WO2006068622A1

WO2006068622A1 - System and method for detecting of pulmonary diseases

Info

Publication number: WO2006068622A1
Application number: PCT/SG2005/000300
Authority: WO
Inventors: Kah Meng Loh
Original assignee: Nanyang Polytechnic
Priority date: 2004-12-20
Filing date: 2005-09-01
Publication date: 2006-06-29
Also published as: SG123633A1; WO2006068622A8

Abstract

A non-invasive system and method for detecting pulmonary diseases by analysing inhalation and exhalation. The system comprises a breath analyser (102) for analysing the inhale and exhale airs of a test person, a computer processor (103) for receiving from the breath analyser (102) the information and processing the received analysis data to give values to different parameters of the inhale and exhale airs of the test person, and a database (104) for storing different parameters of breaths of the public and normal ranges for healthy persons. In another aspect, the invention provides a method comprising acquiring information of inhale and exhale airs of the test person, processing the. acquired information to give values of designated aspects of the inhale and exhale airs of the test person, and comparing the values with the ones stored in a database so as to conclude whether the test person is suffering any pulmonary diseases.

Description

SYSTEM AND METHOD FOR DETECTING OF PULMONARY DISEASES

Field of the Invention

[0001] The present invention generally relates to systems and method for detecting of pulmonary diseases, and more particularly to non-invasive systems and methods for detecting of pulmonary diseases by analyzing inhalation and exhalation.

Background of the Invention

[0002] Lung is a vital organ allowing exchanges of O₂ and CO₂ between the alveolar sockets and bloods. When the lung is impaired, e.g., viral or bacterial infections, its physical appearance and/or functions will be altered, resulting in pulmonary diseases. The current detection of pulmonary diseases is mainly done by X-ray. However, the X-ray is not only invasive, but also only able to detect the physical alterations of lungs. Thus, the X-ray is not applicable for detecting pulmonary diseases at early stages. In addition, there are a broad range of so-called pulmonary function tests. For example, spirometry measures how well the lungs exhale. Lung volume measurement permits detection of restrictive lung diseases. Testing the diffusion capacity permits as estimate of how efficiently the lungs are able to transfer oxygen from the air into the bloodstream. However, all the available function tests are mere measures of systemic metabolism rather than of pulmonary functions.

[0003] It is well known that the components of exhalation could be utilized for diagnosis of certain diseases. For example, U.S. Pat. 4,823,803 discloses a device for testing human exhalation for halitosis by using sensors that are sensitive to malodorant gases of predetermined chemical compositions for producing signals variable with the detected concentrations of the malodorant gases. However, this patent is directed to the detection of malodorant gases rather than the lung functions.

[0004] There are situations in which an early detection of viral infection is critical for treatment of the victim and more importantly for the control of the epidemic. For example, the recent epidemic of SARS (Severe Acute Respiratory Syndrome) has raised concerns about effective detection of such relevant diseases, especially at a much earlier stage of infection in human bodies. Upon early detection, scarce and precious medical resources can be focused on the infected persons. In the early stages of the infections, the X-ray screening may not be useful because the pathogenic damages on the lungs may not be evident enough to be detected by the machine. The current available function tests may not be able to detect the early signs of infections because the functions tests measure the systemic metabolism only. While PCR is powerful in detecting the early infection, it is prone to mutations. Massive mutations will handicap this technique severely. [0005] Therefore, there is an imperative need to develop non-invasive systems and methods for detecting pulmonary diseases, especially ones inflicted by viral or bacterial infections. Furthermore, the detection is independent of the mutations of infectious agents. This invention satisfies this need by disclosing systems and methods of detecting pulmonary diseases by analyzing of the inhalation and exhalation of a test person. Other advantages of this invention will be apparent with reference to the detailed description.

Summary of the Invention

[0006] The present invention provides non-invasive systems and methods for detecting of pulmonary diseases by analyzing inhalation and exhalation. In one aspect, the present invention provides a pulmonary disease detection system for detecting breathing deficiencies of a test person. The pulmonary disease detection system comprises a breath analyser for analysing the inhale and exhale airs of the test person, a computer processor for receiving from the breath analyser the information and processing the received analysis data to give values to different parameters of the inhale and exhale airs of the test person, and a medical database for storing different parameters of breaths of the public and normal ranges for healthy persons; thereby the computer processor compares the values of different parameters of the inhale and exhale airs of the test person with the ones stored in the medical database so as to yield a test result of whether the test person is suffering breath deficiencies. In one embodiment, the computer processor is embedded with an algorithm for processing the information from the breath analyser; wherein the algorithm calculates the breathing rate, oxygen consumption rate and carbon dioxide generation rate from the composition information of the inhale and exhale airs of the test person; thereby comparing the rates of the test person with the ones stored in the medical database so as to conclude whether the test person is suffering any breath deficiencies. In another embodiment, the computer processor is embedded with an algorithm for processing the information from the breath analyser; wherein the algorithm calculates the composition values of the exhale air from the composition values of the inhale air and the assumed normal rates including breathing rate, oxygen consumption rate and carbon dioxide generation rate; thereby comparing the composition values of the exhale air with the ones stored in the medical database so as to conclude whether the test person is suffering any breath deficiencies. In yet another embodiment, the computer processor is embedded with an algorithm for processing the information from the breath analyser; wherein the algorithm calculates the volume compliance and air-flow resistance of the test person from the composition values and pressure values derived from the inhale air rate and exhale air rate; thereby comparing the volume compliance and air-flow resistance values with the ones stored in the medical database so as to conclude whether the test person is suffering any breath deficiencies.

