A breathing signal detection method, system, computer device and storage medium

Abstract

The present application relates to a respiratory signal detection method, system, computer equipment and storage medium, the method comprising real-time acquisition of a respiratory pressure signal sent by a respiratory airflow sensor, data processing of the respiratory pressure signal to obtain an effective respiratory pressure signal that meets the actual respiratory condition of the patient, data calculation of the effective respiratory pressure signal to obtain respiratory frequency data, and respiratory state prediction based on the respiratory frequency data to obtain the patient's next respiratory state, and based on the next respiratory state, generating an oxygen supply adjustment instruction corresponding to the next respiratory state, so as to timely adjust the patient's oxygen supply state to meet the next respiratory state. The present application has the effect of improving the anti-interference ability of respiratory detection and reducing the delay error of oxygen supply state adjustment, thereby improving the accuracy of respiratory signal detection.

Description

Technical Field

[0001] The present invention relates to the technical field of respiratory signal detection, and in particular to a blockchain-based smart health insurance configuration method, system, device and medium. Background Art

[0002] At present, human breathing and heartbeat are direct indicators of human vital signs. Abnormalities in heartbeat and breathing signals often reflect changes in the patient's vital signs. Especially in the process of monitoring the patient's vital signs, accurate detection of breathing signals is particularly important.

[0003] Currently, the detection of respiratory signals is often carried out through the chest impedance method, in which the two electrodes of the ECG lead pick up the signal of respiratory impedance change, and the human breathing condition is obtained according to the impedance change signal difference. However, in the actual operation process, the human body's electrical signal is weak and easily interfered by other factors, such as the resistance fluctuation caused by the expansion and contraction of the chest cavity during human breathing. In addition, the high-frequency electric knife needs to be used during the operation, which is easy to cause irreversible interference to the detection of respiratory signals, making it difficult to obtain accurate respiratory signals during the use of the electric knife.

[0004] With respect to the above-mentioned related technologies, the inventors believe that there is a defect that the anti-interference ability of respiratory signal detection is poor. Summary of the invention

[0005] In order to improve the anti-interference capability of breathing detection, the present application provides a breathing signal detection method, system, computer device and storage medium.

[0006] The above-mentioned invention objective of the present application is achieved through the following technical solutions:

[0007] A breathing signal detection method is provided, the breathing signal detection method comprising:

[0008] Acquire the respiratory pressure signal sent by the respiratory airflow sensor in real time;

[0009] Performing data processing on the respiratory pressure signal to obtain an effective respiratory pressure signal that conforms to the actual respiratory condition of the patient;

[0010] Performing data calculation on the effective respiratory pressure signal to obtain respiratory frequency data, and predicting the respiratory state based on the respiratory frequency data to obtain the next respiratory state of the patient;

[0011] According to the next respiratory state, an oxygen supply adjustment instruction corresponding to the next respiratory state is generated, so as to timely adjust the patient's oxygen supply state to match the next respiratory state.

[0012] By adopting the above technical solution, since the method of picking up human electrical signals through electrodes is easily affected by errors caused by human activities such as chest expansion, especially during surgery, the use of high-frequency electric knife is likely to increase the electrical signals picked up by the electrodes. Therefore, the respiratory changes of the patient are sensed by a respiratory airflow sensor installed in the patient's nasal cavity, and the changes in the patient's respiratory signals are accurately picked up by the sensitive resistor in the respiratory airflow sensor, and the respiratory frequency data is obtained by calculating the respiratory pressure signal, which helps to predict the patient's next respiratory state according to the respiratory frequency data, and dynamically adjust the patient's oxygen supply state through the oxygen supply adjustment instruction, so that when the patient's actual respiratory state reaches the next respiratory state, the discomfort caused by the incompatibility of the oxygen supply state to the patient is reduced, and the patient's respiratory resistance is reduced. The present application dynamically adjusts the oxygen supply state according to the patient's respiratory frequency, so that when the patient reaches the next respiratory state, the influence of respiratory interference factors on the respiratory signal is reduced, so that the changes in the respiratory signal always meet the requirements of the respiratory frequency, thereby improving the anti-interference of the respiratory signal detection, and by timely adjusting the oxygen supply state before the next respiratory state, reducing the delay error of the oxygen supply state adjustment, thereby improving the accuracy of the respiratory signal detection.

