CN113450901A - Control method and device of respiratory support system and respiratory support system - Google Patents
Control method and device of respiratory support system and respiratory support system Download PDFInfo
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Abstract
本公开涉及一种呼吸支持系统的控制方法、装置、呼吸支持系统、电子设备及计算机可读介质。该控制方法包括：获取当前用户的实时监测数据，所述监测数据包括睡眠信息、呼吸信息，环境信息、血氧信息；根据所述用户信息确定目标状态预测函数；通过所述实时监测数据和所述目标状态预测函数生成状态预测指数；在所述状态预测指数满足预设条件时，生成呼吸支持指令，并将所述呼吸支持指令发送至所述呼吸机。本公开涉及的呼吸支持系统的控制方法、装置、呼吸支持系统、电子设备及计算机可读介质，能够基于用户的当前检测数据对用户的一段时间内的状态进行实时预测，在预测到特定情况产生时，可主动调用呼吸机支持用户。
The present disclosure relates to a control method, device, respiratory support system, electronic device, and computer-readable medium of a respiratory support system. The control method includes: acquiring real-time monitoring data of a current user, the monitoring data including sleep information, breathing information, environmental information, and blood oxygen information; determining a target state prediction function according to the user information; The target state prediction function generates a state prediction index; when the state prediction index satisfies a preset condition, a respiratory support instruction is generated, and the respiratory support instruction is sent to the ventilator. The control method, device, respiratory support system, electronic device, and computer-readable medium of the respiratory support system involved in the present disclosure can make real-time prediction of the user's state within a period of time based on the current detection data of the user, and generate a , the ventilator can be actively called to support the user.
Description
Technical Field
The present disclosure relates to the field of computer information processing, and in particular, to a method and an apparatus for controlling a respiratory support system, an electronic device, and a computer-readable medium.
Background
In modern clinical medicine, a ventilator has been widely used in respiratory failure due to various reasons, anesthesia and breathing management during major surgery, respiratory support therapy and emergency resuscitation as an effective means for manually replacing the function of spontaneous ventilation, and has a very important position in the modern medical field. The breathing machine is a vital medical device which can prevent and treat respiratory failure, reduce complications and save and prolong the life of a patient.
Apnea syndrome is the sudden interruption of loud snoring, the patient breathes forcefully but does not work, the patient does not breathe completely, the patient wakes up after several seconds or even tens of seconds, the patient breathes loud, the airway is forced to open, and then the patient continues breathing. Often accompanied by kicking movements of the body and writhing cramps of the body. The various twisted body positions of the patient are for the purpose of opening the airway. The respiratory airflow stops due to obstruction and other reasons in the sleeping process (namely breath holding and breath stopping in sleep), and the sleep apnea is determined when the duration exceeds 10 seconds or the airflow is lower than 20% of the normal airflow.
In the prior art, a breathing machine is adopted to monitor the breathing of a user, and an alarm is given when the breathing pause exists, so that the danger brought to people by the breathing pause is reduced. However, the ventilator in the prior art supports the apnea phenomenon after monitoring the apnea phenomenon, and cannot avoid the apnea phenomenon, so that the problem of insufficient sleep of the person with the apnea syndrome still cannot be solved.
Therefore, a new control method and apparatus for a respiratory support system, an electronic device, and a computer readable medium are needed.
The above information disclosed in this background section is only for enhancement of understanding of the background of the disclosure and therefore it may contain information that does not constitute prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
In view of the above, the present disclosure provides a control method and apparatus for a respiratory support system, an electronic device, and a computer readable medium, which can predict a state of a user in a period of time in real time based on current detection data of the user, and can actively invoke a ventilator to support the user when a specific situation is predicted to occur.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to an aspect of the disclosure, a control method of a respiratory support system, usable with a controller, is provided, the control method comprising: acquiring user information and real-time monitoring data of a current user, wherein the monitoring data comprises sleep information, breathing information, environmental information and blood oxygen information; determining a target state prediction function according to the user information; generating a state prediction index through real-time monitoring data and a target state prediction function; and when the state prediction index meets a preset condition, generating a respiratory support instruction, and sending the respiratory support instruction to the breathing machine.
In an exemplary embodiment of the present disclosure, further comprising: fitting analysis is performed on historical monitoring data of a plurality of users to generate a plurality of state prediction functions.