[0007] In another aspect, the present invention provides a method of detecting a pulmonary disease of a test person by investigating the breath deficiencies of the test person. The method comprises acquiring the information of inhale and exhale airs of the test person, processing the acquired information to give values of designated aspects of the inhale and exhale airs of the test person, and comparing the calculated values with the ones stored in a medical database so as to conclude whether the test person is suffering any pulmonary diseases. In one embodiment, the processing is executed within a computer processor that is embedded with an algorithm for processing the information from the breath analyser; wherein the algorithm calculates the breathing rate, oxygen consumption rate and carbon dioxide generation rate from the composition information of the inhale and exhale airs of the test person; thereby comparing the rates of the test person with the ones stored in the medical database so as to conclude whether the test person is suffering any breath deficiencies. In another embodiment, the processing is executed within a computer processor that is embedded with an algorithm for processing the information from the breath analyser; wherein the algorithm calculates the composition values of the exhale air from the composition values of the inhale air and the assumed normal rates including breathing rate, oxygen consumption rate and carbon dioxide generation rate; thereby comparing the composition values of the exhale air with the ones stored in the medical database so as to conclude whether the test person is suffering any breath deficiencies. In yet another embodiment, the processing is executed within a computer processor that is embedded with an algorithm for processing the information from the breath analyser; wherein the algorithm calculates the volume compliance and air-flow resistance of the test person from the composition values and pressure values derived from the inhale air rate and exhale air rate; thereby comparing the volume compliance and air-flow resistance values with the ones stored in the medical database so as to conclude whether the test person is suffering any breath deficiencies.

[0008] The objectives and advantages of the invention will become apparent from the following detailed description of preferred embodiments thereof in connection with the accompanying drawings.

Brief Description of the Drawings

[0009] Preferred embodiments according to the present invention will now be described with reference to the Figures, in which like reference numerals denote like elements.

[0010] FIG 1 is a block diagram of the pulmonary disease detection system (PDDS) in accordance with one embodiment of the present invention.

[0011] FIG 2 shows a breath analyzer configured in accordance with one embodiment of the present invention.

[0012] FIG 3 shows a flowchart of detecting pulmonary diseases on the basis of altered oxygen consumption and carbon dioxide generation in accordance with one embodiment of the present invention.

[0013] FIG 4 shows another functional flowchart of detecting pulmonary diseases on the basis of lung- volume compliance and airflow resistance in accordance with another embodiment of the present invention.

[0014] FIG 5a shows a diagram illustrating the directional relationships among the different pressures that may be detected or deduced from the air flow rates detected by the breath analyzer as shown in FIG 2.

[0015] FIG 5b shows that different pressures are functions of time. [0016] FIG 6 shows one functional configuration of the disease identification software system and databases in accordance with one embodiment of the present invention.

[0017] FIG 7 shows a flowchart illustrating the performance of the disease identification software system as shown in FIG 6.

Detailed Description of the Invention

[0018] The present invention may be understood more readily by reference to the following detailed description of certain embodiments of the invention. [0019] Throughout this application, where publications are referenced, the disclosures of these publications are hereby incorporated by reference, in their entireties, into this application in order to more fully describe the state of art to which this invention pertains.

[0020] The present invention provides systems and methods for detecting pulmonary diseases. While there are provided more details about the systems and methods hereinafter, it is to be appreciated that the present systems and methods are based on the understanding that anyone developing any pulmonary diseases would demonstrate certain detectable breathing deficiencies. The deficiencies may be manifested by the changes of the oxygen consumption and carbon dioxide generation, or the differences of lung compliance and airflow-resistance.

[0021] There is provided a block diagram of the pulmonary disease detection system (PDDS) as shown in FIG 1 in accordance with one embodiment of the present invention. The PDDS 100 comprises a breath analyzer 102, a computer processor 103, and a medical database 104. The breath analyzer 102 will take in the breath from a test person 101 and output the information of components of the breath from the person to the computer processor 103. The computer processor 103 contains algorithms for manipulating the information of the breath and comparing the manipulated results with the medical database 104, so that the computer processor provides the results of diagnosis 105. [0022] The breath analyzer 102 may be any apparatus that can obtain breathing information from a test person that is sufficient for the application of the algorithms embedded in the computer processor 103. For example, for the application of an algorithm based on oxygen consumption and carbon dioxide generation, the breath analyzer 102 basically comprises of gas sensors such as sensors for oxygen, carbon dioxide and water vapor. The compositions of both inhale and exhale gases are analyzed. Then the respective compositions are further processed by the computer processor 103. [0023] FIG 2 shows a breath analyzer 102 configured in accordance with one embodiment of the present invention. The breath analyzer 102 comprises a mask 1, a data acquisition unit 7, and an air tank 11. The mask 1 is configured to cover the nose and mouth of a test person so that maximum fresh air is delivered to the test person and minimum exhaled air is lost before proper measurement is completed. As shown in FIG 2, the mask 1 includes an air outlet membrane 2 as a seal for preventing air within the mask from leaking; an exhaust flip valve 3 that will open to allow all the expired air to flow out 4 of the mask when the test person breaths out; an air inlet membrane 21 as a seal for preventing air within the mask from leaking; an inhale flip valve 22 that will open to allow the fresh air from the air tank 11 to flow in 23 when the test person breaths in; an oxygen electrode 25 for detecting the oxygen in the air composition; a carbon dioxide gas electrode 24 for detecting the carbon dioxide in the air composition; a nitrogen gas electrode 17 for detecting the nitrogen in the air composition; a water vapor electrode 18 for detecting the water vapor in the air composition; an inspired air flow rate electrode 19 for determining the flow rate of exhale air; an expired air flow rate electrode 20 for determining the flow rate of exhale air; and signal conductors 5 that transmit the information from the electrodes to the data acquisition unit 7. The signal conductors may be ultra-low impedance conductor or fiber optic.

[0024] The air tank 11 contains pressurized air so as to ensure a measurable and controllable air supply to the test person. An air delivery pipe 16 connects the air tank with the mask so as to deliver a stream of controlled air from the air tank to the mask. The air tank 11 also includes four electrodes 12, 13, 14, 15 for detecting the oxygen, carbon dioxide, nitrogen, and water vapor respectively. The signal conductors 10 transmit the data from the four electrodes to the data acquisition unit 7. The signal conductors may be ultra- low impedance conductor or fiber optic.

