Noninvasive ventilator monitoring method and universal monitoring system

Abstract

The invention discloses a noninvasive ventilator monitoring method and a universal monitoring system, and relates to the technical field of medical systems. Comprising the steps that at least one flow sensor is arranged in a breathing machine pipeline to collect airflow data in real time; standard flow information of different breathing machines is obtained or preset, the airflow data is compared with the standard flow information, when it is judged that the current breathing machine is used abnormally according to changes of the airflow data, an alarm mechanism is triggered, and after reminding information is sent to preset equipment, a patient is reminded to check the state of the patient and the state of the breathing machine; a sensor module is arranged in a breathing machine pipeline, software and hardware are matched with breathing machines of various models, and cross-brand universal monitoring is achieved; the airflow sensor collects airflow changes in a breathing machine pipeline in real time, monitoring of the use condition of the breathing machine is achieved by capturing airflow abnormality and establishing a multi-level reminding mechanism, the risk that the abnormal condition is not perceived in time is reduced, and the illness worsening risk is effectively reduced.

Description

Technical Field

[0001] The present application relates to the technical field of medical systems, and in particular to a non-invasive ventilator monitoring method and a universal monitoring system. Background Art

[0002] A non-invasive ventilator is a medical device used to assist or support the patient's breathing. It is widely used in the treatment of chronic obstructive pulmonary disease (COPD), sleep apnea syndrome and other diseases. With the continuous development of non-invasive ventilator technology, many brands and models of equipment have appeared on the market. These devices have their own characteristics in terms of airflow regulation, usage parameter setting and data monitoring. They usually monitor the patient's ventilation volume, respiratory rate and other parameters through internal sensors or external devices to ensure the normal operation of the equipment. However, due to differences in design standards and protocols of equipment from different brands, these monitoring methods mostly rely on the equipment itself and are difficult to monitor uniformly across brands.

[0003] Therefore, the current non-invasive ventilators have certain limitations in compatibility and versatility, especially in the unified management and monitoring of cross-brand devices. When patients experience abnormal pipeline air flow while using non-invasive ventilators, this abnormality is usually difficult for the patients to detect. If not handled in time, it may lead to insufficient oxygen supply or carbon dioxide retention in patients, thus aggravating the condition.

[0004] Therefore, how to develop a universal non-invasive ventilator monitoring method, unify the flow monitoring and abnormal judgment of cross-brand equipment through real-time collection and analysis of pipeline airflow data, and provide timely feedback to patients and doctors has become an urgent problem to be solved in the current technical field. It can not only improve the efficiency of non-invasive ventilators, but also provide patients with more intelligent and personalized medical support. Summary of the invention

[0005] This application aims to address the limitations of current non-invasive ventilators in terms of compatibility and versatility. When patients experience abnormal pipeline airflow when using non-invasive ventilators, this abnormality is usually difficult for the patients to detect by themselves, which leads to worsening of the condition. Therefore, a universal non-invasive ventilator monitoring method is provided. Through real-time collection and analysis of pipeline airflow data by an external flow sensor, flow monitoring and abnormality judgment across brands of equipment are unified, and feedback is provided to patients and doctors in a timely manner. The specific technical solution is as follows: In a first aspect of the present application, a non-invasive ventilator monitoring method is provided, comprising: A sensor module is provided, wherein the sensor module is provided with at least one flow sensor in the ventilator pipeline, which is used to monitor the airflow changes in the pipeline in real time, and collect airflow data in real time through the flow sensor; Obtain or preset standard flow information of different ventilators, compare the airflow data with the standard flow information, and determine whether the current use of the ventilator is normal according to the change of the airflow data; When it is determined that the use is abnormal, the alarm mechanism is triggered and a reminder message is sent to the preset device. After receiving the reminder message, the preset device reminds the patient to check his own status and the status of the ventilator; The preset devices include patient-side devices, relatives-side devices and doctor-side devices. The doctor-side devices obtain the airflow data and remotely monitor the patient's ventilator usage based on the comparison data with the standard flow information.

[0006] In one embodiment of the present application, the sensor module is provided with at least one pressure sensor in the ventilator pipeline, which is used to monitor the pressure changes in the pipeline in real time, and collect pressure data in real time through the pressure sensor; When it is determined according to the airflow data that the current use of the ventilator is abnormal, the current pressure data is obtained, and different alarm mechanisms are triggered in combination with the pressure data, including air leakage alarm and disease aggravation alarm; After the air leakage alarm is triggered, the air flow output of the ventilator is dynamically adjusted according to the degree of air leakage until the ventilator reaches a preset air flow output probability, and an air leakage reminder message is sent to the patient-side device; After the aggravation alarm is triggered, a reminder message of aggravation of the condition is sent to the patient-side device. If the patient does not take action within a preset time, a reminder message is sent to the relatives and friends-side device and / or the doctor-side device.

[0007] In one embodiment of the present application, the sensor module also includes sensors with corresponding data collection functions arranged at corresponding positions of the ventilator according to the patient's needs for collecting different data, including temperature sensors, humidity sensors and gas analyzers, and the working status of the ventilator and the patient's breathing condition are monitored through the obtained multi-dimensional data.

[0008] In one embodiment of the present application, the sensor module also includes an image sensor, which collects facial images of the patient and determines the patient's condition by identifying expression features of the facial images through a machine learning algorithm. The expression features include relaxation type and discomfort type. At the same time, the facial images of the discomfort type are divided into discomfort levels according to the expression features. When it is identified that the facial image reaches a preset discomfort level, an alarm is triggered and / or the ventilator parameters are automatically adjusted.

[0009] In one embodiment of the present application, the image sensor also includes collecting position images of various components of the ventilator, and the position images are used to monitor the position changes of various components of the ventilator. When any component of the ventilator moves, the pressure data is obtained. If the pressure data triggers the air leakage alarm, the moving components of the ventilator and the movement amplitude of the components are recorded.

