Intelligent breathing machine and intelligent ventilation control system and method based on respiratory sound monitoring

Abstract

The embodiment of the application provides a kind of intelligent breathing machine and the intelligent ventilation control system, method based on respiratory sound monitoring.The system includes: respiratory sound acquisition unit, including at least one air pipe sound sensor and at least two chest wall sound sensors, for synchronously collecting the respiratory sound signal of user;Preprocessing unit is used to preprocess the respiratory sound signal;Central processing unit is used to process the respiratory sound signal after preprocessing, obtain the abnormal respiratory sound type and confidence of the user;Breathing machine controller is used to dynamically adjust the ventilation parameter of breathing machine according to the abnormal respiratory sound type and confidence.By real-time acquisition and analysis of patient respiratory sound signal, dynamically adjust ventilation parameter, solve the problem of signal lag, poor adaptability in prior art.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent medical devices, and in particular to an intelligent breathing machine and an intelligent ventilation control system and method based on respiratory sound monitoring. BACKGROUND

[0002] Mechanical ventilation is a medical intervention measure that uses medical devices such as breathing machines to maintain the airway patency of patients and improve the oxygenation effect of patients to reduce respiratory failure. The ventilation mode of the existing breathing machine mainly relies on preset parameters (such as tidal volume, respiratory frequency) or patient physiological signals (such as airway pressure, blood oxygen saturation) for control, lacking real-time and direct sensing of the pathological and physiological state of the lungs. When the patient has airway spasm, atelectasis or secretion retention, medical staff must judge and manually adjust the ventilation parameters through intermittent auscultation or imaging examination, which has problems such as response lag, heavy workload and man-machine difference. Existing research has shown that different abnormal respiratory sounds (wheezing sound, wet rale, dry rale, and pleural friction sound) are closely related to parameters such as airway resistance and alveolar recruitment. If respiratory sounds can be collected and recognized in real time and automatically fed back to the breathing machine control logic, it can achieve the true sense of "giving air according to lung conditions".

[0003] For example, patent CN118490944A discloses a breathing machine and a ventilation mode control method thereof, which calculates the driving ratio of the driving parameters between the breathing machine and the user, sets the pressure adjustment parameter that changes with the driving ratio, and then calculates the pressure adjustment amount of the next breathing cycle according to the tidal volume deviation of the current breathing cycle. In addition, patent CN118022114A discloses a control method for mechanical ventilation duration, which performs blood oxygen saturation detection, inhaled oxygen concentration detection, acid-base value detection, spontaneous breathing detection, and tolerance monitoring in sequence when the breathing machine operates according to the preset ventilation parameters in the preset ventilation mode. In addition, the document "Research on Key Technologies of Intelligent Ventilation of Breathing Machine" points out that the existing breathing machine still has a delay in synchronization and cannot respond to the patient's spontaneous breathing demand in real time.

[0004] However, the above-mentioned existing technology has the following defects:

[0005] 1. Signal lag: existing ventilation technology adjusts comprehensively according to physiological parameters such as pulse blood oxygen saturation and carbon dioxide partial pressure, and mechanical ventilation parameters of the breathing machine, but the human body gas-blood model corresponding to the former has a certain lag, which cannot directly correspond to the state of the breathing process, and is easy to cause delay in adjusting the ventilation mode. In addition, due to the strong compensatory ability of the respiratory system, the oxygenation parameters cannot predict the change of the respiratory state, and the patient's respiratory state may suddenly decline and the breathing machine ventilation mode cannot compensate in advance.

[0006] 2. Insufficient adaptation: The current ventilator preset parameters cannot dynamically match the instantaneous changes of the patient's respiratory state (such as coughing, wheezing, etc.), and the corresponding air pressure, flow adjustment mode cannot respond to abnormal sudden changes of respiratory parameters (such as patient movement, tracheal shedding, sudden illness, etc.), leading to conflict between mechanical ventilation and actual human respiratory state, causing discomfort, and even air pressure injury. SUMMARY

[0007] The present application aims to at least solve one of the technical problems existing in the prior art, and proposes an intelligent ventilator and an intelligent ventilation control system and method based on respiratory sound monitoring.