[0025] The data acquisition unit 7 includes connectors 6, 9 for connecting to the signal conductors 5, 10 so that it will receive all the information from the air tank and the mask. Then the data acquisition unit 7 transmits the received signals to the computer processor 103 which acts as the central processing unit (CPU). The transmitted information may be digitalized packets.

[0026] After the CPU receives the data of the air compositions from the data acquisition unit 7, it will process the air composition data. In one aspect of the present invention, the detection of pulmonary diseases is based on the understanding that anyone developing any pulmonary diseases would demonstrate certain detectable breathing deficiencies. The inventors of the present invention further discovered that the breathing deficiencies are manifested by altered oxygen consumption and carbon dioxide generation. FIG 3 shows a flowchart of detecting pulmonary diseases on the basis of altered oxygen consumption and carbon dioxide generation.

[0027] Referring now to FIG 3, when the PDDS 100 starts 301, it obtains through the data acquisition unit 7 the information including oxygen composition, carbon dioxide composition, nitrogen composition, water vapor composition, and air flow rate 302. It is to be noted that the air flow rate data will be discussed hereinafter when the air flow rate will be used to calculate the volume compliance and air-flow resistance in another algorithm of the present invention. Then the CPU will calculate the overall relatives of all gases compositions 303. Then the processed data is searched against the stored database to determine whether the test person has pulmonary diseases 304. Then diagnostic results will be outputted 305 and the operation comes to an end 306. It is noted from FIG 3 that the stored database is continuously updated so that the database will become more useful when more data is collected.

[0028] Now there is provided a more detailed description of detecting pulmonary diseases on the basis of altered oxygen consumption and carbon dioxide generation. The basic assumption is that the composition of expired air from a patient such as a person with SARS infection is different from that of a normal person. It is further assumed that with an expired air, (a) its O₂ content (or % vol.) will be greater (because of less O₂ consumed from alveoli) and closer to that of inspired air; (b) its CO₂ content will be lesser, and more akin to that of inspired air; and (c) the transfer coefficients for O₂ & CO₂ will be lesser as compared to a medically normal person. Therefore, the mass balance analysis involves (i) compositions of air breathed in and out; and (ii) consumption or generation of O₂, CO₂ and H₂O. [0029] For calculation of inhale and exhale compositions, there are a few general assumptions: (1) Breathing Rate (BR) = 12 breaths/min; (2) P_H20 at 37⁰C = 47mmHg; (3)

O₂ metabolic consumption rate at (at BTP) = 284 m//min; and (4) CO₂ production rate (at

BTP) = 227 m//min. Thus, the expected compositions of the expired air can be calculated from the atmospheric air or vice versa. For example, as shown in Table 1, the expected expired air compositions can be calculated from the numbers of the atmospheric air column:

[0030] N₂ = 393.1 m/

[0031] O₂ = 104.2-(284/12) = 80.53 ml

[0032] CO₂ = 0.2+(227/12) = 19.12 ml

[0033] Total = 492.75 m/ (1)

[0034] Ratio of water vapor/dry gas in the expired air = 49.5mmHg/(760-47) mmHg.= 49.5/713 = 6.94% (2)

[0035] Volume of water vapor in the Expired air = (l)x(2)=492.75 0.0694=34.21m/

(3)

[0036] Total Expired air = [l]+[3]=492.75+34.21=526.96 ml (4)

[0037] Thus the percentage of the gases components in the expired air can be calculated as follows:

[0038] N₂ = 393.1/526.96 = 74.6%;

[0039] O₂ = 80.52/526.96 = 15.28%;

[0040] CO₂ = 19.12/526.96 = 3.63%; and

[0041] H₂O = 34.21/526.96 = 6.49%.

[0042] All the numbers of the atmospheric air and expired air are presented in

Table 1.

[0043] Table 1. An exemplary air compositions

[0044]

[0045] So far it has been shown that if the rates of oxygen consumption and carbon dioxide generation are known, the ideal composition of an expired air can be calculated from the original composition of the atmospheric air. This is important for initializing the stored databases because in the early stages, the PDDS may have to generate part of the database by calculating the components of the expired airs from the atmospheric airs based on certain assumptions. With the gradual accumulation of the database, more and more actual data will supplement or substitute the calculated ones. As discussed early, the breath analyzer 102 of the present invention can acquire the data of individual gas components of the expired air from the test person and the CPU can manipulate the data to give the percentages of each gas component as to the expired air. The processed data of each gas components will be compared with the stored medical database. If the test person suffers from any illness that affects pulmonary functions, it is expected that the processed data of the expired air from a test person will be deviated from that of the stored database for a normal person. If this is so, the CPU will output the diagnostic results showing that the test person is probably having pulmonary diseases. Then the test person can seek further examinations to determine which kind of pulmonary diseases he/she is developing. [0046] As shown in Table 1, it is apparent that the rates of oxygen consumption and carbon dioxide generation may be derived from the actual measurements of individual gas components of the atmospheric air and expired air. It is noted that all of the data of individual gas components can be obtained by the breath analyzer as shown in FIG 2. The calculated rates can be compared with the stored database to determine whether the test person is suffering any pulmonary diseases.

[0047] As mentioned earlier, the choice of a breath analyzer 102 for the present

PDDS is limited by its function only. For example, when the lung compliance and airflow- resistance are used for detecting pulmonary diseases, the breath analyzer 102 may be a commercially available spirometer. Now referring to FIG 4, there is provided another functional flowchart of detecting pulmonary diseases in accordance with another embodiment of the present invention. When the PDDS starts 400, it obtains data from the breath analyzer 401 (the same as the one shown in FIG 2) to determine the volume breath characteristics of the test person 402, and further extract the parameters of PO, Pl, P2, Ra, Ca and W 403. If the extracted parameters are not the best filtered results 404, the program will go back to step 403 to try to get the best parameters. If the extracted parameters are the best filtered results 404, then the VTIl, VTI2 and VTI3 will be determined 405. Then the program will conduct diagnosis 407 by utilizing the medical databases 406, 408, and output the results 409, resulting the end of the program 410.

