Breath detection and electrical impedance imaging based coupled detection method, detection system, storage medium

Abstract

The application provides a coupling detection method and system based on exhalation detection and electrical impedance imaging, and belongs to the technical field of medical information software.The technical points are as follows: S100, acquiring exhalation flow rates corresponding to different moments in a single exhalation period;S200, acquiring a boundary voltage matrix corresponding to different moments in a single exhalation period based on a same excitation frequency;S300, acquiring average impedance change values corresponding to different moments in a single exhalation period based on the data obtained in S200; and S400, solving an exhalation impedance matching index EIMI.The coupling detection method and system based on exhalation detection and electrical impedance imaging can effectively find early-stage chronic obstructive pulmonary disease without needing a bronchodilation test.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of intelligent medical treatment, and particularly relates to a coupling detection method and system based on exhalation detection and electrical impedance imaging, and a storage medium. BACKGROUND

[0002] The gold standard for COPD (Chronic Obstructive Pulmonary Disease) is the bronchodilator test, which mainly relies on FEV1 (forced expiratory volume in one second) / FVC (forced vital capacity) <0.70 after inhaling a bronchodilator to make a diagnosis. This index can objectively evaluate the irreversibility of airflow limitation and is a key basis for differentiating COPD from other respiratory diseases.

[0003] However, the bronchodilator test has the following shortcomings in the evaluation of COPD:

[0004] (1) Unable to locate the lesion area;

[0005] (2) There is a risk of drug stimulation because the patient inhales a bronchodilator;

[0006] (3) Unable to dynamically monitor the lung function of the patient.

[0007] In view of the above shortcomings, many scholars have devoted themselves to applying EIT (Electrical Impedance Tomography) in the field of COPD, such as:

[0008] Reference 1: Xu W N, Feng H, Kai S J, et al. Research progress of electrical impedance tomography in chronic obstructive pulmonary disease [J]. Journal of Chronic Diseases, 2022(003) believes that COV, GI and other indicators can be calculated through EIT images to evaluate the ventilation status of each region of the lung.

[0009] Reference 2: CN118697320B (Guangzhou Medical University, Zhejiang University Medical College Affiliated Shaw Hospital) believes that FEV1 / FVC and GI can be calculated based on EIT images, and thus the damage to the reserve ratio lung function can be evaluated.

[0010] Reference 3: "CN118121183B (Guangzhou Medical University, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences)" argues that: Based on time-series EIT images, a regional time distribution map containing time characteristics is obtained. According to the regional time distribution and its coefficient of variation of different exhaled air volumes obtained from the regional time distribution map, the time required for a unit exhaled air volume can be accurately reflected. This objectively characterizes the explosive power and endurance of human respiratory muscles, as well as the presence of lung damage, and provides a more comprehensive assessment of human lung respiratory function, overcoming the technical difficulty that existing indicators cannot reflect the time required for a unit exhaled air volume.

[0011] Reference 4: "CN118121184B (Guangzhou Medical University, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences)" suggests that by determining the regional temporal distribution and its distribution characteristic parameters based on the variation characteristics of different exhaled air volumes, the time characteristics required to exhale different amounts of gas in the FVC can be characterized. Furthermore, by comparing the regional temporal distribution and distribution characteristic parameters, the results of whether there is regional ventilatory function impairment in the human lungs can be accurately and objectively characterized.

[0012] However, due to the drawbacks of EIT (low imaging resolution and low robustness), it is difficult to achieve accurate screening of chronic lung diseases, especially early-stage chronic obstructive pulmonary disease, by relying solely on EIT as a single technology. Summary of the Invention

[0013] The purpose of this invention is to solve the problems existing in the prior art and to provide a coupled detection method, detection system, and storage medium based on breath detection and electrical impedance imaging.

[0014] A coupled detection method based on breath detection and electrical impedance imaging includes the following steps:

[0015] S100, obtain the different times t0, t1, t2, t3, ..., t within a single expiratory cycle. N The corresponding expiratory flow rates are Q(t0), Q(t1), Q(t2), Q(t3), ..., Q(t). N );

[0016] t0, t N These are the start and end times of a single expiratory cycle;

[0017] S200, obtain t0, t1, t2, t3, ..., t4 at different times within a single expiratory cycle under the same excitation frequency. N The corresponding boundary voltage matrices are V(t0), V(t1), V(t2), V(t3), ..., V(t). N );