[0008] In a first aspect, the present application embodiment provides an intelligent ventilation control system of a ventilator based on respiratory sound monitoring, comprising:

[0009] A respiratory sound acquisition unit comprising at least one tracheal sound sensor and at least two chest wall sound sensors, for synchronously acquiring respiratory sound signals of a user;

[0010] A preprocessing unit for preprocessing the respiratory sound signals;

[0011] A central processing unit for processing the preprocessed respiratory sound signals to obtain abnormal respiratory sound types and confidence levels of the user;

[0012] A ventilator controller for dynamically adjusting ventilation parameters of the ventilator according to the abnormal respiratory sound types and confidence levels.

[0013] In some possible embodiments, the preprocessing unit comprises an analog front end and an analog-to-digital conversion circuit for implementing 50 Hz ~2000 Hz band-pass filtering on the respiratory sound signals and digitizing processing at a sampling rate of no less than 4 kHz.

[0014] In some possible embodiments, the central processing unit is specifically configured to:

[0015] Performing spectral analysis on the preprocessed respiratory sound signals based on a spectral analysis algorithm to obtain a time-frequency domain feature spectrogram corresponding to the respiratory sound signals;

[0016] Performing unsupervised classification processing on the time-frequency domain feature spectrogram based on a FCM clustering algorithm to obtain an effective respiratory sound interval spectrogram;

[0017] Identifying time-frequency features corresponding to the effective respiratory sound interval spectrogram based on a time-frequency domain graph convolution network (GCN) classification model to output abnormal respiratory sound types and confidence levels.

[0018] In some possible embodiments, the central processor is further configured to perform multi-label classification on the effective breath sound interval spectrogram based on the GCN classification model, and output confidence levels of wheezing sound, moist rales, dry rales, pleural friction rub, and normal breath sound.

[0019] In some possible embodiments, the ventilation parameters at least include respiratory rate, inspiration-expiration ratio, positive end-expiratory pressure (PEEP), tidal volume, and peak flow rate.

[0020] In some possible embodiments, the abnormal breath sound types include wheezing sound, moist rales, dry rales, and pleural friction rub, and the ventilator controller is further configured to:

[0021] when the wheezing sound is detected, the expiratory time is prolonged and the respiratory rate is reduced according to a preset gradient;

[0022] when the moist rales are detected, the PEEP is increased by a fixed step until the intensity of the moist rales is lower than a threshold or a safety upper limit is reached;

[0023] when the dry rales are detected, a sputum suction alarm is triggered and a tidal volume impact ventilation is optionally performed;

[0024] when the pleural friction rub is detected, the tidal volume is automatically reduced and the plateau pressure is limited.

[0025] In some possible embodiments, the ventilator controller is further configured to:

[0026] real-time calculation of confidence levels and durations of various types of abnormal breath sounds;

[0027] based on a preset clinical hazard level weight table, a weighted severity score is calculated for multiple types of abnormal breath sounds that exist simultaneously;

[0028] based on the score result, a ventilation parameter adjustment priority is determined, and a hierarchical intervention strategy is executed.

[0029] In some possible embodiments, a human-computer interaction unit is further included.

[0030] The human-computer interaction unit is configured to display real-time spectrograms, abnormal breath sound prompts, and parameter adjustment information.

[0031] In some possible embodiments, a safety monitoring unit is further included.

[0032] The safety monitoring unit is configured to automatically disable the closed-loop adjustment and alarm when a sensor failure or parameter overrun is detected.

[0033] In a second aspect, the embodiments of the present application provide a ventilator intelligent ventilation control method based on breath sound monitoring, including the following steps:

[0034] Synchronously collecting a user's respiratory sound signals through at least one tracheal sound sensor and at least two chest wall sound sensors;

[0035] Preprocessing the respiratory sound signals;

[0036] Processing the preprocessed respiratory sound signals to obtain an abnormal respiratory sound type and a confidence level of the user;

[0037] According to the abnormal respiratory sound type and the confidence level, dynamically adjusting ventilation parameters of a breathing machine.

[0038] In a third aspect, an embodiment of the present application provides an intelligent breathing machine, comprising the breathing machine intelligent ventilation control system based on respiratory sound monitoring as described above.

[0039] In a fourth aspect, an embodiment of the present application provides an electronic device, comprising:

[0040] One or more processors;

[0041] A storage unit, configured to store one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors can implement the breathing machine intelligent ventilation control method based on respiratory sound monitoring as described above.

[0042] In a fifth aspect, an embodiment of the present application provides a computer readable storage medium, having a computer program stored thereon, when the computer program is executed by a processor, the breathing machine intelligent ventilation control method based on respiratory sound monitoring as described above can be implemented.

