CN109350063B - Apparatus and method for detecting respiratory mechanics parameters suitable for monitoring chronic obstructive pulmonary disease - Google Patents

Abstract

Translated fromChinese

本发明涉及一种适用于慢阻肺监控的呼吸力学参数检测装置及方法，包括可实现气体流量、压力监测的信号采集模块，微处理单元，蓝牙通讯模块，移动终端设备，软件分析模块，采集模块实现气道压力及流量的连续监测，采集的连续信号经过微处理单元进行预处理后由蓝牙通讯模块发送至移动终端，并由移动终端上的软件分析模块通过约束优化的方法基于分段式用力呼吸模型估算出呼吸力学参数。本发明提供的适用于慢阻肺监控的呼吸力学参数检测装置，可以实现慢阻肺患者吸气相与呼气相的气道阻力系数与顺应性的实时检测，有助于实现对慢阻肺患者呼吸治疗情况的监控，从而为医生调整治疗方案提供参考与帮助。

The invention relates to a respiratory mechanics parameter detection device and method suitable for COPD monitoring, comprising a signal acquisition module capable of monitoring gas flow and pressure, a microprocessing unit, a Bluetooth communication module, a mobile terminal device, a software analysis module, a The module realizes continuous monitoring of airway pressure and flow. The collected continuous signals are preprocessed by the micro-processing unit and then sent to the mobile terminal by the Bluetooth communication module, and the software analysis module on the mobile terminal is based on the method of segmental optimization through constraint optimization. Respiratory mechanics parameters were estimated by the forced breathing model. The respiratory mechanics parameter detection device suitable for monitoring chronic obstructive pulmonary disease provided by the present invention can realize real-time detection of airway resistance coefficient and compliance in inspiratory and expiratory phases of patients with chronic obstructive pulmonary disease, and is helpful for realizing the detection of chronic obstructive pulmonary disease The monitoring of the patient's breathing treatment can provide reference and help for doctors to adjust the treatment plan.

Description

Respiratory mechanics parameter detection device and method suitable for chronic obstructive pulmonary disease monitoring

Technical Field

The invention belongs to the field of equipment detection and signal processing methods, and particularly relates to a respiratory mechanics parameter detection device and method suitable for monitoring chronic obstructive pulmonary disease, which are used for detecting respiratory mechanics parameters of patients with expiratory limitation and have an auxiliary effect on monitoring chronic obstructive pulmonary disease.

Background

Chronic obstructive pulmonary disease (chronic obstructive pulmonary disease for short) is characterized by incomplete reversible airflow limitation, and the airflow limitation during expiration of a patient affects the normal respiration of the patient and even endangers the life. The respiratory mechanics parameter is an index capable of directly reflecting the performance of the respiratory system of a patient, for example, the airway resistance coefficient of a patient with chronic obstructive pulmonary disease is increased, and the effective monitoring of the respiratory mechanics parameter is of great significance for the monitoring of the condition of the chronic obstructive pulmonary disease. At present, the method for detecting the respiratory mechanics parameter mainly uses a breathing machine to perform a blocking method to measure the respiratory mechanics parameter, or performs the estimation of the respiratory mechanics parameter based on a respiratory mechanics model. The blocking method must block ventilation and cannot realize real-time monitoring, but the method for parameter estimation based on the breathing mechanics model is suitable for patients without spontaneous breathing, once the patients have spontaneous breathing and need invasive measurement of esophageal pressure instead of pleural pressure to complete estimation, the method is invasive and cannot realize real-time monitoring. The invention provides a set of device for collecting airway flow pressure on the premise of not influencing mechanical ventilation, and respiratory mechanics parameters can be estimated in real time by means of a constraint optimization method, so that the device has important significance for monitoring the condition of chronic obstructive pulmonary disease.

Disclosure of Invention

The invention aims to provide a respiratory mechanics parameter detection device and a detection method, and particularly relates to a method for collecting airway flow and pressure, estimating resistance coefficients and compliance of an inspiratory phase airway and an expiratory phase airway in each respiratory cycle by utilizing a constraint optimization method based on a sectional forced respiration model, and providing meaningful reference for a clinician to monitor the condition of chronic obstructive pulmonary disease.

