CN115715673B - Pulmonary ventilation partition assessment system based on degenerate electrode - Google Patents
Pulmonary ventilation partition assessment system based on degenerate electrode Download PDFInfo
- Publication number
- CN115715673B CN115715673B CN202210906680.8A CN202210906680A CN115715673B CN 115715673 B CN115715673 B CN 115715673B CN 202210906680 A CN202210906680 A CN 202210906680A CN 115715673 B CN115715673 B CN 115715673B
- Authority
- CN
- China
- Prior art keywords
- lung
- partition
- impedance
- module
- pulmonary
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000005192 partition Methods 0.000 title claims abstract description 109
- 238000009423 ventilation Methods 0.000 title claims abstract description 75
- 230000002685 pulmonary effect Effects 0.000 title claims abstract description 63
- 210000004072 lung Anatomy 0.000 claims abstract description 134
- 230000005284 excitation Effects 0.000 claims abstract description 26
- 238000004364 calculation method Methods 0.000 claims abstract description 25
- 230000000638 stimulation Effects 0.000 claims abstract description 9
- 238000012545 processing Methods 0.000 claims abstract description 5
- 239000011159 matrix material Substances 0.000 claims description 47
- 238000012546 transfer Methods 0.000 claims description 17
- 238000000034 method Methods 0.000 claims description 14
- 230000000241 respiratory effect Effects 0.000 claims description 12
- 230000008859 change Effects 0.000 claims description 10
- 238000011156 evaluation Methods 0.000 claims description 7
- 230000029058 respiratory gaseous exchange Effects 0.000 claims description 7
- 230000008569 process Effects 0.000 claims description 6
- 230000035565 breathing frequency Effects 0.000 claims 1
- 238000003745 diagnosis Methods 0.000 abstract description 3
- 210000000038 chest Anatomy 0.000 description 20
- 230000000875 corresponding effect Effects 0.000 description 11
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 6
- 230000006870 function Effects 0.000 description 6
- 230000004199 lung function Effects 0.000 description 6
- 238000003384 imaging method Methods 0.000 description 4
- 238000005259 measurement Methods 0.000 description 4
- 230000035945 sensitivity Effects 0.000 description 4
- 230000036387 respiratory rate Effects 0.000 description 3
- 238000000638 solvent extraction Methods 0.000 description 3
- 208000000884 Airway Obstruction Diseases 0.000 description 2
- 230000004075 alteration Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000002593 electrical impedance tomography Methods 0.000 description 2
- 230000009325 pulmonary function Effects 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 210000000115 thoracic cavity Anatomy 0.000 description 2
- 208000002200 Respiratory Hypersensitivity Diseases 0.000 description 1
- 230000010085 airway hyperresponsiveness Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000003990 capacitor Substances 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 230000002441 reversible effect Effects 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 238000013316 zoning Methods 0.000 description 1
Landscapes
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
Abstract
The invention discloses a pulmonary ventilation partition assessment system based on a degenerate electrode, which comprises an electric stimulation module, an excitation power module, a signal acquisition module, a signal processing module, a pulmonary partition impedance calculation module and a pulmonary ventilation state assessment module; the invention adopts the degenerate electrode technology to simply partition the lung, establishes the functional relation between the lung ventilation state parameter and the lung partition impedance, and distinguishes the lung ventilation state to distinguish the quality of the lung ventilation state, thereby providing a basis for the preliminary diagnosis of doctors.
Description
Technical Field
The invention relates to the field of lung function assessment, in particular to a lung ventilation partition assessment system based on a degenerate electrode.
Background
Pulmonary ventilation status is an important factor in determining the degree of pulmonary health in humans and has important reference values for the type and degree of airway obstruction, airway hyperresponsiveness, and airway obstruction reversibility.
Parameters commonly used for pulmonary ventilation status assessment include forced vital capacity FVC, forced expiratory volume in one second FEV1. These parameters may determine the type of pulmonary ventilation function and the severity of the ventilation disorder.