In an exemplary embodiment of the present disclosure, fitting the historical monitoring data of the plurality of users to generate a plurality of state prediction functions includes: extracting specified information from historical monitoring data of a plurality of users; the specified information is fitted to other information in the historical monitoring data to generate a plurality of state prediction functions.
In an exemplary embodiment of the present disclosure, fitting the historical monitoring data of the plurality of users to generate a plurality of state prediction functions includes: acquiring historical monitoring data of a plurality of users; classifying the historical monitoring data based on the user information of a plurality of users to generate a plurality of user characteristic data sets; and performing fitting analysis on the plurality of user characteristic data sets respectively to generate a plurality of state prediction functions.
In an exemplary embodiment of the present disclosure, determining a target state prediction function according to user information includes: and determining a target state prediction function from the plurality of state prediction functions according to the user information.
In an exemplary embodiment of the present disclosure, determining a target state prediction function from a plurality of state prediction functions according to user information includes: extracting user characteristics according to the user information; and determining a target state prediction function from the plurality of state prediction functions according to the user characteristics.
In an exemplary embodiment of the present disclosure, further comprising: acquiring historical monitoring data and a breathing support instruction of a current user by a breathing machine; the state prediction function is updated based on historical monitoring data, respiratory support instructions, and a deep learning model of the current user.
In an exemplary embodiment of the present disclosure, updating the state prediction function based on historical monitoring data, respiratory support operational data, and a deep learning model of a current user includes: dividing historical monitoring data into sample data and verification data according to the generation time of the breathing instruction; training the deep learning model by using the sample data and the verification data to obtain an updating function; and updating the state prediction function through the updating function.
In an exemplary embodiment of the present disclosure, before training the deep learning model by using the sample data and the verification data, the method further includes: and verifying the execution effect of the target state prediction function through the verification data, and training the deep learning model by using the sample data and the verification data when the verification result does not meet the verification threshold.
According to an aspect of the disclosure, a control method of a respiratory support system is provided, which may be used for a ventilator, the control method comprising: acquiring real-time monitoring data of a current user; sending the user information and the real-time monitoring data to a control end; and when receiving a respiratory support instruction sent by the control end, starting respiratory support operation according to the respiratory support instruction.
In an exemplary embodiment of the present disclosure, further comprising: acquiring real-time monitoring data of a user during respiratory support operation; and generating alarm information when the real-time monitoring data does not meet the preset strategy within the appointed time.
In an exemplary embodiment of the present disclosure, initiating respiratory support operations according to respiratory support instructions comprises: obtaining a plurality of operating parameters from the respiratory support instruction; a plurality of executive components of the ventilator are adjusted to predetermined positions based on the plurality of operating parameters.
According to an aspect of the disclosure, a control device of a respiratory support system is provided, usable with a controller, the control device comprising: the data module is used for acquiring user information and real-time monitoring data of a current user, wherein the monitoring data comprises sleep information, respiratory information, environmental information and blood oxygen information; the function module is used for determining a target state prediction function according to the user information; the prediction module is used for generating a state prediction index through real-time monitoring data and a target state prediction function; and the instruction module is used for generating a respiratory support instruction when the state prediction index meets a preset condition, and sending the respiratory support instruction to the breathing machine.
According to an aspect of the disclosure, a control device of a respiratory support system is provided, usable with a ventilator, the control device comprising: the monitoring module is used for acquiring real-time monitoring data of a current user; the sending module is used for sending the user information and the real-time monitoring data to the control end; and the operation module is used for starting the respiratory support operation according to the respiratory support instruction when receiving the respiratory support instruction sent by the control end.
According to an aspect of the disclosure, a respiratory support system is presented, the system comprising: the breathing machine is used for acquiring real-time monitoring data of a current user and sending user information and the real-time monitoring data to the control end; starting a respiratory support operation when a respiratory support instruction sent by a control end is received; the controller is used for acquiring user information and real-time monitoring data of a current user, wherein the monitoring data comprises sleep information, respiratory information, environmental information and blood oxygen information; determining a target state prediction function based on the user information; generating a state prediction index through real-time monitoring data and a target state prediction function; and when the state prediction index is larger than the state threshold value, generating a respiratory support instruction and sending the respiratory support instruction to the breathing machine.