[0048] FIG 5a shows a diagram illustrating the directional relationships among the different pressures that may be detected or induced from the air flow rates detected by the breath analyzer as shown in FIG 2. FIG 5b shows that different pressures are functions of time. From there are derived a few fundamental equations that are the foundation for the algorithm shown in FIG 4: [0049]

(ii) Pei = (2 a h) /R r = 2 T/ r = V/C + Pd, o

(iv) P_L = Po -Pp (v) R(dV/dt) + VIC = P_L - P a o

[0050] Now there is provided a more detailed description of determination of the volume breath characteristics of the test person 402. In one embodiment of the present invention, the lung ventilation function is analyzed by means of a very simple model represented by a first-order differential equation (Deq) in lung-volume (V) dynamics in response to the driving pressure (P_/, = atmospheric pressure - pleural pressure), as shown in FIG 5

[0051] First, the model governing equation derived from the basic equations is as follows:

RV + V/C = P_L(t) -Pel , o = P₀ + Pi cosω t+ P₂ sinω t -P_{d 0} (1)

[0052] wherein (i) P₀, Pi and P₂ are obtained from the given P₁ (= P_o ~ Pp ) data;

(ii) the parameters of this Governing Deq are lung compliance (C) and airflow-resistance (R), wherein in the equation both R and C are instantaneous valves; (iii) V = V(t) - Vo (the lung volume at the end-expiration); and (iv) P_eι,₀ is the lung elastic recoil pressure at the end of expiration and

Pel - V/C. [0053] At end-expiration when ωt = ωT, P_L = P_eι,_o- Hence, [0054] P_eι,o = P₀ + Pi , and the governing equation (1) becomes:

RV + V/C = -P₁ + P₁ cosω t + P₂ sinω t =P_n (2-a)

[0055] where the right-hand side represents the net pleural pressure. (P_n ^~P_atm -Pp -

P_ei,_o) curve. This P_n is in fact the driving pressure (P₀ - P_p) normalized with respect to its value at end-expiration. Equation(2-a) can be rewritten as follows:

V + V/R C =- P₁ / R+ (P₁ / R )cosω t + (P₂ / R) sinω t ; RC=τ (2-b)

[0056] wherein the P(t) clinical data displayed in FIG 5b is assumed to be represented by:

[0057] Po = 9.84 cm H2 O₁ Pl = -1.84 cm H2 O, and P2 = 3.16 cm H2 O (3)

[0058] If, in equation (1), R_a and C_a are designated as the average values (R and Q for the ventilatory cycle, then the solution of equation (1) is given by:

V(t) = -PiC_a + PiCa[ (Cos ωt+ ω Ra C_a Sin ωt)/ (1+ω² R_a ² C_a ²) J +P₂C_a

[(sinω t-ωR_aC_a cosω t) / (1+ω² R_a ² C_a ²)] + He^'t/R^ (4)

[0059] wherein the term (R_aC_a) is denoted by τ_a , and ω = 1.55 rad/s (based on the data in FIG 5b. If V = 0 at t = 0, then, putting V (at t = 0) = 0 gives us:

[0060] Then from equations (4) and (5), the overall expression for V (t) becomes:

V(t) = -P]C_α+ (Pi C_α (cos ft) t + ω τ_a Sin ωt)/(l+ω ²τ_a ² )}

+{P2 C_a (Sin ωt-ωτ_a Cos ωt) / (1+ ω ²τ_a ² )}

+{ e*^a ω Ca τ_a ( P₂ + P₁ ω τ_a) / (1+ ω V )} (6)

[0061] If dV/dt = 0 at t = 0, implying no air-flow at the start of inspiration, then equation (6) can be differentiated into:

^v = (P₁ C₀/ (1+ ω²τ_a ²)} (- ωSinωt+ω² τ_aCos ωt)

+(P₂ C_a/ (1+ ω²τ_a ²)}(ωcosωt+ ω² τ_aSin ωt) +{- C_a e ¹⁷*/ (1+ ω V )} ( ω P₂ + P₁ ω \) (7)

[0062] From equation (7), we get: V = 0 at t - 0, thereby also satisfying this initial condition. By matching the above V(t) expression (6) with the given V(t) data (in FIG 5b), and carrying out parameter-identification, the in vivo values of R_a and C_a can be determined. As a check, it can be verified that the substitution of (6) and (7) satisfies equation (2).

[0063] However, we can also analytically evaluate R_a and C_a by satisfying some conditions. For this purpose, we first note that V is maximum (=Tidal Volume, TV) at about t (= U )= 1.6s, i.e. at ω U = 2.48 rad. Now, for ω U = 2.48 rad, we get : sin(ωt_v)=0.62, cos (ω U) = -0.79 and tan (ω U) = -0.78. Also, for ω t_v = 2.48 rad f and based on the knowledge of the range of τ_a ), the exponential term e ^t/τa ( in equation 6) becomes of the order of e^'3 and less; hence, we decide to neglect it. So then, by and

putting V = 0 in equation. (7), we obtain:

tan (CO t_v) = ( P₂ + CO τ_a P₁ ) / ( P₁ - P₂ ω τ_a ) = -0.78 (8)

[0064] Upon substituting the values of P_/ and P ₂ from equation (3), and putting ω

= 1.55 rad s^"1 , we obtain the value of τ_a = 0.26s. We can also put V=O at t^0.58 or ωt=93 and obtain a similar value for T. Then, we also note that at U — 1- 6s ( for which dv /dt = 0), V = 0.61 . Hence upon substituting for cos(ω U) = -0.79 and sin(ω U) = 0.62 in equation(7), and again neglecting the exponential term we get the following algebraic equation:

- P₁ C₃ -( O. 54 P₁ C_a Z D) + (0. 94 P₂ C_a / D) = 0.6; (9)

