Ventilator control method, apparatus, ventilator, storage medium and program product

Abstract

The application relates to a ventilator control method and device, a ventilator, a storage medium and a program product. The method comprises the following steps: receiving an original diaphragm electromyogram collected by an electrode of a ventilator in a current period; generating a ventilation control instruction according to the original diaphragm electromyogram in the current period based on running parameters of a previous period corresponding to the current period and a first controller; wherein the running parameters of the first period are initialization data; in response to the ventilation control instruction, controlling the ventilator to supply air, and sending the ventilation control instruction to a second controller in the ventilator, so that the second controller determines the running parameters in the current period according to the original diaphragm electromyogram in the current period; and receiving the running parameters in the current period fed back by the second controller. The method can improve the accuracy of ventilator control and improve the response efficiency of the ventilator on this basis.

Description

Technical Field

[0001] This application relates to the field of ventilator control technology, and in particular to a ventilator control method, device, ventilator, storage medium, and program product. Background Technology

[0002] With the development of ventilator technology, the electromyographic signal of the diaphragm (Edi) triggering technology has emerged. This technology can directly monitor the patient's breathing intention, effectively improve the synchronization between the ventilator and the patient, and is of great significance for improving the prognosis of critically ill patients and reducing respiratory-related complications.

[0003] However, Edi signals are weak and easily affected by noise such as electrocardiogram interference and power frequency interference. Furthermore, the Edi signals of the same patient vary greatly at different pathological stages. Current Edi triggering technology is difficult to adapt to such changes in state, resulting in inaccurate and inefficient ventilator control, which urgently needs to be addressed. Summary of the Invention

[0004] Therefore, it is necessary to provide a ventilator control method, device, ventilator, storage medium, and program product to address the above-mentioned technical problems, which can improve the accuracy of ventilator control and, on this basis, improve the ventilator's response efficiency.

[0005] In a first aspect, this application provides a ventilator control method, including a first controller applied in a ventilator, comprising:

[0006] Receive the raw diaphragmatic electrical signal acquired by the ventilator electrodes during the current cycle;

[0007] Based on the operating parameters of the first controller in the current cycle corresponding to the previous cycle, ventilation control commands are generated according to the original diaphragm electrical signals in the current cycle; wherein, the operating parameters of the first cycle are the initialization data;

[0008] In response to a ventilation control command, the ventilator is controlled to deliver air, and a ventilation control command is sent to a second controller in the ventilator so that the second controller can determine the operating parameters for the current cycle based on the original diaphragm electrical signal for the current cycle.

[0009] Receive operating parameters for the current cycle from the second controller.

[0010] In one embodiment, ventilation control commands are generated based on the raw diaphragmatic electrical signal in the current cycle, including:

[0011] Extract the effective diaphragmatic electrical signal from the raw diaphragmatic electrical signal in the current cycle;

[0012] Based on the effective diaphragmatic electrical signal in the current cycle, ventilation control commands are generated.

[0013] In one embodiment, a ventilation control command is generated based on the effective diaphragmatic electrical signal in the current cycle, including at least one of the following:

[0014] If a ventilation control command is generated when there is a signal intensity exceeding the trigger intensity threshold in the effective diaphragmatic electrical signal of the current cycle;

[0015] Based on the effective diaphragmatic electrical signals in the current cycle and the effective diaphragmatic electrical signals in historical cycles, the signal change rate of the effective diaphragmatic electrical signals is determined, and ventilation control commands are generated when the signal change rate exceeds the trigger change rate threshold.

[0016] In one embodiment, the operating parameters include filter parameters; extracting the effective diaphragmatic electrical signal from the original diaphragmatic electrical signal in the current cycle includes:

[0017] The original diaphragm electrical signal in the current cycle is filtered based on the filter parameters to obtain the filtered signal in the current cycle.

[0018] Extract the diaphragm electrical signal envelope from the filtered signal in the current cycle to obtain the effective diaphragm electrical signal in the current cycle.

[0019] In one embodiment, the operating parameters include ventilation reference parameters; based on the effective diaphragmatic electrical signal in the current cycle, ventilation control commands are generated, including:

[0020] Based on the effective diaphragmatic electrical signals in the current cycle, ventilation reference data is generated;

[0021] Based on ventilation reference data and ventilation reference parameters, ventilation control commands are generated.

[0022] Secondly, this application provides a ventilator control method, applied to a second controller in a ventilator, comprising:

[0023] The system receives the raw diaphragmatic electrical signal collected by the electrodes of the ventilator in the current cycle, as well as the ventilation control command sent by the first controller in the ventilator. The ventilation control command is a command generated by the first controller based on the operating parameters of the first controller in the current cycle corresponding to the previous cycle, and according to the raw diaphragmatic electrical signal in the current cycle, to control the ventilator to deliver air.

[0024] Based on the original diaphragm electrical signal in the current cycle, determine the operating parameters for the current cycle;

[0025] The operating parameters for the current cycle are sent to the first controller to generate the ventilation control command for the next cycle.

[0026] In one embodiment, the operating parameters include a trigger intensity threshold; determining the operating parameters for the current cycle based on the original diaphragm electrical signal in the current cycle includes:

[0027] Extract the effective diaphragmatic electrical signal from the raw diaphragmatic electrical signal in the current cycle;

[0028] Determine the average signal intensity of the effective diaphragmatic electrical signal in the current cycle;

[0029] The trigger strength threshold for the current period is determined based on the average signal strength and the trigger strength threshold corresponding to the previous period.

[0030] In one embodiment, the operating parameters include a trigger rate of change threshold; determining the operating parameters for the current cycle based on the original diaphragm electrical signal for the current cycle includes:

[0031] Extract the effective diaphragmatic electrical signal from the raw diaphragmatic electrical signal in the current cycle;

[0032] Based on the effective diaphragmatic electrical signals in the current cycle and the effective diaphragmatic electrical signals in historical cycles, the statistical characteristics and comprehensive sensitivity of the effective diaphragmatic electrical signals are determined.

[0033] Based on statistical characteristics and comprehensive sensitivity, determine the trigger change rate threshold for the current cycle.

[0034] In one embodiment, the comprehensive sensitivity of the effective diaphragm electrical signal is determined based on the effective diaphragm electrical signal in the current cycle and the effective diaphragm electrical signal in historical cycles, including:

[0035] Based on the effective diaphragmatic electrical signals in the current cycle and the effective diaphragmatic electrical signals in historical cycles, the signal quality data of the effective diaphragmatic electrical signals are determined.

[0036] Based on the effective diaphragmatic electrical signals in the same cycle, the respiratory rate in the corresponding cycle is determined, and the trend data of respiratory rate changes in different cycles are determined; different cycles include the current cycle and historical cycles;

[0037] The overall sensitivity of the effective diaphragmatic electrical signal is determined based on signal quality data and / or trend data.

[0038] In one embodiment, the operating parameters include filter parameters; determining the operating parameters for the current cycle based on the raw diaphragm electrical signal for the current cycle includes:

[0039] Spectral analysis is performed on the original diaphragm electrical signal in the current cycle to obtain the initial filter parameters of the original diaphragm electrical signal in the current cycle;

[0040] Determine the initial cutoff frequency that matches the initial filter parameters;

[0041] The initial cutoff frequency is smoothed to obtain the filter parameters for the current period.

[0042] Thirdly, this application also provides a ventilator control device, comprising a first controller configured in the ventilator, including:

[0043] The first receiving module is used to receive the raw diaphragmatic electrical signals collected by the electrodes of the ventilator during the current cycle;

[0044] The instruction generation module is used to generate ventilation control instructions based on the operating parameters of the first controller in the current cycle corresponding to the previous cycle and the original diaphragm electrical signal in the current cycle; wherein, the operating parameters of the first cycle are the initialization data;

[0045] The instruction response module is used to respond to ventilation control instructions, control the ventilator to deliver air, and send ventilation control instructions to the second controller in the ventilator so that the second controller can determine the operating parameters of the current cycle based on the original diaphragm electrical signal of the current cycle.

[0046] The second receiving module is used to receive the operating parameters of the current cycle from the second controller.

[0047] Fourthly, this application also provides a ventilator control device, a second controller disposed in the ventilator, comprising:

[0048] The third receiving module is used to receive the raw diaphragmatic electrical signal collected by the electrodes of the ventilator in the current cycle, as well as the ventilation control command sent by the first controller in the ventilator. The ventilation control command is a command generated by the first controller based on the operating parameters of the first controller in the current cycle corresponding to the previous cycle, and according to the raw diaphragmatic electrical signal in the current cycle to control the ventilator to deliver air.