In addition, it is often necessary to confirm the lung ventilation status of different areas of the lung at the same time as the lung function is detected, and therefore, a zoning technique that can reflect the lung ventilation status of different areas is required.
Electrical impedance tomography is commonly used in clinical procedures, but most of the existing electrical impedance tomography systems use 16 electrodes and 32 and 64 electrodes. Increasing the number of electrodes increases the resolution and imaging quality of the system, but the data throughput increases, which makes a high demand for computational speed.
Disclosure of Invention
The invention aims to provide a pulmonary ventilation partition assessment system based on a degenerate electrode, which comprises an electric stimulation module, an excitation power module, a signal acquisition module, a signal processing module, a pulmonary partition impedance calculation module and a pulmonary ventilation state assessment module;
The electrical stimulation module comprises 4 electrodes;
The four electrodes are arranged on the surface of the chest of the user;
The excitation power supply module respectively applies current excitation to the four electrodes;
The signal acquisition module acquires the voltage information of each electrode and transmits the voltage information to the lung partition impedance calculation module;
The lung partition module divides the cross section of the chest of the user into four lung partitions; wherein each lung partition is provided with an electrode in a projection area on the surface of the chest of the user;
The lung partition impedance calculation module processes voltage information of the electrode and establishes a transfer impedance matrix between the voltage information and current excitation;
The lung partition impedance calculation module converts the transfer impedance matrix into a lung partition impedance matrix corresponding to the lung partition;
The lung partition impedance calculation module transmits the lung partition impedance matrix to the lung ventilation state evaluation module;
the pulmonary ventilation state evaluation module calculates the pulmonary ventilation parameters according to the pulmonary regional impedance matrix.
Further, the chest of the user is the chest of the user, namely, four electrodes are arranged on the surface of the chest of the user, and the lung partitioning module divides the cross section of the chest of the user into four lung partitions.
Further, the transfer impedance matrix is as follows:
Wherein U 1(s)、U2(s)、U3(s)、U4(s) respectively represents voltage information output when four electrodes are used as measuring electrodes; i 1(s)、I2(s)、I3(s)、I4(s) respectively represent the current excitations received when four electrodes are used as excitation electrodes; Δ kj＝Δjk is the node admittance of a network of four electrodes; h=j=1, 2,3,4; delta is the determinant of the node admittance matrix.
Further, the step of converting the transfer impedance matrix into a pulmonary segment impedance matrix corresponding to a pulmonary segment by the pulmonary segment impedance calculation module includes:
1) Converting the transfer impedance matrix (1) to obtain:
In the impedance matrix Impedance of
2) The modulus of the i-th lung partition impedance will be calculated, namely:
Wherein Z i is the partition impedance model of the ith transformed lung partition, i refers to the number of chest partition, i=1, 2,3,4; w pi is the contribution of impedance Z (5-p)p(s) to the impedance of the ith pulmonary segment;
3) A lung partition impedance matrix is established based on a model of all lung partition impedances.
Further, the lung ventilation parameters include forced vital capacity FVC, forced expiratory volume in one second FEV1.
Further, the step of calculating the lung ventilation parameter comprises:
1) Establishing an impedance curve of the regional impedance of the lung along with the change of time, and acquiring peaks and troughs of the impedance curve;
2) Calculating a j-th breathing cycle T j; the respiration period T j is the time difference between adjacent peaks P j+1、Pj;
3) The respiratory rate RR is calculated, namely:
Wherein h is the number of respiratory cycles;
4) Calculating the difference value (delta Z FVC)j) between the adjacent wave crests and wave troughs, and taking the maximum value of the difference value between the adjacent wave crests and wave troughs as the variation extremely difference delta Z FVC of the lung partition impedance modulus;
Starting timing at the wave crest, calculating to obtain the variation of the lung partition impedance modulus value (delta Z FEV1)j, repeating for h times, taking the maximum variation of the lung partition impedance modulus, and recording as delta Z FEV1;
5) Calculating lung ventilation parameters, namely:
Where Z max is the maximum modulus of the pulmonary partition impedance; p f denotes a scale coefficient, p s denotes a power coefficient, and p t denotes a constant coefficient.