[0010] In one embodiment of the present application, the type of sensor of the sensor module is obtained, and the corresponding appropriate data range is preset for various sensors. At the same time, the type of data adjusted by the current external device is obtained, and the data type collected by the sensor and the data type adjusted by the external device are matched, and the successfully matched sensor is set to be linked with the external device; when the data collected by the sensor exceeds the appropriate data range, the sensor is linked with the external device, and a corresponding instruction is sent according to the value exceeding the appropriate data range, so that the data collected by the sensor returns to the appropriate data range.

[0011] In one embodiment of the present application, the sensor module summarizes the collected data and sends a data report to the preset device at a preset time, and the content of the data report received by different types of preset devices is different; the first data report received by the patient-side device includes respiratory data and adjustment suggestions; the second data report received by the friend-side device includes respiratory data and suggested execution data; the third data report received by the doctor-side device includes respiratory data and data trends.

[0012] In one embodiment of the present application, all the third data report information is desensitized and sorted to obtain ventilator parameters corresponding to the improvement and deterioration of the condition under different disease states, and generate ventilator recommended parameters and alarm parameters under different disease states.

[0013] In one embodiment of the present application, it also includes obtaining the patient's historical health data, including past medical history and medication records; generating personalized treatment recommendations based on the historical health data, including ventilator parameter adjustment recommendations; and adjusting the ventilator parameters in real time based on changes in the patient's airflow data and pressure data during the use of the ventilator.

[0014] In a second aspect of the present application, a universal monitoring system for a non-invasive ventilator is provided, comprising: A flow acquisition module is provided with a sensor module, wherein the sensor module is provided with at least one flow sensor in the ventilator pipeline, which is used to monitor the airflow changes in the pipeline in real time, and collect airflow data in real time through the flow sensor; A flow comparison module, which obtains or presets standard flow information of different ventilators, compares the airflow data with the standard flow information, and determines whether the current use of the ventilator is normal according to the change of the airflow data; The alarm prompt module triggers the alarm mechanism when it is judged that the use is abnormal, and sends a reminder message to the preset device. After receiving the reminder message, the preset device reminds the patient to check his own status and the status of the ventilator; The information transmission module includes a patient-side device, a relative-side device and a doctor-side device. The doctor-side device obtains the airflow data and remotely monitors the patient's ventilator usage based on the comparison data with the standard flow information.

[0015] In one embodiment of the present application, a pressure acquisition module is also included, wherein the sensor module is provided with at least one pressure sensor in the ventilator pipeline, which is used to monitor the pressure changes in the pipeline in real time, and to collect pressure data in real time through the pressure sensor; The alarm prompt module also includes, when judging that the current use of the ventilator is abnormal according to the airflow data, obtaining the current pressure data, and triggering different alarm mechanisms in combination with the pressure data, specifically including air leakage alarm and disease aggravation alarm; An air leakage alarm module, after triggering the air leakage alarm, dynamically adjusts the air flow output of the ventilator according to the degree of air leakage until the ventilator reaches a preset air flow output probability, and sends an air leakage reminder message to the patient-side device; The condition alarm module sends a reminder message of worsening condition to the patient-side device after triggering the alarm of worsening condition. If the patient fails to take action within a preset time, a reminder message is sent to the relatives and friends-side device and / or the doctor-side device.

[0016] In one embodiment of the present application, a multidimensional data module is also included. The sensor module also includes sensors with corresponding data collection functions arranged at corresponding positions of the ventilator according to the patient's needs for collecting different data, including temperature sensors, humidity sensors and gas analyzers, and the working status of the ventilator and the patient's breathing condition are monitored through the obtained multidimensional data.

[0017] In one embodiment of the present application, the multidimensional data module also includes a facial image submodule, and the sensor module also includes an image sensor. The image sensor collects the patient's facial image, and determines the patient's condition by identifying the expression features of the facial image through a machine learning algorithm. The expression features include relaxation type and discomfort type. At the same time, the facial image of the discomfort type is divided into discomfort levels according to the expression features. When it is identified that the facial image reaches a preset discomfort level, an alarm is triggered and / or the ventilator parameters are automatically adjusted.

[0018] In one embodiment of the present application, the multidimensional data module also includes a layout image submodule, and the image sensor also includes a device for collecting position images of various components of the ventilator. The position images are used to monitor the position changes of various components of the ventilator. When any component of the ventilator moves, the pressure data is obtained. If the pressure data triggers the air leakage alarm, the moving components of the ventilator and the movement amplitude of the components are recorded.

[0019] In one embodiment of the present application, the multidimensional data module also includes a device linkage submodule, which obtains the type of sensor of the sensor module, presets corresponding appropriate data ranges for various sensors, and simultaneously obtains the type of data adjusted by the current external device, matches the type of data collected by the sensor and the type of data adjusted by the external device, and sets a linkage with the successfully matched sensor; when the data collected by the sensor exceeds the appropriate data range, it is linked with the external device, and a corresponding instruction is sent according to the value exceeding the appropriate data range, so that the data collected by the sensor is restored to the appropriate data range.

[0020] In one embodiment of the present application, it also includes a data reporting module, the sensor module summarizes the collected data, and sends a data report to the preset device at a preset time, and the content of the data report received by different types of preset devices is different; the first data report received by the patient-side device includes respiratory data and adjustment suggestions; the second data report received by the friend-side device includes respiratory data and suggested execution data; the third data report received by the doctor-side device includes respiratory data and data trends.

[0021] In one embodiment of the present application, the data reporting module also includes a parameter adjustment submodule, which desensitizes and organizes all the third data report information, obtains the ventilator parameters corresponding to the improvement and deterioration of the condition under different disease states, and generates ventilator recommended parameters and alarm parameters under different disease states.