[0049] The parameter determination module is used to determine the operating parameters for the current cycle based on the original diaphragm electrical signal in the current cycle.

[0050] The parameter sending module is used to send the operating parameters for the current cycle to the first controller, which is used to generate the ventilation control command for the next cycle.

[0051] Fifthly, this application also provides a ventilator, including a first controller and a second controller;

[0052] The first controller and the second controller respectively receive the raw diaphragmatic electrical signals collected by the electrodes of the ventilator during the current cycle;

[0053] The first controller generates ventilation control commands based on the operating parameters of the previous cycle corresponding to the current cycle and the original diaphragm electrical signal in the current cycle; wherein, the operating parameters of the first cycle are the initialization data;

[0054] The first controller responds to ventilation control commands and controls the ventilator to deliver air;

[0055] The second controller determines the operating parameters for the current cycle based on the original diaphragm electrical signal and sends the operating parameters for the current cycle to the first controller.

[0056] Sixthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0057] Receive the raw diaphragmatic electrical signal acquired by the ventilator electrodes during the current cycle;

[0058] Based on the operating parameters of the first controller in the current cycle corresponding to the previous cycle, ventilation control commands are generated according to the original diaphragm electrical signals in the current cycle; wherein, the operating parameters of the first cycle are the initialization data;

[0059] In response to a ventilation control command, the ventilator is controlled to deliver air, and a ventilation control command is sent to a second controller in the ventilator so that the second controller can determine the operating parameters for the current cycle based on the original diaphragm electrical signal for the current cycle.

[0060] Receive operating parameters for the current cycle from the second controller.

[0061] In a seventh aspect, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0062] The system receives the raw diaphragmatic electrical signal collected by the electrodes of the ventilator in the current cycle, as well as the ventilation control command sent by the first controller in the ventilator. The ventilation control command is a command generated by the first controller based on the operating parameters of the first controller in the current cycle corresponding to the previous cycle, and according to the raw diaphragmatic electrical signal in the current cycle, to control the ventilator to deliver air.

[0063] Based on the original diaphragm electrical signal in the current cycle, determine the operating parameters for the current cycle;

[0064] The operating parameters for the current cycle are sent to the first controller to generate the ventilation control command for the next cycle.

[0065] Eighthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0066] Receive the raw diaphragmatic electrical signal acquired by the ventilator electrodes during the current cycle;

[0067] Based on the operating parameters of the first controller in the current cycle corresponding to the previous cycle, ventilation control commands are generated according to the original diaphragm electrical signals in the current cycle; wherein, the operating parameters of the first cycle are the initialization data;

[0068] In response to a ventilation control command, the ventilator is controlled to deliver air, and a ventilation control command is sent to a second controller in the ventilator so that the second controller can determine the operating parameters for the current cycle based on the original diaphragm electrical signal for the current cycle.

[0069] Receive operating parameters for the current cycle from the second controller.

[0070] Ninthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0071] The system receives the raw diaphragmatic electrical signal collected by the electrodes of the ventilator in the current cycle, as well as the ventilation control command sent by the first controller in the ventilator. The ventilation control command is a command generated by the first controller based on the operating parameters of the first controller in the current cycle corresponding to the previous cycle, and according to the raw diaphragmatic electrical signal in the current cycle, to control the ventilator to deliver air.

[0072] Based on the original diaphragm electrical signal in the current cycle, determine the operating parameters for the current cycle;

[0073] The operating parameters for the current cycle are sent to the first controller to generate the ventilation control command for the next cycle.

[0074] The aforementioned ventilator control method, device, ventilator, storage medium, and program product, during the ventilator control process, employ a first controller and a second controller, which work together to achieve overall control of the ventilator. Specifically, the first controller receives the raw diaphragmatic electrical signal collected by the ventilator electrodes in the current cycle and generates a ventilation control command based on the operating parameters of the previous cycle and the raw diaphragmatic electrical signal of the current cycle. The first controller responds to this ventilation control command by controlling the ventilator to deliver air and simultaneously sends the ventilation control command to the second controller, enabling the second controller to determine the operating parameters for the current cycle based on the raw diaphragmatic electrical signal. Subsequently, the first controller receives the operating parameters of the current cycle from the second controller for generating the ventilation control command for the next cycle. In this process, the first and second controllers have clearly defined roles: the first controller generates the ventilation control command based on the operating parameters and the raw diaphragmatic electrical signal; the second controller updates the operating parameters based on the ventilation control command and the raw diaphragmatic electrical signal. Because the first controller focuses on generating ventilation control commands and does not participate in the calculation of operating parameters, it can effectively improve the efficiency of command generation. Meanwhile, the second controller periodically updates the operating parameters, making them more closely match the dynamic changes of the original diaphragmatic electrical signals, thus making the ventilation control commands generated by the first controller more accurate and reliable. In other words, the above-mentioned ventilator control method can improve the accuracy of ventilator control and, on this basis, improve the ventilator's response efficiency. Attached Figure Description

[0075] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0076] Figure 1 This is a diagram illustrating the application environment of a ventilator control method in one embodiment;

[0077] Figure 2 This is a flowchart illustrating a ventilator control method in one embodiment;

[0078] Figure 3 This is a flowchart illustrating the ventilation control command generation steps in one embodiment;

[0079] Figure 4 This is a flowchart illustrating the ventilator control method in another embodiment;

[0080] Figure 5 This is a flowchart illustrating the steps for determining runtime parameters in one embodiment;

[0081] Figure 6 This is a flowchart illustrating the steps for determining operating parameters in another embodiment;

[0082] Figure 7 This is a flowchart illustrating the steps for determining operating parameters in another embodiment;

[0083] Figure 8 This is a timing diagram of a ventilator control method in one embodiment;

[0084] Figure 9 This is a structural block diagram of a ventilator control device in one embodiment;

[0085] Figure 10 This is a structural block diagram of the ventilator control device in another embodiment;

[0086] Figure 11 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0087] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0088] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0089] The ventilator control method provided in this application embodiment can be applied to, for example... Figure 1In the application environment shown, the first controller 102 communicates with the second controller 104 via a network. The first controller 102 and the second controller 104 respectively receive the raw diaphragmatic electrical signals collected by the ventilator electrodes in the current cycle. Based on the operating parameters corresponding to the previous cycle, the first controller 102 generates a ventilation control command according to the raw diaphragmatic electrical signals of the current cycle; the operating parameters of the first cycle are initialization data. In response to the ventilation control command, the first controller 102 controls the ventilator to deliver air. The second controller 104 determines the operating parameters for the current cycle based on the raw diaphragmatic electrical signals and sends these parameters to the first controller 102. The first controller can be understood as a control microcontroller unit (MCU), and the second controller can be understood as a monitoring MCU.

[0090] In one exemplary embodiment, such as Figure 2 As shown, a ventilator control method is provided, which is applied to... Figure 1 Taking the first controller 102 as an example, the explanation includes the following steps:

[0091] S210 receives the raw diaphragmatic electrical signal acquired by the ventilator electrodes during the current cycle.

[0092] The electrodes used in ventilators are typically diaphragmatic electromyography (EMG) electrodes, such as dedicated surface electrodes or esophageal electrodes. Their working principle is to directly pick up and conduct bioelectrical activity originating from the diaphragm by attaching them to the body surface or inserting them into specific locations in the esophagus. Correspondingly, the so-called raw diaphragmatic electrical signal is the diaphragmatic electrical signal directly acquired by the ventilator electrodes.

[0093] Typically, the electrodes on a ventilator periodically sample the diaphragm's electrical signals; correspondingly, the current period is the sampling period at the current moment. The duration of a single sampling period can be determined based on actual needs, human experience, or extensive testing; this application does not impose any limitations on this.

[0094] In one optional embodiment, a signal transmission line is deployed between the ventilator's electrodes and the first controller. After the ventilator's electrodes complete the acquisition of the raw diaphragm electrical signal for the current cycle, they will send the raw diaphragm electrical signal acquired in the current cycle to the first controller through the signal transmission line. Correspondingly, the first controller receives the raw diaphragm electrical signal acquired by the ventilator's electrodes in the current cycle through the signal transmission line.

[0095] Optionally, to ensure the availability of the raw diaphragm electrical signal, the first controller can perform a basic verification after receiving the raw diaphragm electrical signal, and refer to subsequent steps based on the verified raw diaphragm electrical signal. For example, a preliminary level detection can be performed to determine whether the signal transmission line is normal; another example is a period attribution verification to determine whether the raw diaphragm electrical signal was collected in the current period, and signals collected outside the current period can be discarded.