[0013] In a preferred example, the present application may be further configured as follows: generating an oxygen supply adjustment instruction corresponding to the next respiratory state according to the next respiratory state, so as to timely adjust the patient's oxygen supply state to match the next respiratory state, specifically including:

[0014] Obtain the actual breathing time of the patient in the current breathing state in real time;

[0015] Predicting the expected respiratory change time for the patient to reach the next respiratory state based on the respiratory rate data;

[0016] generating an oxygen supply frequency adjustment instruction that conforms to the respiratory frequency data according to the estimated respiratory change time;

[0017] When the actual breathing time meets the expected breathing change time, the oxygen delivery frequency adjustment instruction is called to timely adjust the patient's oxygen delivery frequency so that the oxygen delivery frequency meets the next breathing state.

[0018] By adopting the above technical scheme, since the breathing of critically ill patients during surgery is relatively weak, if the oxygen supply is always in the delivery state, or is not compatible with the patient's breathing frequency, it is easy to cause resistance to the patient's breathing. For example, the direction of the oxygen supply airflow creates resistance to the patient's exhalation direction, which is not conducive to the patient's oxygen intake, and the change of the oxygen delivery frequency requires a certain reaction time. Therefore, by predicting the expected respiratory change time of the patient's next breathing state, the oxygen supply frequency is timely adjusted through the oxygen supply frequency adjustment instruction, and when the patient's actual breathing time meets the predicted expected respiratory change time, the oxygen supply frequency adjustment instruction is called to timely adjust the patient's oxygen intake state when arriving at the next breathing state, thereby improving the adaptability of the oxygen delivery frequency to the patient's actual breathing state and reducing the adverse resistance caused by oxygen delivery to the patient's breathing state.

[0019] In a preferred example, the present application may be further configured as follows: generating an oxygen supply adjustment instruction corresponding to the next respiratory state according to the next respiratory state, so as to timely adjust the patient's oxygen supply state to match the next respiratory state, and further comprising:

[0020] Obtain the estimated oxygen content in the next breathing state;

[0021] Calculating the blood oxygen concentration based on the predicted oxygen delivery content to obtain expected blood oxygen concentration data that meets the predicted oxygen delivery content;

[0022] Obtain the patient's actual blood oxygen concentration data in real time;

[0023] Comparing the actual blood oxygen concentration data with the expected blood oxygen concentration data to obtain a blood oxygen concentration comparison difference;

[0024] According to the blood oxygen concentration comparison difference, an oxygen supply pressure adjustment instruction corresponding to the blood oxygen concentration comparison difference and used to adjust the oxygen supply pressure is generated.

[0025] By adopting the above technical solution, since it is easy to have illusions when relying solely on respiratory signal detection at the nasal cavity, such as there is breathing movement at the nasal cavity, but the inhaled oxygen does not enter the patient's lungs in sufficient quantity, resulting in the mismatch between the actual blood oxygen concentration and the oxygen content ingested, and respiratory signal detection errors are prone to occur. Therefore, the actual blood oxygen concentration data of the patient is obtained in real time through the blood oxygen sensor, and the blood oxygen concentration is calculated for the expected oxygen delivery content, which helps to provide an effective reference indicator for the change of the actual blood oxygen concentration through the expected blood oxygen concentration data, and adjust the patient's oxygen delivery pressure according to the comparison value of the actual blood oxygen concentration data and the expected blood oxygen concentration data, thereby obtaining the oxygen delivery pressure regulation instruction, and dynamically adjusting the oxygen delivery pressure through the oxygen delivery pressure regulation instruction. The present application dynamically adjusts the oxygen delivery pressure according to the blood oxygen concentration comparison difference, thereby improving the patient's actual oxygen absorption content, reducing the mismatch between the nasal oxygen absorption data and the actual blood oxygen concentration data, and improving the accuracy of respiratory signal detection from multiple dimensions.

[0026] In a preferred example, the present application may be further configured as follows: generating an oxygen supply pressure adjustment instruction corresponding to the blood oxygen concentration comparison difference and used to adjust the oxygen supply pressure according to the blood oxygen concentration comparison difference, and further comprising:

[0027] Comparing the blood oxygen concentration comparison difference with a preset blood oxygen concentration change threshold to obtain a blood oxygen concentration comparison result;

[0028] According to the blood oxygen concentration comparison result, judging whether the actual oxygen content of the patient meets the patient's oxygen demand;

[0029] If not, the oxygen supply pressure adjustment instruction corresponding to the blood oxygen concentration comparison difference is called to adjust the patient's actual blood oxygen concentration to meet the expected blood oxygen concentration requirement.