[0022] In one embodiment of the present application, a personalized module is also included to obtain the patient's historical health data, including past medical history and medication records; generate personalized treatment recommendations based on the historical health data, including ventilator parameter adjustment recommendations; and adjust the ventilator parameters in real time based on changes in the patient's airflow data and pressure data during the use of the ventilator.

[0023] This application has the following beneficial effects: 1. By setting a sensor module in the ventilator pipeline, the hardware can be adapted to various models of ventilators; by obtaining or presetting the standard flow information of non-invasive ventilators of different brands and comparing it with the airflow data, the software can also be adapted to various models of ventilators, realizing cross-brand universal monitoring, breaking through the compatibility limitations caused by different brand-specific standards in the prior art; the airflow sensor collects the airflow changes in the ventilator pipeline in real time, and can capture the abnormal airflow at the first time. When abnormal use is detected, the alarm mechanism is triggered, and the reminder information is sent to the patient-side device, the relatives and friends-side device and the doctor-side device, and a multi-level reminder mechanism is established to realize dynamic monitoring of the patient's ventilator usage, greatly reducing the risk of abnormal conditions not being detected in time. The patient-side device can directly prompt the patient to check his own status and the ventilator status, the relatives and friends-side device can assist the patient in supervising the use of the ventilator, and the doctor-side device can provide remote guidance to the patient to ensure the timeliness and reliability of abnormal handling, and effectively reduce the risk of worsening of the disease.

[0024] 2. Use the pressure sensor in combination with the flow sensor to monitor the flow and pressure changes in the pipeline to determine the operating status of the ventilator, and identify whether the abnormal state is caused by worsening of the disease or air leakage. The triggered alarm mechanism is divided accordingly, and different alarm mechanisms correspond to different processing methods: When it is identified as an air leakage alarm, the ventilator airflow output is dynamically adjusted according to the degree of air leakage until the ventilator reaches the preset airflow output probability. If it still cannot make up for the air pressure demand caused by the air leakage, a leak reminder message is sent to the patient-side device to remind the patient to adjust the ventilator mask to avoid the ventilator being in high-load operation for a long time. , more importantly, to prevent the patient from not getting enough airflow; when the alarm is identified as the aggravation of the condition, first send a reminder message of the aggravation of the condition to the patient-side device to wake up the patient, and the patient takes corresponding measures according to his or her own condition, including taking medicine and going to the hospital for treatment. If the patient is not treated within the preset time, the patient may not be awakened, or the patient may find it difficult to take corresponding measures due to the aggravation of the condition. Therefore, a reminder message is sent to the relative-side device and / or the doctor-side device, and the relative-side device or the doctor-side device is used to check the patient's status, or remotely check and seek help to ensure that the patient is effectively treated in time when the condition worsens.

[0025] 3. The image sensor is used to collect the patient's facial image. When the patient's facial expression is identified as discomfort, the patient is reminded, and the degree of discomfort of the patient is further divided according to the degree of facial muscle tension, frowning, etc. The higher the degree, the worse the patient's current condition. Therefore, when the facial image is identified to reach a preset discomfort level, an alarm is triggered and / or the ventilator parameters are automatically adjusted; at the same time, when the face is blocked by objects such as quilts and the patient's facial image cannot be collected, an abnormal prompt is also issued. On the one hand, it is avoided that the patient's facial image cannot be collected due to the quilt blocking, and on the other hand, it is avoided that the patient has respiratory diseases The poor circulation caused by the quilt blocking; further, considering that air leakage is mostly caused by unconscious movement of the patient in the sleep state, This is caused by a gap between the ventilator mask and the face, so the image sensor collects position images of various components of the ventilator, and the position images are used to monitor the position changes of various components of the ventilator. When any component of the ventilator moves, the pressure data is obtained. If the pressure data triggers the air leakage alarm, the moving components of the ventilator and the movement amplitude of the components are recorded, and the corresponding relationship between the movement amplitude of each component and the probability of air leakage is obtained. Before the subsequent ventilator moves significantly, the patient is prompted to reduce the number of air leaks; further, the image sensor records the patient's sleeping posture and the number of movements and turning over at night, and derives a sleeping posture with less movement and turning over, and gives advice to the patient to reduce the number of movements during sleep and reduce the risk of air leakage.

[0026] 4. According to the data type adjusted by the external device and the data type collected by the sensor, the external device and the sensor are linked. When the data collected by the sensor is not within the normal data range, the external device is used for adjustment. For example, when the sensor detects that the temperature of the patient's exhaled gas is low, the sensor module controls the air conditioning heating, and the patient's ambient temperature rises, so that the patient's exhaled gas temperature returns to the normal range. A suitable environment helps to optimize the function of the ventilator and reduce the risk of worsening of the disease. It can also be linked with home oximeters, heart rate monitors, etc. When the data collected by the linked device is abnormal, the other linked devices are also tested to form a more comprehensive family health management system.