[0043] The intelligent breathing machine and the intelligent ventilation control system and method based on respiratory sound monitoring of the embodiments of the present application have the following advantages:

[0044] 1. Evaluation accuracy is improved: a respiratory dynamics evaluation model with strong generalization ability is constructed based on respiratory sound signal spectrum feature analysis technology, non-invasive high-precision evaluation of the respiratory system is realized, and the ventilation quality of the respiratory system can be directly fed back;

[0045] 2. Time precision is improved: the respiratory dynamics evaluation model based on respiratory sound can synchronously respond to the instantaneous changes of physiological characteristics such as lung volume and airway pressure, and the response speed is improved compared with traditional methods;

[0046] 3. Strong dynamic adaptability: a respiratory pattern classifier based on deep convolutional neural network can accurately identify normal respiratory patterns and abnormal respiratory events (including cough, wheezing, apnea, etc.), and give dynamic adjustment suggestions according to normal and abnormal respiratory patterns;

[0047] 4. Strong anti-noise capability: under the condition of non-invasive physiological monitoring, the influence of environmental noise and other non-ideal factors is relatively small, and the monitoring precision is high and the operation difficulty is small compared with the carbon dioxide partial pressure detection and other methods. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 Structure schematic diagram of an example electronic device for implementing the breathing machine intelligent ventilation control method based on respiratory sound monitoring according to an embodiment of the present application;

[0049] Figure 2 Conceptual schematic diagram of the breathing machine intelligent ventilation control based on respiratory sound monitoring according to another embodiment of the present application;

[0050] Figure 3 Structure schematic diagram of the breathing machine intelligent ventilation control system based on respiratory sound monitoring according to another embodiment of the present application;

[0051] Figure 4 Structure schematic diagram of the breathing machine intelligent ventilation control system based on respiratory sound monitoring according to another embodiment of the present application;

[0052] Figure 5 Flowchart of respiratory sound acquisition and signal processing according to another embodiment of the present application;

[0053] Figure 6 Example spectrogram of abnormal respiratory sound recognition result according to another embodiment of the present application;

[0054] Figure 7 Schematic diagram of closed-loop parameter adjustment logic state machine according to another embodiment of the present application;

[0055] Figure 8 Flowchart of the breathing machine intelligent ventilation control method based on respiratory sound monitoring according to another embodiment of the present application;

[0056] Figure 9 Flowchart of the breathing machine intelligent ventilation control method based on respiratory sound monitoring according to another embodiment of the present application;

[0057] Figure 10 Structure schematic diagram of the intelligent breathing machine according to another embodiment of the present application. DETAILED DESCRIPTION

[0058] The technical solutions of the present application will be described clearly and completely below in conjunction with the drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0059] Figure 1Structural schematic diagram of an example electronic device for implementing a method of intelligent ventilation control of a breathing machine based on respiratory sound monitoring according to an embodiment of the present application. As shown in Figure 1 The electronic device 100 includes one or more processors 110, one or more storage devices 120, one or more input devices 130, one or more output devices 140, and the like, which are interconnected through a bus system 150 and / or other form of connection mechanism. It should be noted that Figure 1 The components and structure of the electronic device shown are only exemplary and not restrictive, and the electronic device can also have other components and structures as needed.

[0060] The processor 110 can be a central processing unit (CPU), or can be other forms of processing units composed of multiple processing cores, or having data processing capability and / or instruction execution capability, and can control other components in the electronic device 100 to perform desired functions.

[0061] The storage device 120 can include one or more computer program products, which can include various forms of computer readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory, etc. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions can be stored on the computer readable storage medium, and the processor can run the program instructions to implement the client functions (implemented by the processor) in the embodiments of the present disclosure described below and / or other desired functions. Various application programs and various data, such as various data used and / or generated by the application programs, etc., can also be stored in the computer readable storage medium.

[0062] The input device 130 can be a device used by a user to input instructions, and can include one or more of a keyboard, a mouse, a microphone, and a touch screen, etc.

[0063] The output device 140 can output various information (such as images or sounds) to the outside (such as a user), and can include one or more of a display, a speaker, etc.

[0064] Figure 2 Conceptual schematic diagram of intelligent ventilation control of a breathing machine based on respiratory sound monitoring according to another embodiment of the present application. Figure 3 Structural schematic diagram of a system for intelligent ventilation control of a breathing machine based on respiratory sound monitoring according to another embodiment of the present application. Figure 4 Structural schematic diagram of a system for intelligent ventilation control of a breathing machine based on respiratory sound monitoring according to another embodiment of the present application.

