Supervisor: Dr. Wai Ho MOW
Project Code: MWH1a-06
Group Member:
KWOK Kam Yu Calina (03733828)
Introduction
Continuous cardiovascular monitoring has many benefits.
• It helps in the diagnosis and treatment of a number of major diseases, like heart diseases
• For healthy individuals, cardiovascular monitoring can also let them know about their health status and
take early treatment if symptoms of disease arise
Mobile Health Monitor System consists of
• Ring size Photoplethysmography (PPG) sensor which is small and can be worn for long time
• Cell Phone for implement the measurement algorithm and displaying the results
• Constraints of the system
o Due to the compact size of the ring sensor, the PPG signal is more likely to suffer from noise
such as motion artifacts which is due to relative movement of the sensor with respect to the skin
and ambient light due to light in the surroundings
o The computation power of cell phone is limited. Therefore it is desirable to have a signal
processing algorithm of low complexity. These have motivated me doing this research
This project aims at designing an low-complexity yet robust signal processing algorithm for the detection of
heart beat rate and breathing rate using the from the PPG signal.
The objectives of the project are to produce a smart and intelligent signal processing algorithm for the PPG
signal. The algorithm should produce accurate measurement in heart beat rate and breathing rate while
providing robust tracking in variation in heart beat rate and breathing rate. It should also be able to resistant
to noise and has low computational complexity.
Photoplethsmograph (PPG)
•
•
•
Ring sensor contains two LEDs and one receiver.
PPG is the waveform measured by the receiver
The periodic pulsatile expansion of the arterial wall
produces an increase in path length, thus amount of
light absorbed by the blood, causing periodic
variation in PPG signal with frequency equal to heart
beat rate
A low frequency variation is present in the PPG
signal that can be used to extract breathing related
information
Fig 1 Three Cycles of PPG signal
Wavelet Transform
Wavelet Transform is used as a time-frequency signal analysis tool. It is
defined as
T ( a, b) =
1
a
∫
∞
−∞
x (t )ψ *(
t −b
) dt
a
By varying the scale variable a and time location variable b, we can obtain
information about the frequency composition of the signal at different time.
The Wavelet Function ψ (t ) can be chosen to suit the purpose of the
application. One way in choosing the wavelet that has similar shape as the
signal to be analyzed. One example is the Gaussian First Wavelet use in the
Heart Beat Rate application.
Fig 2 Gaussian First Wavelet
Heart Beat Rate
Two methods are in measuring heart beat rate is analyzed.
•
Values of Ca,b Coefficients for a = 1 2 3 4 5 ...
FFT
o The frequency with maximum magnitude in the
frequency spectrum is taken as the breathing rate
Wavelet Transform
o Gaussian First Wavelet is used
o Wavelet transform is computed at scale 22 and 24
o Wavelet coefficients shows periodic maxima and
minima
o By searching for modulus maxima (maxima with
respect to time location b) in the wavelet transform
coefficients, we can find the location of the Y-points,
thus calculate the heart beat rate
scales a
•
49
46
43
40
37
34
31
28
25
22
19
16
13
10
7
4
1
27
200
150
100
50
28
30
31
time (or space) b
32
33
Fig 3 Scalogram of Wavelet Coefficients
Below shows a comparison of the heart beat rate obtained from the Wavelet Algorithm, FFT algorithm
compared to that from a commercial pulse oximeter (Nonin Pusle Oximeter).
Fig 4 Comparison of the heart beat rate obtained from the Wavelet Algorithm, FFT Algorithm and Nonin Pulse Oximeter
The heart beat rate measured by the Wavelet transform shows more robust and closer tracking with that from
the Nonin Pusle Oximeter. In fact, the Wavelet Transform algorithm provide measurement of accurate heart
beat rate.
Breathing Rate
•
•
•
•
Complex Morlet Wavelet with centre frequency 1 Hz.is
used for breathing rate measurement.
2 bands with higher magnitude in the scalogram, one from
heart beat band the other breathing band respectively
As the breathing band is of lower magnitude, it is easily
obscured by low frequency noise
“Secondary Wavelet Feature Decoupling” is employed to
solve the problem
o The ridge (locus of scale a with maximum magnitude)
is
located
and
“Ridge
Amplitude
Perturbations”(RAP) signal is obtained
o Wavelet Transform is performed on the RAP signal. A
clearer breathing band can be observed
Complex Morlet wavelet cmor2-1
0.4
0.2
0
-0.2
-0.4
-8
-6
-4
-2
-6
-4
-2
0
Real part
2
4
6
8
0
2
Imaginary part
4
6
8
0.4
0.2
0
-0.2
-0.4
-8
Fig 5 Real and Imaginary part of Complex Morlet
Wavelet
100
0.75
200
0.38
0.25
300
Breathing Band
400
0.19
500
0.15
600
27
40
5
53
Time (s)
10
0.125
80
67
15
20
Frequency(Hz)
Scale a
0
25
Fig 6 Scalogram showing magnitude of coefficients at different time and scale of Wavelet Transform of RAP signal
•
•
4
The Breathing Rate can be determined
from the phase signal of the ridge in the
RAP signal. The Phase signal shows
periodic variation between π and −π .
A drop of 0.9 × 2π in phase represents a
breath. Breathing rate can be calculated
by counting the number of drops in the
time period.
Fig 7 shows the breathing rate measured
using the Wavelet Transform Algorithm
with the subject breathing at a constant
rate of 23 breaths per second. The
measured breathing rate vary between 22
to 25 breaths per minute, which is pretty
close to the breathing rate claimed by the
subject
3
2
Phase (radian)
•
1
0
-1
-2
-3
-4
27
40
53
Time (s)
67
80
Fig 7 Phase of ridge of RAP signal showing periodic variation in
Breathing Rate (breaths per minute)
25
Conclusion
24
23
22
21
20
40
57
Time (second)
63
80
Fig 8 Measured Breathing Rate using the Wavelet Transform
Algorithm with the subject breathing at 23 breaths/min
Algorithm using wavelet transform in measuring heart
beat rate and breathing rate from PPG signal are
designed and studied. It was shown that accurate
measurement in Heart Beat Rate can be obtained
through the Wavelet Transform algorithm. It was also
shown that it is possible to measure breathing rate
from PPG signal. The future of the use of mobile
health monitoring system comprising of ring PPG
sensor and mobile phone in continuous cardiovascular
monitoring is promising.