According to an aspect of the present disclosure, an electronic device is provided, the electronic device including: one or more processors; storage means for storing one or more programs; when executed by one or more processors, cause the one or more processors to implement a method as above.
According to an aspect of the disclosure, a computer-readable medium is proposed, on which a computer program is stored, which program, when being executed by a processor, carries out the method as above.
According to the control method and device of the respiratory support system, the electronic equipment and the computer readable medium, the target state prediction function is determined according to the user information; generating a state prediction index through real-time monitoring data and a target state prediction function; when the state prediction index meets the preset condition, a respiration support instruction is generated and sent to the respirator, the respirator can predict the state of the user within a period of time in real time based on the current detection data of the user in a starting mode of the respiration support instruction, and the respirator can be actively called to support the user when the specific condition is predicted to be generated.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The above and other objects, features and advantages of the present disclosure will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings. The drawings described below are merely some embodiments of the present disclosure, and other drawings may be derived from those drawings by those of ordinary skill in the art without inventive effort.
Fig. 1 is a system block diagram illustrating a respiratory support system according to an exemplary embodiment.
Fig. 2 is a flow chart illustrating a method of controlling a respiratory support system according to an exemplary embodiment.
Fig. 3 is a flow chart illustrating a method of controlling a respiratory support system according to another exemplary embodiment.
Fig. 4 is a flow chart illustrating a method of controlling a respiratory support system according to another exemplary embodiment.
Fig. 5 is a flow chart illustrating a method of controlling a respiratory support system according to another exemplary embodiment.
Fig. 6 is a block diagram illustrating a control arrangement of a respiratory support system according to an exemplary embodiment.
Fig. 7 is a block diagram illustrating a control arrangement of a respiratory support system according to another exemplary embodiment.
FIG. 8 is a block diagram illustrating an electronic device in accordance with an example embodiment.
FIG. 9 is a block diagram illustrating a computer-readable medium in accordance with an example embodiment.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals denote the same or similar parts in the drawings, and thus, a repetitive description thereof will be omitted.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the disclosure.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one element from another. Thus, a first component discussed below may be termed a second component without departing from the teachings of the disclosed concept. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It is to be understood by those skilled in the art that the drawings are merely schematic representations of exemplary embodiments, and that the blocks or processes shown in the drawings are not necessarily required to practice the present disclosure and are, therefore, not intended to limit the scope of the present disclosure.
Fig. 1 is a system block diagram illustrating a respiratory support system according to an exemplary embodiment.
As shown in fig. 1, the respiratory support system 10 may include a ventilator device 101, a network 102, and a control device 103. The network 102 serves as a medium to provide a communication link between the ventilator device 101 and the control device 103. Network 102 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The ventilator device 101 may interact with the control device 103 via the network 102 to receive or transmit instructions or data or the like. Various data transmission type applications, data processing type applications, instruction receiving type applications, and the like may be installed on the ventilator device 101.
The ventilator device 101 may be a controlled mechanical ventilation type ventilator, an assisted mechanical ventilation type ventilator, an intrathoracic or airway compression type ventilator, an extrathoracic type ventilator, or the like.
The control device 103 may be a processing device that provides various services, such as processing of monitoring data transmitted by the ventilator device 101. The control device 103 may analyze the received monitoring data, etc., and feed back the processing results (e.g., breathing instructions) to the ventilator device 101.
The control device 103 may, for example, obtain user information of the current user and real-time monitoring data, including sleep information, respiration information, environmental information, blood oxygen information; the control device 103 may determine a target state prediction function, e.g. from the user information; the control device 103 may generate a state prediction index, for example, from the real-time monitoring data and the target state prediction function; the control device 103 may, for example, generate a respiratory support instruction when the state prediction index meets a preset condition, and send the respiratory support instruction to the ventilator device 101.
The control device 103 may also perform fitting analysis, for example, on historical monitoring data for multiple users to generate multiple state prediction functions.
The control device 103 may also acquire historical monitoring data and respiratory support instructions of the current user, e.g., by the ventilator; the control device 103 may also update the state prediction function, for example, based on historical monitoring data, respiratory support instructions, and a deep learning model of the current user.
The ventilator device 101 may, for example, acquire real-time monitoring data of the current user; the ventilator device 101 may, for example, send the real-time monitoring data to a control end; ventilator device 101 may, for example, upon receiving a respiratory support instruction sent by the control terminal, initiate respiratory support operations in accordance with the respiratory support instruction.
