University of Wismar
Faculty of Engineering
Department of Electrical Engineering and Computer Science
Prof. Dr.-Ing. Jens Kraitl
WINTER SEM-2022/23,
SUBMISSION DATE:20-01-2023
Matriculation Numbers:
Ankita- 457620
Rohith- 451730
BIOSIGNAL PROCESSING
PULSE OXIMETRY
ANKITA KANNAN IYER and
ROHITHVISHAL PENTAKOTA
Research Seminar, Masters in IEE
PRINCIPLE AND PPGSIGNAL AANALYSIS
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ABSTRACT
For older persons, pregnant women, and in many other life-threatening conditions, continuous
measurement of oxygen level and pulse rate is crucial. An oximeter is frequently used to keep an eye
on this. In this report, we'll talk about the PPG-Signal analysis of individual samples and the bio-signal
processing pulse oximetry basis. We will examine the frequency of various blood impulses using
MATLAB. A voltage signal known as photoplethysmography can be used to assess the change in light
intensity passed through tissue as a result of arterial blood pulse (PPG). Blood with oxygen and blood
without oxygen absorb light differently at red and infrared wavelengths. In this study, a moving average
technique is employed to eliminate high-frequency noise, while a low pass filter is utilized to remove
motion distortions.
Keywords: Pulse oximetry, AC and DC components, PPG signal, Savitzky- Golay filter, FFT, STFT,
Wigner-Ville distribution, Continuous Wavelet Transformation.
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INDEX
Sr.
no.
TITLE
Pg
no.
1.
Human Cardiovascular System
3
2.
Pulse Oximetry
8
3.
Oxygen Saturation in Blood
11
4.
Optical Properties of Blood and Tissues
14
5.
Beer- Lamberts Law
17
6.
Photoplethysmography
18
7.
Evaluation of Test Signals
19
7.1 Time, Frequency, and Time-Frequency Analysis of our personalized test signal
7.2 Time, Frequency, and Time-Frequency Analysis of Test Signals
8.
Evaluation of PPG Signals
38
8.1 Time, Frequency, and Time-Frequency Analysis
8.2 Results of FFT, STFT, Wigner-Ville, and Wavelet transform
8.3 Results of derivative of patient data
9.
Calculation of the Ratio OF Ratio
10.
Conclusion
43
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1
HUMAN CARDIOVASCULAR SYSTEM
1.1 Introduction
The cardiovascular system is also called the circulatory system, where cardio is called the “heart” and
the vascular represents the “blood vessels.” This system handles producing oxygen by extracting blood
from the heart and pumping it through the lungs. After this body gets oxygenated blood supplied by the
arteries from the heart, while veins carry the deoxygenated blood back to the heart, and the system is
repeated again and the process continues.
1.2 Functions of the Cardiovascular System
The system discards the waste from both the cells and organs of the body.
The system supplies oxygen, nutrients, and hormones to various parts of the body such as muscles,
tissues, and organs. There are two blood circulatory systems in the body.
The first is the systemic circulatory system. This is the main blood circulatory system that transports
blood to the organs, tissues, and cells throughout the body.
The second is the pulmonary circulatory system. This circulatory system moves blood between the heart
and the lungs. It is where oxygen enters the blood and carbon dioxide leaves the blood.
1.3 Parts of the Cardiovascular system
1.3.1 Heart
The heart is the most important organ of the circulatory system. The matrix of blood vessels handles
carrying the blood to the rest of the body, it controls the blood pressure and heart rate by working with
other parts of the system. The four main sections of the heart are driven by electrical impulses and
the functioning depends on the brain and the nervous system. It can be said that the heart circulates
nearly 2000 gallons (about 7570.82 L) of blood every day in the human body.
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Figure 1.1 Picture standing for the sections of the heart
Figure 1.1 stands for the path of the blood flow through various stages in the heart. There are 12
stages of blood transfer from the heart to all over the body and back to the heart to keep the blood in
circulation. Below we are discussing various stages of blood flow in the heart.
The largest vein in the body is thought to be the vena cava. The Superior Vena Cava and the Inferior
Vena Cava are separated from one another. The inferior vena cava carries blood from the abdomen and
legs, whereas the superior vena cava gathers blood from the upper section of the body, including the
head and arms. These then sent the heart's right atrium deoxygenated blood. The chamber where blood
will be gathered is called the atrium. The Tricuspid valve is the passageway used to deliver the blood
to the right ventricle. The valve opens to let blood pass through it, but it closes as soon as it reaches the
right ventricle to stop backflow. One of the primary chambers in the heart, the right ventricle is located
in the lower region of the organ. The pulmonary valve collects the blood that is low in oxygen and sends
it to the pulmonary artery. Deoxygenated blood can be transferred through this valve. Blood is pushed
to the lungs, where it is oxygenated, as soon as it enters the pulmonary artery. The pulmonary veins
draw oxygen-rich blood from the body and deliver it to the left atrium chamber. The left atrium pumps
oxygen-rich blood to the left ventricle, where it is then pumped through the aortic valve to the aorta.
The lower left heart chamber and the aortic valve are separated by the aortic valve, which controls blood
flow. Blood is then distributed throughout the body by the Aorta, which transports it from the heart.
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1.3.2 Blood Vessels
These are tube-like structures through which blood flows through them. These are classified into three
types known as arteries, veins, and capillaries.
Figure 1.2 Represents the types of blood vessel
Arteries are thin and strong tubes that carry oxygenated blood to all parts of the body. The largest artery
in the heart is Aorta and this connects the heart passing through the chest to the stomach. The arteries
again divide into several small branches called arterioles and grow further into minute small arteries
while traveling away from the heart(fig1.2).
Veins carry less oxygenated blood away from the heart. They are also called venules that start small
and become larger along the way to the heart. The central veins are classified into two types known as
superior vena cava and inferior vena cava. The central veins look after blood traveling to your heart.
The superior vena cava transfers blood from the head and the arms to the heart and the inferior vena
cava transfers the blood from the lower parts of the body such as from the legs. The valves in the veins
present in the lower parts of the body deal with the backward flow of the blood traveling through the
inferior vena cava.
The Capillary is the bridge between the arteries and veins that connect the arterioles and the venules.
Through the capillaries, the waste and the nutrients are transferred between the body tissues and the
body.