Further, the lung ventilation parameter is used to assess a lung ventilation status level;
further, the excitation power supply module excites currents applied to the four electrodes into constant current source signals.
The technical effect of the invention is undoubtedly that the invention has the following beneficial effects:
According to the invention, the test electrode is reduced, so that the calculation amount is greatly reduced, the purpose of simple imaging of lung partition can be achieved, and the lung ventilation state of each region is distinguished by the breathing curve.
The invention adopts the degenerate electrode technology to simply partition the lung, establishes the functional relation between the lung ventilation state parameter and the lung partition impedance, and distinguishes the lung ventilation state to distinguish the quality of the lung ventilation state, thereby providing a basis for the preliminary diagnosis of doctors.
In the prior art, at least 8 electrodes are needed when lung imaging is carried out, the invention adopts a degenerate electrode technology, 4 electrodes are used for simple imaging, and the lung ventilation state of each partition is reflected through the distribution of the partition lung impedance.
The degenerate electrode technique is described as: the lung impedance distribution of the 4 regions can be measured by the 4 electrodes, so that the lung ventilation state of each region is further reflected according to the lung impedance distribution.
According to the functional relation between the lung partition impedance and the lung ventilation state parameters, the impedance distribution of 4 lung partitions in the respiratory process is obtained, and the lung ventilation state of each partition is further reflected through the impedance distribution.
The invention utilizes the method of the degenerate electrode to identify the lung ventilation state partition, and provides basis for preliminary diagnosis of doctors.
The method establishes an equivalent multiport network model of the cross section of the thoracic cavity, and divides the lung into 4 areas according to the different relative positions of the electrodes;
the invention derives the functional relationship between the modes of the lung impedance of different partitions and the measured impedance in the measurement mode;
The invention establishes a functional relationship between the zoned lung impedance and the lung function ventilation status parameter.
Drawings
FIG. 1 is a flow chart of a pulmonary ventilation partition assessment system using degenerate electrode-based;
FIG. 2 is a schematic illustration of lung segmentation;
FIG. 3 is a schematic diagram of a multiport network;
FIG. 4 is a graph of impedance versus time for a pulmonary segment;
fig. 5 shows the lung partition impedance modulus change corresponding to the lung function parameter.
Detailed Description
The present invention is further described below with reference to examples, but it should not be construed that the scope of the above subject matter of the present invention is limited to the following examples. Various substitutions and alterations are made according to the ordinary skill and familiar means of the art without departing from the technical spirit of the invention, and all such substitutions and alterations are intended to be included in the scope of the invention.
Example 1:
Referring to fig. 1 to 5, the pulmonary ventilation partition assessment system based on the degenerate electrode comprises an electrical stimulation module, an excitation power module, a signal acquisition module, a signal processing module, a pulmonary partition impedance calculation module and a pulmonary ventilation state assessment module;
The electrical stimulation module comprises 4 electrodes;
The four electrodes are arranged on the surface of the chest of the user;
The excitation power supply module respectively applies current excitation to the four electrodes;
the excitation power supply module excites currents applied to the four electrodes into constant current source signals.
The signal acquisition module acquires the voltage information of each electrode and transmits the voltage information to the lung partition impedance calculation module;
the lung partition module adopts a cross division mode to divide the cross section of the chest of the user into four lung partitions; wherein each lung partition is provided with an electrode in a projection area on the surface of the chest of the user;
the chest of the user is the chest of the user, namely four electrodes are arranged on the surface of the chest of the user, and the lung partitioning module divides the cross section of the chest of the user into four lung partitions. The user's forechest cross section includes a complete lung cross section.
