Sep 19, 1982 - maximum expiratory air flow as the lung volume becomes smaller. 3. 1.2 .... parameters; Top panel Airflow signal, Lower panel. Fractional CO2 ...
PSZ 19:16 (Pind. 1/07)
UNIVERSITI TEKNOLOGI MALAYSIA DECLARATION OF THESIS / UNDERGRADUATE PROJECT PAPER AND COPYRIGHT
Author’s full name
: MOHSEN KAZEMI
Date of birth
: 19TH SEPTEMBER 1982
Title
: NEW PROGNOSTIC INDEX TO DETECT THE SEVERITY OF ASTHMA
AUTOMATICALLY USING SIGNAL PROCESSING TECHNIQUES OF CAPNOGRAM Academic Session
: SEMESTER II 2012-2013
I declare that this thesis is classified as :
CONFIDENTIAL
(Contains confidential information under the Official Secret Act 1972)*
RESTRICTED
(Contains restricted information as specified by the organization where research was done)*
√
OPEN ACCESS
I agree that my thesis to be published as online open access (full text)
I acknowledged that Universiti Teknologi Malaysia reserves the right as follows: 1. The thesis is the property of Universiti Teknologi Malaysia. 2. The Library of Universiti Teknologi Malaysia has the right to make copies for the purpose of research only.
3. The Library has the right to make copies of the thesis for academic exchange.
Certified by :
SIGNATURE H18933075 (NEW IC NO. /PASSPORT NO.)
Date : 21 November 2013
NOTES :
*
SIGNATURE OF SUPERVISOR Dr. Malarvili Balakrishnan NAME OF SUPERVISOR
Date : 21
November 2013
If the thesis is CONFIDENTAL or RESTRICTED, please attach with the letter from the organization with period and reasons for confidentiality or restriction.
“I hereby declare that I have read this thesis and in my opinion this thesis is sufficient in terms of scope and quality for the award of the degree of Doctor of Philosophy (Biomedical Engineering)”
Signature
: .............................................
Name of Supervisor
: Dr. Malarvili Balakrishnan
Date
: 21 November 2013
NEW PROGNOSTIC INDEX TO DETECT THE SEVERITY OF ASTHMA AUTOMATICALLY USING SIGNAL PROCESSING TECHNIQUES OF CAPNOGRAM
MOHSEN KAZEMI
A thesis submitted in fulfilment of the requirements for the award of the degree of Doctor of Philosophy (Biomedical Engineering)
Faculty of Biosciences and Medical Engineering Universiti Teknologi Malaysia
NOVEMBER 2013
ii
I declare that this thesis entitled “New Prognostic Index to Detect the Severity of Asthma Automatically Using Signal Processing Techniques of Capnogram” is the result of my own research except as cited in the references. The thesis has not been accepted for any degree and is not concurrently submitted in candidature of any other degree.
Signature
: ...................................
Name
: Mohsen Kazemi
Date
: 21 November 2013
iii
To my family For keeping me sane and giving me hope when I was ready to give up
iv
ACKNOWLEDGEMENT
“Those who believe, and whose hearts find comfort in the remembrance of God, surely, it is in the remembrance of God that hearts find comfort.” (Q.13: 28) Firstly, I would like to express my extreme gratitude to Allah for blessing me with abundant of wisdom, patience and health throughout completing this research. It would not have been possible to carry out this research without the help and support of the kind people around me, to only some of whom it is possible to give particular mention here. First of all, I am very thankful to my supervisor, Dr. Malarvili Balakrishnan, for her excellent guidance, support and patience to listen. Her always-cheerful conversations, a friendly behaviour, and her unique way to make her students realise their hidden research talents are extraordinary. I am also thankful to Dr. Teo Aik Howe for his medical advice, and Mr. Tan Teik Kant for his assistance in data collection. Furthermore, I gratefully acknowledge the Universiti Teknologi Malaysia (UTM) for providing facilities and laboratory equipments. I would like to thank my wife, Mahnoosh, with all my heart for her love, encouragement, and great patience at all times. She was always cheering me up and stood by me through the good times and bad, that I have needed it the most. I would also thank my son, Artin. Definitely, his future is one of the major motivations for me in the rest of my life. I also wish to extend my utmost thanks to my parents for their love, continuous support, prayers, helping me to be assertive in difficult times, and encouraging me with their best wishes. They raised me with a love of science and supported me in all my pursuits.