[0030] By adopting the above technical scheme, it is difficult to judge whether the oxygen content actually entering the patient's lungs meets the oxygen supply demand due to the breathing action at the nasal cavity, and it takes a certain reaction time for oxygen to enter the lungs and be converted into blood oxygen. Therefore, by comparing the blood oxygen concentration comparison difference with the preset blood oxygen concentration change threshold, it is helpful to judge the patient's actual oxygen conversion capacity according to the blood oxygen concentration comparison result, so as to know whether the patient's actual oxygen absorption content meets the patient's oxygen absorption demand. When the patient's actual sunset content cannot meet the patient's oxygen absorption demand, the oxygen supply pressure instruction should be called in time to adjust the oxygen supply pressure, which helps to increase the oxygen content inhaled into the patient's lungs through the external pressure effect, so that the patient's actual blood oxygen concentration meets the expected blood oxygen intensity demand, reduces the error between the actual blood oxygen concentration and the oxygen intake content at the nasal cavity, and improves the accuracy of respiratory signal detection.

[0031] In a preferred example, the present application may be further configured as follows: the data processing of the respiratory pressure signal to obtain an effective respiratory pressure signal that conforms to the actual respiratory condition of the patient specifically includes:

[0032] Performing data filtering processing on the respiratory pressure signal to obtain respiratory pressure filtered data after eliminating respiratory interference data;

[0033] Inputting the respiratory pressure filtered data into a pre-built respiratory variation model to obtain an effective respiratory pressure signal that conforms to the actual respiratory variation of the patient;

[0034] The effective respiratory pressure signal is plotted into a respiratory change curve that conforms to the actual respiratory condition of the patient, so as to monitor the actual respiratory condition of the patient according to the respiratory change curve.

[0035] By adopting the above technical solution, since the patient's body movements or changes in the surrounding airflow will have a slight impact on the respiratory airflow sensor, these airflow data that have no reference value are likely to affect the accuracy of respiratory signal detection. Therefore, by filtering the respiratory pressure data, the respiratory interference data that is incomplete and continuously changing in the respiratory pressure data is filtered out, thereby leaving the respiratory pressure filtered data that conforms to the patient's actual respiratory condition, which helps to improve the accuracy of actual respiratory signal detection, and re-process the respiratory pressure filtered data through a pre-constructed respiratory change model, so that the actual respiratory signal is more in line with the patient's actual respiratory changes, improves the fit between the detected respiratory signal and the actual respiratory changes, and intuitively monitors the patient's actual respiratory condition through the respiratory change curve, thereby improving the convenience of monitoring the respiratory signal changes.

[0036] In a preferred example, the present application can be further configured as follows: the respiratory pressure filtering data is input into a pre-built respiratory change model to obtain an effective respiratory pressure signal that conforms to the actual respiratory change of the patient, specifically including:

[0037] Obtain historical breathing data without interference;

[0038] Performing data training on the historical respiratory data to generate a respiratory change model that conforms to the patient's respiratory condition in an undisturbed state;

[0039] Inputting the respiratory pressure filtering data into the respiratory change model, performing data comparison between the respiratory pressure filtering data and the historical respiratory data under the same respiratory state, and obtaining a respiratory data comparison result;

[0040] According to the respiratory data comparison result, error compensation is performed on the respiratory pressure filtering data to obtain a valid respiratory pressure signal that conforms to the historical respiratory frequency.

[0041] By adopting the above technical scheme, since there is no effective reference index for the respiratory signal detected by the respiratory airflow sensor, in order to make the accuracy of the respiratory signal detected by the respiratory airflow sensor meet the medical reference standard for respiratory signal detection, data training is performed through historical respiratory data in an undisturbed state to construct a respiratory change model, wherein the historical respiratory data in an undisturbed state refers to the respiratory signal picked up by the electrode sheet without interference from the electric knife and / or the patient's body movement. The respiratory pressure filtering data is quickly analyzed through the respiratory change model. Since the human body's respiratory state switching time is relatively fast, the respiratory change model helps to reduce the data analysis time of the respiratory pressure filtering data and speed up the analysis efficiency of the respiratory pressure filtering data. The respiratory pressure filtering data is compensated for errors based on the respiratory data comparison results under the same respiratory state, which helps to obtain an effective respiratory pressure signal that conforms to the historical respiratory frequency, so that the respiratory pressure signal detected by the respiratory airflow sensor fits the medical reference index.