The lung partition impedance calculation module processes voltage information of the electrode and establishes a transfer impedance matrix between the voltage information and current excitation;
The transfer impedance matrix is as follows:
Wherein U 1(s)、U2(s)、U3(s)、U4(s) respectively represents voltage information output when four electrodes are used as measuring electrodes; i 1(s)、I2(s)、I3(s)、I4(s) respectively represent the current excitations received when four electrodes are used as excitation electrodes; Δ kj＝Δjk is the node admittance of a network of four electrodes; k=j=1, 2,3,4; delta is the determinant of the node admittance matrix. The node here is the electrode.
The lung partition impedance calculation module converts the transfer impedance matrix into a lung partition impedance matrix corresponding to the lung partition;
The step of converting the transfer impedance matrix into a pulmonary partition impedance matrix corresponding to the pulmonary partition by the pulmonary partition impedance calculation module includes:
1) Converting the transfer impedance matrix (1) to obtain:
In the impedance matrix Impedance of
2) The modulus of the i-th lung partition impedance will be calculated, namely:
Wherein Z i is the partition impedance model of the ith transformed lung partition, i refers to the number of chest partition, i=1, 2,3,4; w pi is the contribution of impedance Z (5-p)p(s) to the impedance of the ith pulmonary segment;
3) A lung partition impedance matrix is established based on a model of all lung partition impedances.
The lung partition impedance calculation module transmits the lung partition impedance matrix to the lung ventilation state evaluation module;
the pulmonary ventilation state evaluation module calculates the pulmonary ventilation parameters according to the pulmonary regional impedance matrix.
The lung ventilation parameters include forced vital capacity FVC, forced expiratory volume in one second FEV1.
The step of calculating a lung ventilation parameter comprises:
a) Establishing an impedance curve of the regional impedance of the lung along with the change of time, and acquiring peaks and troughs of the impedance curve;
b) Calculating a j-th breathing cycle T j; the respiration period T j is the time difference between adjacent peaks P j+1、Pj;
c) The respiratory rate RR is calculated, namely:
Wherein h is the number of respiratory cycles;
d) Calculating the difference value (delta Z FVC)j) between the adjacent wave crests and wave troughs, and taking the maximum value of the difference value between the adjacent wave crests and wave troughs as the variation extremely difference delta Z FVC of the lung partition impedance modulus;
Starting timing at the wave crest, calculating to obtain the variation of the lung partition impedance modulus value (delta Z FEV1)j, repeating for h times, taking the maximum variation of the lung partition impedance modulus, and recording as delta Z FEV1;
e) Calculating lung ventilation parameters, namely:
Where Z max is the maximum modulus of the pulmonary partition impedance; p f denotes a scale coefficient, p s denotes a power coefficient, and p t denotes a constant coefficient.
The coefficients satisfy the following formula:
Wherein, the individual influence parameter of the user is xi L/W =L/W; l, W is the length of the long and short axes of the chest cross section;
In this implementation, the element p ft、pff、pfs in the high-order parameter matrix of the scaling coefficient p f, the element p sf、pss、pst in the high-order parameter matrix of the power coefficient p s, and the element p tf、pts、ptt in the high-order parameter matrix of the constant coefficient p t are as follows:
When Δz is Δz FEV1, the obtained Δv is the calculated FEV1M of the volume of the expired air of the first second of the maximum expiration, and when Δz is Δz FVC, the obtained Δv is the calculated FVCM of the forced vital capacity. A method of calculating the lung function parameter is thus obtained.
The lung ventilation parameter is used to evaluate a lung ventilation status level;
Specifically, the present embodiment calculates the ratio of the forced ventilation amount FEV1 and the forced ventilation amount FVC within one second, and determines the lung ventilation status level from the ratio, which is positively correlated with the lung ventilation status level.