[0018] S300, based on the data obtained from S200, acquires different times t0, t1, t2, t3, ..., t within a single expiratory cycle. N The corresponding average impedance changes are ΔZ(t0), ΔZ(t1), ΔZ(t2), ΔZ(t3), ..., ΔZ(t... N );

[0019] S400, calculate the expiratory impedance matching index EIMI:

[0020] EIMI=[Q(t0)·△Z(t0)·(t1-t0) / 2+Q(t1)·△Z(t1)·(t2-t0) / 2+Q(t2)·△Z(t2)·(t3-t1) / 2+……+Q(t i )·△Z(t i )·(t i+1 -t i-1 ) / 2+……+Q(t N )·△Z(t N )·(t N -t N-1 ) / 2] / [Var(Q)·Var(△Z)] 0.5 ;

[0021] Var(Q) refers to Q(t0) ~ Q(t) N The variance of )

[0022] Var(△Z) refers to △Z(t0) ~ △Z(t) N The variance of ).

[0023] Furthermore, S500, according to EIMI, provides results regarding ventilatory function impairment:

[0024] If EIMI ≥ 0.75, then the person is healthy;

[0025] If 0.75 > EIMI ≥ 0.45, it indicates asthma or mild COPD.

[0026] If 0.45 > EIMI, then it is considered moderate to severe COPD.

[0027] Furthermore, S300 includes the following sub-steps:

[0028] S301, Calculate any time t i The boundary voltage difference matrix ΔV(t) with t0 i ): △V(t) i )= V(t i )-V(t0);

[0029] S302, Calculate any time t i The relative conductivity matrix Δσ(t) with t0i );

[0030] S303, Solve for any time t after normalization. i The relative conductivity matrix Δσ with respect to t0 norm (t i );

[0031] △σ norm (t i )=△σ(t i ) / max{△σ(t i )},max{△σ(t i )} represents the matrix △σ(t) i The maximum value in );

[0032] S304, Solve for any time t i The average impedance change value △Z(t) i ): △Z(t) i )=mean(△σ norm (t i ()), mean means to calculate the average.

[0033] Furthermore, S302 employs the TK-Noser method to solve for the relative conductivity matrix Δσ(t). i ).

[0034] A coupled detection system based on breath detection and electrical impedance imaging includes: a storage module, an average impedance change value calculation module, and an EIMI calculation module;

[0035] The storage module stores the different times t0, t1, t2, t3, ..., t4 within a single expiratory cycle of the patient. N The corresponding expiratory flow rates are Q(t0), Q(t1), Q(t2), Q(t3), ..., Q(t). N Based on different times t0, t1, t2, t3, ..., t within a single expiratory cycle under the same excitation frequency N The corresponding boundary voltage matrices are V(t0), V(t1), V(t2), V(t3), ..., V(t). N ); t0, t N These are the start and end times of a single expiratory cycle;

[0036] S200, the average impedance change calculation module can calculate the value of V(t0) ~ V(t) based on V(t0) ~ V(t0) N Find the values ​​t0, t1, t2, t3, ..., t4 at different times within a single expiratory cycle. N The corresponding average impedance changes are ΔZ(t0), ΔZ(t1), ΔZ(t2), ΔZ(t3), ..., ΔZ(t...N );

[0037] S300, the EIMI solver module is used to solve for the patient's EIMI value;

[0038] EIMI=[Q(t0)·△Z(t0)·(t1-t0) / 2+Q(t1)·△Z(t1)·(t2-t0) / 2+Q(t2)·△Z(t2)·(t3-t1) / 2+……+Q(t i )·△Z(t i )·(t i+1 -t i-1 ) / 2+……+Q(t N )·△Z(t N )·(t N -t N-1 ) / 2] / [Var(Q)·Var(△Z)] 0.5 Var(Q) refers to Q(t0) ~ Q(t). N The variance of ΔZ; Var(ΔZ) refers to the range of ΔZ(t0) to ΔZ(t). N The variance of ).

[0039] Furthermore, the working method of the average impedance change value calculation module is as follows:

[0040] Step a, calculate any time t i The boundary voltage difference matrix ΔV(t) with t0 i ): △V(t) i )= V(t i )- V(t0);

[0041] Step b, calculate any time t i The relative conductivity matrix Δσ(t) with t0 i );

[0042] Step c, ; △σ norm (t i )=△σ(t i ) / max{△σ(t i )},max{△σ(t i )} represents the matrix △σ(t) i The maximum value in );

[0043] Step d: Solve for any time t i The average impedance change value △Z(t) i ): △Z(t) i )=mean(△σ norm (t i ()), mean means to calculate the average.