The control device 103 may be an integrated device connected to the ventilator device 101 by a cable, or may be a wearable device or a mobile phone terminal of the user, as long as it is a device capable of receiving data, analyzing data, and sending instructions. It should be noted that the control method of the respiratory support system provided by the embodiment of the present disclosure may be executed by the ventilator apparatus 101 and the control apparatus 103, and accordingly, the control device of the respiratory support system may be disposed in the ventilator apparatus 101 and the control apparatus 103. While data monitoring sensors that monitor the user are typically located in the ventilator device 101.
The respiratory support system can predict the state of apnea, acquires the relation between the occurrence of the apnea of a user and other factors by monitoring the breathing habit of the user in sleeping through deep learning, accurately predicts the time of the occurrence of the apnea of the user, and actively supports the respiration of the user, thereby avoiding the occurrence of the apnea and bringing good sleep experience to the user.
Fig. 2 is a flow chart illustrating a method of controlling a respiratory support system according to an exemplary embodiment. The control method 20 of the respiratory support system is applicable to a control end and includes at least steps S202 to S208.
As shown in fig. 2, in S202, user information and real-time monitoring data of a current user are acquired, where the monitoring data includes sleep information, respiratory information, environmental information, and blood oxygen information.
In one embodiment, monitoring data may include: the time of sleep onset, the duration and variation of each breath, the pressure and variation of the inspiration and expiration during each breath, the time of occurrence of a change in sleep posture, the time of occurrence of apnea, blood oxygen concentration, external environmental noise condition variations, and the like.
More specifically, real-time monitoring data of the user may be acquired, for example, by a ventilator, and user information may also be acquired, for example, by data input by the user at a control terminal. The user information may include health-related information such as the user's age, weight, height, sleeping habits, and the like. Wherein, the breathing machine and the control end can be started to monitor when the user sleeps.
In S204, a target state prediction function is determined according to the user information. Can be for example: and determining the target state prediction function from a plurality of state prediction functions according to the user information.
In one embodiment, a fitting analysis may be performed on historical monitoring data of a plurality of users to generate a plurality of state prediction functions, the content of which will be described in detail in the corresponding embodiment of fig. 3.
In one embodiment, determining the target state prediction function from a plurality of state prediction functions based on the user information comprises: extracting user characteristics according to the user information; and determining the target state prediction function from the plurality of state prediction functions according to the user characteristics. Each state prediction function corresponds to an applicable condition, which may be an age range and a weight range of the user, or a user characteristic obtained by integrating a plurality of parameters related to the health of the user, and the like. And comparing the user characteristics of the current user with the applicable conditions corresponding to the plurality of state prediction functions, thereby determining the target state prediction function used for the current user.
In S206, a state prediction index is generated by the real-time monitoring data and the target state prediction function. And substituting the real-time monitoring data into the target state prediction function to obtain a state prediction index. Wherein the state prediction index may represent a probability of an apnea occurring for the user over a period of time in the future. The future period of time may be within 10 seconds of the future, or within 1 minute of the future, etc., and the disclosure is not limited thereto. It is pointed out that the state prediction index is only the apnea probability calculated according to the state prediction function, does not represent the apnea probability of the user under the real condition, and does not influence the apnea probability of the user under the real condition.
In S208, when the state prediction index satisfies a preset condition, a respiratory support instruction is generated and sent to a ventilator. The respiratory support instructions may be generated and sent to the ventilator, for example, when the state prediction index is greater than 70%, i.e., the probability of apnea in the user is greater than 70%.
Fig. 3 is a flow chart illustrating a method of controlling a respiratory support system according to another exemplary embodiment. The flow shown in fig. 3 is a detailed description of "performing fitting analysis on historical monitoring data of a plurality of users to generate a plurality of state prediction functions".
As shown in fig. 3, in S302, historical monitoring data of a plurality of users is acquired. Monitoring data of a plurality of users can be obtained from a plurality of ventilators, in order to enable subsequent fitting calculation to be more accurate, the users can be screened according to user information, and historical monitoring data of the users in all age groups, different sexes and different body health states can be covered as much as possible.
In S304, the historical monitoring data is classified based on the user information of the users, and a plurality of user feature data sets are generated. Users of the same age group may for example be classified into the same user profile set, and users with the same chronic disease may for example also be classified into the same user profile set.