Parameters commonly used for pulmonary function status assessment include: the forced vital capacity FVC, the forced expiratory volume FEV1 within one second, the peak expiratory flow PEF, and the forced expiratory phase measure the lung function parameters MMEF, V25, V50, V75, etc. related to the expiratory flow.
Table 1 shows a lung ventilation status evaluation table used in this embodiment, and the present system converts the change of the regional lung impedance model and the functional relationship between the change and the lung ventilation status parameter into corresponding indexes to determine the condition of the lung regional ventilation status.
TABLE 1 Graded basis of pulmonary ventilation status levels
Pulmonary ventilation status of the pulmonary segment includes normal, mild, moderate, severe and extremely severe disorders;
When the ratio of forced expiratory volume FEV1 to forced vital capacity FVC is greater than 70% within one second, the lung ventilation status is normal;
When the ratio of forced expiratory volume FEV1 to forced vital capacity FVC within one second ranges from [60%, 70%), the pulmonary ventilation status is mild disorder;
when the ratio of forced expiratory volume FEV1 to forced vital capacity FVC within one second ranges from [50%, 60%), the pulmonary ventilation status is moderate disorder;
when the ratio of forced expiratory volume FEV1 to forced vital capacity FVC within one second ranges from [35%, 50%), the pulmonary ventilation status is a severe disorder;
When the ratio of forced expiratory volume FEV1 to forced vital capacity FVC is less than 35% within one second, the pulmonary ventilation status is a severe disorder.
The excitation power supply module excites currents applied to the four electrodes into constant current source signals.
Example 2:
The lung ventilation partition assessment system based on the degenerate electrode comprises an electric stimulation module, an excitation power supply module, a signal acquisition module, a signal processing module, a lung partition impedance calculation module and a lung ventilation state assessment module;
the electrical stimulation module comprises 4 electrodes; the four electrodes are arranged on the surface of the chest of the user;
the step of using a degenerate electrode-based pulmonary ventilation partition assessment system includes:
1) Partitioning the chest cross section into four regions as in fig. 1;
2) The multi-port network test circuit for building the thoracic model is shown in fig. 2, excitation currents are respectively applied to four electrodes, then voltage values corresponding to each electrode are measured, and a 4×4-order transfer impedance matrix between output voltage and input current is built. The calculation method comprises the following steps:
first, list the network node equation:
the multiport network is composed of linear time-invariant resistor, inductor and capacitor elements, so that the node admittance matrix is a nonsingular symmetric matrix, the matrix is reversible, and the inverse matrix is also a nonsingular symmetric matrix. Thereby, can obtain:
Δij＝Δji (2)
the formula (4) can be obtained by combining the formula (2) and the formula (3).
3) The transfer impedance matrix is converted into a 2X 2 order lung partition impedance matrix corresponding to the lung partition, and the conversion process is as follows:
And then a model of the pulmonary zonal impedance can be obtained, denoted as the i-th model of the pulmonary zonal impedance:
Wherein Z i is the transformed partition impedance value of the ith lung partition, I refers to the number of the chest partition, i=i, II, III, IV; w pi is the contribution of impedance Z (5-p)p(s) to the impedance of the ith pulmonary segment.
To evaluate the respiratory sensitivity of the pulmonary segment impedance modulus and phase, the maximum value (1.4) and the minimum value (0.05) of the gas filling coefficient are respectively regarded as the beginning and the end of expiration, the pulmonary segment impedance modulus corresponding to the maximum value and the minimum value are respectively marked as Z i(s)(v＝0.05) and Z i(s)(v＝1.4), the respiratory sensitivity of the pulmonary segment impedance modulus is represented by the maximum change rate delta|Z i(s)|max of the pulmonary segment impedance modulus, and the calculation expression is shown in the formula (6).
Similarly, the maximum rate of change of the phase angle of the impedance of the pulmonary segment is defined according to equation (7)And is used to characterize the respiratory sensitivity of the impedance phase angle.