v
Last, but by no means least, I thank my friends in Malaysia (Kuala Lumpur, and Johor Bahru). My times in Malaysia were made enjoyable in large part due to these friends that became a part of my life. I am grateful for time spent with them.
vi
ABSTRACT
Asthma is a chronic inflammatory disease of the bronchial tubes that happens approximately in 3% to 5% of all people in their life. Currently, capnography is a new method to monitor the asthmatic conditions, and unlike traditional methods, it is taken while the patient is breathing as comfortably as able. Previous studies concluded a significant correlation between the capnogram and the asthmatic patient. However, most of them are just manual studies conducted through the conventional time domain method based on the assumption that the capnogram is a stationary signal. However, manual analysis of capnogram is time-consuming and leads to erroneous results due to human factor such as tiredness. Therefore, a new prognostic index to automatic detection the severity of airway obstruction by processing the capnogram signal is presented in this research. First, in order to investigate the property of capnogram signal, the first and the second statistical orders of 73 asthmatic and 23 non-asthmatic patients’ capnogram were calculated. Based on the findings in this research, capnogram signals can be categorised as wide-sense nonstationary random signals. So, non-stationary techniques including linear predictive analysis and Burg algorithm analysis are used to process the capnogram signals. It should be noted that these techniques by windowing signal are based on this assumption that signal is locally stationary. Then, by means of Receiver Operating Characteristic (ROC) curve, the effectiveness of the extracted features to differentiate between asthmatic and non-asthmatic conditions is justified. Finally, selected features are used in a Gaussian radial basis function (GRBF) neural network. The output of this network is an integer prognostic index from 1 to 10 (depending on the severity of asthma) with an average good detection rate of 90.15% and an error rate of 9.85%. This developed algorithm is aspired to provide a fast and low-cost diagnostic system to help healthcare professional involved in respiratory care as it would be possible to monitor the severity of asthma automatically and instantaneously.
vii
ABSTRAK
Asma ialah satu penyakit radang kronik tiub bronkus yang berlaku dalam kira-kira 3 hingga 5% semua orang dalam hidup mereka. Kini, kapnografi digunakan sebagai satu kaedah baru bagi memantau penyakit asma. Tidak seperti kaedah tradisional, ia diukur ketika pesakit sedang bernafas dalam keadaan paling selesa. Kajian terdahulu telah menunjukkan perkaitan antara kapnogram dan pesakit asma. Bagaimanapun, ia adalah kajian secara manual yang menggunakan kaedah domain masa dengan berdasarkan andaian bahawa kapnogram merupakan sejenis isyarat pegun. Tambahan lagi, analisis manual kapnogram memakan masa dan berkemungkinan menghasilkan keputusan yang tidak tepat disebabkan faktor kemanusiaan seperti letih dan kurang kemahiran. Oleh itu, indeks ramalan baru bagi mengesan tahap sekatan salur udara secara automatik dengan memproses isyarat kapnogram telah dibentangkan dalam penyelidikan ini. Pertama, untuk mengkaji sifat-sifat isyarat kapnogram, turutan statistik pertama dan kedua kapnogram bagi 73 pesakit asma dan 23 pesakit tanpa asma telah dianalisa. Berdasarkan penemuan dalam penyelidikan ini, isyarat kapnogram boleh dikategorikan sebagai isyarat rawak tidak pegun yang luas. Jadi, teknik-teknik tidak pegun termasuk pengekodan ramalan linear pekali-pekali dan autoregresif digunakan untuk memproses isyaratkapnogram. Kemudian, dengan cara ciri kendalian penerima (ROC) lengkung, keberkesanan nilai-nilai yang dihasilkan untuk membezakan antara pesakit asma dan tanpa asma ditentukan. Akhirnya, nilai-nilai yang terpilih digunakan dalam fungsi asas jejari Gauss (GRBF) jaringan saraf. Output rangkaian ini ialah indeks ramalan integer dari 1 hingga 10 (bergantung kepada tahap asma) dengan kadar pengesanan yang sederhana baik iaitu 90.15% dan satu kadar ralat, 9.85%. Algoritma yang dihasilkan ini diharap dapat menghasilkan satu sistem diagnostik yang pantas dan murah untuk membantu professional bidang kesihatan dalam penjagaan dan rawatan pernafasan di mana ia tidak mustahil dapat memantau tahap asma secara automatik dan berterusan.