In one embodiment, user portraits of multiple users can be generated through a trained machine learning model, the machine learning model analyzes the multiple users to output user characteristics (user portraits) corresponding to each user, and users with the same user characteristics are divided into the same user characteristic data set.
In S306, fitting analysis is performed on the plurality of user feature data sets respectively to generate the plurality of state prediction functions. Can be for example: extracting specified information from historical monitoring data of a plurality of users; fitting the specified information to other information in the historical monitoring data to generate a plurality of state prediction functions.
The apnea related data may be extracted from historical monitoring data of a plurality of users, for example, in any user characteristic data set, and may specifically be the occurrence time of apnea. The occurrence time of the apnea of the user can be acquired through a first sensor on the breathing machine, and the sleep information, the breathing information, the environmental information, the blood oxygen information and the like of the user can be acquired through other sensors on the breathing machine. And taking the apnea time of the user as a target, and fitting the target through sleep information, respiratory information, environmental information, blood oxygen information and the like to obtain a state prediction parameter corresponding to the user characteristic data set.
Furthermore, the fitting may be performed by a least square method to obtain a fitting curve, and a correlation coefficient r of the fitting curve is obtained, and when the correlation coefficient r is greater than a certain threshold (e.g., 0.95), the fitting equation is used as the state prediction function.
Fig. 4 is a flow chart illustrating a method of controlling a respiratory support system according to another exemplary embodiment. The flow shown in fig. 4 is a supplementary description of the flow shown in fig. 2.
As shown in fig. 4, in S402, historical monitoring data and respiratory support instructions of the current user are acquired by the ventilator. The ventilator can record the historical monitoring data of the user in a log mode, and can also record the receiving condition of each breathing instruction.
In S404, the historical monitoring data is divided into sample data and verification data according to the generation time of the breathing instruction. It can be considered that, before the breathing instruction is sent, data of the user in a normal state is monitored for the breathing machine, and the data can be sample data; the data that assists the user in breathing for the ventilator after the ventilator instruction is sent may be verification data.
And when the verification result does not meet the verification threshold, training the deep learning model by using the sample data and the verification data. The situation of the user's apnea predicted by the target state prediction function can be compared with the situation of the actual user's apnea, if the predicted breathing situation and the actual situation have a high goodness of fit, the target state prediction function can be considered to be capable of well supporting the current user, otherwise, the target state prediction function needs to be updated.
In S406, the deep learning model is trained using the sample data and the verification data to obtain an update function. The sample data and the verification data can be respectively in a data deep learning model, and a deep learning name regenerates a new target state prediction function according to the difference between the sample data and the verification data.
In S408, the state prediction function is updated by the update function. The state prediction function is updated so that the new state prediction function can better support the apnea condition of the user. In the using process of a user, the control end of the respiration support system can continuously monitor the using condition of the user and continuously improve the correlation coefficient of the fitting curve until the apnea of the user is accurately predicted, so that the occurrence of the apnea is completely avoided.
According to the control method of the respiratory support system of the present disclosure, a target state prediction function is determined according to the user information; generating a state prediction index through the real-time monitoring data and the target state prediction function; when the state prediction index meets a preset condition, a breathing support instruction is generated and sent to the breathing machine, the state of the user within a period of time can be predicted in real time based on the current detection data of the user, and the breathing machine can be actively called to support the user when a specific condition is predicted to be generated.
It should be clearly understood that this disclosure describes how to make and use particular examples, but the principles of this disclosure are not limited to any details of these examples. Rather, these principles can be applied to many other embodiments based on the teachings of the present disclosure.
Fig. 5 is a flow chart illustrating a method of controlling a respiratory support system according to an exemplary embodiment. The control method 50 of the respiratory support system is applicable to a ventilator and includes at least steps S502 to S508.
As shown in fig. 5, in S502, real-time monitoring data of the current user is acquired. In one embodiment, monitoring data may include: the time of sleep onset, the duration and variation of each breath, the pressure and variation of the inspiration and expiration during each breath, the time of occurrence of a change in sleep posture, the time of occurrence of apnea, blood oxygen concentration, external environmental noise condition variations, and the like. More specifically, real-time monitoring data of the user may be acquired, for example, by a plurality of sensors on the ventilator.
In S504, the real-time monitoring data is sent to a control end.