[0065] wherein D = 1 + ω² τ_a ² , ω = 1.55 rad/s, and τ_a = 0. 26s; this equation can hence be rewritten as:

C_a (-1.54 Pi + 0. 94 P₂)- 0.7 (10)

[0066] We can substitute, therein, the values of Pj & P₂ from equation (3), and obtain the value of C_a = 0.12 L (cm H₂ O)^'1 . Since we have computed τ_a = 0.26 s, therefore R_a = 2.20 (cm H₂ O) s L^"1 . These are the average values of resistance- to - airflow and lung compliance during the ventilatory cycle shown in FIG 5b. [0067] Since Lung disease will influence the values of R and C, these parameters can be employed to diagnose lung diseases. For instance in the case of emphysema, the destruction of lung tissue between the alveoli produces a more compliant lung, and hence results in a larger value of C. In asthma, there is increased airway resistance (R) due to contraction of the smooth muscle around the airways. In fibrosis of the lung, the membranes between the alveoli thicken and hence lung compliance (C) decreases. Thus by determining the normal and diseased ranges of the parameters R and C, we can employ this simple Lung-ventilation model for differential diagnosis. Let us, however formulate just one non-dimensional number to serve as a ventilatory performance index VTIi (to characterize ventilatory function), as:

VTI₁ = f (R_a C_a)(Ventilatory rate in s ¹) 60 f = τ? (BR)² 60² (11)

where BR is the breathing rate. Now, let us obtain its order-of-magnitude by adopting representative values of R_a and C₀ in normal and disease states. Let us take the above computed values of R_a = 2.2(cm H₂O) s L^'1 and C_a =0.12 L(cm H₂O) ^ml and BR = 12m^'1 or 0.2s^'1, computed for the data of Fig(l) and equation(3). Then, in a supposed normal situation, the value of VTIi is of the order of 9.75. In the case of obstructive lung disease, (with increased R_a), let us take R_a = 3cm H₂ O s L^'1, C₀ = 0.12 L (cm H₂ O)^'1 and BR = 0.3s^'1 ; then we get VTU = 42. For the case of emphysema (with enhanced C), let us take Ra = 2.0cm H₂O s L^'1 , C_a =0.2 L( cm H₂O) ^A and BR = 0.2s^'1 ; then we obtain VTI₁ = 23.04. In the case of lung fibrosis (with decreased C_a ) , we take R_a = 2.0cm H₂O s L^'1 , C_a =0.08 L( cm H₂O) ^'J and BR = 0.2s^'1 ; then we obtain VTI₁ = 3.7. We can, hence summarize that VTIi would be in the range of 2-5 in the case of fibrotic lung disease, 5-15 in normal persons, 15-25 for the case of emphysema, 25-50 in the case of obstructive lung disease. This would of course be needed to be verified by analyzing a big patient population.

[0068] Now, all of this analysis requires pleural pressure data, for which the patient has to be intubated. If now we evaluate the patient in an outpatient clinic, in which we can only monitor lung volume and not the pleural pressure, then we have to develop a non- invasively obtainable Ventilatory index. [0069] In order to formulate a non-invasively determinable Ventilatory index from equation (1), we need to redesignate the model parameters, and indicate their identification procedure. So we make use of the following features from the volume-time data to facilitate evaluation of the following three parameters: : (Pi C), (P₂ C) and τ:

[0070] At t = t_v = 1.6s & ω U = 2.48, V is max & dV/dt = 0; hence we rewrite equation (9) as:

[0071]

tan (ω t_v ) = -0.78 = (P₂ + ω t P₁) / (P₁ -P₂ω t ) (12)

[0072] At t - t_m , V = 0 ; hence by differentiating equation. (7), without the exponential term ,we obtain:

PiC(-a? cos ω t_m - ω³ τsin ω t^ +P₂ C f-ω² sinω t_m - ω³ τsinω Q (1+ ω ²^) V =o=

i.e. tan ω t_m = (-Pj + ωτ P₂) / (P i(oτ + P₂) (13)

[0073] At t = Is &ω t = π/2, V = Vi (whose value is obtainable from FIG 5b); this condition yields (without the exponential term):

V₁= -(PiC) - {ω τ (PiC)Z(I+ ω W)} +{ (P₂ C) /(1+ ω ²T²)) (14)

[0074] At t = 2s & ω t=ττ/2 , V = V₂ (whose value is obtainable from FIG 5b); this condition yields (without the exponential term):

V₂= -(PiC) ~ ((PiC)Z(I+ ω ²T²)) +{ωτ (P₂ C) Z(I+ ω ²T⁷)) (15)

[0075] At t = 0.3s & ω t=270 , V= V₃ gives:

V₃= -(PiC) - {ωτ (PiC)Z(I+ ω'τ²)} -( (P₂ C) Z(I+ ω²i?)} (16)

[0076] From equations (12) & (13) and any one of the equations) (14-16), we can only obtain the values of ω τ (or of τ , since ω = 1.55) and of (Pi C) & (P₂ C) but not of Pj & P₂ by themselves. On the other hand, by also fitting equation (6), (without the exponential term) to the V(t) data, we obtain:

τ= rad s^~l; P₁C= L, P₂C= L (17) [0077] We can nevertheless formulate another non-invasively-determinable non- dimensional ventilatory index (V T Ij) in terms of these parameters, (ωτ, Pi C and P₂ C) as follows: [0078]

V TI₂= 60 (arc) (T V)² /2π (P₁ C)(P₂ C) (18)

[0079] It is seen that VTI₂ can in fact be expressed in terms of Pi₁ P 2 and R, C.

This VTI₂ index can be evaluated by computing the values of τ, alongwith (P; C) & (P₂ C) as given by equation (17). Then, after evaluating V T I₂ for a number of persons, and patients its distribution can enable us to categorize and differentially diagnose patients with various lung disorders and diseases. Between the two indices VTIi and VTI₂ , we can employ the one that enables more distinct separation of subjects with different ventilatory disorders.