viii
TABLE OF CONTENTS
CHAPTER
1
TITLE DECLARATION
ii
DEDICATION
iii
AKNOWLEDGEMENT
iv
ABSTRACT
vi
ABSTRAK
vii
TABLE OF CONTENTS
viii
LIST OF TABLES
xi
LIST OF FIGURES
xii
LIST OF ABBREVIATIONS
xix
LIST OF APPENDIX
xxi
INTRODUCTION 1.1 Motivation of Research
1 1
1.2
Diagnosis of Asthma: Common Techniques and Limitations
3
1.2.1
Spirometry
4
1.2.2
Peak Flow Meter
8
1.2.3 Limitations of Spirometry and Peak Flow Meter 1.2.4
12
Capnography: A New Approach for Diagnosis of Asthma
2
PAGE
13
1.3
Aims and Objectives of the Research
13
1.4
Research Scope
14
1.5
Research Contributions
14
1.6 Thesis Organization
16
1.7 List of Publications
17
LITERATURE REVIEW Introduction
19 19
ix
2.1
Background of Respiratory System and Asthma
21
2.1.1 Respiratory Mechanism
21
2.1.2
2.1.3 2.2
2.4
2.5
Expiratory Flow
24
Mechanism of Airway Narrowing in Asthma
26
Background of Capnography and Capnogram
28
2.2.1
History of Capnograph and Capnogram
28
2.2.2
Basic Principles of Capnography
29
2.2.3
Types of Capnographs
31
2.2.4 Clinical Use of Capnography
33
2.2.5
2.3
The Effect of Asthma on the Maximum
Diffusion of Carbon Dioxide from the Peripheral Tissue Cells
34
2.2.6
Normal Capnogram
36
2.2.7
Abnormal Capnogram
37
Background of Non-stationary Signal Processing
42
2.3.1
43
Linear Predictive Analysis
2.3.2 Autoregressive (AR) Modeling
45
Background of Classifiers
47
2.4.1
Radial Basis Function (RBF) Neural Networks
50
2.4.1.1
53
Learning Algorithms
Review on Researches Used Capnogram for Medical Diagnosis and Detecting Asthmatic Conditions 2.5.1
53
A Review on Medical Diagnosis Using Capnography
53
2.5.2 A Review on Methods to Detect Asthmatic Condition and Bronchospasm through Analysis of Capnogram 2.6 3
Summary
56 62
RESEACH METHODOLOGY Introduction
63 63
3.1
Data Acquisition
65
3.2
Data Preprocessing
68
3.3
Property Analysis of Capnogram Signals
71
3.4
Feature Extraction of Capnogram Signals
75
x
3.4.1
Linear Predictive Analysis
75
3.4.2
Frequency Domain
77
3.4.2.1
3.4.2.2
4
5
Periodogram Analysis (Non-Parametric Spectrum Estimation)
78
3.4.2.1.1
79
Window Selection
Burg Algorithm Analysis
84
3.5
Performance Measure of Features
87
3.6
Design of RBF Neural Network
89
3.7
Summary
91
RESULTS AND DISCUSSION Introduction
93 93
4.1
Non-stationary Analysis of Capnogram Signals
93
4.2
Linear Predictive Analysis
96
4.3
Frequency Analysis
99
4.3.1
Periodogram Analysis Results
99
4.3.2
Burg Algorithm Analysis Results
104
4.4
Neural Network Classification Results
107
4.5
Performance Evaluation
111
4.6 Graphical User Interface (GUI)
116
4.7
118
Summary
CONCLUSION AND FUTURE WORKS Introduction
120 120
5.1
Conclusions
120
5.2
Future Works
122
REFERENCES
124
Appendix A
137
xi
LIST OF TABLES
TABLE NO.