In S506, when the respiratory support instruction sent by the control terminal is received, the respiratory support operation is started according to the respiratory support instruction. Can be for example: obtaining a plurality of operating parameters from the respiratory support instructions; adjusting a plurality of executive components of the ventilator to predetermined positions based on the plurality of operating parameters.
The breathing machine of a certain model is taken as an example in the disclosure to explain the breathing support operation: a certain type of breathing machine can receive a breathing support instruction, wherein the breathing support instruction comprises an inspiratory valve opening parameter, an air flow parameter, a turbofan rotating speed, an output interface opening time and the like.
The breathing valve of a breathing machine of a certain model is opened after receiving an opening signal command, air and oxygen mixed gas which can be mixed according to a preset proportion enters a patient body, furthermore, a pressure sensor in the breathing machine transmits a measured air pressure data signal to a processor in the breathing machine in real time, after the breathing of a user is determined, an ARM processor can send a closing signal command to the breathing valve, and the breathing valve is closed after receiving the closing signal command to complete the breathing support operation.
In S508, real-time monitoring data of the user is acquired while the breathing support operation is performed.
In S510, when the real-time monitoring data does not satisfy a preset policy within a specified time, generating alarm information. An alert message may be generated, for example, if the user is not found to be breathing by himself within 10s, or if the user's blood oxygen concentration is not found to be improved. The alarm information can be buzzing sound for attracting the attention of the user, can also be instant messages, can send the instant messages to the control end, and can also be sent to a medical platform so that medical staff can warn.
According to the control method of the respiratory support system, the real-time monitoring data is sent to a control end; when the respiratory support instruction sent by the control end is received, the mode of starting the respiratory support operation according to the respiratory support instruction can predict the state of the user in a period of time in real time based on the current detection data of the user, and the user can be actively supported when the specific situation is predicted to be generated.
Those skilled in the art will appreciate that all or part of the steps implementing the above embodiments are implemented as computer programs executed by a CPU. When executed by the CPU, performs the functions defined by the above-described methods provided by the present disclosure. The program may be stored in a computer readable storage medium, which may be a read-only memory, a magnetic or optical disk, or the like.
Furthermore, it should be noted that the above-mentioned figures are only schematic illustrations of the processes involved in the methods according to exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods. For details not disclosed in the embodiments of the apparatus of the present disclosure, refer to the embodiments of the method of the present disclosure.
Fig. 6 is a block diagram illustrating a control arrangement of a respiratory support system according to an exemplary embodiment. As shown in fig. 6, the control device 60 of the respiratory support system may be applied to a control end, including: data module 602, function module 604, prediction module 606, instruction module 608.
The data module 602 is configured to obtain user information and real-time monitoring data of a current user, where the monitoring data includes sleep information, respiratory information, environmental information, and blood oxygen information; the monitoring data may include: the time of sleep onset, the duration and variation of each breath, the pressure and variation of the inspiration and expiration during each breath, the time of occurrence of a change in sleep posture, the time of occurrence of apnea, blood oxygen concentration, external environmental noise condition variations, and the like.
The function module 604 is configured to determine a target state prediction function according to the user information; the target state prediction function may be determined from a plurality of state prediction functions, for example, based on the user information. Can include the following steps: extracting user characteristics according to the user information; and determining the target state prediction function from the plurality of state prediction functions according to the user characteristics. Each state prediction function corresponds to an applicable condition, which may be an age range and a weight range of the user, or a user characteristic obtained by integrating a plurality of parameters related to the health of the user, and the like. And comparing the user characteristics of the current user with the applicable conditions corresponding to the plurality of state prediction functions, thereby determining the target state prediction function used for the current user.
The prediction module 606 is configured to generate a state prediction index according to the real-time monitoring data and the target state prediction function; wherein the state prediction index may represent a probability of an apnea occurring for the user over a period of time in the future. The future period of time may be within 10 seconds of the future, or within 1 minute of the future, etc., and the disclosure is not limited thereto. It is pointed out that the state prediction index is only the apnea probability calculated according to the state prediction function, does not represent the apnea probability of the user under the real condition, and does not influence the apnea probability of the user under the real condition.
The instruction module 608 is configured to generate a respiratory support instruction when the state prediction index meets a preset condition, and send the respiratory support instruction to the ventilator. The respiratory support instructions may be generated and sent to the ventilator, for example, when the state prediction index is greater than 70%, i.e., the probability of apnea in the user is greater than 70%.