[0042] In a preferred example, the present application may be further configured as follows: the effective respiratory pressure signal is plotted into a respiratory change curve that conforms to the actual respiratory condition of the patient, so as to monitor the actual respiratory condition of the patient according to the respiratory change curve, and further includes:

[0043] Calculating the peak data of the respiratory change curves of adjacent respiratory states to obtain respiratory differential values ​​of adjacent respiratory states;

[0044] Comparing the respiratory differential value with a preset differential threshold to obtain a differential value comparison result;

[0045] According to the differential value comparison result, judging whether the actual breathing condition of the patient is abnormal;

[0046] If so, a breathing abnormality alarm instruction corresponding to the breathing differential value is generated to facilitate timely processing of the breathing abnormality.

[0047] By adopting the above technical solution, since it takes a certain reaction time from the occurrence of an abnormality in the patient's respiratory signal to the start of treatment, the respiratory differential value is calculated by calculating the peak data in the respiratory change curve under adjacent respiratory states, which helps to accurately monitor each change in the patient's respiratory state according to the respiratory differential value. By comparing the differential value between the respiratory differential value and the preset differential threshold, it is determined whether the patient's actual breathing condition has respiratory abnormalities. If the respiratory differential value exceeds the preset differential threshold, it means that the patient has abnormal breathing, and a respiratory abnormality alarm instruction is generated in time to reduce the reaction time of the respiratory abnormality alarm and improve the processing efficiency of the abnormal alarm.

[0048] The second object of the invention is achieved by the following technical solutions:

[0049] A breathing signal detection system is provided, the breathing signal detection system comprising:

[0050] A data acquisition module, used for acquiring the respiratory pressure signal sent by the respiratory airflow sensor in real time;

[0051] A data processing module, used for processing the respiratory pressure signal to obtain an effective respiratory pressure signal that meets the actual respiratory condition of the patient;

[0052] A respiratory state prediction module, used to perform data calculation on the effective respiratory pressure signal to obtain respiratory frequency data, and perform respiratory state prediction based on the respiratory frequency data to obtain the patient's next respiratory state;

[0053] The instruction generation module is used to generate an oxygen supply adjustment instruction corresponding to the next respiratory state according to the next respiratory state, so as to adjust the patient's oxygen supply state to match the next respiratory state in time.

[0054] By adopting the above technical solution, since the method of picking up human electrical signals through electrodes is easily affected by errors caused by human activities such as chest expansion, especially during surgery, the use of high-frequency electric knife is likely to increase the electrical signals picked up by the electrodes. Therefore, the respiratory changes of the patient are sensed by a respiratory airflow sensor installed in the patient's nasal cavity, and the changes in the patient's respiratory signals are accurately picked up by the sensitive resistor in the respiratory airflow sensor, and the respiratory frequency data is obtained by calculating the respiratory pressure signal, which helps to predict the patient's next respiratory state according to the respiratory frequency data, and dynamically adjust the patient's oxygen supply state through the oxygen supply adjustment instruction, so that when the patient's actual respiratory state reaches the next respiratory state, the discomfort caused by the incompatibility of the oxygen supply state to the patient is reduced, and the patient's respiratory resistance is reduced. The present application dynamically adjusts the oxygen supply state according to the patient's respiratory frequency, so that when the patient reaches the next respiratory state, the influence of respiratory interference factors on the respiratory signal is reduced, so that the changes in the respiratory signal always meet the requirements of the respiratory frequency, thereby improving the anti-interference of the respiratory signal detection, and by timely adjusting the oxygen supply state before the next respiratory state, reducing the delay error of the oxygen supply state adjustment, thereby improving the accuracy of the respiratory signal detection.

[0055] The third objective of the present application is achieved through the following technical solutions:

[0056] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned breathing signal detection method when executing the computer program.

[0057] The fourth objective of the present application is achieved through the following technical solutions:

[0058] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned breathing signal detection method are implemented.