[0080] Thus far, we have adopted the average cyclic values C_a and R₀ for our DE_g model parameters. However, we expect that C will vary with lung volume (V) ,and that R

will perhaps vary with the airflow-rate or (V) or even ω. Hence, for a true representation of the lung properties C & R, let us determine their values for different times during the ventilatory cycle, and compare them with their average values C_a & R_a ,so as to make a case for a non-linear ventilatory-function model.

[0081] Let us hence compute the instantaneous value of compliance (C) at mid- inspiration at (t = tm ), and compare it with that of its average cyclic value of C_a . For this purpose let us differentiate equation (2-a) , giving : [0082]

R V + V /C = - Pi ωSinω t + P₂ ω Cosω t (19)

[0083] Now at about mid-inspiration, when t ≡ 0.87 s and ω t = ω t_m = 1.32 rad or

78^°, V = 0 mά V = 0.5 Us (based on figl). By substituting for V and V in equation (19), we obtain, C = 0.14L/cm H₂O (compared to its C_a value of 0.118). Now, in order to also

compute R at ω t_m =1.32 we substitute V = 0.5 L/s and V = 0.3L (from the figl data) into equation (2-a), to obtain: [0084]

0.5 R + 0.3 /C = -P₁ + P₁ Cos 78° + P₂ Sin 78° = 3.8 (20)

[0085] Now, since C(at ω t_m = 1.32) = O. 14L/cm H₂ O, we obtain R=4.6(cmH₂O) s

L^'1 , compared to R_a = 2.20. This gives us some idea of the order of magnitude of R & C, in comparison to their average values C_a & R_a.. We could also expect C at mid-inspiration to be higher than its value at end-inspiration, when the lung is fully inflated. Also, we

could expect the flow-resistance to be maximum at mid-inspiration, when V is maximal. [0086] We can hence represent lung compliance (C) and resistance (R) as follows:

[0087]

C = H₁ (V)^ - C ₀ or C = C _o e^n(V)) , (21-a)

[0088] wherein V can also be varied by having the subjects breathe at different tidal volumes (TV_S) and ventilation frequency (ω)

[0089] We note as per the conventional formulation of compliance, given by equation(ii) in FIG 5b as:

Pa = V/C + P_el, o ; (22)

[0090] In the above formulation, we assume that C and E(— 1/C) remains constant throughout the ventilation cycle. However at the start of inspiration, C = C₀ at t= 0, and it decreases as the lung volume increases, based on the lung (static) volume vs pressure curve. So let us improve upon this equation(22) model, by making P_eι to be a non-linear function of volume, as follows:

[0091] kv

P_ei=P_ei,o +E₀e ₍23)

[0092] Employing the above format of compliance, the governing DE_q (1) becomes

[0093] R V + E₀ e^kV = PL ( t) - P_ei, o = P₀+ Pi cos ω t + P₂ sin ω t - P_ei_, o (24)

[0094] Again at end-expiration, P_et, o = intra-pulmonary pressure =(Po + Pi)-

Hence equation. (24) becomes: [0095]

R V + E_o e^{k v} = -P] + P₁ COS W t + P₂ sin ω t (25)

[0096] whose RHS is similar to that of equation(2-a), and the values of P; & P₂ are given by equation(3) for the FIG 5b data.

[0097] In order to evaluate these parameters Jc & Eo ,we again bring to bear the situation that at end-inspiration, for t = t_v =1.6 s (for which (O t =ω t_v = 2. 48 rad, sin co t_v = 0.62 & cos ω t_v= -0. 79), we have

V = 0 and V = V_max =TV = 0.6 L Hence, from fig (1) data, and equations (3 & 25), we obtain:

[0098]

E₀ e^06k = 8.75 (26)

[0099] Let us now employ the volume data point at which V =0. For this purpose, we differentiate equation (25) , to obtain:

[00100] RT + E₀ k e ^kv - -P₁G) sin (ω t) + P₂ω cos (ω t) (27)

[00101] From the Fig (1) data at about mid-inspiration, for which t = t_m = 0. 87s &

ω t_m = 1.32 = 78° with cos (ω Q= 0.2 & sin (ω Q = 0.98), we have V =. 0, V =0.5 Ls^~\ V = 0.3 L , from fig(l) data. Substituting these values into equation.(27), we get : [00102]

E₀ e ^OM = 3.8 (28)

[00103] Now from equation(s) (27) & (28), we get: _e-0.3k ₌ J_j8 ^₂₉)

[00104] for which, k = 1.07 and (from equation 26 or 28) E₀ = 2.75 (30)

[00105] Hence, by employing the non-linear formulation,

[00106] we obtain the following expression for lung compliance (or elastance):

[00107] dP , 1 kV 1.07F

^r=E=^E_oke =2.94e (31)

[00108] Based on this expression, we obtain ,for t = t_m & V = 0.3L:

E =IZC = 4.06cm H₂O ZL; and C = 0.25 L/cm H₂ O. (32)

[00109] Equation(32) can now provide us a more realistic characterization of lung compliance as follows:

At

At At

which corresponds to the value of C_a .

[00110] Our non-linear formulation of lung compliance, as depicted by equation. (31

& 33) , indicates that compliance decreases from 0.34 cm H₂ O ZL at start-inspiration to 0.25 cm H₂ O ZL at about mid-inspiration, and then to 0.18 cm H₂ O ZL at the end of inspiration. What this also tells us is that the ventilatory model equation.(l) gives the correct reading of the compliance at V_max , i.e. at end-inspiration. At other times of inspiration and expiration, the C_a parameter underestimates the instantaneous value of lung compliance. Now how about obtaining an analytical solution of equation(25) for V(t), and fitting the expression for V(t) to the lung volume data, to evaluate the parameters (i) R, E₀ & k for an intubated patient and (ii) R, E₀ k & P₁ & P₂ for a non-intubated patient in the out patient clinic.