[0096] S220 generates ventilation control commands based on the operating parameters of the first controller in the current cycle corresponding to the previous cycle and the original diaphragm electrical signal in the current cycle.

[0097] Among them, the operating parameters can be understood as the parameters generated when or after the ventilation control command is generated in the previous cycle corresponding to the current cycle. They are used as the basis for evaluation to analyze and judge the raw diaphragm electrical signals collected in the current cycle.

[0098] To ensure that ventilation control commands adapt to changes in the patient's condition and improve their accuracy, different operating parameters are used for different cycles in this embodiment. For example, when generating ventilation control commands in the current cycle, the operating parameters of the previous cycle are used; correspondingly, when generating ventilation control commands in the next cycle, the operating parameters of the current cycle are used. The operating parameters for the first cycle are initialization data, which can be determined based on human experience or through extensive experimentation; this application does not impose any limitations on this.

[0099] Ventilation control commands are used to guide the ventilator to perform corresponding ventilation actions in order to provide appropriate respiratory support for the patient. Ventilation control commands may carry control information such as the timing of air delivery, ventilation pressure, and ventilation flow rate.

[0100] In one optional implementation, the raw diaphragmatic electrical signal can be processed based on preset signal processing logic to generate signal correlation data. The operating parameters of the previous cycle are then compared with the signal correlation data of the current cycle, and ventilation control commands are generated based on the comparison results. The preset signal processing logic can be determined based on human experience or through extensive experimentation; this application does not impose any limitations on it.

[0101] In another alternative implementation, the operating parameters from the previous cycle and the raw diaphragmatic electrical signal from the current cycle can be input into the command generation model to generate ventilation control commands. The command generation model can be built based on common neural networks, which will not be elaborated upon here. Furthermore, during the training of the command generation model, sample diaphragmatic electrical signals and sample operating parameters can be input into the model to generate predictive control commands. Then, based on the ventilation control command labels corresponding to the predictive control commands and the sample electrical signals, the command generation model is trained to improve its command generation accuracy.

[0102] S230, in response to a ventilation control command, controls the ventilator to deliver air and sends a ventilation control command to a second controller in the ventilator so that the second controller determines the operating parameters for the current cycle based on the original diaphragmatic electrical signal for the current cycle.

[0103] In one optional implementation, the first controller can parse the control information such as the timing of air delivery, ventilation pressure and ventilation flow rate in the ventilation control command, and output corresponding drive signals to the airway execution module of the ventilator according to the parsing results, so as to control the airway execution module to start ventilation and complete the air delivery action according to the command requirements.

[0104] In this embodiment, after the first controller generates a ventilation control command, it sends the ventilation control command to the second controller. The process by which the second controller determines the operating parameters for the current cycle based on the original diaphragm electrical signal for the current cycle is described in the following embodiments.

[0105] Optionally, the original diaphragmatic electrical signal in the current cycle that the second controller uses to determine the operating parameters in the current cycle may be sent along with the ventilation control command sent by the first controller, or it may be sent by the electrodes of the ventilator. This application does not impose any limitations on this.

[0106] S240 receives operating parameters for the current cycle from the second controller.

[0107] In the aforementioned ventilator control method, a first controller and a second controller are configured during the ventilator control process, and the overall control of the ventilator is achieved through their collaborative interaction. Specifically, the first controller receives the raw diaphragmatic electrical signal collected by the ventilator electrodes in the current cycle, and generates a ventilation control command based on the operating parameters of the previous cycle corresponding to the current cycle, combined with the raw diaphragmatic electrical signal of the current cycle. The first controller responds to this ventilation control command by controlling the ventilator to perform the air delivery action, and simultaneously sends the ventilation control command to the second controller, enabling the second controller to determine the operating parameters for the current cycle based on the raw diaphragmatic electrical signal of the current cycle. Subsequently, the first controller receives the operating parameters of the current cycle fed back by the second controller, which are used to generate the ventilation control command for the next cycle. In this process, the first and second controllers have a clear division of labor: the first controller generates the ventilation control command based on the operating parameters and the raw diaphragmatic electrical signal; the second controller updates the operating parameters based on the ventilation control command and the raw diaphragmatic electrical signal. Because the first controller focuses on generating ventilation control commands and does not participate in the calculation of operating parameters, it can effectively improve the efficiency of command generation. Meanwhile, the second controller periodically updates the operating parameters, making them more closely match the dynamic changes of the original diaphragmatic electrical signals, thus making the ventilation control commands generated by the first controller more accurate and reliable. In other words, the above-mentioned ventilator control method can improve the accuracy of ventilator control and, on this basis, improve the ventilator's response efficiency.

[0108] Based on the technical solutions of the above embodiments, this application also provides an optional embodiment. In this optional embodiment, the process of generating ventilation control commands based on the original diaphragm electrical signals in the current cycle is refined.

[0109] See Figure 3 The ventilation control command generation steps shown include:

[0110] S310, extract the effective diaphragmatic electrical signal from the original diaphragmatic electrical signal in the current cycle.

[0111] The so-called effective diaphragmatic electrical signal can be understood as a signal that reflects the patient's true breathing intention.

[0112] In one alternative implementation, the original diaphragmatic electrical signals can be filtered according to a preset amplitude threshold, and signal segments with amplitudes within the threshold range can be identified as valid diaphragmatic electrical signals.

[0113] In another alternative implementation, the original diaphragm electrical signal can be filtered to remove electrocardiogram interference, power frequency interference, and motion artifacts, and the filtered signal can be used as the effective diaphragm electrical signal.

[0114] For example, the operating parameters include filter parameters, which can be used to filter the original diaphragm electrical signal in the current cycle to obtain a filtered signal in the current cycle; the diaphragm electrical signal envelope is extracted from the filtered signal in the current cycle to obtain the effective diaphragm electrical signal in the current cycle. The filter parameters may include at least one of a filter type and a cutoff frequency. The filter type may include low-pass filtering, high-pass filtering, and power frequency notch filtering, etc.; the cutoff frequency is used to limit the frequency range of the filtered signal.

[0115] For example, based on the filter type in the filter parameters, the original diaphragm electrical signal is filtered, removing signals of other filter types besides the desired type and retaining only the filtered signal of the desired type. As another example, based on the cutoff frequency in the filter parameters, the original diaphragm electrical signal is filtered, removing signals of other frequencies besides the desired frequency and retaining only the filtered signal of the desired frequency.

[0116] Optionally, the method for extracting the diaphragm electrical signal envelope from the filtered signal in the current period can be any common envelope extraction method, and no limitation is made here. For example, the filtered signal can be rectified and smoothed. First, the filtered signal is rectified by full-wave rectification to obtain a positive amplitude sequence, and then the rectified signal is smoothed and fitted by low-pass smoothing filter to obtain a stable and continuous diaphragm electrical signal envelope. This envelope is then determined as the effective diaphragm electrical signal in the current period.

[0117] In this embodiment, based on the filter parameters in the operating parameters, irrelevant signals in the original diaphragmatic electrical signal are filtered out, and the diaphragmatic electrical signal envelope in the filtered signal is used as the effective diaphragmatic electrical signal to participate in the generation process of ventilation control commands. On the one hand, it can accurately remove irrelevant signals such as ECG interference, power frequency interference, and motion artifacts in the original diaphragmatic electrical signal, highlighting the effective feature components that reflect the patient's true breathing intention. On the other hand, by extracting the diaphragmatic electrical signal envelope, the effective diaphragmatic electrical signal can be made smoother and more stable, avoiding amplitude fluctuations from interfering with ventilation control judgment, thereby improving the accuracy and reliability of ventilation control command generation.

[0118] S320 generates ventilation control commands based on the effective diaphragmatic electrical signals in the current cycle.

[0119] In one alternative implementation, a ventilation control command may be generated if the effective diaphragmatic electrical signal in the current cycle contains a signal strength exceeding a trigger strength threshold. The trigger strength threshold is used to constrain the signal strength of the effective diaphragmatic electrical signal and can be determined based on human experience or through extensive experimentation; this application does not impose any limitations on this.

[0120] For example, the peak intensity of the effective diaphragmatic electrical signal in the current cycle can be extracted, and the peak intensity of the signal can be compared with the trigger intensity threshold. When the peak intensity of the signal is greater than the trigger intensity threshold, it is confirmed that the ventilation triggering condition is met, and a ventilation control command is generated.