[0044] Furthermore, in step b, the TK-Noser method is used to solve for the relative conductivity matrix Δσ(t). i ).

[0045] Furthermore, based on the results of ventilatory function impairment given by EIMI: if EIMI ≥ 0.75, then the patient is healthy; if 0.75 > EIMI ≥ 0.45, then the patient has asthma or mild COPD; if 0.45 > EIMI, then the patient has moderate to severe COPD.

[0046] Furthermore, the excitation frequency ranges from [80KHz to 120KHz].

[0047] Furthermore, at different times t0, t1, t2, t3, ..., t within a single expiratory cycle N The corresponding expiratory flow rates are Q(t0), Q(t1), Q(t2), Q(t3), ..., Q(t). N Based on the same exhalation frequency, at different times t0, t1, t2, t3, ..., t within a single expiratory cycle, N The corresponding boundary voltage matrices are V(t0), V(t1), V(t2), V(t3), ..., V(t). N All of these were obtained under the condition that the subject completed a forceful inhalation until the lungs were full and then forcefully exhaled until all the air was exhaled (this process is similar to the characteristics of FVC).

[0048] A storage medium characterized in that it stores a program capable of executing the coupling detection method as described above.

[0049] The advantages of the technical solution of this invention are mainly reflected in:

[0050] First, the fundamental concept of this application lies in proposing a new specific parameter: EIMI. The value of EIMI reflects the time-domain synergy between airflow and impedance. Using EIMI offers the following advantages:

[0051] 1.1, Bronchodilator test is not required;

[0052] 1.2, can effectively detect early COPD.

[0053] Second, EIMI is defined as follows:

[0054] EIMI=[Q(t0)·△Z(t0)·(t1-t0) / 2+Q(t1)·△Z(t1)·(t2-t0) / 2+Q(t2)·△Z(t2)·(t3-t1) / 2+……+Q(t i )·△Z(t i )·(t i+1 -t i-1 ) / 2+……+Q(tN )·△Z(t N )·(t N -t N-1 ) / 2] / [Var(Q)·Var(△Z)] 0.5 ;

[0055] Var(Q) refers to Q(t0) ~ Q(t) N The variance of )

[0056] Var(△Z) refers to △Z(t0) ~ △Z(t) N The variance of )

[0057] In the formula, EIMI is the Expiratory Impedance Matching Index; Q(t) is the expiratory velocity signal measured by the flow sensor; ∆Z(t) is the average lung impedance change; t0, t N represents the start and end times of the expiratory cycle; Var[] represents the variance of the signal.

[0058] In the EIMI calculation process, the normalized relative conductivity matrix Δσ is used. norm (t i This avoids measurement differences caused by different excitation frequencies and amplitude differences caused by different measurement conditions. Attached Figure Description

[0059] The present invention will be further described in detail below with reference to the embodiments shown in the accompanying drawings, but this does not constitute any limitation on the present invention.

[0060] Figure 1 This illustrates the specific manifestations of EIMI in COPD, health, and asthma.

[0061] Figure 2 The threshold analysis results of EIMI are shown.

[0062] Figure 3 The physical meaning of the symbols in this application is illustrated. Detailed Implementation

[0063] The objectives, advantages, and features of this invention will be explained through the following non-limiting description of preferred embodiments. These embodiments are merely typical examples of applying the technical solutions of this invention, and all technical solutions formed by equivalent substitutions or equivalent transformations fall within the scope of protection claimed by this invention.

[0064] <I. Research and Development Strategies for Early-Stage COPD Characteristics>

[0065] The research team discovered subtle abnormalities in respiratory physiological parameters in early-stage COPD, particularly in expiratory flow fluctuations, end-tidal humidity changes, and changes in respiratory impedance patterns. The key to diagnosing early-stage COPD lies in selecting appropriate quantifiable biomarkers from these findings to form specific parameters.

[0066] The specific parameter proposed in this application is the expiratory impedance matching index, which can be called EIMI (Expiratory Impedance Matching Index).

