 0.04 breaths per minute.
Conference Paper
Estimation of respiration rates from electrocardiogram (ECG), blood pressure (BP) and photoplethysmographic (PPG) signals would be an alternative approach for obtaining respiration related information. This process is useful in situations when, ECG, BP and PPG but not respiration is routinely monitored or in cases where, the cardiac arrhythmias are to be studied in correlation with respiratory information and is extremely important. There have been several efforts on ECG-Derived Respiration (EDR), BP-Derived Respiration (BDR) and PPG Derived Respiration (PDR). These methods are based on different signal processing techniques like filtering, wavelets and other statistical methods, which work by extraction of respiratory trend embedded into various physiological signals, as an additive component, or an amplitude modulated (AM) component or frequency modulated (FM) component. The proposed method is a robust, yet simple and makes use of derived Intrinsic Mode Functions (IMF) using Empirical Mode Decomposition (EMD). Test results on ECG, BP and PPG signals of the well known MIMIC database from Physiobank archive reveal that the proposed EMD method has efficiently extracted respiratory information from ECG, BP and PPG signals. The evaluated similarity parameters in both time and frequency domains for original and estimated respiratory rates have shown the superiority of the method.
Article
The newly inaugurated Research Resource for Complex Physiologic Signals, which was created under the auspices of the National Center for Research Resources of the National Institutes of Health, is intended to stimulate current research and new investigations in the study of cardiovascular and other complex biomedical signals. The resource has 3 interdependent components. PhysioBank is a large and growing archive of well-characterized digital recordings of physiological signals and related data for use by the biomedical research community. It currently includes databases of multiparameter cardiopulmonary, neural, and other biomedical signals from healthy subjects and from patients with a variety of conditions with major public health implications, including life-threatening arrhythmias, congestive heart failure, sleep apnea, neurological disorders, and aging. PhysioToolkit is a library of open-source software for physiological signal processing and analysis, the detection of physiologically significant events using both classic techniques and novel methods based on statistical physics and nonlinear dynamics, the interactive display and characterization of signals, the creation of new databases, the simulation of physiological and other signals, the quantitative evaluation and comparison of analysis methods, and the analysis of nonstationary processes. PhysioNet is an on-line forum for the dissemination and exchange of recorded biomedical signals and open-source software for analyzing them. It provides facilities for the cooperative analysis of data and the evaluation of proposed new algorithms. In addition to providing free electronic access to PhysioBank data and PhysioToolkit software via the World Wide Web (http://www.physionet. org), PhysioNet offers services and training via on-line tutorials to assist users with varying levels of expertise.
Article
To determine if an automatic algorithm using wavelet analysis techniques can be used to reliably determine respiratory rate from the photoplethysmogram (PPG). Photoplethysmograms were obtained from 12 spontaneously breathing healthy adult volunteers. Three related wavelet transforms were automatically polled to obtain a measure of respiratory rate. This was compared with a secondary timing signal obtained by asking the volunteers to actuate a small push button switch, held in their right hand, in synchronisation with their respiration. In addition, individual breaths were resolved using the wavelet-method to identify the source of any discrepancies. Volunteer respiratory rates varied from 6.56 to 18.89 breaths per minute. Through training of the algorithm it was possible to determine a respiratory rate for all 12 traces acquired during the study. The maximum error between the PPG derived rates and the manually determined rate was found to be 7.9%. Our technique allows the accurate measurement of respiratory rate from the photoplethysmogram, and leads the way for developing a simple non-invasive combined respiration and saturation monitor.
  • A L Goldberger
  • L A N Amaral
  • L Glass
  • J M Hausdorff
  • P Ivanov
  • Ch
  • R G Mark
  • J E Mietus
  • G B Moody
  • C.-K Peng
  • H E Stanley
Goldberger A. L., Amaral L. A. N., Glass L., Hausdorff J. M., Ivanov P. Ch., Mark R. G., Mietus J. E., Moody G. B., Peng C.-K., Stanley H. E. "PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23), e215-e220, 2000.
Estimation of respiration rate from ECG, BP and PPG signals using empirical mode decomposition
  • K V Madhav
  • M R Ram
  • E H Krishna
  • N R Komalla
  • K A Reddy
Madhav, K. V., Ram, M. R., Krishna, E. H., Komalla, N. R., & Reddy, K. A. "Estimation of respiration rate from ECG, BP and PPG signals using empirical mode decomposition". In: Instrumentation and Measurement Technology Conference (I2MTC), 2011 IEEE. IEEE, 1-4, 2011. A fast algorithm for extracting the breathing rate from PPG signal