[0121] In this embodiment, ventilation control commands are generated only when the signal strength exceeds the trigger strength threshold, which can effectively reduce false triggers and missed triggers, synchronize the timing of ventilator ventilation with the patient's actual breathing needs, and thus improve the accuracy of ventilation control command generation.

[0122] In another alternative implementation, the signal change rate of the effective diaphragm electrical signal can be determined based on the effective diaphragm electrical signal in the current cycle and the effective diaphragm electrical signal in historical cycles, and a ventilation control command can be generated if the signal change rate exceeds the trigger change rate threshold.

[0123] The signal change rate is used to characterize the trend of the effective diaphragmatic electrical signal in the current cycle relative to the effective diaphragmatic electrical signal in historical cycles. The trigger change rate threshold is used to constrain the signal change rate of the effective diaphragmatic electrical signal. It can be determined based on human experience or through a large number of experiments, and this application does not impose any limitations on it.

[0124] For example, the effective diaphragmatic electrical signal in the current cycle and the effective diaphragmatic electrical signal in historical cycles can be acquired. Based on the difference in signal intensity and the time difference between the two, the signal change rate of the effective diaphragmatic electrical signal is calculated. The calculated signal change rate is then compared with the trigger change rate threshold in the operating parameters. If the signal change rate exceeds the trigger change rate threshold, a ventilation control command is generated.

[0125] In this embodiment, since the rate of change of the signal can reflect the rate of increase of the patient's breathing and the trend of inspiratory triggering, generating ventilation control commands based on the rate of change of the signal can effectively identify the patient's rapid inspiratory effort and effective breathing triggering intention, thereby improving the accuracy of ventilation control command generation.

[0126] In another alternative implementation, the operating parameters include ventilation reference parameters. Accordingly, ventilation reference data can be generated based on the effective diaphragmatic electrical signal in the current cycle; and ventilation control commands can be generated based on the ventilation reference data and ventilation reference parameters.

[0127] Ventilation reference parameters can be understood as various reference bases used to generate ventilation control commands; ventilation reference data can be understood as characteristic data that can reflect the signal characteristics of the effective diaphragmatic electrical signal under the current cycle.

[0128] Optionally, ventilation reference data for different dimensions can correspond to ventilation reference parameters for the respective dimensions. For example, ventilation reference data may include the aforementioned trigger intensity threshold, with the corresponding ventilation reference data representing the signal intensity of the effective diaphragmatic electrical signal; ventilation reference data may also include the aforementioned trigger rate of change threshold, with the corresponding ventilation reference data representing the signal rate of change of the effective diaphragmatic electrical signal; ventilation reference data may further include a signal integration threshold, with the corresponding ventilation reference data representing the electrical signal integration of the effective diaphragmatic electrical signal; and ventilation reference data may further include a signal quality score threshold, with the corresponding ventilation reference data representing the signal quality score of the effective diaphragmatic electrical signal.

[0129] It should be noted that the processes for generating ventilation control commands based on the trigger intensity threshold and the signal intensity of the effective diaphragmatic electrical signal, and for generating ventilation control commands based on the trigger rate of change threshold and the signal rate of change of the effective diaphragmatic electrical signal, have been described in the above embodiments and will not be repeated here. The signal integration threshold and quality score threshold can both be determined based on human experience or extensive experimentation, and are not limited here. The process for determining the electrical signal integration and signal quality score of the effective diaphragmatic electrical signal is described in the following embodiments.

[0130] In the above embodiments, by extracting the effective diaphragmatic electrical signal from the original diaphragmatic electrical signal, interference noise can be filtered out while retaining effective features that reflect the patient's true breathing intention. Generating ventilation control commands based on the effective diaphragmatic electrical signal can effectively avoid false triggering, improve patient-ventilator synchronization and control accuracy, and make ventilator ventilation more closely match the patient's actual breathing needs, that is, improve the precision of ventilation control commands.

[0131] In one exemplary embodiment, such as Figure 4 As shown, a ventilator control method is provided, which is applied to... Figure 1 Taking the second controller 104 as an example, the following steps are included:

[0132] S410 receives the raw diaphragmatic electrical signal collected by the electrodes of the ventilator during the current cycle, as well as the ventilation control command sent by the first controller in the ventilator.

[0133] The ventilation control command is a command generated by the first controller based on the operating parameters of the first controller in the current cycle corresponding to the previous cycle, and according to the original diaphragmatic electrical signal in the current cycle, to control the ventilator's air delivery. The process of the first controller generating the ventilation control command has been described in the above embodiments and will not be repeated here. The ventilation control command is used to instruct the second controller to generate the operating parameters for the current cycle.

[0134] S420 determines the operating parameters for the current cycle based on the original diaphragm electrical signal during the current cycle.

[0135] In one alternative implementation, the raw diaphragmatic electroencephalogram (EEG) signal can be input into the running parameter generation model to generate running parameters for the current cycle. The running parameter generation model can be built based on common neural networks, which will not be elaborated upon here. Furthermore, during the training of the running parameter generation model, sample diaphragmatic EEG signals can be input into the model to generate predicted running parameters. The model is then trained based on the running parameter labels corresponding to the sample diaphragmatic EEG signals and the predicted running parameters to improve the model's accuracy.

[0136] S430 sends the operating parameters for the current cycle to the first controller for generating the ventilation control command for the next cycle.

[0137] In the aforementioned ventilator control method, a first controller and a second controller are configured during the ventilator control process, and the overall control of the ventilator is achieved through their collaborative interaction. Specifically, the first controller receives the raw diaphragmatic electrical signal collected by the ventilator electrodes in the current cycle, and generates a ventilation control command based on the operating parameters of the previous cycle corresponding to the current cycle, combined with the raw diaphragmatic electrical signal of the current cycle. The first controller responds to this ventilation control command by controlling the ventilator to perform the air delivery action, and simultaneously sends the ventilation control command to the second controller, enabling the second controller to determine the operating parameters for the current cycle based on the raw diaphragmatic electrical signal of the current cycle. Subsequently, the first controller receives the operating parameters of the current cycle fed back by the second controller, which are used to generate the ventilation control command for the next cycle. In this process, the first and second controllers have a clear division of labor: the first controller generates the ventilation control command based on the operating parameters and the raw diaphragmatic electrical signal; the second controller updates the operating parameters based on the ventilation control command and the raw diaphragmatic electrical signal. Because the first controller focuses on generating ventilation control commands and does not participate in the calculation of operating parameters, it can effectively improve the efficiency of command generation. Meanwhile, the second controller periodically updates the operating parameters, making them more closely match the dynamic changes of the original diaphragmatic electrical signals, thus making the ventilation control commands generated by the first controller more accurate and reliable. In other words, the above-mentioned ventilator control method can improve the accuracy of ventilator control and, on this basis, improve the ventilator's response efficiency.

[0138] Based on the technical solutions of the above embodiments, this application also provides an optional embodiment. In this optional embodiment, when the operating parameters include a trigger intensity threshold, the process of determining the operating parameters for the current cycle based on the original diaphragm electrical signal of the current cycle is described in detail.

[0139] See Figure 5 The steps for determining the operating parameters shown include:

[0140] S510, extract the effective diaphragmatic electrical signal from the raw diaphragmatic electrical signal in the current cycle.

[0141] In this embodiment, the method by which the second controller extracts the effective diaphragm electrical signal from the original diaphragm electrical signal in the current cycle can be the same as the extraction method of the first controller, and will not be described in detail here.

[0142] Optionally, to further reduce the computing power overhead of the first controller, the second controller can send the extracted effective diaphragm electrical signals to the first controller, avoiding the first controller from performing calculations related to the extraction of effective diaphragm electrical signals, thereby reducing computing power consumption and improving the generation efficiency of ventilation control commands.

[0143] S520 determines the average signal intensity of the effective diaphragmatic electrical signal in the current cycle.

[0144] Among them, the effective diaphragm electrical signal corresponds to multiple sampling points, and the signal intensity of the effective diaphragm electrical signal is different at different sampling points.

[0145] In one optional implementation, the number of sampling points included in the current period and the signal intensity corresponding to each sampling point can be determined, and then the ratio of the sum of the signal intensities corresponding to each sampling point to the number of sampling points can be used as the average signal intensity of the effective diaphragmatic electrical signal in the current period.

[0146] In another alternative implementation, the average signal intensity of the effective diaphragmatic electrical signal in the current period can be dynamically tracked based on an exponentially weighted moving average algorithm to determine the average signal intensity of the effective diaphragmatic electrical signal in the current period.