TITLE
PAGE
1.1
Spirometry parameters and their meaning
8
2.1
Summary of net functions
49
2.2
Neuron activation functions
50
2.3
Review on medical diagnosis associated with capnogram
3.1
54
Comparison of commonly used windows (M is the length of window)
80
4.1
The performance indices for extracted LPC coefficients
98
4.2
Performance indices of the magnitude, frequency, and bandwidth of the main-lobe in the FFT of CNPs, and CAPs
4.3
103
Performance indices of the frequency of the first component, its magnitude, and the total power in the PSD estimation of CNPs, and CAPs
106
4.4
The detection rate of RBF network output in detail
111
4.5
The performance indices of presented method in this research in comparison with two existing algorithms
115
xii
LIST OF FIGURES
FIGURE NO. 1.1
TITLE
PAGE
(A) Collapse of the respiratory passageway during Maximum expiratory effort, an effect that limits expiratory flow rate, (B) Effect of lung volume on the maximum expiratory air flow, showing decreasing maximum expiratory air flow as the lung volume becomes smaller
1.2
3
Early spirometer collected exhaled air in a plastic cylinder floating on top of a tub of water. The more air exhaled, the higher the cylinder rose, and the lower the mark left by the attached marking pen. By attaching the recording paper to a rotating drum, both the volume of air exhaled and the time elapsed during exhalation could be recorded
5
1.3
One of the newest spirometer which is used
6
1.4
Recordings during the forced vital capacity manoeuvre (A) in a healthy person and (B) in a person with partial airway obstruction (The “zero” on the volume scale is residual volume)
1.5
7
The flow-volume curve plots expiratory flow on the vertical axis and volume on the horizontal axis (from full
1.6
breath in on the left to maximal breath out on the right)
9
Wright peak flow meters
10
xiii
1.7
A typical home use peak flow meters
10
1.8
The normal values for peak expiratory flow (PEF)
11
1.9
The overall organization of this thesis
16
2.1
The block diagram of general topics discussed in this chapter
2.2
20
Diagram of the respiratory system illustrates the air passageways from nose and mouth, larynx and trachea, into multiple generations of branching bronchi, until finally the air alveoli are reached
2.3
22
Changes in lung volume, alveolar pressure, pleural pressure, and transpulmonary pressure during normal breathing
2.4
24
Effect of two respiratory abnormalities, constricted lungs and asthma, on the maximum expiratory flowvolume curve; TLC total lung capacity, RV residual volume
2.5
25
Depicts airway smooth muscles surrounding the lumen of a normal bronchus and contracting to constrict the airway during an asthmatic attack
2.6
27
An actual histology section from the lung of an individual who died of asthma, showing airway smooth muscle surrounding a narrowed small airway [4]
2.7
Capnography monitor displaying waveform, end-tidal CO2, and other features
2.8
27
30
Adapter that resembles a nasal cannula, and is placed on a non-intubated patient
30
2.9
Adapter that is placed on ventilator tubing
31
2.10
Capnography adapter placed on the non-intubated
xiv
patient 2.11
31
A sidestream capnograph (A) and a mainstream capnograph (B)
32
2.12
Comparing normal and bronchospastic capnograms
33
2.13
Uptake of carbon dioxide by the blood in the tissue capillaries
2.14
35
Diffusion of carbon dioxide from the pulmonary blood into the alveolus
36
2.15
Normal capnogram
37
2.16
Incomplete exhalation
38
2.17
Gradual rises in baseline
39
2.18
Exponential decrease
40
2.19
Sudden decrease
40
2.20
Loss of plateau
41
2.21
Capnogram of a patient with chronic respiratory disease shows (A) transition phase is longer than normal (shaded area) (B) A large tidal volume with a prolonged expiratory phase reflects PaO2
2.22
Capnogram representing a flickering expiratory valve with rebreathing
2.23
41
41
Capnogram displaying patient-ventilator asynchronies during intermittent mandatory ventilation (The arrows indicate spontaneous breaths)