[0065] As Figures 2 to 4 shown, the embodiment of the present application relates to a breathing machine intelligent ventilation control system based on respiratory sound monitoring, comprising: a respiratory sound acquisition unit 301, a preprocessing unit 302, a central processing unit 303 and a breathing machine controller 304.

[0066] Specifically, as Figure 3 and Figure 4 shown, the respiratory sound acquisition unit 301 comprises at least one tracheal sound sensor and at least two chest wall sound sensors for synchronously acquiring the respiratory sound signals of the user. The preprocessing unit 302 is used for preprocessing the respiratory sound signals; the central processing unit 303 is used for processing the preprocessed respiratory sound signals to obtain the abnormal respiratory sound type and confidence of the user; and the breathing machine controller 304 is used for dynamically adjusting the ventilation parameters of the breathing machine according to the abnormal respiratory sound type and confidence.

[0067] The breathing machine intelligent ventilation control system based on respiratory sound monitoring has the following beneficial effects:

[0068] 1. The evaluation accuracy is improved: a respiratory dynamics evaluation model with strong generalization ability is constructed based on respiratory sound signal spectrum feature analysis technology, realizing non-invasive high-precision evaluation of the respiratory system and directly feeding back the ventilation quality of the respiratory system.

[0069] 2. The time precision is improved: the respiratory dynamics evaluation model based on respiratory sound can synchronously respond to the instantaneous changes of physiological characteristics such as lung volume and airway pressure, and the response speed is improved compared with traditional methods.

[0070] 3. Strong dynamic adaptability: the respiratory pattern classifier based on deep convolutional neural network can accurately identify normal respiratory patterns and abnormal respiratory events (including cough, wheezing, apnea, etc.), and give dynamic adjustment suggestions according to normal and abnormal respiratory patterns.

[0071] 4. Strong anti-noise ability: under the condition of non-invasive physiological monitoring, the influence of environmental noise and other non-ideal factors is relatively small, and the monitoring precision is high and the operation difficulty is small compared with carbon dioxide partial pressure detection.

[0072] Exemplarily, as Figure 3 and Figure 4As shown, the preprocessing unit 302 includes an analog front end and an analog-to-digital conversion circuit, which is used to implement 50 Hz ~2000 Hz band-pass filtering on the respiratory sound signal and digitize the signal at a sampling rate of no less than 4 kHz. Specifically, in the present embodiment, a high-sensitivity acceleration contact microphone sensor is used, and an MCU compensation algorithm is used to compensate for the bandwidth defects and tape coupling strength defects of the accelerometer, so as to collect real respiratory sound signals of a patient in a wide frequency band in real time; the respiratory sound signal is time-synchronized with the airway pressure / flow signal.

[0073] For example, in combination Figure 3 , Figure 4 and Figure 5 , Figure 5 is the flow chart of the respiratory sound collection and signal processing of another embodiment of the present application. The central processing unit 303 is specifically configured to: perform spectral analysis on the preprocessed respiratory sound signal based on a spectral analysis algorithm to obtain a time-frequency domain feature spectrogram corresponding to the respiratory sound signal; perform unsupervised classification processing on the time-frequency domain feature spectrogram based on an FCM clustering algorithm to obtain an effective respiratory sound interval spectrogram; and perform identification on the time-frequency features corresponding to the effective respiratory sound interval spectrogram based on a time-frequency domain graph convolution network (GCN) classification model, and output an abnormal respiratory sound type and a confidence. As shown in Figure 6 , the extracted abnormal respiratory sound identification result example spectrogram.

[0074] In some embodiments, the central processing unit 303 implements signal filtering, performs spectral analysis algorithms such as short-time Fourier transform, wavelet transform, and mel-frequency spectrum transform; uses FCM clustering to identify effective signal intervals, uses AIC criterion for accurate picking, and realizes real-time signal monitoring with low algorithm complexity; uses a time-frequency domain graph convolution network (GCN) classification model to identify respiratory sound features (such as inspiration phase, expiration phase, and abnormal sound), and extracts key parameters such as respiratory frequency, respiratory intensity, and airway resistance.

[0075] For example, as shown in Figure 3 and Figure 4 , the central processing unit 303 is specifically further configured to: perform multi-label classification on the effective respiratory sound interval spectrogram based on the GCN classification model, and output the confidence of wheezing sound, wet rale, dry rale, pleural friction rub, and normal respiratory sound.

