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
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
, 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.
Madhav et al.  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  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.
(*) firstname.lastname@example.org ; (†) email@example.com
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.
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
This work was developed in and supported by Empatica Inc.
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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)