PROJECT REPORT ON
RESPIRATORY RATE
ESTIMATION USING PPG
SIGNALS
CONTENTS
1.INTRODUCTION

What is respiration

Respiratory rate
2.Respiration rate monitoring
3.Conventional method
4.Photoplethysmograph
5.Respiratory rate measurments using ppg signal
6.PPG sensor
7.Respiratory measurment
8.Plethysmography
9.Methodology
10.PPG processing
11.Identification of peak points
12.Genration of peak vs time and peak interval vs time data
13.Performing FFT of the signal
14.FFT spectrum of RIFV signal
15.Result
16.Table
17.Challenge in processing the PPG signals
18.Conclusion
What is Respiration ?
Respiration is a complex metabolic biochemical process where in, the living cells of
an organism obtains energy, in the form of
Adenosine Triphosphate (ATP) by taking in
oxygen and liberating carbon dioxide from
the oxidation of complex organic substances
Respiratory Rate
• Respiration rate is an important vital sign. It is, the number of
breaths taken within a set amount of time.
Newborn: 30-60 breaths per minute
Infant (1 to 12 months): 30-60
breaths per minute
Toddler (1-2 years): 24-40 breaths
per minute
Preschooler (3-5 years): 22-34
breaths per minute
School-age child (6-12 years): 18-30
breaths per minute
Adolescent (13-17 years): 12-16
breaths per minute
RESPIRATION RATE MONITORING


Respiratory rate is a critical vial sign that provides
early detection of respiratory compromise and
patient distress
Continous monitoring of respiratory rate is
especially important for post–surgical patients
receiving patient- controlled analgesia (PCA) for
pain management as the sedation can induce
respiratory depression and place patients at
considerable risk of serious injury or death
Conventional Methods
• An efficient and accurate method of respiratory measurement is still not present in
healthcare sector.
• There are different approaches for respiration monitoring, but generally they can
be classed as contact or noncontact. For contact methods, the sensing device (or
part of the instrument containing it) is attached to the subject's body.
• Concerns related to the patient's recording comfort, recording hygiene, and the
accuracy of respiration rate monitoring have resulted in the development of a
number of noncontact respiration monitoring approaches.
What is Photoplethysmograph?
• The word plethysmograph is a combination of two ancient
Greek words ‘plethysmos’ which means increase and ‘graph’
which is the word for write.
Photoplethysmography (PPG) is used to estimate the skin blood
flow using infrared light. Researchers from different domains of
science have become increasingly interested in PPG because of
its advantages as non-invasive, inexpensive, and convenient
diagnostic tool.
Traditionally, it measures the oxygen saturation, blood pressure,
cardiac output, and for assessing autonomic functions.
Respiratory rate measurment using PPG
signal

Photoplethysmography(PPG) is the measurement of blood
volumetric changes with each heartbeat. The PPG pulse
obtained by the optical sensor is used for various
cardiovascular parameter measurement.
PPG SENSOR
Among
the PPG
sensors under
the category of
contact type PPG
sensors, the
Respiration Measurement
• The irregularity in respiration process can cause illness problem to a person. With
this regard it can be said that the respiratory rate is one of the earliest indicators of
physiological instability including cardiac problem or other health related issues.
• The failures in Multi Organ Systems are indicated by changes in respiration pattern
as shown in Fig
Fig.15 Respiration pattern in normal and disease state
Plethysmography:
•
Plethysmography is a method of evaluating pulmonary ventilation
by measuring the movement of the chest and abdominal wall.
Plethysmography measures changes in volume in different areas of
our body.
• It measures these changes with blood
pressure cuffsor other
sensors. These are attached to a machine called a plethysmograph.
Plethysmography is especially effective in detecting changes caused
by blood flow. It can also help to determine a blood clot in subject
arm or leg.
Methodology
The proposed methodology consist of the major parts as shown in figure
PPG Preprocessing
Identification
of peak points
Estimating the
respiratory rate
Generation of
peak vs Time
and peak
interval vs time
data
Performing FFT
of the signals
PPG preprocessing
•
•
The quality of ppg signal depends on the location and the properties of the subjects
skin at measurement, including the individual skin structure, the blood oxygen
saturation ,blood flow rate ,skin temperatures and the measuring environment.
The PPG signal available in DEAP database is considered for the present work. The
original signal is found to contain few artifacts like powerline interference which
needs to be filtered out before further processing. As sampling frequency is 250Hz
,5 point Moving average filter has been used for removing power line interference
noise.
Identification of peak points
•
•
We have developed a peak detection algorithm for accurate determination of respiratory rate
,using ppg signals.
Peak points detection is the first step for the analysis of respiratory rate estimation.

Identification of peak points of data 1
Generation of peak vs time and peak interval vs time data


RIAV(Respiratory induced amplitude variation )signal is obtained by plotting the
amplitudes of the peaks against time as shown in figure
In order to obtained the RIFV (Respiratory induced frequency variation) signal, the
difference between two consecutive peak instance is computed
Fig RIAV and RIFV for emotion Excited
Performing FFT of the signal
The FFT algorithm is used to convert a digital signal with length (N) from the time domain into a
signal in the frequency domain.
Fig FFT of RIAV signal for emotion Excited
The FFT spectrum of RIFV signal is shown in figure:
Fig FFT of RIFV signal for emotion Excited
RESULT
• FFT has been performed over above mention signals for extracting the frequency components. From the spectrum,
the frequency having the max amplitude denotes the respiratory rate per second
• In order to validate our method we have compared the FFT of RIAV and RIFV with the FFT of the actual respiratory
signal. The FFT of the actual respiratory signal for emotion excitation is given in figure.
Fig FFT of actual respiratory signal for emotion Excited
TABLE
•
•
SUBJECT(Emotion)
Respiration
Rate from
RIAV signal
Respiration rate Respiratory Rate
from RIFV
from actual
respiratory
signal
signal
EXCITED
(0.3182*60)=19.092
(0.2835*60)=17.0149
(0.2857*60)=17.142
When the values of RR(Respiratory Rate) from RIAV and RIFV are compared with the
RR(Respiratory Rate) from the actual respiratory signal they are found to be close to each
other.
From here we can conclude that our proposed method is capable enough to estimate the
respiratory rate from the PPG signal without using any direct respiratory sensor.
Challenges in processing the PPG signals
• Powerline Interference
• Motion Artifact
• Low Amplitude PPG Signal
• Premature Ventricular Contraction
• These factors generate several types of additive artefact which
may be contained within the PPG signals. This may affect the
extraction of features and hence the overall diagnosis,
especially, when the PPG signal and its derivatives will be
assessed in an algorithmic fashion.
• In order to improve the accuracy of respiratory rate extracted
from photoplethysmography (PPG) signal, a method based on
moving average filtering is proposed.
CONCLUSION
•
•
•
•
•
Extraction of respiratory rate from Photoplethysmographic (PPG) signals will
potentially eliminate the use of sensor intended to record respiration. In this
report, a novel based algorithm is presented and applied to extract respiratory
rate from Photoplethysmographic (PPG ) signals.
The derived respiratory signal using principal component was compared with
the recorded respiratory signals available in DEAP database and shown a strong
correlation.
In this contribution, we showed that it is possible to track respiratory rate from
PPG signal with high accuracy at a low computational cost.
From our work we can conclude that our proposed method is capable enough
to estimate the respiratory rate from the ppg signal without using any direct
respiratory sensor.
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 Respiratory rate ; this indirect estimation can be
extremely useful in situations when a continuous monitoring is required
THANKYOU