[0147] For example, the effective diaphragmatic electrical signal within the current cycle can be extracted by identifying the end of expiration of the patient's current respiratory cycle. The extracted effective diaphragmatic electrical signal can be squared, summed, averaged, and squared to obtain the root mean square value of the effective diaphragmatic electrical signal at the corresponding moment. This root mean square value can then be used as the average signal intensity of the effective diaphragmatic electrical signal in the current cycle.

[0148] S530 determines the trigger strength threshold for the current period based on the average signal strength and the trigger strength threshold of the previous period corresponding to the current period.

[0149] In one alternative implementation, the average signal intensity of the effective diaphragm electrical signal in the current cycle can be weighted and calculated with the trigger intensity threshold of the previous cycle, and the trigger intensity threshold in the current cycle can be determined based on the weighted fusion result.

[0150] For example, the trigger strength threshold for the current period can be determined based on the following formula:

[0151] B(t) = α · B(t-1) + (1-α) · RMS_end-tidal(t);

[0152] In the formula, B(t) is the trigger intensity threshold at time t, which is also the trigger intensity threshold in the current cycle; B(t-1) is the trigger intensity threshold at time t-1, which is also the trigger intensity threshold in the previous cycle corresponding to the current cycle; α is the smoothing coefficient, which can be determined based on human experience or through a large number of experiments, and is usually set between 0.85 and 0.95 to ensure that the trigger intensity threshold in the current cycle can drift slowly with the average signal intensity in the current cycle, and will not jump drastically due to a single abnormal fluctuation; RMS_end-tidal(t) is the average signal intensity of the effective diaphragmatic electrical signal in the current cycle.

[0153] In the above embodiments, the trigger strength threshold is not directly a fixed value, but is adjusted according to the average signal strength of the current cycle to obtain the trigger strength threshold of the previous cycle corresponding to the current cycle. This allows the trigger strength threshold to match the patient's state. For example, when the patient's position changes or the electrode undergoes a slight displacement that causes a change in the static DC bias of the signal, the trigger strength threshold of the current cycle can automatically compensate for this drift to ensure the accuracy of the trigger reference point.

[0154] Based on the technical solutions of the above embodiments, this application also provides an optional embodiment. In this optional embodiment, when the operating parameters include a trigger change rate threshold, the process of determining the operating parameters for the current cycle based on the original diaphragm electrical signal of the current cycle is described in detail.

[0155] See Figure 6 The steps for determining the operating parameters shown include:

[0156] S610, extract the effective diaphragmatic electrical signal from the original diaphragmatic electrical signal in the current cycle.

[0157] In this embodiment, the method by which the second controller extracts the effective diaphragm electrical signal from the original diaphragm electrical signal in the current cycle can be the same as the extraction method of the first controller, and will not be described in detail here.

[0158] S620 determines the statistical characteristics and overall sensitivity of the effective diaphragm electrical signal based on the effective diaphragm electrical signal in the current cycle and the effective diaphragm electrical signal in historical cycles.

[0159] The number of historical cycles can be determined based on human experience or through a large number of experiments. For example, the historical cycle may include a preset number of effective diaphragmatic electrical signals, such as 20.

[0160] The statistical characteristics include at least one of the following: mean, standard deviation, and variance.

[0161] In one optional implementation, a sliding window can be predetermined, and corresponding statistical characteristics can be determined based on the signal intensity of the effective diaphragmatic electroencephalogram (EEG) signals within the sliding window. For example, the arithmetic mean of the signal intensity of each effective diaphragmatic EEG signal within the sliding window is used as the average value characteristic; the calculated dispersion of the signal intensity of each effective diaphragmatic EEG signal within the sliding window relative to the average value is used as the standard deviation characteristic. The sliding window uses the current period as its last period.

[0162] In one optional implementation, the signal quality data of the effective diaphragm electrical signal can be determined based on the effective diaphragm electrical signal in the current cycle and the effective diaphragm electrical signal in historical cycles; the respiratory degree in the corresponding cycle can be determined based on the effective diaphragm electrical signal in the same cycle, and the trend data of the respiratory degree in different cycles can be determined; different cycles include the current cycle and historical cycles; the comprehensive sensitivity of the effective diaphragm electrical signal can be determined based on the signal quality data and / or the trend data.

[0163] The signal quality data can be determined based on at least one of the signal-to-noise ratio (SNR), the residual ratio of the electrocardiogram (ECG), and the baseline variance. For example, a first scoring rule corresponding to the SNR, a second scoring rule corresponding to the ECG residual ratio, and a third scoring rule corresponding to the baseline variance are determined. According to different scoring rules, the evaluation data under the corresponding evaluation dimensions are scored to obtain the score data under the corresponding dimensions. Then, based on the score values ​​under each dimension, the signal quality data of the effective diaphragmatic electrical signal is determined.

[0164] Breathing intensity is used to characterize the overall breathing effort intensity during the noise period. The overall sensitivity is typically a normalized dimensionless parameter between 0 and 1.

[0165] For example, the signal-to-noise ratio can be determined as follows: the effective diaphragmatic electromyography (EMG) signal under the current period and historical periods is segmented in the frequency domain, the signal components belonging to the effective frequency band of the diaphragmatic EMG signal are extracted, and the sum of the signal energy corresponding to the signal components is calculated as the effective electromyographic signal energy (EMG Energy); the noise components outside the effective frequency band are extracted, and the sum of the signal energy in the out-of-band region is calculated as the noise energy; the ratio of the effective EMG energy to the noise energy is taken as the signal-to-noise ratio of the effective diaphragmatic EMG signal.

[0166] For example, the ECG residual ratio can be determined as follows: determine the total signal energy of the effective diaphragmatic electrical signal, extract the residual ECG interference energy in the effective diaphragmatic electrical signal, and use the ratio of the residual ECG interference energy to the total signal energy as the ECG residual ratio.

[0167] For example, the baseline variance can be determined as follows: determine the difference between the effective electrical signal and the corresponding trigger strength threshold under different periods, and take the variance of the difference corresponding to each period as the baseline variance.

[0168] In one alternative implementation, signal quality data can be presented in a graded format. For example, the scores from at least one dimension can be weighted and fused to obtain a signal quality score for the effective diaphragmatic electroencephalogram (EEG). The grade to which the signal quality score falls is taken as the signal quality level of the effective diaphragmatic EEG. For instance, it can be divided into four levels: Level 3 (Excellent, signal quality score > 0.8): The system operates at full speed, maintaining maximum sensitivity. Level 2 (Good, signal quality score: 0.6~0.8): The trigger strength threshold can be appropriately increased. Level 1 (Average, signal quality score: 0.4~0.6): An extremely conservative triggering strategy is adopted. Level 0 (Ineffective, signal quality score < 0.4): The signal is extremely degraded, and the triggering mode is downgraded.

[0169] For example, numerical integration can be performed on all effective diaphragmatic electrical signals within a single cycle, and the integration result can be used as the respiratory level corresponding to that cycle to characterize the patient's respiratory effort intensity in the current cycle. After calculating the corresponding respiratory level for multiple consecutive respiratory cycles, trend fitting and analysis can be performed based on the time-series arranged multi-cycle respiratory level data to determine the rising, falling, or stable change trend data of respiratory level under different cycles.

[0170] Optionally, a lookup table between signal quality data and reference sensitivity can be predetermined, and the reference sensitivity corresponding to the determined signal quality data can be used as the comprehensive sensitivity of the effective diaphragmatic electrical signal; alternatively, a lookup table between trend data and reference sensitivity can be predetermined, and the reference sensitivity corresponding to the determined trend data can be used as the comprehensive sensitivity of the effective diaphragmatic electrical signal; alternatively, the two comprehensive sensitivities can be weighted and fused to obtain the final comprehensive sensitivity of the effective diaphragmatic electrical signal.

[0171] In this embodiment, the overall sensitivity is dynamically determined by combining signal quality and respiratory trend, which can adaptively adjust the triggering strategy, effectively reduce false triggering and missed triggering, and improve the stability and safety of respiratory control.

[0172] S630 determines the trigger change rate threshold for the current cycle based on statistical characteristics and comprehensive sensitivity.

[0173] In one alternative implementation, statistical features and comprehensive sensitivity can be input into a pre-trained threshold generation model to obtain the trigger change rate threshold for the current period.