The technical scheme of the invention is as follows: the utility model provides a respiratory mechanics parameter detection device suitable for slow-stopping lung control, includes the signal acquisition module that can realize gas flow, pressure monitoring, little processing unit to and the mobile terminal equipment who has installed software analysis module, wherein, signal acquisition module access person's examined's gas circuit, gather person's mouth end pressure and flow signal in succession, and with mouth end pressure and flow signal transmission to little processing unit, carry out signal preprocessing, mouth end pressure and flow signal after the preprocessing send to mobile terminal equipment, realize by software analysis module through the method of restraint optimization based on the respiratory mechanics parameter estimation of power consumption breathing model, obtain the air flue resistance coefficient and the compliance in a respiratory cycle that inspiratory phase corresponds with the expiration in every respiratory cycle.

Further, the respiratory mechanics parameter detection device suitable for monitoring the chronic obstructive pulmonary disease as described above, wherein the preprocessed mouth end pressure and flow signal are sent to the mobile terminal device through the bluetooth communication module.

A method for detecting respiratory mechanics parameters by adopting the device realizes respiratory mechanics parameter estimation based on a sectional type force-applied respiratory model by a constraint optimization method, wherein the sectional type force-applied respiratory model describes a respiratory cycle in a nonlinear modeRespiratory mechanics phenomenon, setting up the resistance coefficient of inspiratory airway and the resistance coefficient of expiratory airway in the form of P (t) ═ R_inF(t)+E∫F(t)dt+P_mus(t)，P(t)＝R_exF(t)+E∫F(t)dt+P_mus(t), wherein P (t) is the gas pressure change at the mouth end obtained by the signal acquisition module, P_mus(t) is the equivalent pressure of the respiratory muscle group during forced respiration, F (t) is the gas flow at the mouth end obtained by the signal acquisition module, R_inIs the inspiratory airway resistance coefficient, R_exFor the expiratory phase airway resistance coefficient, E is the compliance in one respiratory cycle.

Further, a method for detecting respiratory mechanics parameter as described above, wherein the equivalent pressure P of respiratory muscle group action in one respiratory cycle is used for forced respiration_mus(t) is described using a bounded two-corner continuous curve, t at the beginning of a respiratory cycle₀Time P_mus(t₀) At inspiration phase P of 0_mus(t) monotonically decreasing at t_m+t₀Reaches the inflection point-a at a moment₁I.e. P_mus(t_m+t₀)＝-a₁Then monotonically increases at t_p+t₀Time P_mus(t_p+t₀) 0, then continues to rise monotonically at t_q+t₀The moment reaches another inflection point a₂I.e. P_mus(t_q+t₀)＝a₂Then monotonically decreases at t_s+t₀Time P_mus(t_s+t₀)＝0。

Further, the method for detecting respiratory mechanics parameters as described above, wherein the method for constrained optimization refers to that the respiratory mechanics parameters are estimated by an optimization method that minimizes a cost equation under a constrained condition, and the cost equation is specifically formed by:

the constraint conditions are as follows:

-a₁≤P_mus(t_k+1)-P_mus(t_k)≤0,k＝1,2,...,m-1，

0≤P_mus(t_k+1)-P_mus(t_k)≤a₁+a₂,k＝m,m+1,...,q-1，

-a₂≤P_mus(t_k+1)-P_mus(t_k)≤0,k＝q,q+1,...,s-1，

P_mus(t_k+1)-P_mus(t_k)＝0,k＝s,s+1,...,N-1，

0≤R_in≤100，

0≤R_ex≤100，

0≤E≤100，

-30≤P_mus(t_k)≤30，

in the above formula, k represents the sampling count within one equally spaced sampling period, and N represents the sampling count at the end of one period;

the constraints describe the range of normal physiological parameters, ensuring that the parameter estimates have physiological significance, where a₁And a₂The equivalent pressure values representing spontaneous forced respiration are obtained based on a large number of sample experiments.