[0059] In summary, the present application includes at least one of the following beneficial technical effects:

[0060] 1. The respiratory changes of the patient are sensed by a respiratory airflow sensor installed in the patient's nasal cavity, and the changes of the patient's respiratory signal are accurately picked up by the sensitive resistor in the respiratory airflow sensor, and the respiratory frequency data is obtained by calculating the respiratory pressure signal, so as to help predict the patient's next respiratory state according to the respiratory frequency data, and dynamically adjust the patient's oxygen supply state through the oxygen supply adjustment instruction, so as to reduce the discomfort caused by the incompatibility of the oxygen supply state to the patient when the patient's actual respiratory state reaches the next respiratory state, and reduce the patient's respiratory resistance. The present application dynamically adjusts the oxygen supply state according to the patient's respiratory frequency, so as to reduce the influence of respiratory interference factors on the respiratory signal when the patient reaches the next respiratory state, so that the changes of the respiratory signal always meet the requirements of the respiratory frequency, thereby improving the anti-interference of the respiratory signal detection, and by timely adjusting the oxygen supply state before the next respiratory state, reducing the delay error of the oxygen supply state adjustment, thereby improving the accuracy of the respiratory signal detection;

[0061] 2. By predicting the expected respiratory change time of the patient's next respiratory state, the oxygen supply frequency is adjusted in time through the oxygen supply frequency adjustment instruction. When the patient's actual breathing time meets the predicted expected respiratory change time, the oxygen supply frequency adjustment instruction is called to adjust the oxygen uptake state of the patient who reaches the next respiratory state in time, thereby improving the adaptability of the oxygen delivery frequency to the patient's actual respiratory state and reducing the adverse resistance caused by oxygen delivery to the patient's respiratory state;

[0062] 3. The actual blood oxygen concentration data of the patient is obtained in real time through the blood oxygen sensor, and the blood oxygen concentration is calculated for the expected oxygen delivery content, which helps to provide an effective reference indicator for the change of the actual blood oxygen concentration through the expected blood oxygen concentration data, and adjust the patient's oxygen delivery pressure according to the comparison value of the actual blood oxygen concentration data and the expected blood oxygen concentration data, thereby obtaining the oxygen delivery pressure adjustment instruction, and dynamically adjusting the oxygen delivery pressure through the oxygen delivery pressure adjustment instruction. This application dynamically adjusts the oxygen delivery pressure according to the blood oxygen concentration comparison difference, thereby improving the patient's actual oxygen absorption content, reducing the incompatibility between nasal oxygen absorption data and actual blood oxygen concentration data, and improving the accuracy of respiratory signal detection from multiple dimensions. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a flow chart of an implementation method of a breathing signal detection method in an embodiment of the present application.

[0064] Figure 2 This is a flow chart for implementing step S20 of a breathing signal detection method in an embodiment of the present application.

[0065] Figure 3 This is a flow chart for implementing step S102 of a breathing signal detection method in an embodiment of the present application.

[0066] Figure 4 It is a flow chart for implementing step S103 of a breathing signal detection method in an embodiment of the present application.

[0067] Figure 5 This is a flowchart for implementing step S40 of a breathing signal detection method in an embodiment of the present application.

[0068] Figure 6 This is another implementation flow chart of step S40 of a breathing signal detection method in an embodiment of the present application.

[0069] Figure 7 This is another implementation flow chart of step S505 of a breathing signal detection method in an embodiment of the present application.

[0070] Figure 8 It is a structural schematic diagram of a breathing signal detection system in an embodiment of the present application.

[0071] Fig. 9 It is a schematic diagram of the internal structure of a computer device used to implement a breathing signal detection method. DETAILED DESCRIPTION

[0072] The present application is further described in detail below in conjunction with the accompanying drawings.

[0073] In one embodiment, if Figure 1 As shown, the present application discloses a breathing signal detection method, which specifically includes the following steps:

[0074] S10: Acquire the respiratory pressure signal sent by the respiratory airflow sensor in real time.