2.24
42
Capnogram in which the arrow points to a small spontaneous inspiratory effort that did not trigger the ventilator
42
xv
2.25
Capnogram exhibiting weaning failure. There is rapid breathing, with rebreathing (A), Spontaneous breaths (B) during mandatory (ventilator-delivered) breaths
2.26
42
Some common techniques to process non-stationary signals
43
2.27
AR process (a), and linear predictor (b)
46
2.28
The block diagram of common methods used to model the classifiers
47
2.29
McCulloch and Pitts’ neuron model
48
2.30
Architecture of a standard GRBF network
51
2.31
Difference classification mechanisms for pattern classification in two-dimension space; (a) RBF network, (b) Separation results of RBF networks, (c) MLP network, (d) Separation result of MLP network
2.32
Slopes of volume segments used as input for back propagation neural network
2.33
52
57
Simulation of a capnogram, using default model parameters; Top panel Airflow signal, Lower panel Fractional CO2 concentrations in the rebreathe volume (solid line) and alveoli (dashed line)
2.34
58
Description of the capnogram and its indices in normal and obstructive conditions; (a) The inspiratory (I1 and I2) and expiratory phases (E1, E2, E3) of a normal capnogram, and (b) Schematic description of the capnographic indices measured on a normal (upper) and on an obstructive (lower) capnogram [88]
60
3.1
The overall algorithm of used method
64
3.2
A non-asthmatic patient during data collection
66
xvi
3.3
The block diagram for data collection
67
3.4
The capnogram signal of CNP2 before filtering
68
3.5
The capnogram signal of CNP6 before filtering
68
3.6
The correlation coefficients of some capnogram signals after filtering with different spans
70
3.7
The capnogram signal of CNP2 after filtering
70
3.8
The capnogram signal of CNP6 after filtering
71
3.9
The algorithm that used to analyze the property of capnogram signals
73
3.10
The CNP2 and its separated cycles
74
3.11
The CAP7 and its separated cycles
74
3.12
The developed algorithm for linear predictive analysis
76
3.13
The overall algorithm used to analyze capnogram in frequency domain
3.14
A Blackman window with M=256 in time and frequency domain
3.15
82
FFT of a non-asthmatic capnogram (CNP6) using Hamming window
3.18
82
FFT of a non-asthmatic capnogram (CNP6) using Kaiser window
3.17
81
FFT of a non-asthmatic capnogram (CNP6) using Rectangular window
3.16
78
83
FFT of a non-asthmatic capnogram (CNP6) using Blackman window
83
3.19
Lattice filter
85
3.20
AIC values for different model orders
87
xvii
3.21
A typical ROC curve [130]
3.22
Overall algorithm used for training and designing the RBF network
4.1
90
The mean of different cycles of CNP1 to CNP5, and CAP1 to CAP5
4.2
88
94
The variance of different cycles of CNP1 to CNP5, and CAP1 to CAP5
94
4.3
The normalized autocorrelation of CNP1
95
4.4
The normalized autocorrelation of CAP1
96
4.5
Correlation coefficients between the original capnogram signal and estimated signals using different LPC orders
97
4.6
The FFT of a non-asthmatic capnogram (CNP2)
99
4.7
The FFT of an asthmatic capnogram (CAP9)
100
4.8
The normalized magnitude of the main-lobe for both CAPs, and CNPs
4.9
The frequency of the main-lobe for both CAPs, and CNPs
4.10
102
Power spectral density of a non-asthmatic capnogram (CNP2)
4.12
102
The bandwidth of the main-lobe for both CAPs, and CNPs
4.11
101
104
Power spectral density of an asthmatic capnogram (CAP9)
105
4.13
Overall algorithm used for RBF network
109
4.14
The performance of the designed RBF network
110
xviii
4.15
The ‘S’ parameters on the 1st cycle of CNP1
112
4.16
The ‘S’ parameters on the 2nd cycle of CNP1
113
4.17
The ‘S’ parameters on the 3rd cycle of CNP1
113
4.18
The ‘S’ parameters on the 4th cycle of CNP1
114
4.19
The ‘S’ parameters on the 5th cycle of CNP1
114
4.20
The patient information layout
117
4.21
The loading capnogram layout
117
4.22
The analysis window
118
xix
LIST OF ABBREVIATIONS
AIC
-
Akaike Information Criterion
AR
-
Autoregressive
ASI
-