[0076] In some embodiments, the ventilation parameters at least include: respiratory frequency, inspiration-expiration ratio, positive end-expiratory pressure (PEEP), tidal volume, and peak flow rate.

[0077] For example, as shown in Figure 3 and Figure 4As shown, the ventilator controller 304 is further configured to, when wheezing is detected, extend the expiratory time and reduce the respiratory rate according to a preset gradient; when wet rales are detected, increase the PEEP by a fixed step until the intensity of the wet rales is below a threshold or a safety upper limit is reached; when dry rales are detected, trigger a sputum suction alarm and optionally perform a tidal volume impact ventilation; and when pleural rub is detected, automatically reduce the tidal volume and limit the plateau pressure.

[0078] In some embodiments, the ventilator controller 304 is further configured to, in combination with the above-mentioned functions, Figure 7 , Figure 7 The schematic diagram of the closed-loop parameter adjustment logic state machine is another embodiment of the present application. The ventilator controller 304 dynamically switches the ventilation mode (such as auxiliary control ventilation, pressure support ventilation) according to the respiratory sound characteristics, adjusts the parameters such as tidal volume and inspiration-expiration ratio in real time through a closed-loop feedback system, ensures human-machine synchronization, and quickly adjusts the ventilation pressure in real time when a sudden abnormality occurs.

[0079] Wheezing: extend the expiration, relatively reduce the respiratory rate and shorten the inspiration time, and the ratio can be adjusted to 1:3-1:4;

[0080] Wet rales: increase the end-expiratory pressure (PEEP) by a fixed step of 2 cm H2O each time until the rales are significantly weakened;

[0081] Dry rales: issue a sputum suction alarm and select airway impact ventilation;

[0082] Pleural rub: automatically reduce the tidal volume, limit the plateau pressure, and increase the synchronization triggering sensitivity.

[0083] As shown in the above-mentioned embodiments, the ventilator controller 304 is further configured to, in combination with the above-mentioned functions, Figure 3 and Figure 4 As shown, the ventilator controller 304 is further configured to: calculate the confidence and duration of each type of abnormal respiratory sound in real time; based on a preset clinical hazard level weight table, perform a weighted severity score on multiple types of abnormal respiratory sounds that exist simultaneously; determine the ventilation parameter adjustment priority according to the score result, and execute a hierarchical intervention strategy.

[0084] As shown in the above-mentioned embodiments, the ventilator controller 304 is further configured to, in combination with the above-mentioned functions, Figure 3 and Figure 4 The system further comprises a human-computer interaction unit 305; the human-computer interaction unit 305 is configured to display real-time spectrograms, abnormal respiratory sound prompts, and parameter adjustment information. Specifically, the human-computer interaction unit 305 can realize real-time spectrograms, abnormal type color marking, adjustment suggestion pop-up window and confirmation logic, one-key switching between automatic and manual, log and historical trend tracking, and the like.

[0085] As shown in the above-mentioned embodiments, the ventilator controller 304 is further configured to, in combination with the above-mentioned functions, Figure 3 and Figure 4Further shown, the safety monitoring unit 306 is configured to automatically deactivate the closed-loop regulation and alarm when a sensor failure or parameter out-of-limit is detected.

[0086] Based on the same inventive concept, the embodiment of the present application provides a breathing machine intelligent ventilation control method based on respiratory sound monitoring.

[0087] Figure 8 A flowchart of the breathing machine intelligent ventilation control method based on respiratory sound monitoring according to another embodiment of the present application. Figure 9 A flowchart of the breathing machine intelligent ventilation control method based on respiratory sound monitoring according to another embodiment of the present application. Figure 8 And Figure 9 As shown in the figures, the embodiment of the present application relates to a breathing machine intelligent ventilation control method based on respiratory sound monitoring, which comprises the following steps S801 to S804:

[0088] Step S801, synchronously collecting the respiratory sound signals of a user through at least one tracheal sound sensor and at least two chest wall sound sensors.

[0089] Step S802, preprocessing the respiratory sound signals.

[0090] Step S803, processing the preprocessed respiratory sound signals to obtain the abnormal respiratory sound type and confidence of the user.

[0091] Step S804, dynamically adjusting the ventilation parameters of the breathing machine according to the abnormal respiratory sound type and confidence.