[0075] Specifically, the patient's respiratory airflow is picked up by a respiratory airflow sensor installed at the nasal cavity of the patient to be tested, wherein the respiratory airflow includes respiratory inhalation airflow and respiratory exhalation airflow. A respiratory pressure signal corresponding to the respiratory airflow is obtained through a pressure sensitive resistor in the respiratory airflow sensor. When the respiratory airflow is detected by the respiratory airflow sensor, the resistance value of the pressure sensitive resistor is changed due to the change of the respiratory airflow. The change of the resistance value of the pressure sensitive resistor is recorded to obtain a respiratory pressure signal.

[0076] S20: Process the respiratory pressure signal to obtain an effective respiratory pressure signal that meets the actual respiratory condition of the patient.

[0077] Specifically, the respiratory pressure signal is amplified and filtered by a signal amplifying circuit and a signal filtering circuit connected to the respiratory airflow sensor to obtain respiratory pressure filtered data after filtering out invalid interference signals, and the filtered respiratory pressure filtered data is converted into a data format by a preset DAC digital-to-analog conversion circuit to obtain a respiratory pressure signal in a digital signal format recognizable by a computer, and the respiratory pressure signal is adjusted by comparing it with historical respiratory data to obtain an effective respiratory pressure signal that meets the actual respiratory condition of the patient, such as Figure 2 As shown, step S20 specifically includes:

[0078] S101: Performing data filtering processing on the respiratory pressure signal to obtain respiratory pressure filtered data after eliminating respiratory interference data.

[0079] Specifically, since the patient's respiratory airway is relatively weak, the generated respiratory pressure signal is also relatively small, which is not convenient for data screening. Therefore, the respiratory pressure signal is first amplified by a signal amplification circuit connected to the respiratory airflow sensor, and the amplified respiratory interference data that does not conform to the respiratory change frequency is filtered through a signal filtering circuit, such as an LC filter circuit. Among them, the respiratory interference data that does not conform to the respiratory change frequency mainly includes breakpoint data or respiratory pressure signals with incomplete change curves, etc., and a continuous and evenly changing complete respiratory pressure signal is screened out from a number of respiratory pressure data as respiratory pressure filtered data.

[0080] S102: Inputting the respiratory pressure filtered data into a pre-built respiratory change model to obtain an effective respiratory pressure signal that conforms to the actual respiratory change of the patient.

[0081] Specifically, the respiratory pressure filtering data is quickly compared with the historical respiratory frequency data in the respiratory change model, thereby improving the fit between the respiratory pressure filtering data and the patient's actual respiratory changes, such as Figure 3 As shown, step S102 specifically includes:

[0082] S201: Acquire historical breathing data in a non-interference state.

[0083] Specifically, the historical respiratory data in an undisturbed state is a respiratory signal with medical reference value without external interference, such as respiratory signal data with medical reference value picked up by the electrodes of the ECG lead before the patient undergoes electrosurgery or when the patient is in a stable breathing state.

[0084] S202: Perform data training on the historical respiratory data to generate a respiratory change model that conforms to the patient's respiratory condition in an undisturbed state.

[0085] Specifically, historical respiratory data is used as a training set, and the respiratory signals of the patient in each respiratory state are recorded, and the peak data in the historical respiratory data is marked, such as the respiratory signal value at the respiratory state transition of each breath, the respiratory signal difference between adjacent respiratory states is calculated, and the respiratory fluctuation amplitude is calculated according to the corresponding respiratory state transition time to obtain the respiratory slope between adjacent respiratory states, and a historical respiratory change curve is drawn according to the corresponding respiratory slope, and the historical number of breaths in a period of time is obtained using hours, days, and weeks as time units, and the patient's respiratory frequency is obtained by dividing the historical number of breaths by the corresponding time value. The respiratory frequency in different time intervals is calculated through an artificial intelligence algorithm such as a convolutional neural network algorithm, and the respiratory frequency value with the best completeness and fluctuation is selected as a reference indicator for constructing a respiratory change model, so as to construct a respiratory change model according to the correlation between the respiratory frequency value and the historical respiratory change curve.

[0086] S203: Input the respiratory pressure filtering data into the respiratory change model, and compare the respiratory pressure filtering data under the same respiratory state with the historical respiratory data to obtain a respiratory data comparison result.