[0174] In another alternative implementation, a formula for determining the trigger rate of change threshold can be pre-constructed, and the trigger rate of change threshold for the current period can be determined based on this formula. For example, the formula can be as follows:

[0175] S_th(n) = μ_S(n) + k · σ_S(n);

[0176] In the formula, S_th(n) is the trigger change threshold in the current cycle; μ_S(n) represents the mean of the statistical characteristics of the effective diaphragmatic electrical signal; σ_S(n) represents the standard deviation of the statistical characteristics of the effective diaphragmatic electrical signal; and k represents the influence coefficient (usually between 1.5 and 2.5), which is controlled by the overall sensitivity. For example, there is a mapping relationship between the influence coefficient and the overall sensitivity. For instance, the higher the overall sensitivity, the smaller the k value, and the more sensitive the corresponding ventilator control; the lower the overall sensitivity, the larger the k value, and the more conservative the corresponding ventilator control.

[0177] In the above embodiments, effective diaphragmatic electrical signals are accurately extracted from the original diaphragmatic electrical signals. By combining the current cycle and historical cycle signal data, the signal statistical characteristics and comprehensive sensitivity are obtained. The trigger change rate threshold of the current cycle is dynamically determined in this way. This can adaptively match the real-time change characteristics of the patient's respiratory signals and effectively suppress the risk of false triggering caused by noise, electrocardiogram interference and baseline fluctuations.

[0178] Based on the technical solutions of the above embodiments, this application also provides an optional embodiment. In this optional embodiment, when the operating parameters include filter parameters, the process of determining the operating parameters for the current cycle based on the original diaphragm electrical signal for the current cycle is described in detail.

[0179] See Figure 7 The steps for determining the operating parameters shown include:

[0180] S710 performs spectral analysis on the original diaphragm electrical signal in the current cycle to obtain the initial filter parameters of the original diaphragm electrical signal in the current cycle.

[0181] The initial filter parameters characterize the passband range of the effective electrical components in the original diaphragm electrical signal during the current cycle. The initial filter parameters can include low-frequency filter parameters and high-frequency filter parameters.

[0182] In one alternative implementation, the original diaphragm electrical signal under the current cycle can be subjected to spectral analysis and wavelet decomposition to obtain the basis for the initial filter parameters, and the initial filter parameters of the original diaphragm electrical signal under the current cycle can be obtained based on the basis.

[0183] For example, the original diaphragm electrical signal is subjected to wavelet decomposition with a preset number of layers (e.g., 5 layers) to extract the coefficients of the lower, for example, lowest frequency band (e.g., layer A5, approximately 0–30 Hz), and the energy centroid of this frequency band is calculated to estimate the dominant frequency band of motion artifacts, f_motion. Low-frequency filter parameters are determined based on the dominant frequency band of motion artifacts, for example, based on the following formula:

[0184] flow = max(20 Hz, f_motion + 5 Hz);

[0185] In the formula, flow represents the low-frequency filter parameter; 20Hz is the absolute hard lower limit for eliminating DC drift, which is an empirical value; f_motion is the main frequency band of motion artifacts; 5Hz is a safety margin to ensure that motion artifacts are completely excluded from the passband, which is also an empirical value.

[0186] For example, frequency analysis and wavelet decomposition can be performed on the raw diaphragm electrical signal in the current cycle to analyze the energy distribution of the high-frequency wavelet layers (e.g., D1 and D2 layers, corresponding to 125~1000Hz), obtaining a frequency point f90 containing a preset proportion (e.g., 90%) of the effective signal energy. High-frequency filter parameters are then determined based on this frequency point, for example, based on the following formula:

[0187] fhigh = min( 450 Hz, f90 );

[0188] In the formula, fhigh represents the parameters of the high-frequency filter; 450 Hz is the lower limit for eliminating high-frequency electromagnetic noise (such as interference from the electrosurgical unit in the operating room), which is an empirical value; and f90 is the frequency point of the effective signal energy.

[0189] Based on the above formula, the parameters of the high-frequency filter are determined. Under strong high-frequency electromagnetic noise (such as interference from an electrosurgical unit in an operating room), f90 will decrease significantly, thereby automatically narrowing the high-frequency passband and protecting the triggering characteristics from being damaged by high-frequency glitches.

[0190] S720 determines the initial cutoff frequency that matches the initial filter parameters.

[0191] The initial cutoff frequency may include an initial low-frequency cutoff frequency and an initial high-frequency cutoff frequency, which serve as a reference for subsequent filtering to extract the effective diaphragm electrical signal.

[0192] For example, the low-frequency filter parameters in the initial filter parameters can be used as the initial low-frequency cutoff frequency; and the high-frequency filter parameters in the initial filter parameters can be used as the initial high-frequency cutoff frequency.

[0193] The S730 smooths the initial cutoff frequency to obtain the filter parameters for the current cycle.

[0194] In one alternative implementation, the initial cutoff frequency calculated in the current cycle can be weighted and averaged with the filter parameters determined in the previous cycle. A smoothing algorithm, such as a first-order inertial filter (exponential smoothing) algorithm, can be used to suppress abrupt changes in frequency points, so that the filter parameters in the current cycle remain continuously and smoothly changing, avoiding drastic changes in filter parameters due to instantaneous interference, thereby ensuring the stability of signal processing.

[0195] In the above embodiments, smoothing the initial cutoff frequency before using it as the filter parameter for the current cycle can prevent distortion of the diaphragm electrical signal waveform and abnormal fluctuations in trigger characteristics caused by sudden changes in filter coefficients, avoid false triggering or missed triggering caused by sudden changes in frequency points, and improve the stability of filtering and the reliability of respiratory detection.

[0196] Based on the technical solutions of the above embodiments, this application also provides an optional embodiment. In this optional embodiment, the ventilator control method provided by this application will be described in detail.

[0197] See Figure 8 The timing diagram of the ventilator control method shown includes:

[0198] S801, the first controller and the second controller receive the raw diaphragmatic electrical signals acquired by the electrodes of the ventilator during the current cycle;

[0199] S802, the first controller extracts the effective diaphragmatic electrical signal from the original diaphragmatic electrical signal in the current cycle;

[0200] For example, the first controller filters the original diaphragm electrical signal in the current cycle based on the filter parameters to obtain the filtered signal in the current cycle; the diaphragm electrical signal envelope in the filtered signal in the current cycle is extracted to obtain the effective diaphragm electrical signal in the current cycle.

[0201] S803, the first controller generates a ventilation control command when there is a signal strength exceeding the trigger strength threshold in the effective diaphragm electrical signal in the current cycle; or, generates a ventilation control command when the signal change rate of the effective diaphragm electrical signal exceeds the trigger change rate threshold.

[0202] The signal change rate is determined by the first controller based on the effective diaphragm electrical signal in the current cycle and the effective diaphragm electrical signal in the historical cycle.

[0203] S804, the first controller responds to ventilation control commands and controls the ventilator to deliver air;

[0204] S805, the first controller sends a ventilation control command to the second controller in the ventilator;

[0205] S806, the second controller extracts the effective diaphragm electrical signal from the raw diaphragm electrical signal in the current cycle, and executes S807A, S807B and S807C in parallel;

[0206] S807A, the second controller determines the average signal strength of the effective diaphragmatic electrical signal in the current cycle;

[0207] S808A, the second controller determines the trigger strength threshold for the current period based on the average signal strength and the trigger strength threshold of the current period corresponding to the previous period;

[0208] In one alternative implementation, the average signal intensity of the effective diaphragm electrical signal in the current cycle can be weighted and calculated with the trigger intensity threshold of the previous cycle, and the trigger intensity threshold in the current cycle can be determined based on the weighted fusion result.

[0209] For example, the trigger strength threshold for the current period can be determined based on the following formula:

[0210] B(t) = α · B(t-1) + (1-α) · RMS_end-tidal(t);

[0211] In the formula, B(t) is the trigger intensity threshold at time t, which is also the trigger intensity threshold in the current cycle; B(t-1) is the trigger intensity threshold at time t-1, which is also the trigger intensity threshold in the previous cycle corresponding to the current cycle; α is the smoothing coefficient, which can be determined based on human experience or through a large number of experiments, and is usually set between 0.85 and 0.95 to ensure that the trigger intensity threshold in the current cycle can drift slowly with the average signal intensity in the current cycle, and will not jump drastically due to a single abnormal fluctuation; RMS_end-tidal(t) is the average signal intensity of the effective diaphragmatic electrical signal in the current cycle;

[0212] S807B, the second controller determines the statistical characteristics and comprehensive sensitivity of the effective diaphragm electrical signal based on the effective diaphragm electrical signal in the current cycle and the effective diaphragm electrical signal in historical cycles;

[0213] The comprehensive sensitivity determination method is as follows: based on the effective diaphragmatic electrical signals in the current cycle and the effective diaphragmatic electrical signals in historical cycles, the signal quality data of the effective diaphragmatic electrical signals are determined; based on the effective diaphragmatic electrical signals in the same cycle, the respiratory degree in the corresponding cycle is determined, and the trend data of the respiratory degree in different cycles is determined; different cycles include the current cycle and historical cycles; based on the signal quality data and / or the trend data, the comprehensive sensitivity of the effective diaphragmatic electrical signals is determined.