[0092] The breathing machine intelligent ventilation control method based on respiratory sound monitoring according to the embodiment of the present application has the following beneficial effects:

[0093] 1. The evaluation accuracy is improved: a respiratory dynamics evaluation model with strong generalization ability is constructed based on respiratory sound signal spectrum feature analysis technology, realizing non-invasive high-precision evaluation of the respiratory system and directly feeding back the ventilation quality of the respiratory system.

[0094] 2. The time accuracy is improved: the respiratory dynamics evaluation model based on respiratory sound can synchronously respond to the instantaneous changes of physiological characteristics such as lung volume and airway pressure, and the response speed is improved compared with traditional methods.

[0095] 3. Strong dynamic adaptability: the respiratory pattern classifier based on deep convolutional neural network can accurately identify normal respiratory patterns and abnormal respiratory events (including cough, wheezing, apnea, etc.), and give dynamic adjustment suggestions according to normal and abnormal respiratory patterns.

[0096] 4. Strong anti-noise capability: relatively less affected by environmental noise and other non-ideal factors under the condition of non-invasive physiological monitoring, higher monitoring accuracy and less operation difficulty compared with carbon dioxide partial pressure detection and other methods.

[0097] In some embodiments, the parameter adjustment includes: wheezing sound triggers prolonged exhalation and reduced respiratory rate, wet rale triggers increased PEEP, dry rale triggers sputum suction prompt, and pleural friction sound triggers reduced tidal volume.

[0098] In some embodiments, all parameter adjustment amplitudes are limited by safety thresholds, and are recorded in an unchangeable log. In addition, in some embodiments, a one-key switching function is provided to switch between automatic mode and manual mode.

[0099] In some embodiments, the real-time spectrogram, abnormality determination and parameter adjustment suggestion are also displayed through the human-computer interface. The processing delay of the central processing unit is not more than 50 ms, so that the acquisition-determination-control closed loop is completed within a single breathing cycle.

[0100] Based on the same inventive concept, as shown in Figure 10 The embodiment of the present application also provides an intelligent ventilator, which comprises the intelligent ventilation control system of the ventilator based on respiratory sound monitoring as described above, and specific reference can be made to the related description above, which will not be repeated here.

[0101] Based on the same inventive concept, the embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the computer program can realize the off-line page development method based on the low-code development platform according to the above description when executed by a processor.

[0102] The computer readable medium can be included in the device, equipment or system of the present disclosure, or can exist independently.

[0103] The computer readable storage medium can be any tangible medium containing or storing a program, which can be an electrical, magnetic, optical, electromagnetic, infrared, semiconductor system, device or equipment, and more specific examples include but are not limited to: an electrical connection with one or more wires, a portable computer diskette, a hard disk, an optical fiber, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0104] The computer readable storage medium can also include a data signal propagating in a baseband or as a carrier wave in a propagated signal, which carries the computer readable program code, and specific examples include but are not limited to an electromagnetic signal, an optical signal, or any suitable combination thereof.

[0105] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions recorded in the above embodiments can still be modified, or some or all of the technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A respiratory machine intelligent ventilation control system based on respiratory sound monitoring, characterized in that, The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine. The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine. The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine. The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine. The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine.

2. The respiratory sound monitoring based ventilator intelligent ventilation control system according to claim 1, wherein, The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine.

3. The respiratory sound monitoring based ventilator intelligent ventilation control system according to claim 1, wherein, The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine.

4. The respiratory sound based monitor ventilator intelligent ventilation control system according to any one of claims 1 to 3, characterized in that, The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine.

5. The respiratory sound based monitor ventilator intelligent ventilation control system according to any one of claims 1 to 3, wherein, The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine. The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine. The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine. The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine.

6. The respiratory sound based monitor ventilator intelligent ventilation control system according to any one of claims 1 to 3, wherein, The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine. The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine.

7. The respiratory sound monitoring based ventilator intelligent ventilation control system according to any one of claims 1 to 3, wherein, The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine. The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine.

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The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine. The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine. The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine. The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine. The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine. The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine. The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine. The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine. The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine. The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine. The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine. The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine. The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine. The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine. The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine. The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine. The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine. The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine. The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine. The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine. The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine. The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine. The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine. The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine. The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine. The application relates to a breathing sound monitoring-based intelligent ventilation control system of a breathing machine. The application relates to a breathing sound monitoring-based intelligent ventilation control system of

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