Prescribed delta|Z pq(s)|max andThe larger the value of (a) the higher the modulus (or phase angle) respiratory sensitivity of the corresponding pulmonary segment impedance.
5) Establishing a relationship between the lung ventilation status assessment parameter and the lung partition impedance:
FVC (forced vital capacity) refers to the total amount of gas exhaled as soon as possible after forced inhalation, FEV1 (forced vital capacity within 1 second) refers to the total amount of gas exhaled as soon as possible after forced inhalation as much as possible within 1 second. The two are main parameters for evaluating the pulmonary ventilation function, and a method for measuring the pulmonary function parameters based on the tracing method is proved to be feasible by experimental study, and the core idea is as follows: the lung partition impedance modulus change and the corresponding lung gas change are plotted one by one, and then the mathematical relationship of the two is constructed through function fitting.
The waveform of the pulmonary regional impedance over time is shown in fig. 3, with the ordinate representing the modulus of the pulmonary regional impedance and the abscissa representing the measurement time. P j and V j respectively represent the peak and trough of the impedance curve of the lung partition in the jth respiratory process, and the time difference between two adjacent peaks P j and P j+1 is referred to as the respiratory cycle T j. To reduce the measurement error, the average value of 3 consecutive respiratory cycles is taken as the respiratory cycle, and the reciprocal thereof is the respiratory rate RR, as shown in formula (8).
The image of fig. 4 is used to illustrate the pulmonary regional impedance parameters that need to be recorded during the measurement and calculation of FVC, FEV1 respectively.
And calculating the difference value (delta Z FVC)j, taking the maximum value as the variation extremely difference delta Z FVC of the model value of the lung partition impedance according to the drawn waveform curve, and calculating the variation of the model value of the lung partition impedance within 1 second (delta Z FEV1)j, repeating for 3 times, taking the maximum value as delta Z FEV1) from the time of starting from the peak.
There was a clear functional relationship between FVC and Δz FVC、ΔZFEV1 and FEV1 found based on the trace. And respectively carrying out data fitting on the two power functions, wherein the best fitting effect of the two power functions is found, and the function expression is shown as the formula (9):
In the formula (9), p f denotes a scale coefficient, p s denotes a power coefficient, and p t denotes a constant coefficient. When Δz takes Δz FEV1, the obtained Δv is FEV1; when Δz is Δz FVC, the obtained Δv is FVC, and thus a calculation method of FVC and FEV1 is obtained.
Claims (2)
1. The lung ventilation partition assessment system based on the degenerate electrode is characterized by comprising an electric stimulation module, an excitation power supply module, a signal acquisition module, a signal processing module, a lung partition impedance calculation module and a lung ventilation state assessment module;
The electrical stimulation module comprises 4 electrodes;
The four electrodes are arranged on the surface of the chest of the user;
The excitation power supply module respectively applies current excitation to the four electrodes;
The signal acquisition module acquires the voltage information of each electrode and transmits the voltage information to the lung partition impedance calculation module;
The lung partition module divides the cross section of the chest of the user into four lung partitions; wherein each lung partition is provided with an electrode in a projection area on the surface of the chest of the user;
The lung partition impedance calculation module processes voltage information of the electrode and establishes a transfer impedance matrix between the voltage information and current excitation;
The lung partition impedance calculation module converts the transfer impedance matrix into a lung partition impedance matrix corresponding to the lung partition;
The lung partition impedance calculation module transmits the lung partition impedance matrix to the lung ventilation state evaluation module;