[0067] Specifically, EIMI is defined as follows:

[0068] EIMI=[Q(t0)·△Z(t0)·(t1-t0) / 2+Q(t1)·△Z(t1)·(t2-t0) / 2+Q(t2)·△Z(t2)·(t3-t1) / 2+……+Q(t i )·△Z(t i )·(t i+1 -t i-1 ) / 2+……+Q(t N )·△Z(t N )·(t N -t N-1 ) / 2] / [Var(Q)·Var(△Z)] 0.5 ;

[0069] Var(Q) refers to Q(t0) ~ Q(t) N The variance of )

[0070] Var(△Z) refers to △Z(t0) ~ △Z(t) N The variance of )

[0071] In the formula, EIMI is the Expiratory Impedance Matching Index; Q(t) is the expiratory velocity signal measured by the flow sensor; ∆Z(t) is the average lung impedance change; t0, t N represents the start and end times of the expiratory cycle; Var[] represents the variance of the signal.

[0072] <II. Specific Expression of EIMI>

[0073] This study initially recruited 168 participants from a dual-center cohort at the Second Affiliated Hospital of Soochow University and the First Affiliated Hospital of Jinan University. All participants signed informed consent forms and completed a full FVC (forced vital capacity) breathing test.

[0074] The following two types of data were collected simultaneously from each subject:

[0075] (1) Expiratory flow rate signal Q(t): acquired by the mask flow rate sensor, with a sampling frequency ≥50Hz;

[0076] (2) Chest boundary voltage signal V(t): acquired by a 16-channel EIT device in adjacent excitation-adjacent measurement mode, with a total of 208 voltage groups;

[0077] During the data quality control phase, 18 subjects were excluded based on the following criteria:

[0078] (1) There is a loss or saturation of ≥1.5s in the flow velocity signal;

[0079] (2) The EIT reconstructed image contains ≥5 consecutive frames of artifacts, reconstruction failure, or obvious abnormal electrode contact;

[0080] (3) Non-standard FVC operation during exhalation (such as premature interruption, repeated inhalation, etc.).

[0081] The final sample size included in the analysis was 150 cases, including: normal control group (50 cases), asthma group (50 cases), and COPD group (50 cases).

[0082] like Figure 1 As shown, using the EIMI index as a continuous respiratory coupling metric, statistical analysis based on training samples revealed significant differences in EIMI among healthy individuals, those with asthma or mild COPD, and those with moderate to severe COPD. The threshold distributions for these three groups can be determined as follows: Figure 2 As shown.

[0083] Therefore, in practical applications, the lung function status of a subject can be determined by comparing the EIMI with a preset grading threshold.

[0084] <III. Coupled Detection Method Based on Breath Detection and Electrical Impedance Imaging>

[0085] The coupled detection method based on breath detection and electrical impedance imaging includes the following steps:

[0086] S100, obtain the different times t0, t1, t2, t3, ..., t within a single expiratory cycle. N The corresponding expiratory flow rates are Q(t0), Q(t1), Q(t2), Q(t3), ..., Q(t). N );

[0087] t0, t N These are the start and end times of a single expiratory cycle;

[0088] S200, obtain t0, t1, t2, t3, ..., t4 at different times within a single expiratory cycle under the same excitation frequency. N The corresponding boundary voltage matrices are V(t0), V(t1), V(t2), V(t3), ..., V(t). N );

[0089] S300, based on the data obtained from S200, acquires different times t0, t1, t2, t3, ..., t within a single expiratory cycle. N The corresponding average impedance changes are ΔZ(t0), ΔZ(t1), ΔZ(t2), ΔZ(t3), ..., ΔZ(t... N );

[0090] S300 includes the following sub-steps:

[0091] S301, Calculate any time t i The boundary voltage difference matrix ΔV(t) with t0 i ): △V(t) i )=V(t i )-V(t0);

[0092] S302, Calculate any time t i The relative conductivity matrix Δσ(t) with t0 i ):

[0093] △σ(t i )=(J T ·J+k t ·I+k n W) -1 ·J T ·△V(t i );

[0094] S303, in order to eliminate the influence of signal amplitude differences under different individuals and measurement conditions on the calculation results, Δσ(t) i Perform normalization, for example, normalize by its maximum absolute value:

[0095] △σ norm (t i )=△σ(t i ) / max{△σ(t i )}, that is, matrix △σ(t) i Divide each data point in the matrix by the maximum value of the matrix; max{△σ(t)} i )} represents the matrix △σ(t) i The maximum value in );

[0096] △σ norm (t i() represents the normalized conductivity matrix;

[0097] S304. To simplify subsequent analysis, the conductivity change across the entire image is averaged regionally to obtain a representative impedance change index for the global or lung region:

[0098] △Z(t i )=mean(△σ norm (t i (), mean means to calculate the average;

[0099] S400, calculate the expiratory impedance matching index EIMI:

[0100] EIMI=[Q(t0)·△Z(t0)·(t1-t0) / 2+Q(t1)·△Z(t1)·(t2-t0) / 2+Q(t2)·△Z(t2)·(t3-t1) / 2+……+Q(t i )·△Z(t i )·(t i+1 -t i-1 ) / 2+……+Q(t N )·△Z(t N )·(t N -t N-1 ) / 2] / [Var(Q)·Var(△Z)] 0.5 ;

[0101] Var(Q) refers to Q(t0) ~ Q(t) N The variance of )

[0102] Var(△Z) refers to △Z(t0) ~ △Z(t) N The variance of )

[0103] S500, based on EIMI results of ventilatory function impairment.