[0087] Specifically, according to the correlation between the respiratory states between the respiratory pressure signals, the real-time respiratory pressure filtered data is input into the respiratory change model, and the respiratory pressure filtered data and the historical respiratory data under the same respiratory state are compared in the respiratory change model. For example, if the impedance value in the historical respiratory data under the inhalation state is 3 ohms, and the impedance value in the respiratory pressure filtered data under the inhalation state is 5 ohms, then the respiratory data comparison result is that the respiratory difference between the respiratory pressure filtered data and the historical respiratory data is 2 ohms.

[0088] S204: According to the respiratory data comparison result, error compensation is performed on the respiratory pressure filtering data to obtain a valid respiratory pressure signal that conforms to the historical respiratory frequency.

[0089] Specifically, according to the respiratory impedance difference between the respiratory pressure filtering data and the historical respiratory data, the respiratory pressure filtering data is compensated for errors. For example, the respiratory pressure filtering data is amplified according to the respiratory impedance difference, and the patient's oxygen supply status is adjusted to adjust the patient's respiratory signal changes, thereby reducing the respiratory impedance difference between the respiratory pressure filtering data and the historical respiratory data. For example, with the impedance value of 3 ohms in the historical respiratory data as a reference, the oxygen supply status is adjusted to reduce the patient's abnormal respiratory fluctuations. For example, when the patient's oxygen supply is insufficient, shortness of breath, too fast or too slow breathing, etc., will cause the respiratory pressure filtering data to deviate from the accuracy requirements of the historical respiratory data. Therefore, the respiratory pressure filtering data is compensated for errors by adjusting the patient's oxygen supply status to obtain a valid respiratory pressure signal that meets the historical respiratory frequency.

[0090] S103: Plotting the effective respiratory pressure signal into a respiratory change curve that conforms to the actual respiratory condition of the patient, so as to monitor the actual respiratory condition of the patient according to the respiratory change curve.

[0091] Specifically, the effective respiratory pressure signal is sent to an electrically connected waveform display for waveform display. Specifically, in the waveform display, the respiratory state conversion impedance value in each respiratory state is marked according to the occurrence order of the patient's breathing time, and connected in chronological order. According to the continuous change of the effective respiratory pressure signal, the broken line of the respiratory state conversion impedance value is smoothed by a preset function to obtain a respiratory change curve that conforms to the patient's actual respiratory condition, such as by smoothing the effective respiratory pressure signal by an interpolation method. The obtained respiratory change curve passes through each effective respiratory pressure signal, so that the patient's actual respiratory condition is monitored through the respiratory change curve. Figure 4 As shown, step S103 specifically includes:

[0092] S301: Calculate the peak data of the respiratory change curves of adjacent respiratory states to obtain the respiratory differential values ​​of adjacent respiratory states.

[0093] Specifically, for the peak data under adjacent breathing states, that is, the peak value and trough value between each complete breathing wave in the breathing change curve, if the peak value under adjacent inhalation states is 10 ohms and the trough value under exhalation states is 5 ohms, then the breathing differential value between the ringing breathing states is 5 ohms.

[0094] S302: Compare the respiratory differential value with a preset differential threshold to obtain a differential value comparison result.

[0095] Specifically, the differential threshold is calculated based on the patient's historical breathing data, such as obtaining historical abnormal data when the patient has abnormal breathing in the historical breathing data, and screening out the optimal solution as the maximum breathing abnormality value that the patient can bear based on the historical abnormal data, and using the maximum breathing abnormality value as a preset differential threshold such as 6 ohms. The differential value comparison result between the breathing differential value of 5 ohms and the preset differential threshold is a positive value; if the breathing differential value is 8 ohms, the differential value comparison result between the breathing differential value and the preset differential threshold is a negative value.

[0096] S303: Determine whether the patient's actual breathing condition is abnormal based on the differential value comparison result.

[0097] Specifically, when the differential value comparison result is a positive value, that is, the respiratory differential value does not exceed the controllable range of the preset differential threshold, it means that the patient's actual breathing condition is still in a controllable state and no abnormal breathing has occurred; when the differential value comparison result is a negative value, that is, the respiratory differential value exceeds the controllable range of the preset differential threshold, it means that the patient's actual breathing condition has respiratory abnormalities, such as shortness of breath, bradypnea and / or rapid breathing and other abnormal conditions.

[0098] S304: If yes, generate a breathing abnormality alarm instruction corresponding to the breathing differential value, so as to handle the breathing abnormality in time.