The invention has the following beneficial effects: the respiratory mechanics parameter detection device suitable for monitoring the chronic obstructive pulmonary disease is suitable for patients who are treated by mechanical ventilation, can realize breath-by-breath parameter detection without influencing normal treatment, and is not limited by a mechanical ventilation mode. The detection method adopted by the device fully considers the characteristic that the examinee with the chronic obstructive pulmonary disease breathes spontaneously due to forced expiration, provides a sectional forced respiration model which is more consistent with the respiratory mechanics characteristics of the examinee with the chronic obstructive pulmonary disease, adds the constraint condition which is consistent with the respiratory mechanics characteristics of the examinee with the chronic obstructive pulmonary disease in combination with a large number of sample experiments, realizes effective estimation of respiratory mechanics parameters through constrained optimization, and further provides meaningful reference for a clinician to monitor the chronic obstructive pulmonary disease.

Drawings

FIG. 1 is a schematic diagram of a system structure of a respiratory mechanics parameter detection device suitable for monitoring chronic obstructive pulmonary disease according to the present invention;

FIG. 2 is a graph of the equivalent pressure exerted by the respiratory muscle group during vigorous breathing;

fig. 3 is a flow chart of the operation of the respiratory mechanics parameter detection device suitable for monitoring the chronic obstructive pulmonary disease.

Detailed Description

The invention is described in further detail below with reference to specific embodiments and with reference to the following drawings.

The system structure of the respiratory mechanics parameter detection device suitable for monitoring the chronic obstructive pulmonary disease is shown in fig. 1, and the system comprises an acquisition module 01 of gas flow and pressure monitoring signals, amicro-processing unit 04, a Bluetoothcommunication module 05, a mobile terminal device 06 and a software analysis module 07, wherein the software analysis module adopts a constraint optimization method 08 based on a sectional forcedrespiration model 09 to obtain an airway resistance coefficient 10 andcompliance 11 of a detected person. During specific implementation, the pressure and flow acquisition module 01 of the system is connected in series to the artificial airway between the respirator and the patient, and the gas pressure sensor and the flow sensor in the pressure and flow acquisition module acquire ventilation pressure andflow signals 03 in the artificial airway. In order to ensure that the connecting equipment is as small as possible during clinical use, the collected original pressure signal andflow signal 03 are sent to the mobile terminal device 06 by the Bluetooth module after being preprocessed (filtered) by the microprocessor.

The mobile terminal device 06 has strong computing ability, a software analysis module 07 is built in, a constraint optimization method 08 is used in the software analysis module 07, parameter optimization estimation under constraint conditions is achieved by utilizing a pressure signal and aflow signal 03 based on a segmented forcedrespiration model 09, and an airway resistance coefficient 10 of an inspiratory phase and an expiratory phase andcompliance 11 of a respiratory system can be obtained.

The sectional forcedrespiration model 09 describes the respiratory mechanics phenomenon in a respiration cycle in a nonlinear manner, and an inspiratory airway resistance coefficient and an expiratory airway resistance coefficient 10 are respectively set, wherein the form of the inspiratory airway resistance coefficient and the expiratory airway resistance coefficient is P (t) ═ R_inF(t)+E∫F(t)dt+P_mus(t)，P(t)＝R_exF(t)+E∫F(t)dt+P_mus(t), wherein P (t) is a gas flow rate,The gas pressure change P of the port end obtained by the pressure signal acquisition module 01_mus(t) is the equivalent pressure of the respiratory muscle group during forced respiration, F (t) is the gas flow and the gas flow at the mouth end obtained by the pressure signal acquisition module 01, R_inIs the inspiratory airway resistance coefficient, R_exFor the expiratory phase airway resistance coefficient, E is thecompliance 11 in one respiratory cycle.

The constraint optimization method 08 is an optimization method for minimizing the cost equation under the constraint condition, and can realize the optimized estimation of the model parameters by adding the constraint condition under the condition of not determining the spontaneous respiratory signal of the patient.

The cost equation is in the specific form:

the constraint conditions are as follows:

-a₁≤P_mus(t_k+1)-P_mus(t_k)≤0,k＝1,2,...,m-1，

0≤P_mus(t_k+1)-P_mus(t_k)≤a₁+a₂,k＝m,m+1,...,q-1，

-a₂≤P_mus(t_k+1)-P_mus(t_k)≤0,k＝q,q+1,...,s-1，

P_mus(t_k+1)-P_mus(t_k)＝0,k＝s,s+1,...,N-1，

0≤R_in≤100，

0≤R_ex≤100，

0≤E≤100，

-30≤P_mus(t_k)≤30，

in the above formula, k represents the sampling count within one equally spaced sampling period, and N represents the sampling count at the end of one period; the time is the corresponding time point of the equal interval sampling, whether a new period starts or not can be judged by the sudden change of the pressure in the respiration, and the starting point of the new period is the ending point of the original period.

The constraints describe the range of normal physiological parameters, ensuring that the parameter estimates have physiological significance, where a₁And a₂The equivalent pressure value representing the spontaneous forced respiration is obtained on the basis of a large number of sample experiments.

As shown in fig. 2, the segmented forcedrespiration model 09 adopts a bounded double-inflection-point continuous curve to describe the equivalent pressure P of the respiratory muscle group in a respiratory cycle when a patient with slow obstructive pulmonary disease breathes forcefully_mus(t) of (d). T at the beginning of a respiratory cycle₀Time P_mus(t₀) At inspiration phase P of 0_mus(t) monotonically decreasing at t_m+t₀Reaches the inflection point-a at a moment₁I.e. P_mus(t_m+t₀)＝-a₁Then monotonically increases at t_p+t₀Time P_mus(t_p+t₀) 0, then continues to rise monotonically at t_q+t₀The moment reaches another inflection point a₂I.e. P_mus(t_q+t₀)＝a₂Then monotonically decreases at t_s+t₀Time P_mus(t_s+t₀)＝0。

As shown in fig. 3, the operation flow of the respiratory mechanics parameter detection device suitable for monitoring the chronic obstructive pulmonary disease starts with collecting pressure and flow signals of an artificial airway in the mechanical ventilation process, preprocessing of original pressure signals and flow signals is realized in a microprocessor, the pressure and flow signals are displayed for confirming that the collection process is normal, the pressure signals and the flow signals are transmitted to a mobile terminal through bluetooth, detection of respiratory mechanics parameters is completed by corresponding software programs on the mobile terminal, and the respiratory mechanics parameters are given in real time according to each respiratory cycle.

In conclusion, the respiratory mechanics parameter detection device suitable for monitoring the slow obstructive pulmonary disease is built, and the respiratory mechanics parameters of the detected person are estimated by collecting flow and pressure signals of the artificial airway in the ventilation process. The device has fully considered the characteristics that the person being examined who has the chronic obstructive pulmonary disease has the spontaneous breathing of exhaling hard, has proposed that the sectional type is breathed hard the model and is more accorded with the respiratory mechanics characteristic of the person being examined who has the chronic obstructive pulmonary disease, has combined a large amount of sample experiments to add the constraint condition that accords with the respiratory mechanics characteristic of the person being examined who has the chronic obstructive pulmonary disease, has realized the effective estimation of respiratory mechanics parameter through the optimization of having the constraint, and then provides meaningful reference for the clinician to monitor the state of an illness of chronic obstructive pulmonary disease.

The respiratory mechanics parameter detection device suitable for monitoring the chronic obstructive pulmonary disease is suitable for patients who are treated by mechanical ventilation, can realize breath mechanics parameter detection of breath-by-breath without influencing normal treatment, and is not limited by a mechanical ventilation mode.

The above example is only one embodiment of the present invention. It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is intended to include such modifications and variations.

Claims (4)

1. The utility model provides a respiratory mechanics parameter detection device suitable for control of chronic obstructive pulmonary disease, includes the signal acquisition module that can realize gas flow, pressure monitoring, micro-processing unit to and the mobile terminal who has installed software analysis module, its characterized in that: the system comprises a signal acquisition module, a micro-processing unit, a software analysis module, a force-applied breathing model and a power consumption module, wherein the signal acquisition module is connected to an air passage of a detected person, continuously acquires a mouth end pressure and flow signal of the detected person, transmits the mouth end pressure and flow signal to the micro-processing unit for signal preprocessing, sends the preprocessed mouth end pressure and flow signal to mobile terminal equipment, and realizes respiratory mechanics parameter estimation based on the sectional type force-applied breathing model through a constraint optimization method by the software analysis module to obtain an airway resistance coefficient corresponding to an inspiratory phase and an expiratory phase in each breathing cycle and compliance in one breathing cycle;

the sectional forced breathing model describes the breathing mechanics phenomenon in a breathing cycle in a nonlinear mode, and an inspiratory airway resistance coefficient and an expiratory airway resistance coefficient are respectively set, wherein the form of the breathing mechanics phenomenon is P (t) R (t)_inF(t)+E∫F(t)dt+P_mus(t)，P(t)＝R_exF(t)+E∫F(t)dt+P_mus(t), wherein P (t) is the gas pressure change at the mouth end obtained by the signal acquisition module, P_mus(t) is the equivalent pressure of the respiratory muscle group during forced respiration, F (t) is the gas flow at the mouth end obtained by the signal acquisition module, R_inIs the inspiratory airway resistance coefficient, R_exFor the expiratory phase airway resistance coefficient, E is the compliance in one respiratory cycle.

2. The respiratory mechanics parameter detection device suitable for slow obstructive lung monitoring of claim 1, wherein: and the preprocessed port-end pressure and flow signals are sent to the mobile terminal equipment through the Bluetooth communication module.

3. A method for respiratory mechanics parameter detection using the device of any of claims 1-2, wherein: equivalent pressure P of respiratory muscle group action in one respiratory cycle during forced respiration_mus(t) is described using a bounded two-corner continuous curve, t at the beginning of a respiratory cycle₀Time P_mus(t₀) At inspiration phase P of 0_mus(t) monotonically decreasing at t_m+t₀Reaches the inflection point-a at a moment₁I.e. P_mus(t_m+t₀)＝-a₁Then monotonically increases at t_p+t₀Time P_mus(t_p+t₀) 0, then continues to rise monotonically at t_q+t₀The moment reaches another inflection point a₂I.e. P_mus(t_q+t₀)＝a₂Then monotonically decreases at t_s+t₀Time P_mus(t_s+t₀)＝0。

4. A method of respiratory mechanics parameter detection according to claim 3, wherein: the respiratory mechanics parameter estimation based on the segmented forced respiration model is realized through a constraint optimization method, wherein the constraint optimization method is that the respiratory mechanics parameter is obtained by estimation through an optimization method which enables a cost equation to be minimum under a constraint condition, and the specific form of the cost equation is as follows:

the constraint conditions are as follows:

-a₁≤P_mus(t_k+1)-P_mus(t_k)≤0,k＝1,2,...,m-1，

0≤P_mus(t_k+1)-P_mus(t_k)≤a₁+a₂,k＝m,m+1,...,q-1，

-a₂≤P_mus(t_k+1)-P_mus(t_k)≤0,k＝q,q+1,...,s-1，

P_mus(t_k+1)-P_mus(t_k)＝0,k＝s,s+1,...,N-1，

0≤R_in≤100，

0≤R_ex≤100，

0≤E≤100，

-30≤P_mus(t_k)≤30，

in the above formula, k represents the sampling count within one equally spaced sampling period, and N represents the sampling count at the end of one period;

the constraints describe the range of normal physiological parameters, ensuring that the parameter estimates have physiological significance, where a₁And a₂The equivalent pressure values representing spontaneous forced respiration are obtained based on a large number of sample experiments.

Images