[0104] For expiratory flow rate Q(t0) ~Q(t) N For example, the exhalation flow rate signal can be collected by the flow sensor in the user's face breathing mask, with a sampling frequency of ≥50Hz, to reflect airway patency and lung rebound capacity.

[0105] It should be noted that for electrical impedance tomography (EIT), EIT testing devices such as CN215738930U and CN219557274U are worn on the patient (generally, 16 electrodes are used for chest and lung testing). Sensors on a wearable vest with 16 electrodes collect boundary voltage signals. The lung EIT image sequence is obtained from the boundary voltage signals acquired by the 16 electrodes attached to the chest, combined with a two-dimensional conductivity image sequence reconstructed from a finite element model. The 16 electrodes are distributed sequentially around the chest, using an adjacent excitation and adjacent acquisition method to collect 208 sets of voltages.

[0106] It should be noted that the relative conductivity matrix Δσ can be obtained using various methods. The most common method is the TK-Noser method (reference: CN115177234A), which first solves for the sensitivity matrix J based on the patient's lung shape; then, the relative conductivity matrix Δσ is obtained using the following formula: Δσ(t i )=(J T ·J+k t ·I+k n W) -1 ·J T ·△V(t i J is the sensitivity matrix, J T It is the transpose of J, k t k n These are the regularization parameters, where W represents a diagonal matrix of the same order and diagonal elements as J; and I represents an identity matrix with the same number of columns as J.

[0107] It should be noted that the methods in Examples 2-4 are not for the direct purpose of disease diagnosis and treatment.

[0108] It should be noted that the method of this application does not require all the data of the FVC action cycle (a single FVC action includes inhalation + exhalation). When applying this application, only the data corresponding to the exhalation process in the FVC action cycle needs to be taken.

[0109] It should be noted that the method of this application does not require strict adherence to the FVC action criteria of the pulmonary function instrument (i.e., it is not limited to the use of the pulmonary function instrument when acquiring data), but it must ensure that the subject completes a breathing process of forcefully inhaling until the lungs are full and then forcefully exhaling until all air is exhaled (a breathing process similar to the characteristics of the FVC action) to ensure the validity of the EIMI calculation.

[0110] The above-described embodiments are preferred embodiments of the present invention and are only used to facilitate the illustration of the present invention. They are not intended to limit the present invention in any way. Any person skilled in the art who makes local modifications or alterations to the technical content disclosed in the present invention without departing from the scope of the technical features of the present invention shall still fall within the scope of the technical features of the present invention.

Claims

1. A coupled detection method based on breath detection and electrical impedance imaging, characterized in that, Includes the following steps: S100, obtain the different times t0, t1, t2, t3, ..., t within a single expiratory cycle. N The corresponding expiratory flow rates are Q(t0), Q(t1), Q(t2), Q(t3), ..., Q(t). N ); t0, t N These are the start and end times of a single expiratory cycle; S200, obtain t0, t1, t2, t3, ..., t4 at different times within a single expiratory cycle under the same excitation frequency. N The corresponding boundary voltage matrices are V(t0), V(t1), V(t2), V(t3), ..., V(t). N ); S300, based on the data obtained from S200, acquires different times t0, t1, t2, t3, ..., t within a single expiratory cycle. N The corresponding mean lung impedance changes are ΔZ(t0), ΔZ(t1), ΔZ(t2), ΔZ(t3), ..., ΔZ(t... N ); S400, calculate the expiratory impedance matching index EIMI: EIMI=[Q(t0)·△Z(t0)·(t1-t0) / 2+Q(t1)·△Z(t1)·(t2-t0) / 2+Q(t2)·△Z(t2)·(t3-t1) / 2+……+Q(t i )·△Z(t i )·(t i+1 -t i-1 ) / 2+……+Q(t N )·△Z(t N )·(t N -t N-1 ) / 2] / [Var(Q)·Var(△Z)] 0.5 ; Var(Q) refers to Q(t0) ~ Q(t) N The variance of ) Var(△Z) refers to △Z(t0) ~ △Z(t) N The variance of ).