[00111] Finally, while it is important to determine the normal and pathological diagnostic ranges of C_a & R_a , or better still of the parameters (Eo & k ) of the C vs F and R

vs V relationships, it would be more useful to construct and employ a non-dimensional ventilatory index We have already formulated VT Ii & VT I₂ in equations (11) & (18), respectively. We will now formulate yet another index : [00112]

VT I₃ = [60 ( R_a / C_a ) (BR) (TV)²]/ P₁ P₂ (34)

[00113] wherein (i) BR (the breathing rate in # / sec) = 0.5 ωZπ (ii) and / Pi P₂ I is the absolute value of the product Pi P₂ (because of Pi being negative). For the fig (1) (3) clinical data, of BR= 0.25, with TV=0.6L & IP₁ P₂/ = 18.1, and for the computed value of

RJC_a=l8.33cm H₂O s L^'1, we obtain VTI₃ = 5.47. Between VTI₁ and VTI₃ , we can decide which index enables us to better differentially diagnose subjects with ventilatory disorders.

[00114] Now, let us go one step further and recognize that, for non-intubated patients, we cannot monitor P₁ and P₂ , and hence cannot evaluate R_a & C_a as demonstrated in [0051] to [0068]. However for evaluating ventilatory index in out-patient clinics, we can infact adopt (P₁ C) and (P₂ C ) to be the model-system parameters, and evaluate them as delineated in [0069] to [0083]. We can hence adopt the non-invasively-obtainable ventilatory-performance index VTI₂ (given by equation 18):

[00115]

VTI₂ = 30 ωτ (TV)² / π (P₁ C) (P₂ C); ω = 2π(BR)(in U¹)

= 60 (BR)(R / C) (TV)² / (P₁ P₂) = VTI₃ (35)

[00116] which is noted to be the same expression as for index VTI₃ , except that it can be evaluated without intubating the patient. Hence, it would be even more useful to determine the distribution of VTI₂ for patients with a wide range of lung pathologies and ventilatory disorders. Then, we can delineate the normal and pathological ranges of this index, and employ this information to diagnose patients into different disease categories. [00117] Now referring to FIG 6, there is provided a more detailed description of the functional configuration 600 of the disease identification software system and databases in accordance with one embodiment of the present invention. When the user 610 inputs all the parameters through the GUI 620 into the computer processor, the inputs will be queued in the query module 640. Then, when the parameters are transformed into indexes as discussed above, the indexes will go to the disease identification software system 650 through the intelligent DBMS to obtain the matched diseases; the indexes at the same time are compared with the expert DB 630 to verify the diagnosis. The final diagnosis will be output 105 as shown in FIG 1.

[00118] Now referring to FIG 7, there is provided a more detailed description of the performance 700 of the disease identification software system as shown in FIG 6. The index 710 first fires up numerous search engines in finding the best match diseases in the intelligent DBMS 720; the search engines will search the disease identification software system (DISS) 730. The DISS contains many types of diseases 750, 760, 770 where the search engines will search all the types of diseases and output the search results to the Query Module 780. There is a possibility that there is more than one type of diseases being suspected. Statistical means are used to determine which is the best match diseases and thereby putting all these best fit diseases to the expert database system to further refine the possibilities 740. Then the verified diagnostic results will be output to the user 800 throughthe GUI 790.

[00119] While the present invention has been described with reference to particular embodiments, it will be understood that the embodiments are illustrative and that the invention scope is not so limited. Alternative embodiments of the present invention will become apparent to those having ordinary skill in the art to which the present invention pertains. Such alternate embodiments are considered to be encompassed within the spirit and scope of the present invention. Accordingly, the scope of the present invention is described by the appended claims and is supported by the foregoing description.

Claims

CLAIMSWhat is claimed is:

1. A pulmonary disease detection system for detecting breathing deficiencies of a test person, comprising: a breath analyser for analysing the inhale and exhale airs of the test person; a computer processor for receiving from the breath analyser the information and processing the received analysis data to give values to different parameters of the inhale and exhale airs of the test person; and a medical database for storing different parameters of breaths of the public and normal ranges for healthy persons; thereby the computer processor compares the values of different parameters of the inhale and exhale airs of the test person with the ones stored in the medical database so as to yield a test result of whether the test person is suffering breath deficiencies.

2. The pulmonary disease detection system of claim 1, wherein the breathing deficiencies may be caused by bacterial infection, viral infection, physical injuries and cancer.

3. The pulmonary disease detection system of claim 1, wherein the breath analyser comprises: a mask for covering the nose and mouth of the test person so as to maximizing the delivery of fresh air and minimizing the loss of the exhaled air; an air tank for supplying measurable and controllable air to the mask; and an acquisition unit electrically connected with the mask and the air tank so as to receive all the information from the air tank and the mask.

4. The pulmonary disease detection system of claim 3, wherein the mask comprises a group of electrodes including an oxygen electrode, a carbon dioxide gas electrode, a nitrogen electrode, a water vapor electrode, an inspired air flow rate electrode and an expired air flow rate electrode; wherein each electrode detects each designated component of the inhale and exhale airs.

5. The pulmonary disease detection system of claim 4, wherein the computer processor is embedded with an algorithm for processing the information from the breath analyser; wherein the algorithm calculates the breathing rate, oxygen consumption rate and carbon dioxide generation rate from the composition information of the inhale and exhale airs of the test person; thereby comparing the rates of the test person with the ones stored in the medical database so as to conclude whether the test person is suffering any breath deficiencies.

6. The pulmonary disease detection system of claim 4, wherein the computer processor is embedded with an algorithm for processing the information from the breath analyser; wherein the algorithm calculates the composition values of the exhale air from the composition values of the inhale air and the assumed normal rates including breathing rate, oxygen consumption rate and carbon dioxide generation rate; thereby comparing the composition values of the exhale air with the ones stored in the medical database so as to conclude whether the test person is suffering any breath deficiencies.