The pulmonary ventilation state evaluation module calculates and obtains pulmonary ventilation parameters according to the pulmonary regional impedance matrix;
The chest of the user is the chest of the user, namely, four electrodes are arranged on the surface of the chest of the user, and the lung partition module divides the cross section of the chest of the user into four lung partitions;
The transfer impedance matrix is as follows:
Wherein U 1(s)、U2(s)、U3(s)、U4(s) respectively represents voltage information output when four electrodes are used as measuring electrodes; i 1(s)、I2(s)、I3(s)、I4(s) respectively represent the current excitations received when four electrodes are used as excitation electrodes; Δ kj＝Δjk is the node admittance of a network of four electrodes; k=j=1, 2,3,4; delta is determinant of node admittance matrix;
The step of converting the transfer impedance matrix into a pulmonary partition impedance matrix corresponding to the pulmonary partition by the pulmonary partition impedance calculation module includes:
a1 -transforming the transfer impedance matrix (1) to obtain:
In the impedance matrix Impedance of
A2 A) a modulus of the i-th lung partition impedance will be calculated, i.e.:
Wherein Z i is the partition impedance model of the ith transformed lung partition, i refers to the number of chest partition, i=1, 2,3,4; w pi is the contribution of impedance Z (5-p)p(s) to the impedance of the ith pulmonary segment;
3) Establishing a lung partition impedance matrix based on the modes of all lung partition impedances;
the lung ventilation parameters include forced vital capacity FVC, forced expiratory volume in one second FEV1;
the step of calculating a lung ventilation parameter comprises:
b1 Establishing an impedance curve of the regional impedance of the lung along with the change of time, and acquiring peaks and troughs of the impedance curve;
b2 Calculating a j-th breathing cycle T j; the respiration period T j is the time difference between adjacent peaks P j+1、Pj;
b3 Calculating the breathing frequency RR, namely:
Wherein h is the number of respiratory cycles;
b4 Calculating the difference value (delta Z FVC)j) between the adjacent wave crest and the wave trough, and taking the maximum value of the difference value between the adjacent wave crest and the wave trough as the variation extremely difference delta Z FVC of the lung partition impedance modulus;
Starting timing at the wave crest, calculating to obtain the variation of the lung partition impedance modulus value (delta Z FEV1)j, repeating for h times, taking the maximum variation of the lung partition impedance modulus, and recording as delta Z FEV1;
b5 Calculating lung ventilation parameters, namely:
where Z max is the maximum modulus of the pulmonary partition impedance; p f denotes a scale coefficient, p s denotes a power coefficient, and p t denotes a constant coefficient;
the lung ventilation parameter is used to assess a lung ventilation status level.
2. The degenerate electrode-based pulmonary ventilation partition assessment system of claim 1, wherein the excitation power module applies current excitation to four electrodes as constant current source signals.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210906680.8A CN115715673B (en) | 2022-07-29 | 2022-07-29 | Pulmonary ventilation partition assessment system based on degenerate electrode |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210906680.8A CN115715673B (en) | 2022-07-29 | 2022-07-29 | Pulmonary ventilation partition assessment system based on degenerate electrode |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115715673A CN115715673A (en) | 2023-02-28 |
| CN115715673B true CN115715673B (en) | 2024-07-19 |
Family
ID=85253898
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210906680.8A Active CN115715673B (en) | 2022-07-29 | 2022-07-29 | Pulmonary ventilation partition assessment system based on degenerate electrode |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115715673B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118216898A (en) * | 2024-05-22 | 2024-06-21 | 博联众科（武汉）科技有限公司 | State monitoring method based on lung resistance tomography and application thereof |
Family Cites Families (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7758503B2 (en) * | 1997-01-27 | 2010-07-20 | Lynn Lawrence A | Microprocessor system for the analysis of physiologic and financial datasets |