[0214] S808B, the second controller determines the trigger change rate threshold for the current cycle based on statistical characteristics and comprehensive sensitivity;

[0215] In one alternative implementation, a formula for determining the trigger rate of change threshold can be pre-constructed, and the trigger rate of change threshold for the current period can be determined based on this formula. For example, the formula can be as follows:

[0216] S_th(n) = μ_S(n) + k · σ_S(n);

[0217] In the formula, S_th(n) is the trigger change threshold in the current cycle; μ_S(n) represents the mean of the statistical characteristics of the effective diaphragmatic electrical signal; σ_S(n) represents the standard deviation of the statistical characteristics of the effective diaphragmatic electrical signal; k represents the influence coefficient (usually between 1.5 and 2.5), which is controlled by the overall sensitivity. For example, there is a mapping relationship between the influence coefficient and the overall sensitivity. For instance, the higher the overall sensitivity, the smaller the k value, and the more sensitive the corresponding ventilator control; the lower the overall sensitivity, the larger the k value, and the more conservative the corresponding ventilator control.

[0218] S807C, the second controller performs spectrum analysis on the original diaphragm electrical signal in the current cycle to obtain the initial filter parameters of the original diaphragm electrical signal in the current cycle;

[0219] For example, the original diaphragm electrical signal is subjected to wavelet decomposition with a preset number of layers (e.g., 5 layers) to extract the coefficients of the lower, for example, lowest frequency band (e.g., layer A5, approximately 0–30 Hz), and the energy centroid of this frequency band is calculated to estimate the dominant frequency band of motion artifacts, f_motion. Low-frequency filter parameters are determined based on the dominant frequency band of motion artifacts, for example, based on the following formula:

[0220] flow = max(20 Hz, f_motion + 5 Hz);

[0221] In the formula, flow represents the low-frequency filter parameter; 20Hz is the absolute hard lower limit for eliminating DC drift, which is an empirical value; f_motion is the main frequency band of motion artifacts; 5Hz is a safety margin to ensure that motion artifacts are completely excluded from the passband, which is also an empirical value.

[0222] For example, frequency analysis and wavelet decomposition can be performed on the raw diaphragm electrical signal in the current cycle to analyze the energy distribution of the high-frequency wavelet layers (e.g., D1 and D2 layers, corresponding to 125~1000Hz), obtaining a frequency point f90 containing a preset proportion (e.g., 90%) of the effective signal energy. High-frequency filter parameters are then determined based on this frequency point, for example, based on the following formula:

[0223] fhigh = min( 450 Hz, f90 );

[0224] In the formula, fhigh represents the parameters of the high-frequency filter; 450 Hz is the lower limit for eliminating high-frequency electromagnetic noise (such as interference from the electrosurgical unit in the operating room), which is an empirical value; and f90 is the frequency point of the effective signal energy.

[0225] S808C, the second controller determines the initial cutoff frequency that matches the initial filter parameters, and smooths the initial cutoff frequency to obtain the filter parameters for the current cycle;

[0226] S809, the second controller sends the operating parameters for the current cycle to the first controller;

[0227] S810, the first controller generates ventilation control commands for the next cycle based on the operating parameters of the current cycle.

[0228] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0229] Based on the same inventive concept, this application also provides a ventilator control device for implementing the ventilator control method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more ventilator control device embodiments provided below can be found in the limitations of the ventilator control method described above, and will not be repeated here.

[0230] In one exemplary embodiment, such as Figure 9 As shown, a ventilator control device is provided, comprising: a first receiving module 910, an instruction generation module 920, an instruction response module 930, and a second receiving module 940, wherein:

[0231] The first receiving module 910 is used to receive the raw diaphragmatic electrical signal collected by the electrodes of the ventilator in the current cycle;

[0232] The instruction generation module 920 is used to generate ventilation control instructions based on the operating parameters of the first controller in the current cycle corresponding to the previous cycle and the original diaphragm electrical signal in the current cycle; wherein, the operating parameters of the first cycle are initialization data;

[0233] The instruction response module 930 is used to respond to ventilation control instructions, control the ventilator to deliver air, and send ventilation control instructions to the second controller in the ventilator so that the second controller can determine the operating parameters of the current cycle based on the original diaphragm electrical signal of the current cycle.

[0234] The second receiving module 940 is used to receive the operating parameters of the current cycle fed back by the second controller.

[0235] In one embodiment, the instruction generation module 920 includes a first signal extraction unit for extracting the effective diaphragm electrical signal from the original diaphragm electrical signal in the current cycle; and a first instruction generation unit for generating a ventilation control instruction based on the effective diaphragm electrical signal in the current cycle.

[0236] In one embodiment, the instruction generation module 920 includes at least one of the following: a second instruction generation unit, configured to generate a ventilation control instruction when there is a signal intensity exceeding a trigger intensity threshold in the effective diaphragm electrical signal in the current cycle; and a third instruction generation unit, configured to determine the signal change rate of the effective diaphragm electrical signal based on the effective diaphragm electrical signal in the current cycle and the effective diaphragm electrical signal in historical cycles, and generate a ventilation control instruction when the signal change rate exceeds a trigger change rate threshold.

[0237] In one embodiment, the operating parameters include filter parameters; the first signal extraction unit includes a filtering subunit, used to filter the original diaphragm electrical signal in the current cycle based on the filter parameters to obtain the filtered signal in the current cycle; and an envelope extraction subunit, used to extract the diaphragm electrical signal envelope in the filtered signal in the current cycle to obtain the effective diaphragm electrical signal in the current cycle.

[0238] In one embodiment, the operating parameters include ventilation reference parameters; the instruction generation module 920 includes a data generation unit for generating ventilation reference data based on the effective diaphragmatic electrical signal in the current cycle; and a fourth instruction generation unit for generating ventilation control instructions based on the ventilation reference data and ventilation reference parameters.

[0239] In one exemplary embodiment, such as Figure 10 As shown, a ventilator control device is provided, including: a third receiving module 1010, a parameter determining module 1020, and a parameter sending module 1030; wherein:

[0240] The third receiving module 1010 is used to receive the raw diaphragm electrical signal collected by the electrodes of the ventilator in the current cycle, as well as the ventilation control command sent by the first controller in the ventilator. The ventilation control command is a command generated by the first controller based on the operating parameters of the first controller in the current cycle corresponding to the previous cycle, and according to the raw diaphragm electrical signal in the current cycle to control the ventilator to deliver air.

[0241] The parameter determination module 1020 is used to determine the operating parameters for the current cycle based on the original diaphragm electrical signal in the current cycle.

[0242] The parameter sending module 1030 is used to send the operating parameters of the current cycle to the first controller for generating the ventilation control command for the next cycle.

[0243] In one embodiment, the operating parameters include a trigger intensity threshold. The parameter determination module 1020 includes a second signal extraction unit for extracting the effective diaphragm electrical signal from the original diaphragm electrical signal in the current cycle; a signal intensity determination unit for determining the average signal intensity of the effective diaphragm electrical signal in the current cycle; and an intensity threshold determination unit for determining the trigger intensity threshold in the current cycle based on the average signal intensity and the trigger intensity threshold corresponding to the previous cycle.

[0244] In one embodiment, the operating parameters include a trigger change rate threshold. The parameter determination module 1020 includes a third signal extraction unit for extracting the effective diaphragm electrical signal from the original diaphragm electrical signal in the current cycle; a sensitivity determination unit for determining the statistical characteristics and comprehensive sensitivity of the effective diaphragm electrical signal based on the effective diaphragm electrical signal in the current cycle and the effective diaphragm electrical signal in historical cycles; and a change rate threshold determination unit for determining the trigger change rate threshold in the current cycle based on the statistical characteristics and comprehensive sensitivity.

[0245] In one embodiment, the sensitivity determination unit includes a first data determination subunit, used to determine the signal quality data of the effective diaphragm electrical signal based on the effective diaphragm electrical signal in the current cycle and the effective diaphragm electrical signal in historical cycles; a second data determination subunit, used to determine the respiratory degree in the corresponding cycle based on the effective diaphragm electrical signal in the same cycle, and to determine the trend data of the respiratory degree in different cycles; different cycles include the current cycle and historical cycles; and a sensitivity determination subunit, used to determine the comprehensive sensitivity of the effective diaphragm electrical signal based on the signal quality data and / or the trend data.