Fig. 7 is a block diagram illustrating a control arrangement of a respiratory support system according to another exemplary embodiment. As shown in fig. 7, the control device 70 of the respiratory support system is applicable to a ventilator including: a monitoring module 702, a sending module 704, and an operation module 706.
The monitoring module 702 is configured to obtain real-time monitoring data of a current user; real-time monitoring data of the user may be acquired, for example, by a plurality of sensors on the ventilator.
The sending module 704 is configured to send the real-time monitoring data to a control end;
the operation module 706 is configured to start a respiratory support operation according to the respiratory support instruction when receiving the respiratory support instruction sent by the control end. Can include the following steps: obtaining a plurality of operating parameters from the respiratory support instructions; adjusting a plurality of executive components of the ventilator to predetermined positions based on the plurality of operating parameters.
According to the control device of the respiratory support system, real-time monitoring data of a current user are obtained, wherein the monitoring data comprise sleep information, respiratory information, environmental information and blood oxygen information; determining a target state prediction function according to the user information; generating a state prediction index through the real-time monitoring data and the target state prediction function; when the state prediction index meets a preset condition, a breathing support instruction is generated and sent to the breathing machine, the state of the user within a period of time can be predicted in real time based on the current detection data of the user, and the breathing machine can be actively called to support the user when a specific condition is predicted to be generated.
FIG. 8 is a block diagram illustrating an electronic device in accordance with an example embodiment.
An electronic device 800 according to this embodiment of the disclosure is described below with reference to fig. 8. The electronic device 800 shown in fig. 8 is only an example and should not bring any limitations to the functionality and scope of use of the embodiments of the present disclosure.
As shown in fig. 8, electronic device 800 is in the form of a general purpose computing device. The components of the electronic device 800 may include, but are not limited to: at least one processing unit 810, at least one memory unit 820, a bus 830 connecting the various system components (including the memory unit 820 and the processing unit 810), a display unit 840, and the like.
Wherein the storage unit stores program code executable by the processing unit 810 to cause the processing unit 810 to perform steps according to various exemplary embodiments of the present disclosure described in the electronic prescription flow processing method section described above in this specification. For example, the processing unit 810 may perform the steps as shown in fig. 2, 3, 4, 5.
The memory unit 820 may include readable media in the form of volatile memory units such as a random access memory unit (RAM)8201 and/or a cache memory unit 8202, and may further include a read only memory unit (ROM) 8203.
The memory unit 820 may also include a program/utility 8204 having a set (at least one) of program modules 8205, such program modules 8205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 800 may also communicate with one or more external devices 800' (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 800, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 800 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 850. Also, the electronic device 800 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via the network adapter 860. The network adapter 860 may communicate with other modules of the electronic device 800 via the bus 830. It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with the electronic device 800, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, as shown in fig. 9, the technical solution according to the embodiment of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, or a network device, etc.) to execute the above method according to the embodiment of the present disclosure.
The software product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
The computer readable medium carries one or more programs which, when executed by a device, cause the computer readable medium to perform the functions of: acquiring user information and real-time monitoring data of a current user, wherein the monitoring data comprises sleep information, breathing information, environmental information and blood oxygen information; determining a target state prediction function according to the user information; generating a state prediction index through the real-time monitoring data and the target state prediction function; and when the state prediction index meets a preset condition, generating a respiratory support instruction, and sending the respiratory support instruction to the breathing machine. The computer readable medium may also implement the following functions: acquiring real-time monitoring data of a current user, wherein the monitoring data comprises sleep information, respiratory information, environmental information and blood oxygen information; determining a target state prediction function based on the user information; generating a state prediction index through the real-time monitoring data and the target state prediction function; when the state prediction index is larger than a state threshold value, generating a respiratory support instruction and sending the respiratory support instruction to the breathing machine
Those skilled in the art will appreciate that the modules described above may be distributed in the apparatus according to the description of the embodiments, or may be modified accordingly in one or more apparatuses unique from the embodiments. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
Exemplary embodiments of the present disclosure are specifically illustrated and described above. It is to be understood that the present disclosure is not limited to the precise arrangements, instrumentalities, or instrumentalities described herein; on the contrary, the disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (17)
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