1.3.3 Blood
It is a fluid that supplies nutrients and oxygen to the cells and absorbs the carbon dioxide and other
waste products from the cells present in the body. These cells are dispersed through a network of fluids
called plasma. Blood is categorized into four types namely white blood cells, red blood cells, plasma,
and platelets(fig 1.3).
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Figure 1.3 Types of cells present in Blood
White blood cells are technically named leukocytes. These cells play a vital role in the human immune
system that protects your body from various infections and kills the virus or bacteria that enter your
body. The white blood cells are fewer in number compared to red blood cells in our body and account
for 1% of the blood. White blood cells have no color but under the microscope, they can be seen as
light pink or purple and are circular in shape with a distinct membrane called the nucleus.
Red blood cells are also technically named erythrocytes which are a cellular part of blood. The blood
cells carry oxygen from the lungs to the tissues of the body. The size of red blood cells is small, they
are circular and it looks dumbbell-shaped. These blood cells are covered by a membrane made up of
lipids and proteins, which lack a nucleus, they are enriched with hemoglobin- a red iron-rich protein
that holds oxygen. The average life span of red blood cells in the adult human body is 100-120 days
(about 4 months). It is estimated that around 5.2 million red blood cells per cubic millimeter of blood
are present in the human body.
Plasma is also named blood plasma as it is present in a liquid. It acts as a medium for carrying nutrients
to the cells present in the organs of the human body and can also excrete waste substances or products
from the kidneys, liver, and lungs. This fluid helps keep blood pressure and distributes heat throughout
the body, and keeps a good PH value in the bloodstream and the blood. It has more than 90% of water
and has solutes that are necessary to sustain human life. Plasma has several elements such as:
1. Minerals present: sodium, chloride, potassium, magnesium, and calcium.
2. Other substances: Amino acids, vitamins, organic acids, pigments, and enzymes.
3. Hormones: Insulin, corticosteroids, and thyroxine.
Platelets are also known as thrombocytes, which are small and help prevent the blood from clotting.
Platelets are formed in the tissue of your bone marrow The largest cell present in the bone marrow is
the megakaryocytes, which are responsible for the production of the platelets.
Function: The blood platelets will act as a natural healer for the blood to stop bleeding.
For example, when an injury happened at some place in your body, the platelets are attached to the
walls of blood vessels and thereby allowing the plasma to flow through and help in recovering process
of the blood clot and prevent excess blood from exceeding the body. Platelets account for 1% of your
whole blood as same as white blood cells. For every 10-20 red blood cells there is one platelet present.
The estimated range of blood platelets in the adult body ranges from 1,50,000 to 4,00,00 platelets per
microliter of blood.
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1.3.4 Respiration
The respiratory system and the lungs handle breathing. There are two ways to let the breath in and out,
called inhalation and exhalation. The respiratory system and the lungs handle bringing oxygen into our
body; thus, this process is known as inhalation. Secondly, the respiratory system and the lungs carry
out carbon dioxide (CO2), which is known as exhalation. The respiratory system consists of the mouth,
nose, throat, voice box, windpipe, and lungs.
Figure 1.4: Stands for the respiratory system functioning in the human body.
Air enters the nostrils, also known as nares, where it is warmed and humidified. Tiny hair follicles
present in the nostrils, protect the nasal passage of the respiratory tract, filtering out dust and other
particles that enter the nose through the breathed air. There are two openings known as the nasal cavity
and mouth which meet at the pharynx, present at the back of the mouth or throat. Pharynx acts as a main
passage for the air to pass. Food and air can be entered via the oral cavity. The pathway is divided into
two: one for the food and the other for the air. Esophagus leads the food to enter the stomach, while the
Epiglottis (a small layer of tissue) prevents the food from entering the lungs and acts as a gateway. The
larynx (also called the voice box) acts as air only pipe which has a pair of vocal cards. The trachea or
windpipe is the extension of the airway, where the air is channeled to the left and right of the bronchi.
The membrane helps keep the lungs flexible and efficient. Within the lungs, the bronchi stem into
smaller bronchi known as bronchioles. The exchange of O2 and CO2 takes place in Alveoli, every
human has hundreds of alveoli present in their lungs(fig 1.4).
The cells inside our body are present due to the active oxygen content present. Oxygen gets inhaled into
our body with the help of the lungs and the respiratory system and carbon dioxide get exhaled. The
diaphragm moves downwards toward the abdomen as you breath-in, the rib muscles pull ribs upward
and outwards, making a larger chest cavity thereby letting the air breathe in. while, exhalation the
diaphragm moves upwards, thus shrinking the chest cavity and pushing out the air out of the respiratory
system from the nose or by mouth. With inhalation, air fills a substantial part of the millions of alveoli.
In a process called diffusion, oxygen moves from the alveoli to the blood through the capillaries (tiny
blood vessels) lining the alveolar walls(fig 1.5). Once it reaches the bloodstream, oxygen gets picked
up by the hemoglobin in red blood cells. This oxygen-rich blood then flows back to the heart, which
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pumps it through the arteries to oxygen-hungry tissues throughout the body. Oxygen through capillaries
in tissues and hemoglobin and moves into the cells. Carbon dioxide moves out of the cells into the
capillaries, where most of it dissolves in the plasma of the blood. Blood rich in carbon dioxide then
returns to the heart via veins. From the heart, this blood is pumped into the lungs, where carbon dioxide
is exhaled.
Figure 1.5: Simplified representation of oxygen exchange from the lungs to the blood.
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2
PULSEOXIMETERY
2.1 History
In 1933, Karl Mathis conceived invented the current pulse oximeter. He developed a tool that used red
and infrared light to continually gauge an individual's ear's oxygen saturation. Glenn Millikan created
the first real-world use for this technology during World War II. He connected an ear oximeter—a term
he coined—to a system that sent oxygen directly into pilots' masks as soon as their oxygen levels were
too low to address the issue of pilot fainting during high-altitude flights. Over the following two
decades, oximetry started to leave the laboratory and into the clinical setting. The discovery of an eightwavelength ear oximeter, which was cumbersome and pricey, marked the climax in 1964.. The
culmination came in 1964 with the invention of an eight-wavelength ear oximeter, which, as it was
bulky and expensive, was limited primarily to pulmonary examinations. The first true pulse oximeter
was invented in 1972 by Takuo Aoyagi, a bioengineer, who was trying to track the dilution of dye using
an ear oximeter to measure cardiac output. While searching for a way to combat the signal artifact
caused by the subject's pulse, he found that the noise caused by the pulse was entirely the cause of
changes in arterial blood flow. After several years of work, he was able to develop a dual-wavelength
device that used the changes in arterial blood flow to measure oxygen intake more accurately in the
blood.
The necessity of SpO2 measurement during surgery got recognized, and in 1986 the American Society
of Anesthesiologists adopted intraoperative pulse oximetry as part of their standard of treatment.
Following this development, pulse oximetry spread to other hospital departments, with the introduction
of the first autonomous fingertip pulse oximeter in 1995. Since then, the usage of pulse oximeter 0 has
increased in the areas of healthcare. To the present day, a rapid SpO2 measurement is often taken during
a regular doctor's visit.
2.2 Introduction
Pulse oximetry is a noninvasive method used for accurately estimating oxygen saturation (SaO2) by
reading the peripheral oxygen saturation (SpO2). As SaO2 and SpO2 are correlated and pulse oximetry
has the advantages of being safe, convenient, inexpensive, and noninvasive, this approach is clinically
accepted for monitoring oxygen saturation. Pulse oximetry is easy to use. It uses two different light
sources and a photodiode. Depending on the part of the body which is to be measured (such as on the
feet, forehand, wrist, chest, or finger) it is classified into transmissive pulse oximetry and reflectance
pulse oximetry.
2.3 Working Principle of Pulse Oximeter
The idea that the amount of Red and InfraRed light absorbed varies with the cardiac cycle, as the arterial
blood volume grows during systole and falls during diastole, is the foundation for pulse oximetry's
ability to measure SpO2 of solely arterial blood. Contrarily, the volume of skin, fat, bone, and other
tissues, as well as the blood volume in veins and capillaries, all remain constant. A portion of the light
that does not absorb in tissues instead reaches the photodetector of the probe, producing signals that
have both a pulsatile "alternating current" (AC) component and a relatively stable "direct current" (DC)
component. The non-pulsatile (DC) and pulsatile (AC) sections of arteries are depicted in a crosssectional schematic of an artery and a vein during systole and diastole, respectively. A cross-sectional
diagram of an artery and a vein during systole and diastole illustrates the non-pulsatile (DC) and
pulsatile (AC) compartments of arteries and the relative absence of volume change in veins and
capillaries (fig 2.1).
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Figure 2.1: Schematic diagram of light absorbance by a pulse oximeter.
With its capacity to continually track the functional oxygen saturation of hemoglobin in arterial blood,
the pulse oximeter has transformed modern medicine (SaO2). Pulse oximetry is used in medical care
so frequently that it is frequently regarded as a fifth vital sign. Erroneous readings might result in
pointless testing, therefore it's critical to understand how the equipment works and its limitations. By
diverting caregivers, frequent false alarms in the intensive care unit might further jeopardize patient
safety. Understanding these two fundamental pulse oximetry concepts is necessary to identify the
conditions where oxygen saturation (SpO2) readings from pulse oximeters may give inaccurate
estimations of the true SaO2: How oxyhemoglobin (O2Hb) and deoxyhemoglobin (HHb) are separated,
and how the SpO2 is calculated only from the arterial compartment.
Figure 2.2: Wavelengths of oxygenated hemoglobin (HbO2) and deoxygenated hemoglobin (Hb) absorption spectra of
hemoglobin.
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2.4 Methods of Pulse Oximeter
There are two methods of pulse oximetry, Transmissive pulse oximetry, and Reflectance pulse
oximetry(fig 2.3).
Transmissive pulse oximetry: In this method, a sensor device is placed on some part of the human
body’s fingertip, earlobe, or an infant’s foot. The blood flow is higher at the fingertips and the earlobes
when compared to other tissues which help the heat to transfer. The device then passes the two
wavelengths of light through the body to a photodetector. The photodetector measures the changing
absorbances at different wavelengths, allowing it to determine the absorbances due to pulsing arterial
blood alone and in most cases, the device cannot measure the absorbance of the venous blood, skin,
bone, muscle, and fat. It is an abundantly used method and has the highest accuracy rate as well as
stability rate.
Figure2.3: stands for the device (pulse oximetry) which is used to check a person’s oxygen saturation
Reflectance pulse oximetry: The demand for reflectance pulse oximetry (to measure oxygen
saturation) is increasing day by day as it can be used on different measurement sites such as feet, chest,
forehead, and wrists. In the reflectance pulse oximetry mode, the light sources, and the photodiode are
on the same side, and the reflected light is passed through the photodiode at the measurement site.
Reflectance pulse oximetry is used for monitoring the oxygen saturation at the measurement site of the
wrist, but it is not clinically practiced. Recently, many reflectance pulse oximeters have become
commercially available, but they are only for personal monitoring of oxygen saturation and for
entertainment purposes. The focus is on developing oximeters for medical purposes, and research is
still in process. Furthermore, most research papers discuss only the basic principle of pulse oximetry
and its utilization in smart devices; only a few papers discuss the challenges associated with reflective
pulse oximetry.
Figure2.4: LED-PD configuration
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3
OXYGEN SATURATION IN BLOOD
3.1 What is oxygen saturation?
Oxygen saturation describes what percentage of Haemoglobin (Hb) is loaded with oxygen
molecules. The oxygen inhaled via the lungs is absorbed by the haemoglobin and transported to the
tissues via the bloodstream. There, the charged oxygen molecules are released to the cells. Haemoglobin
(Hb) is a protein composed of four subunits. The most important function of haemoglobin is the
transport of oxygen in the blood. It is predominantly found in red blood cells (erythrocytes). In small
amounts, it is also found in plasma as free Hb bound to haptoglobin. Oxygen is taken up from the air
we breathe by haemoglobin in the small pulmonary vessels, transported throughout the body by the
bloodstream, and delivered to the cells in the tissues. The oxygen-loaded haemoglobin is called
oxyhaemoglobin. When it has released all the O2 molecules, it is called deoxyhaemoglobin. In its
discharged form, it can absorb carbon dioxide and transport it back to the pulmonary vessels. There, the
CO2 is released and exhaled (fig 3.1).
Figure 3.1: Simplified representation of loading and unloading of oxygen in the hemoglobin of blood.
3.2 Oxygen Saturation in blood
The measurement of oxygen saturation provides conclusions about the function of the lungs. By
determining the saturation of the venous blood, the blood flow, oxygen uptake as well as metabolic
activity of the tissue can be assessed. Peripheral oxygen saturation is also used to calculate arterial
oxygen content (CaO2). Blood oxygen saturation is also frequently measured for continuous monitoring
of patients who are under anaesthesia during surgery or are receiving intensive care. For intensive care
medicine, mixed venous or central venous oxygen saturation is particularly important, as it allows
conclusions to be drawn about cardiac output. In emergency medicine, the measurement of blood
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oxygen saturation is used to obtain an impression of the patient’s oxygen content and respiratory gas
balance. In addition, oxygen saturation is measured to monitor the course of chronic lung diseases such
as asthma. If oxygen saturation is too low, this is referred to as either hypoxemia or hypoxia. With
hypoxemia, the oxygen content in the arterial blood is reduced. Hypoxia is a lack of oxygen in the whole
organism or certain tissues. Symptoms may include anxiety and restlessness, dyspnoea (shortness of
breath), tachycardia (high heart rate), and an increase in blood pressure.
The partial pressure of oxygen is the pressure exerted by gaseous oxygen in the blood and is measured
either in kilopascals (kPa) or millimetres of mercury (mmHg). The higher the partial pressure of oxygen
(pO2) in the blood, the higher the oxygen saturation. This relationship is not linear, but s-shaped and
can be represented in the oxygen dissociation curve (see fig 3.2). Thus, above a certain pO2, oxygen
saturation
increases
only
slightly.
Figure 3.2: The oxygen binding curve describes the relationship between the O2 saturation and partial pressure of oxygen
(pO2) in the blood.
3.3 Calculation to determine the arterial oxygen saturation
Oxygen content is a component of a blood gas analysis test that helps to evaluate acid-base balance, gas
exchange, and voluntary respiratory control. In the blood gas analysis test, blood is drawn from an artery
and analysed in the lab for pH, partial pressure of carbon dioxide, partial pressure of oxygen,
bicarbonate content per 100 mL of blood, oxygen saturation, and oxygen content. Oxygen content is
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the sum of the oxygen that is combined with haemoglobin and the oxygen dissolved in the plasma. The
calculation for oxygen content is the following: arterial oxygen content = (Hgb x 1.36) (Oxygen
saturation/100) + (partial pressure of oxygen x 0.0031).
The principal form of oxygen transport in the blood is haemoglobin-bound. Each gram of haemoglobin
can maximally bind 1.34 mL of oxygen. The oxygen-carrying capacity of the blood is calculated as the
concentration of haemoglobin (in g / dL blood) times 1.34 mL O2 / g Hb. In a healthy person, with a
haemoglobin concentration of 15 g / dL blood, the oxygen-carrying capacity is 20.1 mL O2 / dL blood.
But as we have seen, the actual amount of oxygen normally bound to haemoglobin does not always
reflect 100% saturation. In fact, according to the oxyhaemoglobin dissociation curve(fig 3.3), arterial
blood is about 97% saturated with oxygen when arterial oxygen tension is 100 mmHg.
Figure 3.3: Oxygen hemoglobin dissociation curve.
Although the vast majority of arterial oxygen is transported in haemoglobin-bound form, a small
amount is also transported and dissolved in plasma. According to Henry’s law, the amount of dissolved
oxygen is proportional to the partial pressure of oxygen and its solubility coefficient. So, dissolved
oxygen in the blood is calculated as the arterial oxygen tension (PaO2) times the solubility coefficient
of oxygen (Sol O2 = 0.0031 mL O2 / dL blood / mmHg). The oxygen content of arterial blood (CaO2)
is the sum of haemoglobin-bound and dissolved oxygen.
Figure 3.4: Total oxygen in arterial blood where, hog=hemoglobin,1.34=oxygen combining capacity, SaO2=arterial be
saturated with oxygen, 0.0031=solubility coefficient of oxygen, PaO2=partial pressure of oxygen.
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4 OPTICAL PROPERTIES OF BLOOD AND TISSUES
4.1 Absorption Coefficient
Our body consists of blood plasma and tissues in which, blood contains 50%of water with some clots.
Water has the maximum absorption coefficient, less content of water is directly proportional to less
absorption that’s the reason the absorption coefficient gets affected. Absorption is the extraction of
energy from light by a molecular species. Absorbing the energy works as the primary source of light to
produce or change the physical structures of tissue for treatment purposes. Absorption of UV and visible
light promotes the transition between vibrational energy levels. We can calculate the Absorption
coefficient by Molar law. Absorption coefficient μA is proportional to the hematocrit.
Figure 4.1: Total oxygen in arterial blood where, hog=hemoglobin,1.34=oxygen combining capacity, SaO2=arterial be
saturated with oxygen, 0.0031=solubility coefficient of oxygen, PaO2=partial pressure of oxygen.
4.2 Scattering Coefficient
When we talk about light propagation in the tissues, it’s a turbid media so the properties are similar to
that of milk. Milk and Blood both consist of minute particles that will affect the light transmitted through
and thus will lead to scattering by the refraction concept of physics. Scattering depends on certain tissue
and wavelength. During light propagation in tissue, light is multiple times deflected from its original
direction, generally over short distances (1mm) light propagates on average in all directions in space
leading to isotropic scattering. It’s said that the scattering phase function of red blood cells cannot be
applied to whole blood as interference of waves scattered by neighboring cells needs to be considered
in the phase function. No accurate results for the optical properties can be obtained as a broadening of
the scattering characteristics is associated with the interference. Scattering coefficient μS saturates for
that = t>10%, i.e, the scattering coefficient is underestimated for high hct values concerning a linear
relationship between the two parameters.
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Figure 4.2: Total oxygen in arterial blood where, hog=hemoglobin,1.34=oxygen combining capacity, SaO2=arterial be
saturated with oxygen, 0.0031=solubility coefficient of oxygen, PaO2=partial pressure of oxygen.
4.3 Composition of Blood
In normal, Human blood consists of red blood cells (also known as erythrocytes) occupies a composition
of ±4,500 × 103/μL blood, while the composition of white blood cells (also known as leukocytes)
present in the blood is ±8 × 103/μL, furthermore, the composition of platelets (also known as
thrombocytes) present in the blood is ±300 × 103/μL and finally the plasma which has water,
electrolytes, plasma proteins, carbohydrates, lipids, and various extracellular vesicles.
Hematocrit: The volume percentage of red blood cells present in the blood, and measured as a part of
the blood test. The measurement depends upon the available numbers and the number of red blood cells.
The red blood cells in male accounts for 40.7–50.3%, while in females, the volume percentage of red
blood cells accounts for 36.1–44.3%. The purpose of red blood cells is to carry oxygen from the lungs
to body tissues, with the help of the blood the d sample’s hematocrit – one can take as a point of
reference its capability of delivering oxygen. Some studies show how too high or too low hematocrit
can cause blood disorders, dehydration, and other medical conditions. Abnormally low hematocrit leads
to anemia (Blood has a reduced capability to transport oxygen due a to decrease in hemoglobin). Persons
with anemia can suffer from tiredness, weakness, and shortness of breath. High hematocrit leads to
polycythemia (Increased hemoglobin concentration in the blood). Polycythemia is defined as serum
hematocrit (Hct) or hemoglobin (hgb) exceeding normal ranges expected for age and sex, typically Hct
> 49% in healthy adult men and >48% in women, or HgB >16.5g/dL in men or >16.0g/dL in women.
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Figure 4.3: Total oxygen in arterial blood where, hog=hemoglobin,1.34=oxygen combining capacity, SaO2=arterial hbg
saturated with oxygen, 0.0031=solubility coefficient of oxygen, PaO2=partial pressure of oxygen.
When considering the absorption contribution, red blood cells are the most absorbing element in the
blood with a wavelength of 250-1,100 nm (which is two- or three-times higher order of magnitude when
compared to other components of the blood). The light is absorbed by the red blood cells is due to the
presence of hemoglobin which shows specific absorption features for its various derivatives: bound to
oxygen (oxyhemoglobin, HbO2), unbound to oxygen (deoxyhemoglobin, Hb), bound to carbon
monoxide (carboxyhemoglobin) and oxidized (methemoglobin). Oxyhemoglobin and
deoxyhemoglobin are the two most important derivatives and are the most abundant types present in
healthy adult human blood.
4.4 Optical measurement of blood and tissues
Red blood cells are the main contributor to the optical properties of the blood. Although most
measurements on the optical properties of blood are performed on blood samples where the plasma has
been replaced by saline/phosphate buffer, Meinke et al. Measured that this affects the complex
refractive index mismatch, resulting in an overestimation of the scattering coefficient of 5.5-9.4 % with
respect to the red blood cells in plasma. In tissue, there are several properties reflation, refraction,
Absorption, and scattering. Absorption extraction of energy from light by a molecular species.
Transitions between two energy levels of a molecule that are well-defined at specific wavelengths could
serve as spectral fingerprints of the molecule. Absorption of the energy is the primary mechanism that
allows light from a source(laser) to produce physical effects on a tissue for treatment purposes.
Absorption occurs when the photon frequency matches the ‘frequency’ associated with the ‘molecules’
energy transition. Each electronic energy level is associated with many vibrational energy levels.
Absorption of UV and visible light promotes the transition between electronic energy levels. The
absorption of infrared light promotes transitions between vibrational energy levels.
It is an instrument used to decide and register the variations in the blood volume or blood flow in the
body which occur with each heartbeat. There are several types of plethysmographs in existence and
each of them measures the changes in blood volume in a unique way with a separate transducer. With
this transducer we can check the heart and respiratory rates, oxygen saturation, assessment of blood
vessel viscosity, venous function, measuring ankle pressure and, genital responses, measuring cold
sensitivity, measuring cold sensitivity, measuring blood pressure, and assessment of cardiac output.
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5
BEER-LAMBERTS LAW
The practical expression of the beer-Lambert law relates the optical attenuation of a physical material
containing a single attenuating species of uniform concentration to the optical path length through the
sample and absorptivity of the species. It’s a relation concerning the absorption of radiant energy by an
absorbing medium. The relationship can be expressed as
A = εlc
Where, A = absorbance,
ε = molar extinction coefficient
l = length of the path light
c = concentration of the given solution
There is a linear relationship between the concentration and the absorbance of the solution, which
enables the concentration of a solution to be calculated by measuring its absorbance. The molar
absorption coefficient is a sample-dependent property and is a measure of how strong an absorber the
sample is at a particular wavelength of light. The concentration is simply the moles L-2 (M) of the
sample dissolved in the solution, and the length is the length of cuvette used for the absorbance
measurement and typically 1 cm.
Figure 5.1: Beer Lamberts Law working principal for skin arterial pulse extraction.
Figure 5.2: Beer Lamberts Law in pulse oximetry.
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6. PHOTOPLETHYSMOGRAPHY
The interaction of light with biological tissue is complex and includes the optical processes of scattering,
absorption, reflection, transmission and fluorescence. By researching on the optical properties we come
to know the key factors that can affect the amount of light received by the photodetector: the blood
volume, blood vessel wall movement and the orientation of red blood cells. The orientation effect has
been demonstrated by recording pulsatile waveforms from dental pulp and in glass tube where
volumetric changes should not be possible. The main constituent of tissues is water hence, it absorbs
light very strongly in ultraviolet and infrared wavelengths. The shorter wavelengths of light are also
strongly absorbed by melanin. There is a window in the absorption spectra of water that allows visible
or near infrared light to pass more easily, thereby facilitating the measurement of blood flow or volume
at these wavelengths. In PPG the data depends on the probe design, that can be of order 1 cm 3 for
transmission modes systems.
There are two main PPG operational configurations:
transmission mode operation where the tissue sample is placed between source and detector.
Reflection mode operation where the LED and detector are placed aside to one another. If we analyze
we can see that the transmission side PPG has demerits over the reflection mode PPG, due to limitations
of testing over body parts.
Figure 6.1: Blood Pressu re monitoring using PPG.
Changes in blood flow in peripheral blood vessels according to the respiratory cycle can be measured
to monitor patients with respiratory distress or heart failure. This technique is also used to evaluate the
intrathoracic pressure cardiac function correlation. It can be used to detect ventricular tachycardia and
ventricular fibrillation. After some research by Takazawa et al. (1998), it was observed that the
derivative ppg and second derivative ppg are advantageous for representing spatiotemporal variations
of PPG with respect to peak position, inflection point, number of peaks, ascending slope, and
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descending slope. They can be used as an alternative method to detect dicrotic and diastolic peaks that
are difficult to detect in the original PPG waveform.
7. EVALUATION OF PERSONALISED TEST SIGNALS
Analysis of our personalized test signal is achieved here by using different methods. Some of the
methods we used to achieve the output waveforms of personalized data given to us are by using filters
and spectrograms such as the Savitzky-Golay filter, the Normalization method and the obtaining
spectrogram by Wigner-Ville transformation.
The data can be obtained by two types, either in TIME domain or in FREQUENCY domain. In our case
Signal was not visible in the Time plot. As the signal amplitude is much less than the noise power. We
tried to obtain the result for our data in the Frequency domain. To explain a little about Savitzky-Golay
filter, this filter is applied in order to smooth the digital plotting data . Smoothening of the data then
leads to increasing precision of the data without distorting the signal tendency. By the process of
convolution i.e fitting of subsets into adjacent data by using a lower polynomial you can achieve the
desired data. Secondly, Fast Fourier Transform termed as FFT is used to achieve frequency information
about the signal as it converts the signal into individual spectral components. Thirdly, STFT called as
Short Term Fourier Transform, in this method the signal is broken down into smaller windows and then
FFT of each is taken for further calculations
∞
∫ (𝜏)𝑤(𝑡 − 𝜏)𝑒 −𝑗2𝜋𝑓𝜏 𝑑𝜏
−∞
7.1 Time analysis, frequency analysis, and time-frequency analysis of our personalized test
signals
➢ Analysis of the Personalized Test signal
IMPLEMENTATION: Starting the script by loading the file. After that we load the time
vector column and the raw data that must be analysed. Then, the raw data has been obtained
in time domain by plotting the figure. The obtained time domain signal is then filtered by
using a filtering method known as Savitzky-Golay filter. In this Filter, the noise is filtered out
because smoothening of the signal is necessary to analyse the data accurately. Then the
filtered signal undergoes Fast Fourier transform. In Fast Fourier transform we are calculating
the sampling frequency and plotting it in frequency domain. Then spectrogram method
(STFT) is done by selecting a proper window size and using a special function [B, f, t] where
B represents the complex matrix containing the magnitude and phase of the STFT frequency
spectrum with the rows encoding the time-axis and the column representing the frequencyaxis and t and f are optimal argument vectors that can be helpful in plotting.
The plotted PPG signal shown in the figure has noise in it. To remove the noise or the
unwanted frequencies from the PPG signal, filtering of the signal is done by using the
filtering method known as the Savitzky_Golay filter.
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Figure 7.1a: Unfiltered data of Iyer_Ankita_Kannan.txt.
Figure 7.1b: Unfiltered data of Pentakota_Rohithvidhal.txt.
Figure 7.1c: Iyer_Ankita_Iyer.txt data after using Savitzky-Golay filter.
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Figure 7.1d: Pentakota_Rohithvishal .txt data after using Savitzky-Golay filter.
➢
After Applying Fourier Transformation to our filtered signals
Figure 7.1e: Iyer_Ankita_Kannan .txt data after applying Fast Fourier Transform.
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Figure 7.1f: Pentakota_Rohithvidhal.txt data after applying Fast Fourier Transform
Frequency analysis using Fourier transform can help to detect signals, even when they are not visible
in the time series plots, in other words, SNR is low.
FFT of the entire data is done and plotted from -Fs/2 to -Fs/2. Considering the above frequencies as the
signal of interest, SNR is calculated. When completely unknown data is to be analyzed, frequency
domain analysis provides additional information compared to time domain data, especially when SNR
is suspected to be low. Further, the frequency spectrum can be analyzed to design filters that can filter
the additional noise in the data without degrading the signal of interest.
One drawback of FFT analysis is that the entire data is analyzed as a batch. FFT provides the amplitude
or strength of all the frequencies present in the data. The variation of frequency of the signal with time
is preserved in FFT. STFT or Spectrogram can help us analyze the variation of signal frequencies with
time. Iyer_Ankita_Kannan.txt data Signal to Noise ratio is -10db, Pentakota_Rohithvishal.txt data
Signal to Noise ratio is -12.09db.
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Figure 7.1g: Iyer_Ankita_Kannan.txt data calculation of Signal to Noise ratio with respect to FFT.
Figure 7.1h: Pentakota_Rohithvishal.txt data calculation of Signal to Noise ratio with respect to FFT.
The above plots help us in interpreting the signal to noise ratio in DB.
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7.2 Time Analysis, Frequency analysis and time-frequency analysis of the data file.
➢ Test signal Analysis 1 Plots
Figure 7.2.1.1: Represents the signal in the time domain of the Test signal file.
The given signal has a set of samples that are connected linearly, in the range (-1 to 1) within the time
period of 0-1 second. We can observe that during the early stage, the intensity of the samples is low as
compared to the range i.e, between (0.6- 1). To simplify the waveform, we then use a filter to get the
required data. In fig 7.2.1.2 we can see the waveform of the data after applying the filter. To analyze
the Power Spectra of the waveform we then applied the Fast Fourier Transform to the filtered data
(fig7.2.1.3).
Through this, we can see the spectrum in the Time domain which is a much faster way than the
Frequency domain. As FFT considers all the data required to get the desired output and we need to
obtain the precise results of the data we then plot STFT over FFT, to obtain shorter segments of equal
length of the Time signal. We then used the Wigner-Ville distribution waveform as it allows better
localization of auto terms compared to a smeared spectrogram.
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Figure 7.2.1.2: Time analysis of the given data after filtering.
Figure 7.2.1.3: Test signal 1 in the frequency domain after applying FFT.
The above result is obtained by applying the Fast Fourier Transform. We observed that there are two
higher frequencies at 40 Hz and at 160 Hz. The frequencies between 40 and 160 Hz are irregular.
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➢
STFT algorithm implementation
Fast Fourier Transform is applied using state forward approach using the separate
function [B,t,f] coded in matlab. Here B is a complex matrix containing the
mangnitude and phase of the STFT frequency spectrum with the rows encoding the
time axis and the column representing the frequency axis. Where as, t and f are the
optional argument vectors that can be helpful in plotting.
Figure 7.2.1.4: STFT spectrogram of the data in time frequency domain.
Figure 7.2.1.5: Spectrogram of the test signal in the time domain.
The above plot represents the Time-Frequency contour using STFT. We can observe that the
signal in the frequency domain is increasing linearly from 45 Hz to 170 Hz.
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Figure 7.2.1.6: Wavelet transform of the test signal in the time domain.
Figure 7.2.1.7: Wavelet transform of the test signal in the time-frequency domain.
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Figure 7.2.1.8: Wiegner vile distribution of the test signal data.
Figure 7.2.1.9: Wiegner ville waveform in time frequency domain.
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➢ Test Signal 2 plots
Figure 7.2.2.1: Test Signal2.txt Time Domain of the data .
Figure 7.2.2.2: Time analysis of the given data after filtering
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.
Figure 7.2.2.3: FFT of the test signal in Frequency domain
Figure 7.2.2.4: STFT of the data in time frequency domain.
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Figure 7.2.2.5: STFT of the data in Time domain
Figure 7.2.2.6: Wavelet transformstion of the data in Time domain.
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Figure 7.2.2.7: Wavelet transformstion of the data in Frequency- Time domain.
Figure 7.2.2.8: Wigner ville countor waveform in Time- Frequency domain.
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.
Figure 7.2.2.9: Weigner ville spectrogram waveform in Frequency- Time domain.
The sampling points are connected as a line and the length of the test signal is 1 second. The first plots
looks like Sinusoidal Signal with certain variations in amplitude. Gradually lot of signals changes their
frequencies over time. As discussed earlier, FFT is not concerned with signal timing. Classical or
modern spectral methods provides a complete and opposite solution of waveforms that are stationary.
These waveforms do not change their basic properties over the length of the analysis.
Plenty waveforms are not stationary for example: PPG signals which frequently changes with respevt
to time.
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➢ Test signal 3 plots
Figure 7.2.3.1: Test Signal3.txt Time Domain of the data .
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Figure 7.2.3.2: Filtered data of the signal in Time Domain .
Figure 7.2.3.3: FFT of the filtered data in the Frequency domain .
Figure 7.2.3.4: Contour plot of two sinusoids using STFT.
We can see two sinusoidal waves with different frequencies with respect to time. At first, we can see a
sinusoidal wave at 30hz from the time period of 0.25 to 0.55 seconds. The waveform then steps upto u
65 hz within the time period of 0.55 to 0.79 seconds. Further, when the window length incrases, the
frequency resolution increases but there is a significant decrease in the time resolution.
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Figure 7.2.3.5: STFT of the data in the Time- Frequency domain.
Figure 7.2.3.6: Wavelet Waveform of the data in Time domain.
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Figure 7.2.3.7: Wavelet Waveform of the data in Time- Frequency domain.
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Figure 7.2.3.8: Weigner- ville contour waveform of the data.
Figure 7.2.3.9: Weigner – ville waveform of the data in Time- Frequency domain.
8
EVALUATION OF PPG SIGNALS
8.1 Time analysis, frequency analysis, and time-frequency analysis of the data
file
There are different types of data for Analysis. Here, we have selected a Research report data of the
STFT_WVD_WT file. Each wavelength has its importance for measuring or calculating different
things, because blood absorption is different at different wavelengths. The raw data of the selected file
is loaded in MATLAB. The visualization of the data obtained in time domain. Then the visualization
of the data in time domain is then analyzed further by applying Fast Fourier transform. In fig 8.1.1 we
can observe that the plot represents the two-sided FFT. Two maximum frequencies are obtained at
certain frequencies. Then STFT is implemented using straight forward approach using the separate
function [B,t,f], coded in MATLAB. Here, B is a complex matrix containing the magnitude and phase
of the STFT frequency spectrum with the rows encoding the time-axis and the column representing the
frequency-axis where t and f are optimal argument vectors that can be helpful in plotting. Then wignerville method is to use the analytic signal in time-frequency representation of a real signal.
The wigner-ville Distribution(WVD) is the most powerful and fundamental time frequency
representation. The superior properties of the WVD and SFT technique make it ideal for signal
processing in such diverse fields such as radar, sonar, and in biomedical analysis. In the end, wavelet
transform is used to analyze signals in time-frequency space and reduce noise, while retaining
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components in the original signal. The key advantage of the wavelet transform compared to FFT is to
extract both spectral and temporal information.
Figure 8.1.1: Figures above show the data visualization and Spectral analysis of the signal in the Frequency domain
respectively
Figure 8.1.2: figure shows the STFT Spectrogram Contour of the data.
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figure 8.1.4: figure shows the Wavelet transform waveform of the data.
Figure 8.1.3: Shows the STFT Spectrogram of data in the Time-Frequency domain.
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figure 8.1.5: shows the Wavelet transform waveform of the data in the Time domain.
figure 8.1.6: figure shows the Wigner- Ville contour waveform of the data.
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figure 8.1.7: figure shows the Wiegner- ville waveform of the data in the Time- Frequency domain.
8.2 Results of FFT, STFT analysis with the patient data:
figure 8.2.1: shows the data in the Time-domain of adult volunteer and in FFT
At first, we will be taking the raw data of both Adult volunteer and the Pacemaker, then the data is
displayed in the time domain. we can see in the above time domain plot of the adult volunteer, that
there is much noise involved and the pattern is samples irregular over a period of time. We observe
that there are peaks at certain frequencies. But out of these peaks, only the first peak (80 bpm) denotes
the heart rate, and the rest of the peaks are harmonics.
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figure 8.2.2: shows the Spectrogram (STFT) in the Time-Frequency domain(a)Adult volunteer and (b)Pacemaker DDD4_001
From the above spectrogram, the change in frequency occurs at 55 sec for the Adult volunteer Patient,
and for the pacemaker patient, there is a frequency shift at 1000 sec.
figure 8.2.3: visualization of the data of (a)Adult volunteer and (b)Pacemaker DDD4_001
figure 8.2.4: shows the FFT for the patient pacemaker DDD4_001
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8.3 Results of Derivation of the Patient data
figure 8.3a: Shows derivative of the data for a 10 weeks old Infant.
We carried out the derivative of the data by using the diff function in MATLAB which is a central
method to use for the Savitzky-Golay filter. This method removes the DC part of the PPG-filtered
signal, the resultant signal is a heartbeat signal, although noisy. In order to smoothen the signal we
implement an FIR filter.
Fig8.3a shows three plots. The first plot is the filtered PPG signal of the 10 week old infant. The second
plot is the first derivative of the filtered PPG signal and the third plot is the signal that results from
performing FIR filtering on the filtered PPG signal.
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figure 8.3b: Shows derivative of the data of a Diabetic patient
Here we observe that the derivative signal and the FIR-filtered signal follow a definite pattern that
denotes the rhythmic beating of the heart. However, in the time window of 1 second between 1049
and 1050 seconds the heartbeat signal is a little irregular. If such irregularities repeat it could mean
that the patient is having heart problems.
9
CALCULATION OF THE RATIO OF RATIO
Figure 9.1: Visualisation of the wavelengths 670 and 905nm.
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figure 9.2: Filtered data of wavelengths 670 and 905 nm.
figure 9.3: AC and DC wavelengths 670 and 905 nm.
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figure 9.4: SPO2 caliberation wavelengths 670 and 905 nm.
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Oxygen Saturation (SPO2) is calculated by using Ratio of Ratios as follows:
Ratio 1 = AC 670nm/DC 670nm
Ratio 2 = AC 905nm/DC905nm
Where, Ratio_1 is AC and DC component at 670nm.
And Ratio_2 is AC and DC component of 905nm.
SPO2 = (-732.08* R2) - (20585*R) + 11187
Where, R IS THE RATIO OF RATIO_1 AND RATIO_
Wavelength 670 nm (for Hypoxia patient)
AC = ACmax – ACmin
= 5.5 – 4.8
= 0.7
DC = 5.2
Ratio 1 = AC/DC
= 0.7/5.2
= 0.13461
Wavelength 905 nm (for Hypoxia patient)
AC = ACmax – ACmin
= 4.5 – 3.5
=1
DC = 4
Ratio 2 = AC/DC
= 1/ 4
= 0.25
R = R1/R2
= 0.135/ 0.25
= 0.54
SPO2 = (-7.3208* R2) - (20.585*R) + 111.87
= 2.1347 – 11.1159 + 111.87
SPO2 = 98.62%
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CONCLUSION
We have discussed a method for employing PPG signals to continuously track a subject's heart rate
while they are experiencing various medical conditions. This study sought to assess a variety of
information gathered from several subjects using pulse oximeters (adults, infants, hypoxia, and
diabetics). This aided in our analysis of the disparities between persons with various medical issues and
those who are healthy. We compared various data sets and examined them using various filtering
algorithms to get data with the least amount of noise. After doing data analysis using the SavitzkyGolay, FIR, FFT, and Wigner-Ville distribution functions (WVD). As a result of the data sets gathered
in four separate scenarios, we estimated arterial oxygen content (SPO2) and heartbeats per minute
(bpm). We covered first using the original PPG signal. A feature that uses the second derivative for
various PPG signals. The informative inflection point could be found more precisely by taking the first
and second derivatives of the signals. A promising technology, photoplethysmography is easy to use,
inexpensive, and non-invasive. MATLAB was used for all practical work that was done. One of the
greatest tools for studying these data sets is MATLAB. One of the most crucial patient monitoring
techniques is pulse oximetry, a non-invasive tool and continuous measurement technique used in
surgical procedures, critical care, and emergency medical services. A regular person can use these noninvasive tools at home, such as pulse oximetry, to test oxygen saturation in a painless and
straightforward manner.
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(usable space for professor)
University of Wismar
Faculty of Engineering
Department of Electrical Engineering and Computer Science
Prof. Dr.-Ing. Jens Kraitl
REFERENCES
➢ Photoplethysmography and its application in clinical physiological
➢
measurementwww.researchgate.net
Savitzky–Golay filter - Wikipediaen.wikipedia.org
➢ A literature review and novel theoretical approach on the optical properties of whole blood
https://link.springer.com/article/10.1007/s10103-013-1446-7
➢ Multi-wavelength photoplethysmography method for skin arterial pulse
➢
extractionopg.optica.org
Beer–Lambert law - Wikipediaen.wikipedia.org
➢ https://oar.ptb.de/files/download/5d7b9cdb4c939007700049e3 Determination of optical and
geometrical properties of blood cells and microparticles from light scattering measurements
➢ Pulse Oximetry: The Working Principle, Signal Formation, and Applications.
https://link.springer.com/chapter/10.1007/978-3-031-06413-5_12
➢ Oxygen saturation – better measured than calculatedacutecaretesting.org
➢ https://kidshealth.org/en/teens/lungs.html#:~:text=The%20lungs%20and%20respiratory%20s
ystem%20allow%20us%20to%20breathe.,carbon%20dioxide%20is%20called%20respiration.
➢ https://www.pinterest.com/pin/695524736181517858/ respiratory functioning image
➢ https://medicine.uiowa.edu/iowaprotocols/pulse-oximetry-basic-principles-and-interpretation
➢ https://www.sciencedirect.com/science/article/pii/S2405959516301205/reflective pulse
oximetry
➢ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3953607/optical properties of blood and
tissues
➢ https://en.wikipedia.org/wiki/Hematocrit
➢ https://en.wikipedia.org/wiki/Polycythemia
➢ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3394104/photoplethysmography
➢ https://www.news-medical.net/health/Photoplethysmography-(PPG).aspx
➢ https://asp-eurasipjournals.springeropen.com/articles/10.1186/s13634-020-00714-2
➢ https://www.britannica.com/science/Beers-law
➢ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8553265/#:~:text=This%20law%20is%20wi
dely%20applied,(OPL)%20through%20a%20tissue.
51
Research Seminar 2022/2023
Notes/Rating/Comments:
(usable space for professor)