[0246] In one embodiment, the operating parameters include filter parameters; the parameter determination module 1020 includes a parameter determination unit for performing spectral analysis on the original diaphragm electrical signal in the current cycle to obtain the initial filter parameters of the original diaphragm electrical signal in the current cycle; a frequency determination unit for determining an initial cutoff frequency that matches the initial filter parameters; and a frequency processing unit for smoothing the initial cutoff frequency to obtain the filter parameters in the current cycle.

[0247] The modules in the aforementioned ventilator control device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0248] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 11As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a ventilator control method. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0249] Those skilled in the art will understand that Figure 11 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0250] In one exemplary embodiment, a ventilator is provided that includes a computer device, the computer device including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps in the above-described method embodiments.

[0251] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0252] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0253] It should be noted that the signals (including but not limited to raw diaphragmatic electrical signals) and parameters (including but not limited to operating parameters) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with relevant regulations.

[0254] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0255] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0256] The above embodiments merely illustrate several implementation methods of this application, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of this application's patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A ventilator control method, characterized in that, The first controller used in a ventilator includes: Receive the raw diaphragmatic electrical signal acquired by the electrodes of the ventilator in the current cycle; Based on the operating parameters of the first controller corresponding to the previous cycle in the current cycle, and according to the original diaphragm electrical signal in the current cycle, a ventilation control command is generated; wherein, the operating parameters of the first cycle are initialization data; In response to the ventilation control command, the ventilator is controlled to deliver air, and the ventilation control command is sent to a second controller in the ventilator so that the second controller determines the operating parameters for the current cycle based on the original diaphragm electrical signal for the current cycle. Receive the operating parameters for the current cycle from the feedback of the second controller.

2. The method according to claim 1, characterized in that, The step of generating ventilation control commands based on the original diaphragm electrical signal in the current cycle includes: Extract the effective diaphragmatic electrical signal from the original diaphragmatic electrical signal in the current cycle; The ventilation control command is generated based on the effective diaphragmatic electrical signal in the current cycle.

3. The method according to claim 2, characterized in that, The step of generating the ventilation control command based on the effective diaphragmatic electrical signal in the current cycle includes at least one of the following: If there is a signal intensity exceeding the trigger intensity threshold in the effective diaphragm electrical signal during the current cycle, the ventilation control command is generated. Based on the effective diaphragmatic electrical signal in the current cycle and the effective diaphragmatic electrical signal in the historical cycle, the signal change rate of the effective diaphragmatic electrical signal is determined, and the ventilation control command is generated when the signal change rate exceeds the trigger change rate threshold.

4. The method according to claim 2, characterized in that, The operating parameters include filter parameters; the extraction of the effective diaphragmatic electrical signal from the original diaphragmatic electrical signal in the current cycle includes: Based on the filter parameters, the original diaphragm electrical signal in the current cycle is filtered to obtain the filtered signal in the current cycle. Extract the diaphragm electrical signal envelope from the filtered signal in the current period to obtain the effective diaphragm electrical signal in the current period.

5. The method according to claim 2, characterized in that, The operating parameters include ventilation reference parameters; the generation of the ventilation control command based on the effective diaphragmatic electrical signal in the current cycle includes: Based on the effective diaphragmatic electrical signal in the current cycle, ventilation reference data is generated; The ventilation control command is generated based on the ventilation reference data and the ventilation reference parameters.

6. A ventilator control method, characterized in that, The second controller used in a ventilator includes: The device receives the raw diaphragmatic electrical signal collected by the electrodes of the ventilator in the current cycle, as well as the ventilation control command sent by the first controller in the ventilator. The ventilation control command is a command generated by the first controller based on the operating parameters of the first controller in the current cycle corresponding to the previous cycle, and according to the raw diaphragmatic electrical signal in the current cycle, to control the ventilator to deliver air. Based on the original diaphragm electrical signal in the current cycle, determine the operating parameters for the current cycle; The operating parameters for the current cycle are sent to the first controller to generate the ventilation control command for the next cycle.

7. The method according to claim 6, characterized in that, The operating parameters include a trigger intensity threshold; determining the operating parameters for the current cycle based on the original diaphragm electrical signal of the current cycle includes: Extract the effective diaphragmatic electrical signal from the original diaphragmatic electrical signal in the current cycle; Determine the average signal intensity of the effective diaphragmatic electrical signal in the current cycle; The trigger strength threshold for the current period is determined based on the average signal strength and the trigger strength threshold corresponding to the previous period.

8. The method according to claim 6, characterized in that, The operating parameters include a trigger rate of change threshold; determining the operating parameters for the current cycle based on the original diaphragm electrical signal of the current cycle includes: Extract the effective diaphragmatic electrical signal from the original diaphragmatic electrical signal in the current cycle; Based on the effective diaphragmatic electrical signals in the current cycle and the effective diaphragmatic electrical signals in historical cycles, the statistical characteristics and comprehensive sensitivity of the effective diaphragmatic electrical signals are determined. Based on the statistical characteristics and the comprehensive sensitivity, the trigger change rate threshold for the current period is determined.

9. The method according to claim 8, characterized in that, Based on the effective diaphragmatic electrical signals in the current cycle and the effective diaphragmatic electrical signals in historical cycles, the comprehensive sensitivity of the effective diaphragmatic electrical signals is determined, including: Based on the effective diaphragmatic electrical signals in the current cycle and the effective diaphragmatic electrical signals in historical cycles, the signal quality data of the effective diaphragmatic electrical signals are determined. Based on the effective diaphragmatic electrical signals within the same cycle, the respiratory rate in the corresponding cycle is determined, and the trend data of respiratory rate changes in different cycles are determined; the different cycles include the current cycle and the historical cycles; The overall sensitivity of the effective diaphragmatic electrical signal is determined based on the signal quality data and / or the trend data.

10. The method according to claim 6, characterized in that, The operating parameters include filter parameters; determining the operating parameters for the current cycle based on the original diaphragm electrical signal of the current cycle includes: Spectral analysis is performed on the original diaphragm electrical signal in the current cycle to obtain the initial filter parameters of the original diaphragm electrical signal in the current cycle; Determine an initial cutoff frequency that matches the initial filter parameters; The initial cutoff frequency is smoothed to obtain the filter parameters for the current period.

11. A ventilator control device, characterized in that, The first controller configured in the ventilator includes: The first receiving module is used to receive the raw diaphragmatic electrical signal collected by the electrodes of the ventilator in the current cycle; The instruction generation module is used to generate ventilation control instructions based on the operating parameters of the first controller in the current cycle corresponding to the previous cycle and the original diaphragm electrical signal in the current cycle; wherein, the operating parameters of the first cycle are initialization data; The instruction response module is used to respond to the ventilation control instruction, control the ventilator to deliver air, and send the ventilation control instruction to the second controller in the ventilator so that the second controller determines the operating parameters of the current cycle based on the original diaphragm electrical signal of the current cycle. The second receiving module is used to receive the operating parameters for the current cycle fed back by the second controller.

12. A ventilator control device, characterized in that, The second controller configured in the ventilator includes: The third receiving module is used to receive the raw diaphragmatic electrical signal collected by the electrodes of the ventilator in the current cycle, and the ventilation control command sent by the first controller in the ventilator; the ventilation control command is a command generated by the first controller based on the operating parameters of the first controller in the current cycle corresponding to the previous cycle, and according to the raw diaphragmatic electrical signal in the current cycle to control the ventilator to deliver air. The parameter determination module is used to determine the operating parameters for the current cycle based on the original diaphragm electrical signal in the current cycle. The parameter sending module is used to send the operating parameters for the current cycle to the first controller for generating ventilation control commands for the next cycle.

13. A ventilator, characterized in that, Includes a first controller and a second controller; The first controller and the second controller respectively receive the raw diaphragmatic electrical signals collected by the electrodes of the ventilator in the current cycle; The first controller generates a ventilation control command based on the operating parameters of the previous cycle corresponding to the current cycle and the original diaphragm electrical signal in the current cycle; wherein, the operating parameters of the first cycle are initialization data; The first controller responds to the ventilation control command and controls the ventilator to deliver air; The second controller determines the operating parameters for the current cycle based on the original diaphragm electrical signal for the current cycle, and sends the operating parameters for the current cycle to the first controller.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-10.

15. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-10.

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