[0027] 5. Integrate patients, relatives, friends and doctors into the remote collaborative diagnosis and treatment process, adjust the treatment plan in real time through intelligent analysis technology, and manage the condition under the cooperation of multiple parties; wherein, the patient side receives the first data report, and helps the patient understand the effect of ventilator use through daily respiratory data comparison, promptly discovers problems and actively improves equipment settings or environment, and enhances the patient's ability to actively participate in and improve treatment; the relatives and friends side receives the second data report, and judges the patient's self-management ability through the suggestion execution data, that is, the execution status of the suggestions made to the patient. For example, because the ventilator mask has fallen off several times, the adjustment suggestion gives a suggestion for adjusting the sleeping position. The suggestion execution data identifies that the patient's sleeping position has not changed, but the ventilator mask still falls off, indicating that the patient's self-management ability is poor. When the patient has difficulty in self-management or the ventilator accessories are aging or damaged, relatives and friends provide corresponding support to assist relatives and friends in supervising the patient's treatment situation The doctor side receives the third data report, and provides comprehensive data support to the doctor through various data trends, helping him to optimize the treatment plan and equipment settings, and respond to changes in the condition in advance; through this classified sending and targeted suggestions, it can better assist patients, relatives, friends and doctors to jointly manage the disease, improve the effectiveness of non-invasive ventilator treatment and the quality of life of patients; further, all the third data report information is desensitized and sorted, and the ventilator parameters corresponding to the improvement and deterioration of the condition under different condition states are obtained, and the ventilator recommended parameters and alarm parameters under different condition states are generated to identify the current condition stage of different patients, and generate the ventilator recommended parameters according to the ventilator parameters set for patients with improvement in similar condition stages; the alarm parameters are the ventilator parameters set for patients with deterioration in similar condition stages, and when the ventilator parameters set by the patient are within the alarm parameter range, the patient is prompted.

[0028] 6. By combining the patient's historical health data (such as past medical history, medication records, etc.), provide patients with personalized treatment recommendations, including ventilator parameter adjustment recommendations, and make personalized adjustments to the ventilator parameters before use. Further, during the use of the ventilator, the patient can make corresponding parameter adjustments based on the patient's current breathing conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0030] Figure 1 A schematic diagram of the electronic device structure of the hardware operating environment involved in an embodiment of the present application.

[0031] Figure 2 It is a step flow chart of a universal monitoring system for a non-invasive ventilator provided in an embodiment of the present application.

[0032] Figure 3 It is a functional module diagram of a non-invasive ventilator monitoring method provided in an embodiment of the present application.

[0033] Figure 4 This is a module construction diagram of a non-invasive ventilator monitoring method provided in an embodiment of the present application.

[0034] Symbols in the figure: 1001 - processor, 1002 - communication bus, 1003 - user interface, 1004 - network interface, 1005 - memory. DETAILED DESCRIPTION

[0035] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0036] The solution of the present application is further described below in conjunction with the accompanying drawings.

[0037] like Figure 1 As shown, the electronic device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (WIreless-FIdelity, WI-FI) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM) memory, or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0038] Those skilled in the art will understand that Figure 1 The structure shown in the figure does not constitute a limitation on the electronic device, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0039] like Figure 1As shown, the memory 1005 as a storage medium may include an operating system, a network communication module, a user interface module and a data storage module.

[0040] exist Figure 1 In the electronic device shown, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the electronic device of the present invention can be set in the electronic device, and the electronic device calls a non-invasive ventilator general monitoring system stored in the data storage module in the memory 1005 through the processor 1001, and executes a non-invasive ventilator monitoring method provided in an embodiment of the present application.

[0041] Based on the aforementioned hardware operating environment and system architecture, in the first aspect of the present application, refer to Figure 2 As shown, a non-invasive ventilator monitoring method is provided, comprising: A sensor module is provided, wherein the sensor module is provided with at least one flow sensor in the ventilator pipeline, which is used to monitor the airflow changes in the pipeline in real time, and collect airflow data in real time through the flow sensor; It should be noted that the ventilator in the present application at least includes a ventilator body for providing airflow, a ventilator pipe for conveying airflow, and a ventilator mask for fitting the patient's face, mouth, and nose; the flow sensor for real-time monitoring of airflow changes in the pipe is arranged in the ventilator pipe, and the specific arrangement forms include but are not limited to embedding a small flow sensor inside the ventilator pipe, installing a flow sensor on the outer wall of the pipe, non-contact measurement of airflow by ultrasonic or laser, and connecting the flow sensor at the end of the ventilator pipe, and the flow sensor connection pipe is connected to the ventilator mask; Obtain or preset standard flow information of different ventilators, compare the airflow data with the standard flow information, and determine whether the current use of the ventilator is normal according to the change of the airflow data; It should be noted that due to the different factory parameters of different ventilators, the design standards and protocols of different brands of equipment are different, and it is difficult to uniformly monitor across brands; therefore, by obtaining or presetting the standard flow information of the current ventilator, the flow information includes multiple types, not limited to peak flow: the maximum value reached by the airflow in a breathing cycle; average flow: the average level of airflow in a breathing cycle; inspiratory flow: the airflow rate when the patient inhales; expiratory flow: the airflow rate when the patient exhales; respiratory frequency: the number of breathing cycles completed by the patient per unit time; the airflow data collected by the flow sensor includes the above types. When the collected airflow data is not within the standard flow information range during the detection process, it is judged to be abnormal; When it is determined that the use is abnormal, the alarm mechanism is triggered and a reminder message is sent to the preset device. After receiving the reminder message, the preset device reminds the patient to check his own status and the status of the ventilator; The preset devices include patient-side devices, relatives-side devices and doctor-side devices. The doctor-side devices obtain the airflow data and remotely monitor the patient's ventilator usage based on the comparison data with the standard flow information.