| US7907998B2 (en) * | 2002-07-03 | 2011-03-15 | Tel Aviv University Future Technology Development L.P. | Bio-impedance apparatus and method |
| DE102011106405B4 (en) * | 2011-07-02 | 2021-08-12 | Drägerwerk AG & Co. KGaA | Electro-impedance tomography device |
| EP2853196B1 (en) * | 2013-09-27 | 2016-05-11 | Drägerwerk AG & Co. KGaA | Electro-impedance tomography apparatus and method |
| KR101696791B1 (en) * | 2015-07-31 | 2017-01-17 | 연세대학교 원주산학협력단 | Pulmonary function test apparatus using chest impedance and thereof method |
| KR101765423B1 (en) * | 2016-11-18 | 2017-08-07 | 경희대학교 산학협력단 | Method and apparatus for pulmonary function test |
| US11490815B2 (en) * | 2017-03-24 | 2022-11-08 | Oxford University Innovation Limited | Methods for extracting subject motion from multi-transmit electrical coupling in imaging of the subject |
| CN213077063U (en) * | 2020-05-21 | 2021-04-30 | 南京宇川医疗科技有限公司 | Integrated double-cavity branch pipe conduit |
| CN114533036A (en) * | 2022-01-17 | 2022-05-27 | 深圳市安保科技有限公司 | Visual lung ventilation monitoring method and device and storage medium |
| CN114748052A (en) * | 2022-04-12 | 2022-07-15 | 广州国家实验室 | Respiratory electrical impedance imaging excitation parameter selection method, device, equipment and medium |
-
2022
- 2022-07-29 CN CN202210906680.8A patent/CN115715673B/en active Active
Non-Patent Citations (1)
| Title |
|---|
| 基于多端口网络转移阻抗的肺功能参数测量与状态评估;张一鸣;《中国优秀硕士学位论文全文数据库》;20221231;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115715673A (en) | 2023-02-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115715673B (en) | Pulmonary ventilation partition assessment system based on degenerate electrode | |
| EP0904730B1 (en) | Determination of airway resistance and lung compliance | |
| Ionescu et al. | A theoretical study on modeling the respiratory tract with ladder networks by means of intrinsic fractal geometry | |
| JP2012506046A (en) | Impedance measurement circuit and method | |
| CN106768245A (en) | A kind of fat scale intelligent Matching user method | |
| CN103976737B (en) | A kind of pulmo respiratory impedance correlation analysis and system | |
| CN116936104B (en) | Health detector data analysis system and method based on artificial intelligence | |
| CN112842321A (en) | Lung ventilation function detection method, device and medium based on flow-volume loop diagram | |
| CN111887847A (en) | Visceral fat measuring method and device based on human body composition meter, computer equipment and storage medium | |
| Ionescu et al. | Parametric models for characterizing respiratory input impedance | |
| Johnson et al. | Incremental threshold loading: a standard protocol and establishment of a reference range in naive normal subjects | |
| JP2015510427A (en) | Bioimpedance Spectroscopy System and Method | |
| CN117310455B (en) | Oral cavity scanner circuit board fault detection method based on electric signals | |
| CN110208605A (en) | A method of inhibit the electrical resistance tomography distribution of conductivity of alias to rebuild | |
| Avanzolini et al. | A versatile identification method applied to analysis of respiratory mechanics | |
| Kazemi et al. | Frequency analysis of capnogram signals to differentiate asthmatic and non-asthmatic conditions using radial basis function neural networks | |
| Bhatawadekar et al. | A study of artifacts and their removal during forced oscillation of the respiratory system | |
| CN111973188A (en) | Method for estimating respiratory mechanics parameter based on neural network | |
| Avanzolini et al. | Role of the mechanical properties of tracheobronchial airways in determining the respiratory resistance time course | |
| CN114886406A (en) | Health state monitoring method and device, isolator and storage medium | |
| CN201453268U (en) | Remote respiration monitoring medical system | |
| CN113822420A (en) | Dead space fraction model building method and system based on volumetric carbon dioxide map | |
| TW201911210A (en) | Method of fast evaluation for the moderate to severe obstructive sleep apnea | |
| Ligas et al. | Comparison of measures of forced expiration | |
| CN109350024A (en) | A kind of wearable respiratory disorder monitoring system and method based on Internet of Things |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |