CN116965783A - Integrated machine for realizing physiological information detection and diaphragm electrical stimulation and control method thereof - Google Patents
Integrated machine for realizing physiological information detection and diaphragm electrical stimulation and control method thereof Download PDFInfo
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Abstract
The invention provides an integrated machine for realizing physiological information detection and diaphragm electrical stimulation and a control method thereof.
Description
Technical Field
The invention belongs to the technical field of life support integrated machines, and relates to an integrated machine for realizing physiological information detection and diaphragm electrical stimulation and a control method thereof.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Cardiac arrest is one of the leading causes of death worldwide. Cardiopulmonary resuscitation (Cardiopulmonary resuscitation, CPR) is considered an effective treatment for providing minimal perfusion, cardiac and brain cell oxygen delivery to a patient with cardiac arrest. CPR includes three parts, chest compressions, defibrillation and ventilation. The chest compression is provided by an external chest compression machine, and the external chest compression machine ensures the brain perfusion of the patient by providing 100-120 times of external chest compressions with the depth of 5cm for the patient; defibrillation is provided by a defibrillator, and the defibrillator positions the R wave position by detecting the ventricular fibrillation waveform and performs defibrillation operation to recover the normal electrocardio of the patient; respiratory ventilation is provided by a ventilator, which through control of pressure or flow to the ventilator ensures the delivery of oxygen to the heart and brain cells during cardiopulmonary resuscitation of the patient.
Photoplethysmography (PPG) technology has been widely used in clinic as a non-invasive multi-physiological parameter detection method. The technology realizes accurate measurement of multiple physiological parameters by detecting pulse waves through the pulse oximeter sensor worn on the fingertip or the earlobe, has the advantages of no wound, convenience and accuracy, but is particularly unsuitable for use under the conditions of burns, scalds or open wounds and the like because the sensor is fixed on the earlobe or the fingertip by a spring clamp and can cause discomfort to the human body after being worn for a long time. Therefore, how to develop a non-contact physiological parameter detection method based on PPG technology gradually becomes a research hot spot. Imaging photo-volume (Imaging photoplethysmography, IPPG) technology has evolved in this context. The IPPG technology is based on the basic principle of PPG detection, and can capture the blood volume information in a contact manner by using imaging equipment as a sensor, thereby obtaining various physiological information and well overcoming the defects of the PPG detection technology. The IPPG technology can detect non-contact physiological information of various parameters such as blood pressure, blood oxygen, heart rate, respiratory rate and the like, has outstanding practicality, high efficiency, non-contact performance and the like, can complete real-time monitoring of various physiological parameters of patients in the CPR process, and provides physiological feedback of each first-aid device.
The chest compression machine, the defibrillator and the breathing machine are independent in CPR process both outside and inside the hospital, effective communication cannot be carried out between the chest compression machine, the defibrillator and the breathing machine, cooperation work cannot be carried out, and real-time intelligent adjustment for physiology of a patient cannot be carried out. Meanwhile, the three emergency equipment are large in size, and are not beneficial to rapid emergency rescue. In the CPR process, besides the large volume of each first-aid device and the incapability of communication among the devices for intelligent cooperative work, the CPR operation has the defects. The real-time detection of physiological parameters and decision and feedback guidance of emergency operation during CPR have not been realized; compression during CPR results in a decrease in tidal volume during ventilation and an increase in airway pressure.
Disclosure of Invention
In order to solve the problems, the invention provides an integrated machine for realizing physiological information detection and diaphragm electrical stimulation and a control method thereof.
According to some embodiments, the present invention employs the following technical solutions:
an all-in-one machine for realizing physiological information detection and diaphragmatic electrical stimulation, comprising:
the non-contact physiological information detection module is used for detecting blood pressure, blood oxygen, respiratory rate, heart rate parameters and body temperature parameters;
the minimum intelligent decision system is used for judging whether cardiac arrest occurs according to the non-contact physiological information detection parameters, and if the cardiac arrest does not occur, starting a first life support decision state, and providing basic respiratory support for a patient;
if the cardiac arrest occurs, entering a second life support decision state, carrying out continuous chest compression and respiratory ventilation, determining compression parameters and ventilation parameters according to the non-contact physiological information detection parameters, judging whether the cardiac electricity can not be defibrillated, and if the cardiac electricity can be defibrillated, carrying out external defibrillation; judging whether high airway pressure exists, and if so, starting diaphragm electrical stimulation;
and the executing mechanism is used for performing chest compression, ventilation or/and defibrillation according to the instruction of the minimum intelligent decision system.
As an alternative embodiment, the non-contact physiological information detection module includes an IPPG camera for detecting blood pressure, blood oxygen, respiration rate, and heart rate parameters, and a thermal imaging camera for detecting body temperature parameters.
As an alternative embodiment, the executing mechanism comprises an external chest compressor module, an external defibrillation module, a respiratory ventilation module and a diaphragm electrical stimulation module, wherein the external chest compressor module is used for completing adjustment of depth, frequency and pressing duty ratio, the external defibrillation module is used for completing adjustment of defibrillation energy, the respiratory ventilation module is used for completing adjustment of respiratory flow, airway pressure and respiratory time, and the diaphragm electrical stimulation module is used for completing adjustment of frequency, waveform and current amplitude.
As an alternative embodiment, the minimal intelligent decision system is configured to comprise a first decision subsystem and a second decision subsystem, the first decision subsystem being configured to make decisions using an expert knowledge base; the second decision subsystem is configured to make decisions by utilizing a trained random forest trainer in combination with a multi-criterion decision mode;
when the non-contact physiological information detection parameters and the information fed back by the executing mechanism meet the requirements of the expert knowledge base, the first decision subsystem is preferably selected to make decisions, otherwise, the second decision subsystem is preferably selected to make decisions.
As an optional implementation manner, the second decision subsystem is configured to train the non-contact physiological information detection parameters by using a random forest trainer to obtain multiple groups of alternative schemes, determine an optimal decision scheme by using the multi-criterion decision mode, supplement the optimal decision scheme and the non-contact physiological information detection parameters corresponding to the optimal decision scheme into an expert knowledge base, and update the expert knowledge base.
As an alternative embodiment, the second decision subsystem is configured to form an initial decision matrix based on the multiple sets of alternatives, determine a normalized value of each tuple in the matrix, construct a weighted normalized decision matrix based on the normalized values, determine to find the best choice and worst choice, determine the better choice and worse choice, calculate the value of each choice, and rank all alternatives with maximizing the value to obtain the optimal decision scheme.
A control method of an integrated machine for realizing physiological information detection and diaphragm electrical stimulation comprises the following steps:
non-contact physiological information detection parameters for detecting blood pressure, blood oxygen, respiration rate, heart rate parameters and body temperature parameters;
judging whether the cardiac arrest occurs according to the non-contact physiological information detection parameters, and if the cardiac arrest does not occur, starting a first life support decision state to provide basic respiratory support for the patient;
if the cardiac arrest occurs, entering a second life support decision state, carrying out continuous chest compression and respiratory ventilation, determining compression parameters and ventilation parameters according to the non-contact physiological information detection parameters, judging whether the cardiac electricity can not be defibrillated, and if the cardiac electricity can be defibrillated, carrying out external defibrillation; judging whether high airway pressure exists, and if so, starting diaphragm electrical stimulation;
chest compressions, ventilation, or/and defibrillation are performed according to instructions of the minimum intelligent decision system.
As an alternative implementation manner, when the non-contact physiological information detection parameter and the information fed back by the executing mechanism meet the requirements of the expert knowledge base, the expert knowledge base is preferentially selected to make a decision, otherwise, the trained random forest trainer is preferentially selected to make a decision by combining a multi-criterion decision mode.
As an alternative implementation mode, a random forest trainer is utilized to train non-contact physiological information detection parameters to obtain a plurality of groups of alternative schemes, then an optimal decision scheme is determined by utilizing the multi-criterion decision mode, the optimal decision scheme and the non-contact physiological information detection parameters corresponding to the optimal decision scheme are supplemented into an expert knowledge base, and the expert knowledge base is updated.
As an alternative embodiment, an initial decision matrix is formed based on the multiple groups of alternatives, normalized values of the tuples in the matrix are determined, a weighted normalized decision matrix is established based on the normalized values, searching for the best option and the worst option is determined, better options and worse options are determined, the value of each option is calculated, and all alternatives are ranked by maximizing the value, so that the optimal decision scheme is obtained.
As an alternative implementation manner, the specific process of constructing the identification weighted normalization decision matrix comprises the steps of determining normalization matrix elements, determining the performance of each alternative scheme according to the determined normalization matrix elements, calculating the removal of the performance of each alternative scheme with respect to each criterion, further calculating the removal effect of each criterion, and determining the corresponding weight according to the ratio of the removal effect to the removal effect sum of all the criteria.
Compared with the prior art, the invention has the beneficial effects that:
the invention constructs the full-automatic life support integrated machine with non-contact physiological information detection and diaphragm electrical stimulation. The integrated machine integrates a physiological parameter non-contact detection module, an external chest compression module, an external defibrillation module, a breathing ventilation module and a diaphragm electrical stimulation module on hardware, all the modules are mutually communicated and cooperated, a server can interact with all the modules simultaneously under the same local area network, the data transmission, processing, storage and feedback are included, and the external chest compression module, the external defibrillation module, the breathing ventilation module and the diaphragm electrical stimulation module are regulated in real time according to physiological parameters of a patient to provide an optimal life support scheme.
In the invention, the diaphragm electrical stimulation is applied to the CPR process, and after the breathing machine detects the dangerous airway pressure with the airway pressure exceeding the set value, the integrated machine prompts the diaphragm electrical stimulation module to start to perform external diaphragm pacing on the patient, thereby reducing the airway pressure and increasing the inspiration tidal volume.
The invention can realize closed-loop feedback of multiple physiological parameter input, multiple physiological parameter decision and multiple mechanical parameter output, and realize automatic life support. The full-automatic life support integrated machine detects physiological parameters of blood pressure, blood oxygen, respiratory rate, body temperature and heart rate, carries out disease classification through an intelligent decision-making system, automatically adjusts mechanical parameters of an external chest compression machine, an external defibrillator, a breathing machine and diaphragm electrical stimulation, and obtains personalized full-automatic life support emergency operation.
The invention constructs a network knowledge base with self-learning capability and an expert knowledge base containing definite rules, and introduces a random forest training device for receiving data which is not mentioned, imperfect or can not be decided by the expert knowledge base, data and a method base, and solving the data so as to update the expert knowledge base. Multiple groups of life support schemes can be obtained after training by the random forest trainer, and an optimal scheme is determined by utilizing a multi-criterion decision method so as to update the expert knowledge base, thereby ensuring the comprehensiveness, the accuracy and the rapidity of decision.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
Fig. 1 is a general structure of a full-automatic life support integrated machine of the present embodiment.
Fig. 2 is a system general block diagram of the full-automatic life support integrated machine of the present embodiment.
Fig. 3 shows a ventilation strategy of the full-automatic life support integrated machine of the present embodiment, which can reduce airway pressure by increasing respiratory rate to maintain minute tidal volume, and by using two schemes of diaphragm electrical stimulation.
Fig. 4 is a closed-loop decision system of the full-automatic life support integrated machine of the present embodiment. The personalized life support operation is accomplished through multiple inputs, multiple feedback, and multiple adjustments.
Fig. 5 is a diagram showing the intelligent decision of the full-automatic life support integrated machine according to the present embodiment. The shortcomings of weak learning ability and difficult updating of expert decisions are overcome by combining a traditional expert knowledge base and a random forest trainer, and the decisions of events in a non-expert base are completed.
Fig. 6 is a fully automatic life support integrated machine minimum intelligent decision system according to the present embodiment.
Detailed Description
The invention will be further described with reference to the drawings and examples.
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
As shown in fig. 1, the full-automatic life support integrated machine is integrated in a box 1, and the non-contact physiological information detection module comprises a camera 2, a local area network transmitter 12, an integrated machine server 11 and a non-contact physiological information detection module 3. The camera 2 is an IPPG camera and a thermal imaging camera, the IPPG camera is used for detecting parameters of blood pressure, blood oxygen, respiratory rate and heart rate, and the thermal imaging camera is used for detecting parameters of body temperature; the LAN transmitter 12 establishes a LAN required by the all-in-one machine; the server 11 performs processing and storage of data, updating of emergency policy, and control between modules. The man-machine interaction is done through the display screen 10. The compression machine 5 provides chest compressions during CPR. The electrical defibrillation module contains a defibrillation accessory 4 and a defibrillator 9 for external defibrillation. The respiratory ventilation module contains a ventilator accessory 6 and a ventilator 7 to provide ventilation for the CPR process. Diaphragmatic electrical stimulation module 8 provides diaphragmatic electrical stimulation to reduce airway pressure during CPR.
Of course, in some embodiments, the above components may be selected from existing devices.
As shown in fig. 2, all modules are in the same lan, the thermal imaging camera is connected to the server through a network cable, and the IPPG camera uploads image data through USB 3.0. The full-automatic life support integrated machine is provided with a man-machine interaction interface so as to complete monitoring and adjustment of a user on the server, each module and the client. The server communicates with the chest compressor, defibrillator, ventilator and diaphragm electrical stimulation via a TCP network protocol. Wherein the chest compressor completes the adjustment of depth, frequency and compression duty cycle; the defibrillator completes the adjustment of defibrillation energy; the breathing machine completes the adjustment of the breathing flow, the airway pressure and the breathing time; diaphragmatic electrical stimulation accomplishes adjustments to frequency, waveform, and current amplitude.
This example proposes two solutions to the problems of elevated ventilation pressure and loss of ventilation due to compression. (1) The compression frequency is adjusted to obtain the maximum tidal volume, and airway pressure is reduced while tidal volume is ensured by varying the ventilation frequency and the initially set tidal volume. The initial setting of the pressing machine of the full-automatic life support integrated machine is 120 times/min, and the pressing frequency of 120 times/min is considered to be capable of obtaining higher tidal volume, and particularly, the tidal volume of 10% -20% is improved when the pressing frequency of 120 times/min is used for pressing when the preset tidal volume is below 500 ml.
(2) The ventilation strategy of the full-automatic life support integrated machine is shown in figure 3. Ventilation was started using a ventilation frequency of 10 times/min, inspiration time of 1s, ventilation tidal volume preset at V1 ml, compression frequency of 120 times/min, and airway pressure and flow parameters were monitored each time of ventilation. If the airway pressure is not too high in the ventilation process, the original ventilation strategy is maintained; if airway hypertension occurs, the ventilation frequency is changed to 20 times/min, the inspiration time is 1s, and the tidal volume is v2=0.7v1. Judging whether high airway pressure appears again after the new ventilation strategy, and if the high airway pressure does not appear, keeping the new ventilation strategy unchanged; if high airway pressure occurs, diaphragm electrical stimulation is started, so that the lung performs deep breathing, active ventilation of a patient is started, airway pressure is reduced, and tidal volume is increased. Judging whether high airway pressure occurs again after diaphragm electrical stimulation, and if the high airway pressure does not occur, maintaining original diaphragm electrical stimulation parameters; if high airway pressure occurs, the electrical stimulation current intensity, electrical stimulation frequency and electrical stimulation waveform parameters are changed.
The closed loop decision system of the all-in-one machine is shown in fig. 4. And immediately detecting non-contact physiological information after receiving the patient to obtain parameters of blood pressure, blood oxygen, heart rate, respiratory rate and body temperature of the patient, inputting the obtained parameters into an intelligent decision system, classifying cases by the intelligent decision system, and starting corresponding life support equipment. Meanwhile, the life support equipment provides force, chest impedance, end-tidal carbon dioxide, airway pressure and tidal volume in the CPR process to the intelligent decision-making system, so that the intelligent decision-making system combines blood pressure, blood oxygen, heart rate, respiratory rate, body temperature, force feedback, chest impedance parameters, end-tidal carbon dioxide, airway resistance and tidal volume parameters to make comprehensive decisions, and updates life support equipment parameter settings in real time to carry out personalized life support.
The intelligent decision scheme of the closed-loop decision system is shown in fig. 5. The main body of the system is composed of two parts: a network knowledge base with self-learning capability and an expert system (or expert knowledge base) containing explicit rules.
An expert system is a decision making system that can solve life support operations like a human expert. The method effectively utilizes the experience and professional medical knowledge accumulated by the expert for many years, and solves the actual medical problem by simulating the thinking process of the expert. However, conventional expert systems have some limitations in the decision making process. The knowledge acquisition behavior is static and passive, a self-learning mechanism is lacking, the actual decision problem can be analyzed and processed only according to the established rule, the automatic accumulation of knowledge and experience is difficult to carry out, the knowledge acquisition mode is lack of flexibility and poor in adaptability, and the knowledge base is difficult to update.
In order to solve the problem that the traditional expert system cannot update the decision knowledge base, the embodiment provides a solution. It introduces a random forest trainer for receiving data that is not mentioned, imperfect or otherwise unable to be decided by the expert knowledge base and the data, method base, and solving it to update the expert knowledge base. Multiple sets of life support scenarios are obtained after training by the random forest trainer, which are stored in a network database, each of which is subject to multiple different expected physiological state changes, and multiple criteria decisions (Multi-criteria decision, MCDM) are incorporated into the system in order to obtain the optimal life support scenario. The MCDM procedure is as follows:
the first step: forming an initial decision matrix:
wherein m is an alternative scheme, n is a standard number, x mn Is the value of the standard n in alternative m.
And a second step of: the normalized value k is determined by ij ：
And a third step of: identifying a weighted normalized decision matrix:
l ij ＝w j ×k ij (3)
fourth step, by the following formula (4) finding the best choice A + And worst option a - ：
And->Is the best and worst value of the criterion (j=1, 2, …, n).
Fifth step: determining better optionsAnd worse option->
Sixth step: the value R of each option is calculated by the following method i ：
Seventh step: the alternatives are ordered by maximizing the value of R:
weight w of criterion of 3 j The determination of (2) is as follows:
the first step: the initialization matrix is formed as shown in formula (1):
and a second step of: determining a normalized matrix element:
and a third step of: finding an alternative S according to i The performance of (2):
fourth step: calculation of the ith alternative S i ' j With respect to the removal of the j-th criterion, the following formula is used:
fifth step: calculate the j-th criterion E j The removal effect of (2):
E j ＝∑ i |S’ ij -S i | (13)
sixth step: the weights for each criterion were determined using the following formula:
the trained network knowledge base, updated expert knowledge base, and multi-criteria decisions are used to address actual medical decision-making problems. Based on the decision resources provided, the system can reasonably select an expert system or a neural network to make independent decisions. When the physiological and mechanical information fed back by the non-contact physiological information detection and life support device meets the requirements of the expert system knowledge base, the system can preferentially select the expert system to make decisions. However, when the acquired information cannot match the expert knowledge base or a matching item cannot be found in the knowledge base, the intelligent decision system will transmit the physiological information to a trained random forest trainer, perform reasoning and decision by the trained neural network, and then perform preferential selection by the MCDM.
The fully automatic life support all-in-one minimum intelligent decision system is shown in fig. 6. After receiving a patient, the non-contact physiological information detection module carries out parameter detection and intelligent decision making system to judge that cardiac arrest occurs, and if cardiac arrest does not occur, the non-contact physiological information detection module enters a low-level life support decision making to provide basic respiratory support for the patient; if sudden cardiac arrest occurs, an advanced life support decision is started, and based on continuous chest compressions and respiratory ventilation of the patient, whether the electrocardio can not be defibrillated is judged at the same time, and if the electrocardio can be defibrillated, external defibrillation is carried out; and judging whether high airway pressure occurs, and if so, starting diaphragm electrical stimulation.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
While the foregoing description of the embodiments of the present invention has been presented in conjunction with the drawings, it should be understood that it is not intended to limit the scope of the invention, but rather, it is intended to cover all modifications or variations within the scope of the invention as defined by the claims of the present invention.
Claims (10)
1. An all-in-one machine for realizing physiological information detection and diaphragm electrical stimulation, which is characterized by comprising:
the non-contact physiological information detection module is used for detecting blood pressure, blood oxygen, respiratory rate, heart rate parameters and body temperature parameters;
the minimum intelligent decision system is used for judging whether cardiac arrest occurs according to the non-contact physiological information detection parameters, and if the cardiac arrest does not occur, starting a first life support decision state, and providing basic respiratory support for a patient;
if the cardiac arrest occurs, entering a second life support decision state, carrying out continuous chest compression and respiratory ventilation, determining compression parameters and ventilation parameters according to the non-contact physiological information detection parameters, judging whether the cardiac electricity can not be defibrillated, and if the cardiac electricity can be defibrillated, carrying out external defibrillation; judging whether high airway pressure exists, and if so, starting diaphragm electrical stimulation;
and the executing mechanism is used for performing chest compression, ventilation or/and defibrillation according to the instruction of the minimum intelligent decision system.
2. The integrated machine for realizing physiological information detection and diaphragm electrical stimulation according to claim 1, wherein the non-contact physiological information detection module comprises an IPPG camera and a thermal imaging camera, wherein the IPPG camera is used for detecting blood pressure, blood oxygen, respiratory rate and heart rate parameters, and the thermal imaging camera is used for detecting body temperature parameters.
3. The integrated machine for realizing physiological information detection and diaphragm electrical stimulation according to claim 1, wherein the executing mechanism comprises an external chest compressor module, an external defibrillation module, a respiratory ventilation module and a diaphragm electrical stimulation module, wherein the external chest compressor module is used for completing adjustment of depth, frequency and pressing duty ratio, the external defibrillation module is used for completing adjustment of defibrillation energy, the respiratory ventilation module is used for completing adjustment of respiratory flow, airway pressure and respiratory time, and the diaphragm electrical stimulation module is used for completing adjustment of frequency, waveform and current amplitude.
4. The integrated machine for achieving physiological information detection and diaphragmatic electrical stimulation of claim 1, wherein the minimal intelligent decision system is configured to include a first decision subsystem and a second decision subsystem, the first decision subsystem being configured to make decisions using an expert knowledge base; the second decision subsystem is configured to make decisions by utilizing a trained random forest trainer in combination with a multi-criterion decision mode;
when the non-contact physiological information detection parameters and the information fed back by the executing mechanism meet the requirements of the expert knowledge base, the first decision subsystem is preferably selected to make decisions, otherwise, the second decision subsystem is preferably selected to make decisions.
5. The integrated machine for realizing physiological information detection and diaphragm electrical stimulation according to claim 4, wherein the second decision subsystem is configured to train non-contact physiological information detection parameters by using a random forest trainer to obtain multiple groups of alternative schemes, determine an optimal decision scheme by using the multi-criterion decision mode, supplement the optimal decision scheme and the non-contact physiological information detection parameters corresponding to the optimal decision scheme into an expert knowledge base, and update the expert knowledge base.
6. An all-in-one machine for achieving physiological information detection and diaphragmatic electrical stimulation according to claim 4 or 5, wherein the second decision subsystem is configured to form an initial decision matrix based on the plurality of sets of alternatives, and to determine normalized values for each of the tuples in the matrix, to construct a recognition weighted normalized decision matrix based on the normalized values, to determine the best and worst options to find, to determine the better and worse options, to calculate the value of each option, to rank all alternatives using maximizing the value, to obtain an optimal decision.
7. A control method of an integrated machine for realizing physiological information detection and diaphragm electrical stimulation is characterized by comprising the following steps:
non-contact physiological information detection parameters for detecting blood pressure, blood oxygen, respiration rate, heart rate parameters and body temperature parameters;
judging whether the cardiac arrest occurs according to the non-contact physiological information detection parameters, and if the cardiac arrest does not occur, starting a first life support decision state to provide basic respiratory support for the patient;
if the cardiac arrest occurs, entering a second life support decision state, carrying out continuous chest compression and respiratory ventilation, determining compression parameters and ventilation parameters according to the non-contact physiological information detection parameters, judging whether the cardiac electricity can not be defibrillated, and if the cardiac electricity can be defibrillated, carrying out external defibrillation; judging whether high airway pressure exists, and if so, starting diaphragm electrical stimulation;
chest compressions, ventilation, or/and defibrillation are performed according to instructions of the minimum intelligent decision system.
8. The control method according to claim 7, wherein when the non-contact physiological information detection parameters and the information fed back by the executing mechanism meet the requirements of the expert knowledge base, the expert knowledge base is preferentially selected for decision making, otherwise, the trained random forest trainer is preferentially selected for decision making in combination with a multi-criterion decision making mode;
training the non-contact physiological information detection parameters by using a random forest trainer to obtain a plurality of groups of alternative schemes, determining an optimal decision scheme by using the multi-criterion decision mode, supplementing the optimal decision scheme and the non-contact physiological information detection parameters corresponding to the optimal decision scheme into an expert knowledge base, and updating the expert knowledge base.
9. The control method of claim 8 wherein an initial decision matrix is formed based on the plurality of sets of alternatives, and normalized values for each tuple in the matrix are determined, a weighted normalized decision matrix is constructed based on the normalized values, searching for best and worst options is determined, better and worse options are determined, the value of each option is calculated, and all alternatives are ranked using maximizing the value to obtain an optimal decision scheme.
10. The control method of claim 9, wherein the specific process of constructing the recognition weighted normalization decision matrix includes determining normalization matrix elements, determining performance of each alternative according to the determined normalization matrix elements, calculating removal of performance of each alternative with respect to each criterion, further calculating removal effect of each criterion, and determining corresponding weight according to a ratio of the removal effect and a sum of removal effects of all criteria.
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