2. The coupled detection method based on breath detection and electrical impedance imaging according to claim 1, characterized in that, S300 includes the following sub-steps: S301, Calculate any time t i The boundary voltage difference matrix ΔV(t) with t0 i ): △V(t) i )=V(t i )-V(t0); S302, Calculate any time t i The relative conductivity matrix Δσ(t) with t0 i ); S303, Solve for any time t after normalization. i The relative conductivity matrix Δσ with respect to t0 norm (t i ); △σ norm (t i )=△σ(t i ) / max{△σ(t i )},max{△σ(t i )} represents the matrix △σ(t) i The maximum value in ); S304, Solve for any time t i The change in mean lung impedance ΔZ(t) i ): △Z(t) i )=mean(△σ norm (t i ()), mean means to calculate the average.

3. The coupled detection method based on breath detection and electrical impedance imaging according to claim 2, characterized in that, S302 uses the TK-Noser method to solve the relative conductivity matrix Δσ(t). i ).

4. A coupled detection system based on breath detection and electrical impedance imaging, characterized in that, include: Storage module, average impedance change value calculation module, EIMI solution module; The storage module stores the different times t0, t1, t2, t3, ..., t4 within a single expiratory cycle of the patient. N The corresponding expiratory flow rates are Q(t0), Q(t1), Q(t2), Q(t3), ..., Q(t). N Based on different times t0, t1, t2, t3, ..., t within a single expiratory cycle under the same excitation frequency N The corresponding boundary voltage matrices are V(t0), V(t1), V(t2), V(t3), ..., V(t). N ); t0, t N These are the start and end times of a single expiratory cycle; The average impedance change calculation module can calculate the average impedance change based on V(t0) ~ V(t). N Find the values ​​t0, t1, t2, t3, ..., t4 at different times within a single expiratory cycle. N The corresponding mean lung impedance changes are ΔZ(t0), ΔZ(t1), ΔZ(t2), ΔZ(t3), ..., ΔZ(t... N ); The EIMI solver module is used to solve for a patient's EIMI value; EIMI=[Q(t0)·△Z(t0)·(t1-t0) / 2+Q(t1)·△Z(t1)·(t2-t0) / 2+Q(t2)·△Z(t2)·(t3-t1) / 2+……+Q(t i )·△Z(t i )·(t i+1 -t i-1 ) / 2+……+Q(t N )·△Z(t N )·(t N -t N-1 ) / 2] / [Var(Q)·Var(△Z)] 0.5 ; Var(Q) refers to Q(t0) ~ Q(t) N The variance of ΔZ; Var(ΔZ) refers to the range of ΔZ(t0) to ΔZ(t). N The variance of ).

5. The coupled detection system based on breath detection and electrical impedance imaging according to claim 4, characterized in that, The working method of the average impedance change calculation module is as follows: Step a, calculate any time t i The boundary voltage difference matrix ΔV(t) with t0 i ): △V(t) i )=V(t i )-V(t0); Step b, calculate any time t i The relative conductivity matrix Δσ(t) with t0 i ); Step c: Solve for the normalized value at any time t. i The relative conductivity matrix Δσ with respect to t0 norm (t i );△σ norm (t i )=△σ(t i ) / max{△σ(t i )},max{△σ(t i )} represents the matrix △σ(t) i The maximum value in ); Step d: Solve for any time t i The change in mean lung impedance ΔZ(t) i ): △Z(t) i )=mean(△σ norm (t i ()), mean means to calculate the average.

6. The coupled detection system based on exhalation detection and electrical impedance imaging according to claim 5, characterized in that, In step b, the TK-Noser method is used to solve for the relative conductivity matrix Δσ(t). i ).

7. The coupled detection system based on breath detection and electrical impedance imaging according to claim 4, characterized in that, According to the results of ventilatory function impairment given by EIMI: if EIMI ≥ 0.75, it is considered healthy; if 0.75 > EIMI ≥ 0.45, it is considered asthma or mild COPD; if 0.45 > EIMI, it is considered moderate to severe COPD.

8. The coupled detection system based on breath detection and electrical impedance imaging according to claim 4, characterized in that, The excitation frequency range is [80KHz, 120KHz].

9. A storage medium, characterized in that, It stores a program capable of executing the coupling detection method as described in any one of claims 1 to 3.

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