Asthmatic Severity Index
AUC
-
Area Under the Curve
BPNN
-
Back Propagation Neural Network
CAES
-
Capnogram Analyzer Expert System
CAP
-
Capnogram of Asthmatic Patient
CNP
-
Capnogram of Non-asthmatic Patient
COPD
-
Chronic Obstructive Pulmonary Disease
CPR
-
Cardiopulmonary Resuscitation
DFT
-
Discrete Fourier Transform
DKA
-
Diabetic Ketoacidosis
DSP
-
Digital Signal Processing
ECG
-
Electrocardiography
EEG
-
Electroencephalography
ETT
-
Endotracheal Tube
FEV
-
Forced Expired Volume
FFT
-
Fast Fourier Transform
FVC
-
Forced Vital Capacity
GRBF
-
Gaussian Radial Basis Function
xx
GUI
-
Graphical User Interface
IR
-
Infrared Radiation
LPC
-
Linear Predictive Coding
LSE
-
Least Squared Error
MLP
-
Multilayer Perceptron
PEF
-
Peak Expiratory Flow
PEFR
-
Peak Expiratory Flow Rate
PSD
-
Power Spectral Density
RBF
-
Radial Basis Function
RLS
-
Recursive Least Squares
ROC
-
Receiver Operating Characteristic
RV
-
Residual Volume
TLC
-
Total Lung Capacity
xxi
LIST OF APPENDIX
APPENDIX A
TITLE
PAGE
The Normalized Autocorrelation Function Graph for All Capnogram Signals Used in This Research
135
CHAPTER 1
INTRODUCTION
1.1
Motivation of Research The word asthma is derived from the Greek word “azein” meaning “to
breathe hard”. It is a chronic inflammatory disease of the bronchial tubes in which the bronchi narrow excessively and generally reversibly in response to a variety of stimuli and it occurs in 3 to 5 % of all people in their life [4]. Also, the dramatic increase of asthma during the last few decades has made it an important public health concern, and continues to be a significant cause of morbidity and mortality [8]. A National Health and Morbidity Survey in 2006 indicated that the prevalence of asthma in Malaysia was 10-13%. The survey also shows that the prevalence of asthma varied according to the socioeconomic status of the patients, and the geographical area. In fact, the study found that asthma was high among lower socioeconomic groups, and in rural areas, also by ethnicity, Chinese have a lower prevalence of asthma than the other races. In terms of age, the study found that the prevalence of asthma decreased by age but increased again after 40 years and above. The study also found that the majority of mild asthma (65%) and moderate asthma cases (52%) were on non-inhaler treatment. Moreover, the survey revealed that there was inadequate treatment and monitoring of asthmatic patients especially those with severe cases of asthma [9]. So it needs to be more considered not only by physicians but also by bioengineers to enhance current methods of diagnosis asthma and monitoring its severity. There are many signs of asthma and the symptoms of an asthma attack include shortness of breath, chest tightness, trouble sleeping, whistling sound while exhaling, and coughing or wheezing attacks. These symptoms generally do not occur
2
between asthma attacks, and asthma sufferers can live a normal, physically fit life in between their attacks [2]. As mentioned in next sections, traditionally, spirometry and peak flow meter are useful diagnostic tools, but the reliable results cannot be obtained if a patient is unable to understand the instructions, has chest pain preventing a forceful effort, or does not choose to cooperate. Also there are more limitations that could be found in [10]. Capnography is a new method used to monitor the asthmatic condition [27]. It is able to show the different respiratory situation of patient including asthma and is taken while the patient is breathing as comfortably as able, without requiring following any breathing instructions. In the previous studies, as mentioned in chapter 2, some studies on capnogram signal have been conducted by several researchers. All previous researches show significant correlation between the capnogram and asthmatic patient. However most of them are manual study conducted through the conventional time domain method. So, it is time-consuming and led to erroneous due to human factor such as tiredness