7. The pulmonary disease detection system of claim 4, wherein the computer processor is embedded with an algorithm for processing the information from the breath analyser; wherein the algorithm calculates the volume compliance and air-flow resistance of the test person from the composition values and pressure values derived from the inhale air rate and exhale air rate; thereby comparing the volume compliance and air-flow resistance values with the ones stored in the medical database so as to conclude whether the test person is suffering any breath deficiencies.

8. The pulmonary disease detection system of claim 7, wherein the derived pressure values include pleural pressure and alveolar pressure.

9. The pulmonary disease detection system of claim 8, wherein the volume compliance and air-flow resistance are calculated by the equations (1) - (10) so that VTI₁ is computed as follows:

VTI₁ = [ (R_a C_a χVentilatory rate in s^"1) 60 f = τ² (BR)² 60² wherein VTIi is denoted as ventilatory performance index; C as lung-volume compliance; and R as air-flow resistance; and wherein R_a and C_a are designated as the average values (R and C) for the ventilatory cycle, and BR is the breathing rate.

10. The pulmonary disease detection system of claim 8, wherein (ωτ), (P₁ C) and (P₂ C) are calculated by the equations (12) - (17) so that V T I₂ is computed as follows:

V T I₂= 60 (ωτ) (T V)² /2π (P₁ C )(P₂ C) =30 ω (R/C) (T V)² /π P₁ P₂ wherein V T I₂ is denoted as ventilatory performance index; C as lung-volume compliance; and R as air-flow resistance; and wherein TV is tidal volume; Y\8c P₂ as pleural pressures and ωτ.

11. The pulmonary disease detection system of claim 8, wherein all the required parameters involved are calculated by the equations (1) to (10) and (12)-(17) so that V T I₃ is computed as follows:

VT I₃ = [60 ( R_a / C_a ) (BR) (TV)²]/ P₁ P₂ wherein VTI3 is denoted as ventilatory performance index; C as lung-volume compliance; and R as air-flow resistance; and wherein TV is tidal volume; and

P₂ as pleural pressures.

12. A method of detecting a pulmonary disease of a test person by investigating the breath deficiencies of the test person, said method comprising the steps of: acquiring the information of inhale and exhale airs of the test person; processing the acquired information to give values of designated aspects of the inhale and exhale airs of the test person; and comparing the calculated values with the ones stored in a medical database so as to conclude whether the test person is suffering any pulmonary diseases.

13. The method of claim 12, wherein the breathing deficiencies may be caused by bacterial infection, viral infection, physical injuries and cancer.

14. The method of claim 12, wherein the information of inhale and exhale airs of the test person is acquired by a breath analyser; and wherein the breath analyser comprises: a mask for covering the nose and mouth of the test person so as to maximizing the delivery of fresh air and minimizing the loss of the exhaled air; an air tank for supplying measurable and controllable air to the mask; and an acquisition unit electrically connected with the mask and the air tank so as to receive all the information from the air tank and the mask.

15. The method of claim 14, wherein the mask comprises a group of electrodes including an oxygen electrode, a carbon dioxide gas electrode, a nitrogen electrode, a water vapor electrode, an inspired air flow rate electrode and an expired air flow rate electrode; wherein each electrode detects each designated component of the inhale and exhale airs.

16. The method of claim 15, wherein the processing is executed within a computer processor that is embedded with an algorithm for processing the information from the breath analyser; wherein the algorithm calculates the breathing rate, oxygen consumption rate and carbon dioxide generation rate from the composition information of the inhale and exhale airs of the test person; thereby comparing the rates of the test person with the ones stored in the medical database so as to conclude whether the test person is suffering any breath deficiencies.

17. The method of claim 15, wherein the processing is executed within a computer processor that is embedded with an algorithm for processing the information from the breath analyser; wherein the algorithm calculates the composition values of the exhale air from the composition values of the inhale air and the assumed normal rates including breathing rate, oxygen consumption rate and carbon dioxide generation rate; thereby comparing the composition values of the exhale air with the ones stored in the medical database so as to conclude whether the test person is suffering any breath deficiencies.

18. The method of claim 15, wherein the processing is executed within a computer processor that is embedded with an algorithm for processing the information from the breath analyser; wherein the algorithm calculates the volume compliance and air-flow resistance of the test person from the composition values and pressure values derived from the inhale air rate and exhale air rate; thereby comparing the volume compliance and airflow resistance values with the ones stored in the medical database so as to conclude whether the test person is suffering any breath deficiencies.

19. The method of claim 18, wherein the derived pressure values include pleural pressure and alveolar pressure.

20. The method of claim 19, wherein the volume compliance and air-flow resistance are calculated by the equations (1) — (10) so that V T I₁ is computed as follows:

VTI₁ = [(R_a Ca) (Ventilatory rate in s^'1) 60 J² = i? (BR)² 60² wherein VTIi is denoted as ventilatory performance index; C as lung-volume compliance; and R as air-flow resistance; and wherein R_a and C_a are designated as the average values (R and C) for the ventilatory cycle, and BR is the breathing rate.

21. The method of claim 19, wherein (ωτ), (P₁ C) and (P₂ C) are calculated by the equations (12) - (17) so that V T I₂ is computed as follows:

V T I₂= 60 (ωτ) (T V)² /2π (P₁ C )(P₂ C) =30 ω (R/C) (T V)² /π P₁ P₂ wherein V T I₂ is denoted as ventilatory performance index; C as lung-volume compliance; and R as air-flow resistance; and wherein TV is tidal volume; Pi& P₂ as pleural pressures and ωτ.

22. The method of claim 19, wherein all the required parameters involved are calculated by the equations (1) to (10) and (12)-(17) so that V T I₃ is computed as follows:

VT I₃ = [60 ( Re / Ca ) (BR) (TV)²]/ P₁ P₂ wherein VTB is denoted as ventilatory performance index; C as lung-volume compliance; and R as air-flow resistance; and wherein TV is tidal volume and V_\8c P₂ as pleural pressures.