[0042] It should be noted that there are two situations that may cause the airflow data to be judged as abnormal. One is the abnormal airflow caused by the patient's physical condition deteriorating and the serious condition, and the other is the abnormal airflow caused by ventilator failure or abnormal ventilator mask leakage; the preset device is a pre-set device for receiving prompt information, including patient-side devices, relatives and friends-side devices and doctor-side devices. The preset device can be a dedicated device for receiving prompt information, and can also be connected to the current mobile device to receive prompt information through SMS, app, etc.; the alarm mechanism includes but is not limited to voice prompts, relatives and friends prompts and remote alarms; In this embodiment, by setting a sensor module in the ventilator pipeline, the hardware can be adapted to various models of ventilators; by obtaining or presetting the standard flow information of non-invasive ventilators of different brands and comparing it with the airflow data, the software can also be adapted to various models of ventilators, realizing cross-brand universal monitoring, breaking through the compatibility limitations caused by different brand-specific standards in the prior art; the airflow sensor collects the airflow changes in the ventilator pipeline in real time, and can capture the abnormal airflow at the first time. When abnormal use is detected, the alarm mechanism is triggered, and the reminder information is sent to the patient-side device, the relatives and friends-side device, and the doctor-side device, and a multi-level reminder mechanism is established to realize dynamic monitoring of the patient's ventilator usage, greatly reducing the risk of abnormal conditions not being detected in time. The patient-side device can directly prompt the patient to check his own status and the ventilator status, the relatives and friends-side device can assist the patient in supervising the use of the ventilator, and the doctor-side device can provide remote guidance to the patient to ensure the timeliness and reliability of abnormal processing, and effectively reduce the risk of worsening of the disease.

[0043] In one embodiment of the present application, the sensor module is provided with at least one pressure sensor in the ventilator pipeline, which is used to monitor the pressure changes in the pipeline in real time, and collect pressure data in real time through the pressure sensor; When it is determined according to the airflow data that the current use of the ventilator is abnormal, the current pressure data is obtained, and different alarm mechanisms are triggered in combination with the pressure data, including air leakage alarm and disease aggravation alarm; After the air leakage alarm is triggered, the air flow output of the ventilator is dynamically adjusted according to the degree of air leakage until the ventilator reaches a preset air flow output probability, and an air leakage reminder message is sent to the patient-side device; After the aggravation alarm is triggered, a reminder message of aggravation of the condition is sent to the patient-side device. If the patient does not take action within a preset time, a reminder message is sent to the relatives and friends-side device and / or the doctor-side device.

[0044] It should be noted that when patients use a ventilator during sleep, it is easy for the ventilator mask to move or even fall off due to unconscious movement and turning over, which in turn causes the airflow data to be abnormal; in addition, when the patient's condition worsens, the airflow data may also be abnormal; the two can be distinguished by the difference in the pressure data in the ventilator pipeline, where worsening of the condition often manifests as overall dyspnea, usually resulting in a decrease in respiratory flow, but this decrease is relatively stable; and air leakage can cause unstable airflow, usually with obvious airflow fluctuations or irregular changes, especially during the exhalation phase of the ventilator; the pressure data is collected through the pressure sensor, and the pressure sensor and the flow sensor belong to the same sensor module and are set together with the flow sensor; at the same time, the sensor module can adjust the output parameters of the ventilator by adjusting the ventilation volume and setting the infrared remote control function; In this embodiment, the pressure sensor is used in combination with the flow sensor to monitor the flow and pressure changes in the pipeline at the same time to determine the operating status of the ventilator, and the abnormal state is identified as being caused by worsening of the disease or air leakage, and the triggered alarm mechanism is divided accordingly, and different alarm mechanisms correspond to different processing methods: when it is identified as an air leakage alarm, the ventilator airflow output is dynamically adjusted according to the degree of air leakage until the ventilator reaches the preset airflow output probability. If it still cannot make up for the air pressure demand caused by the air leakage, a leak reminder message is sent to the patient-side device to remind the patient to adjust the ventilator mask to avoid the ventilator being in high-load state for a long time. Load operation, more importantly, to prevent the patient from not getting enough airflow; when it is identified as the alarm of worsening condition, first send a reminder message of worsening condition to the patient-side device to wake up the patient, and the patient will take corresponding measures according to his or her own condition, including taking medicine and going to the hospital for treatment. If the patient is not treated within the preset time, the patient may not be awakened, or the patient may find it difficult to take corresponding measures due to worsening condition. Therefore, a reminder message is sent to the relative-side device and / or the doctor-side device, and the relative-side device or the doctor-side device is used to check the patient's condition, or remotely check and seek help to ensure that the patient is effectively treated in time when the condition worsens.

[0045] In one embodiment of the present application, the sensor module also includes sensors with corresponding data collection functions arranged at corresponding positions of the ventilator according to the patient's needs for collecting different data, including temperature sensors, humidity sensors and gas analyzers, and the working status of the ventilator and the patient's breathing condition are monitored through the obtained multi-dimensional data.

[0046] It should be noted that the sensor module can realize sensors with multiple data type collection functions at the same time through high integration, and can also realize sensors with different data type collection functions to be freely installed and removed according to needs through the sensor module; through the collection of multiple data types, a multidimensional data set is formed, and more data with reference value is obtained through combined analysis; for example, the patient's EtCO can be obtained through a gas analyzer 2 The change in the content of end-tidal Carbon Dioxide (ECD) is combined with the airflow data to obtain the stage of the patient's condition; the temperature and humidity of the ventilator output gas and the patient's exhaled gas are used to determine whether the temperature and humidity of the ventilator output gas need to be adjusted.

[0047] In one embodiment of the present application, the sensor module also includes an image sensor, which collects facial images of the patient and determines the patient's condition by identifying expression features of the facial images through a machine learning algorithm. The expression features include relaxation type and discomfort type. At the same time, the facial images of the discomfort type are divided into discomfort levels according to the expression features. When it is identified that the facial image reaches a preset discomfort level, an alarm is triggered and / or the ventilator parameters are automatically adjusted.

[0048] In one embodiment of the present application, the image sensor also includes collecting position images of various components of the ventilator, and the position images are used to monitor the position changes of various components of the ventilator. When any component of the ventilator moves, the pressure data is obtained. If the pressure data triggers the air leakage alarm, the moving components of the ventilator and the movement amplitude of the components are recorded.

[0049] It should be noted that the image sensor is arranged at a position where the patient's facial image and the images of various components of the ventilator can be collected, including the head of the bed and the ceiling; the facial expression features of the facial image are identified by a machine learning algorithm, including the recognition of facial expressions by applying a deep learning algorithm (such as a convolutional neural network CNN), which can classify various facial expressions (such as pain, tension, relaxation, slight frowning, etc.), and further identify possible minor facial changes in the sleeping state, such as frowning, clenched lips, tense facial muscles, flushed or pale face caused by dyspnea; the image sensor includes an infrared sensing function, and can still perform image acquisition and recognition when the lights are turned off at night; In this embodiment, the patient's facial image is collected by the image sensor, and when the patient's facial expression is recognized as discomfort, the patient is reminded, and the patient's discomfort is further divided into degrees according to the degree of facial muscle tension, frowning, etc. The higher the degree, the worse the patient's current state is. Therefore, when the facial image is recognized to reach a preset discomfort level, an alarm is triggered and / or the ventilator parameters are automatically adjusted; at the same time, when the face is blocked by objects such as a quilt, making it impossible to collect the patient's facial image, an abnormal prompt is also issued. On the one hand, it is avoided that the patient's facial image cannot be collected due to the quilt blocking, and on the other hand, it is avoided that the patient has respiratory diseases. Poor gas circulation caused by the quilt blocking; further, considering that air leakage is mostly due to unconsciousness in the patient's sleep state, Movement causes a gap between the ventilator mask and the face, so the image sensor collects position images of various components of the ventilator, and the position images are used to monitor the position changes of various components of the ventilator. When any component of the ventilator moves, the pressure data is obtained. If the pressure data triggers the air leakage alarm, the moving components of the ventilator and the movement amplitude of the components are recorded, and the corresponding relationship between the movement amplitude of each component and the probability of air leakage is obtained. Before the subsequent ventilator moves significantly, the patient is prompted to reduce the number of air leaks; further, the image sensor records the patient's sleeping posture and the number of movements and turning over at night, and obtains a sleeping posture with less movement and turning over, and gives advice to the patient to reduce the number of movements during sleep and reduce the risk of air leakage.

[0050] In one embodiment of the present application, the type of sensor of the sensor module is obtained, and the corresponding appropriate data range is preset for various sensors. At the same time, the type of data adjusted by the current external device is obtained, and the data type collected by the sensor and the data type adjusted by the external device are matched, and the successfully matched sensor is set to be linked with the external device; when the data collected by the sensor exceeds the appropriate data range, the sensor is linked with the external device, and a corresponding instruction is sent according to the value exceeding the appropriate data range, so that the data collected by the sensor returns to the appropriate data range.

[0051] It should be noted that the external device includes a humidifier, an air conditioner with a temperature adjustment function, an air purifier, and other home medical devices, and the sensor module controls the external device by infrared remote control or the like; In this embodiment, the external device and the sensor are linked according to the type of data adjusted by the external device and the type of data collected by the sensor. When the data collected by the sensor is not within the normal data range, the external device is used for adjustment. For example, when the sensor detects that the temperature of the patient's exhaled gas is low, the sensor module controls the air conditioning heating, and the patient's ambient temperature rises, so that the patient's exhaled gas temperature returns to the normal range. A suitable environment helps to optimize the function of the ventilator and reduce the risk of worsening of the disease. It can also be linked with home oximeters, heart rate monitors, etc. When the data collected by the linked device is abnormal, the other linked devices are also tested to form a more comprehensive family health management system.

[0052] In one embodiment of the present application, the sensor module summarizes the collected data and sends a data report to the preset device at a preset time, and the content of the data report received by different types of preset devices is different; the first data report received by the patient-side device includes respiratory data and adjustment suggestions; the second data report received by the friend-side device includes respiratory data and suggested execution data; the third data report received by the doctor-side device includes respiratory data and data trends.

[0053] In one embodiment of the present application, all the third data report information is desensitized and sorted to obtain ventilator parameters corresponding to the improvement and deterioration of the condition under different disease states, and generate ventilator recommended parameters and alarm parameters under different disease states.

[0054] In this embodiment, patients, relatives, friends and doctors are integrated into the remote collaborative diagnosis and treatment process, and the treatment plan is adjusted in real time through intelligent analysis technology, and the disease is managed under the cooperation of multiple parties; wherein, the patient end receives the first data report, and helps the patient understand the effect of ventilator use through daily respiratory data comparison, timely discovers problems and actively improves equipment settings or environment, and enhances the patient's ability to actively participate in and improve treatment; the relatives and friends end receives the second data report, and judges the patient's self-management ability through the suggestion execution data, that is, the execution status of the suggestion after the patient is given the suggestion. For example, because the ventilator mask falls off several times, the adjustment suggestion gives a suggestion for adjusting the sleeping position. The suggestion execution data identifies that the patient's sleeping position has not changed, and the ventilator mask still falls off, indicating that the patient's self-management ability is poor. When the patient has difficulty in self-management or the ventilator accessories are aging and damaged, relatives and friends provide corresponding support to assist relatives and friends in supervising the patient's treatment treatment situation; the doctor side receives the third data report, and provides comprehensive data support to the doctor through various data trends, helping him to optimize the treatment plan and equipment settings, and respond to changes in the condition in advance; through this classified sending and targeted suggestions, it can better assist patients, relatives, friends and doctors to jointly manage the disease, improve the effectiveness of non-invasive ventilator treatment and the quality of life of patients; further, all the third data report information is desensitized and sorted, and the ventilator parameters corresponding to the improvement and deterioration of the condition under different condition states are obtained, and the ventilator recommended parameters and alarm parameters under different condition states are generated to identify the current condition stage of different patients, and generate the ventilator recommended parameters according to the ventilator parameters set for patients with improvement in similar condition stages; the alarm parameters are the ventilator parameters set for patients with deterioration in similar condition stages. When the ventilator parameters set by the patient are within the alarm parameter range, the patient is prompted.

[0055] In one embodiment of the present application, it also includes obtaining the patient's historical health data, including past medical history and medication records; generating personalized treatment recommendations based on the historical health data, including ventilator parameter adjustment recommendations; and adjusting the ventilator parameters in real time based on changes in the patient's airflow data and pressure data during the use of the ventilator.

[0056] In this embodiment, by combining the patient's historical health data (such as past medical history, medication records, etc.), personalized treatment suggestions are provided to the patient, including ventilator parameter adjustment suggestions. The ventilator parameters are personalized adjusted before use. Further, during the use of the ventilator, the patient can make corresponding parameter adjustments based on the patient's current breathing condition.

[0057] In the second aspect of this application, see Figure 3 and Figure 4 As shown, a universal monitoring system for a non-invasive ventilator is provided, comprising: A flow acquisition module is provided with a sensor module, wherein the sensor module is provided with at least one flow sensor in the ventilator pipeline, which is used to monitor the airflow changes in the pipeline in real time, and collect airflow data in real time through the flow sensor; A flow comparison module, which obtains or presets standard flow information of different ventilators, compares the airflow data with the standard flow information, and determines whether the current use of the ventilator is normal according to the change of the airflow data; The alarm prompt module triggers the alarm mechanism when it is determined that the use is abnormal, and sends a reminder message to the preset device. After receiving the reminder message, the preset device reminds the patient to check his own status and the status of the ventilator; The information transmission module includes a patient-side device, a relative-side device and a doctor-side device. The doctor-side device obtains the airflow data and remotely monitors the patient's ventilator usage based on the comparison data with the standard flow information.

[0058] In one embodiment of the present application, a pressure acquisition module is also included, wherein the sensor module is provided with at least one pressure sensor in the ventilator pipeline, which is used to monitor the pressure changes in the pipeline in real time, and to collect pressure data in real time through the pressure sensor; The alarm prompt module also includes, when judging that the current use of the ventilator is abnormal according to the airflow data, obtaining the current pressure data, and triggering different alarm mechanisms in combination with the pressure data, specifically including air leakage alarm and disease aggravation alarm; An air leakage alarm module, after triggering the air leakage alarm, dynamically adjusts the air flow output of the ventilator according to the degree of air leakage until the ventilator reaches a preset air flow output probability, and sends an air leakage reminder message to the patient-side device; The condition alarm module sends a reminder message of worsening condition to the patient-side device after triggering the alarm of worsening condition. If the patient fails to take action within a preset time, a reminder message is sent to the relatives and friends-side device and / or the doctor-side device.

[0059] In one embodiment of the present application, a multidimensional data module is also included. The sensor module also includes sensors with corresponding data collection functions arranged at corresponding positions of the ventilator according to the patient's needs for collecting different data, including temperature sensors, humidity sensors and gas analyzers, and the working status of the ventilator and the patient's breathing condition are monitored through the obtained multidimensional data.

[0060] In one embodiment of the present application, the multidimensional data module also includes a facial image submodule, and the sensor module also includes an image sensor. The image sensor collects the patient's facial image, and determines the patient's condition by identifying the expression features of the facial image through a machine learning algorithm. The expression features include relaxation type and discomfort type. At the same time, the facial image of the discomfort type is divided into discomfort levels according to the expression features. When it is identified that the facial image reaches a preset discomfort level, an alarm is triggered and / or the ventilator parameters are automatically adjusted.

[0061] In one embodiment of the present application, the multidimensional data module also includes a layout image submodule, and the image sensor also includes a device for collecting position images of various components of the ventilator. The position images are used to monitor the position changes of various components of the ventilator. When any component of the ventilator moves, the pressure data is obtained. If the pressure data triggers the air leakage alarm, the moving components of the ventilator and the movement amplitude of the components are recorded.

[0062] In one embodiment of the present application, the multidimensional data module also includes a device linkage submodule, which obtains the type of sensor of the sensor module, presets corresponding appropriate data ranges for various sensors, and simultaneously obtains the type of data adjusted by the current external device, matches the type of data collected by the sensor and the type of data adjusted by the external device, and sets a linkage with the successfully matched sensor; when the data collected by the sensor exceeds the appropriate data range, it is linked with the external device, and a corresponding instruction is sent according to the value exceeding the appropriate data range, so that the data collected by the sensor is restored to the appropriate data range.

[0063] In one embodiment of the present application, it also includes a data reporting module, the sensor module summarizes the collected data, and sends a data report to the preset device at a preset time, and the content of the data report received by different types of preset devices is different; the first data report received by the patient-side device includes respiratory data and adjustment suggestions; the second data report received by the friend-side device includes respiratory data and suggested execution data; the third data report received by the doctor-side device includes respiratory data and data trends.

[0064] In one embodiment of the present application, the data reporting module also includes a parameter adjustment submodule, which desensitizes and organizes all the third data report information, obtains ventilator parameters corresponding to improvement and deterioration of the condition under different disease states, and generates ventilator recommended parameters and alarm parameters under different disease states.

[0065] In one embodiment of the present application, a personalized module is also included to obtain the patient's historical health data, including past medical history and medication records; generate personalized treatment recommendations based on the historical health data, including ventilator parameter adjustment recommendations; and adjust the ventilator parameters in real time based on changes in the patient's airflow data and pressure data during the use of the ventilator.

[0066] It should be noted that the specific implementation of a universal monitoring system for a non-invasive ventilator in an embodiment of the present application refers to the specific implementation of a non-invasive ventilator monitoring method proposed in the first aspect of the aforementioned embodiment of the present application, and will not be repeated here.