and lack of expertise. Furthermore, all of reported research conducted on capnogram signals were based on the fact that it is a stationary one. The analysis based on this assumption is limited to the use of certain conventional time domain methods that imagine the contents of the signal are constant in different time interval. As such, it is presented here to develop a computerized system and introducing a new prognostic index to monitor asthmatic conditions and to detect the severity of asthma by processing the capnogram signal using digital signal processing techniques. The introduced prognostic index to detect the severity of asthma is an innovative idea that is useful for heathcare professional involved in respiratory care as it can be used in capnographs and it would be possible to monitor severity of asthma automatically, instantaneously, and accurately.
3
1.2
Diagnosis of Asthma: Common Techniques and Limitations In many respiratory diseases, particularly in asthma, the resistance to airflow
becomes especially great during expiration, sometimes causing tremendous difficulty in breathing that has led to the concept called maximum expiratory flow, which can be defined as follows: when a person expires with great force, the expiratory airflow reaches a maximum flow beyond which the flow cannot be increased any more even with greatly increased additional force [1]. The maximum expiratory flow is much greater when the lungs are filled with a large volume of air than when they are almost empty. These principles can be understood by referring to figure 1.1.
Figure 1.1: (A) Collapse of the respiratory passageway during maximum expiratory effort, an effect that limits expiratory flow rate, (B) Effect of lung volume on the maximum expiratory air flow, showing decreasing maximum expiratory air flow as the lung volume becomes smaller
Figure 1.1(A) shows the effect of increased pressure applied to the outsides of the alveoli and air passageways caused by compressing the chest cage. The arrows indicate that the same pressure compresses the outsides of both the alveoli and the
4
bronchioles. Therefore, not only does this pressure force air from the alveoli toward the bronchioles, but it also tends to collapse the bronchioles at the same time, which will oppose movement of air to the exterior. Once the bronchioles have almost completely collapsed, further expiratory force can still greatly increase the alveolar pressure, but it also increases the degree of bronchiolar collapse and airway resistance by an equal amount, thus preventing further increase in flow. Therefore, beyond a critical degree of expiratory force, a maximum expiratory flow has been reached. Figure 1.1(B) shows the effect of different degrees of lung collapse, and therefore of bronchiolar collapse as well, on the maximum expiratory flow. The curve recorded in this section shows the maximum expiratory flow at all levels of lung volume after a healthy person first inhales as much air as possible and then expires with maximum expiratory effort until he can expire at no greater rate. It is obvious that as the lung volume becomes smaller, the maximum expiratory flow rate also becomes less. The main reason for this is that in the enlarged lung the bronchi and bronchioles are held open partially by way of elastic pull on their outsides by lung structural elements; however, as the lung becomes smaller, these structures are relaxed, so that the bronchi and bronchioles are collapsed more easily by external chest pressure, thus progressively reducing the maximum expiratory flow rate as well [1]. The bottom line is that, asthma is one of abnormalities of maximum expiratory flow and this characteristic of asthma is the principle of its diagnosis. So, in following three subsections, traditional diagnosis tools for asthmatic conditions besides their limitations are discussed. Moreover, in subsection 1.2.4, a brief introduction to capnography as a new approach for diagnosis of asthma is presented.
1.2.1 Spirometry One of the pulmonary tests is to make a record on a spirometer of the forced expiratory vital capacity (FVC). Spirometry involves a maximal inspiration followed
5
by a rapid, forceful, and complete exhalation until there is absolutely no more air to blow out. The reason, in part, is that with a mouthpiece in your mouth, if somebody is asked to breathe normally, he cannot do it. On the other hand, the maximal forced expiratory manoeuvre of spirometry proves to be highly reproducible and rich with information about the functioning of the lungs [4]. Figure 1.2 shows the early spirometer collected exhaled air in a plastic cylinder floating on top of a tub of water. Also, figure 1.3 shows one of the latest spirometer which is used.
Figure 1.2: Early spirometer collected exhaled air in a plastic cylinder floating on top of a tub of water. The more air exhaled, the higher the cylinder rose, and the lower the mark left by the attached marking pen. By attaching the recording paper to a rotating drum, both the volume of air exhaled and the time elapsed during exhalation could be recorded
6
Figure 1.3: One of the newest spirometer which is used
To interpret the results of spirometry, it is necessarily to know some parameters. The first is the forced vital capacity, FVC, which is given in litres and is the total amount of air exhaled from the lungs when starting at the top of a full inhalation and ending at the bottom of a complete exhalation. The second is the onesecond forced expired volume, FEV1, which is likewise given in litres and is the amount of air exhaled in the first second of this forced expiratory manoeuvre and is an amount per unit of time that tells us a rate of exhalation. The third element is the ratio of these first two results because is important to know that what portion of the vital capacity can be exhaled in the first second. So, this measurement is expressed as a ratio of the 1-second forced expired volume divided by the forced vital capacity (FEV1/FVC) and the results are typically displayed as a percentage. For instance, if the FEV1 is 3 litters and the FVC is 4 litres, the FEV1/FVC is 75%. Such a record is shown in Figure 1.4(A) for a person with normal lungs and in Figure 1.4(B) for a person with partial airway obstruction. In performing the FVC manoeuvre, the person first inspires maximally to the total lung capacity, and then exhales into the spirometer with maximum expiratory effort as rapidly and as completely as possible. The total distance of the down-slope of the lung volume record represents the FVC, as shown in the figure.
7
Figure 1.4: Recordings during the forced vital capacity manoeuvre: (A) in a healthy person and (B) in a person with partial airway obstruction (The “zero” on the volume scale is residual volume)
In both two records, the total volume changes of the FVCs are not greatly different, indicating only a moderate difference in basic lung volumes in the two persons. There is, however, a major difference in the amounts of air that these persons can expire each second, especially during the first second. Therefore, it is customary to compare the recorded forced expiratory volume during the first second (FEV1) with the normal. In the normal person as shown in figure 1.4(A), the percentage of the FVC that is expired in the first second divided by the total FVC (FEV1/FVC %) is 80 per cent. However, as shown in figure 1.4(B) with airway obstruction, this value decreased to only 47 percent. In serious airway obstruction, as often occurs in acute asthma, this can decrease to less than 20 percent. Table 1.1 shows these three parameters and their meaning briefly.
8
Table 1.1: Spirometry parameters and their meaning Measure
Definition
The Meaning FVC is not a reliable indicator of total
FVC (Forced
Total air forcibly
lung capacity or restriction, especially
Vital Capacity)
exhaled after
in the setting of airflow obstruction;
maximal inhalation
however it can be an indicator of restrictive disease • FEV1 >80% of predicted = normal
FEV1 (Forced
• FEV1 60%–79% of predicted = mild
Expiratory
Maximal volume
obstruction
Volume in 1
forcibly exhaled
• FEV1 40%–59% of predicted =
second)
within 1 second
moderate obstruction • FEV1