[0067] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that an article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such article or device. In the absence of more restrictions, the elements defined by the sentence "includes..." do not exclude the existence of other identical elements in the article or device including the elements.

[0068] The above is a detailed introduction to the non-invasive ventilator monitoring method provided. This article uses specific examples to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the non-invasive ventilator monitoring method of the present application and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A non-invasive ventilator monitoring method, characterized in that: include: A sensor module is provided, wherein the sensor module is provided with at least one flow sensor in the ventilator pipeline, which is used to monitor the airflow changes in the pipeline in real time, and collect airflow data in real time through the flow sensor; Obtain or preset standard flow information of different ventilators, compare the airflow data with the standard flow information, and determine whether the current use of the ventilator is normal according to the change of the airflow data; When it is determined that the use is abnormal, the alarm mechanism is triggered and a reminder message is sent to the preset device. After receiving the reminder message, the preset device reminds the patient to check his own status and the status of the ventilator; The preset devices include patient-side devices, relatives-side devices and doctor-side devices. The doctor-side devices obtain the airflow data and remotely monitor the patient's ventilator usage based on the comparison data with the standard flow information.

2. A non-invasive ventilator monitoring method according to claim 1, characterized in that: The sensor module is provided with at least one pressure sensor in the ventilator pipeline, which is used to monitor the pressure changes in the pipeline in real time and collect pressure data in real time through the pressure sensor; When it is determined according to the airflow data that the current use of the ventilator is abnormal, the current pressure data is obtained, and different alarm mechanisms are triggered in combination with the pressure data, including air leakage alarm and disease aggravation alarm; After the air leakage alarm is triggered, the air flow output of the ventilator is dynamically adjusted according to the degree of air leakage until the ventilator reaches a preset air flow output probability, and an air leakage reminder message is sent to the patient-side device; After the aggravation alarm is triggered, a reminder message of aggravation of the condition is sent to the patient-side device. If the patient does not take action within a preset time, a reminder message is sent to the relatives and friends-side device and / or the doctor-side device.

3. A non-invasive ventilator monitoring method according to claim 2, characterized in that: The sensor module also includes sensors with corresponding data collection functions arranged at corresponding positions of the ventilator according to the patient's needs for collecting different data, including temperature sensors, humidity sensors and gas analyzers, and the working status of the ventilator and the patient's breathing condition are monitored through the obtained multi-dimensional data.

4. A non-invasive ventilator monitoring method according to claim 3, characterized in that: The sensor module also includes an image sensor, which collects facial images of the patient and determines the patient's condition by identifying the expression features of the facial images through a machine learning algorithm. The expression features include relaxation type and discomfort type. At the same time, the facial images of the discomfort type are divided into discomfort levels according to the expression features. When it is recognized that the facial image reaches a preset discomfort level, an alarm is triggered and / or the ventilator parameters are automatically adjusted.

5. A non-invasive ventilator monitoring method according to claim 4, characterized in that: The image sensor also includes collecting position images of various components of the ventilator, and the position images are used to monitor the position changes of various components of the ventilator. When any component of the ventilator moves, the pressure data is obtained. If the pressure data triggers the air leakage alarm, the moving components of the ventilator and the movement range of the components are recorded.

6. A non-invasive ventilator monitoring method according to claim 5, characterized in that: The type of sensor of the sensor module is obtained, and the corresponding appropriate data range is preset for various sensors. At the same time, the type of data adjusted by the current external device is obtained, and matching is performed according to the type of data collected by the sensor and the type of data adjusted by the external device. The successfully matched sensor is set to be linked with the external device; when the data collected by the sensor exceeds the appropriate data range, the sensor is linked with the external device, and a corresponding instruction is sent according to the value exceeding the appropriate data range, so that the data collected by the sensor is restored to the appropriate data range.

7. A non-invasive ventilator monitoring method according to any one of claims 1 to 6, characterized in that: The sensor module summarizes the collected data and sends a data report to the preset device at a preset time. The content of the data report received by different types of preset devices is different; the first data report received by the patient-side device includes respiratory data and adjustment suggestions; the second data report received by the friend-side device includes respiratory data and suggested execution data; the third data report received by the doctor-side device includes respiratory data and data trends.

8. A non-invasive ventilator monitoring method according to claim 7, characterized in that: All the third data report information is desensitized and sorted to obtain the ventilator parameters corresponding to the improvement and deterioration of the condition under different disease states, and generate the ventilator recommended parameters and alarm parameters under different disease states.

9. A non-invasive ventilator monitoring method according to any one of claims 1 to 6, characterized in that: It also includes obtaining the patient's historical health data, including past medical history and medication records; generating personalized treatment recommendations based on the historical health data, including ventilator parameter adjustment recommendations; and adjusting the ventilator parameters in real time based on changes in the patient's airflow data and pressure data during the use of the ventilator.

10. A universal monitoring system for non-invasive ventilators, characterized in that: include: A flow acquisition module is provided with a sensor module, wherein the sensor module is provided with at least one flow sensor in the ventilator pipeline, which is used to monitor the airflow changes in the pipeline in real time, and collect airflow data in real time through the flow sensor; A flow comparison module, which obtains or presets standard flow information of different ventilators, compares the airflow data with the standard flow information, and determines whether the current use of the ventilator is normal according to the change of the airflow data; The alarm prompt module triggers the alarm mechanism when it is determined that the use is abnormal, and sends a reminder message to the preset device. After receiving the reminder message, the preset device reminds the patient to check his own status and the status of the ventilator; The information transmission module includes a patient-side device, a relative-side device and a doctor-side device. The doctor-side device obtains the airflow data and remotely monitors the patient's ventilator usage based on the comparison data with the standard flow information.

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