[0099] Specifically, when a patient has abnormal breathing, a breathing abnormality alarm instruction is generated according to the actual breathing differential value and sent to the corresponding monitoring personnel end, so as to facilitate the monitoring personnel to provide timely treatment to the patient with abnormal breathing.

[0100] S305: If not, continue to monitor the respiratory differential conditions under adjacent respiratory states.

[0101] Specifically, if the patient does not have abnormal breathing, the respiratory differential conditions under adjacent respiratory states in the respiratory change curve are continuously monitored, so that abnormal respiratory differential conditions can be discovered in time.

[0102] S30: Calculate the effective respiratory pressure signal to obtain respiratory frequency data, and predict the respiratory state based on the respiratory frequency data to obtain the patient's next respiratory state.

[0103] Specifically, according to preset unit time, such as hour, day and / or week as a unit time node, the respiratory pressure signal interval within the unit time is obtained, and the quotient of the number of breaths within the respiratory pressure signal interval, that is, the number of respiratory impedance value conversions, and the corresponding unit time is used as the respiratory frequency. The respiratory frequency is combined with the length of time for each respiratory state transition of the patient to predict the patient's future respiratory state, so as to obtain the patient's next respiratory state. For example, the patient's respiratory frequency is 12 times per minute, and each respiratory state transition takes 5 seconds. If the current respiratory state is the inhalation state, the patient's respiratory state will be adjusted to the exhalation state after 5 seconds.

[0104] S40: Generate an oxygen supply adjustment instruction corresponding to the next breathing state according to the next breathing state, so as to timely adjust the patient's oxygen supply state to match the next breathing state.

[0105] Specifically, according to the patient's next breathing state, the patient's oxygen supply state, including oxygen supply frequency and oxygen supply pressure, is adjusted, so that the oxygen supply state fits the patient's actual breathing state and reduces the patient's discomfort during breathing, such as Figure 5 As shown, step S40 specifically includes:

[0106] S401: Acquire the actual breathing time of the patient in the current breathing state in real time.

[0107] Specifically, the patient's current breathing state is obtained based on the detection of the direction of airflow in the respiratory airflow sensor. For example, pressure-sensitive resistors are set in both positive and negative directions. When the pressure-sensitive resistor close to the nasal cavity detects airflow, it indicates that the patient's current breathing state is an exhalation state; when the pressure-sensitive resistor far from the nasal cavity detects airflow, it indicates that the patient's current breathing state is an inhalation state; and the patient's actual breathing time is judged based on the resistance change of the pressure-sensitive resistor. For example, the resistance of the pressure-sensitive resistor rises from 0 to 5 ohms and then drops to 0, which is one breathing state of the patient. The length of time the resistance of the pressure-sensitive resistor changes is detected as the patient's actual breathing time. For example, the time it takes for the resistance to rise from 0 to 5 and then drop to 0 is 5 seconds, then the actual breathing time of the patient's current breathing state is 5 seconds.

[0108] S402: Predicting the expected respiratory change time for the patient to reach the next respiratory state based on the respiratory rate data.

[0109] Specifically, if the patient's breathing rate is 12 times per minute, each breathing state of the patient takes 5 seconds, and the estimated breathing change time for the patient to reach the next breathing state is 5 seconds, and so on.

[0110] S403: Generate an oxygen supply frequency adjustment instruction that conforms to the respiratory frequency data according to the expected respiratory change time.

[0111] Specifically, since the adjustment of the oxygen supply status requires a certain reaction time, when the patient is in the current breathing state, the oxygen supply frequency adjustment instruction that matches the patient's breathing frequency is generated in advance according to the expected breathing change time. For example, if the patient's breathing frequency is 12 times per minute, the patient's oxygen supply frequency is adjusted to 12 times per minute accordingly, that is, the frequency of oxygen delivery is adjusted according to the patient's actual breathing situation.

[0112] S404: When the actual breathing time meets the expected breathing change time, the oxygen delivery frequency adjustment instruction is called to timely adjust the patient's oxygen delivery frequency so that the oxygen delivery frequency meets the next breathing state.

[0113] Specifically, when the actual breathing time meets the expected breathing change time, that is, the patient has completed the current breathing state and is about to switch to the next breathing state, the oxygen supply frequency adjustment instruction is called in time to adjust the oxygen supply frequency. If the patient's next breathing state is the inhalation state, the oxygen supply state is adjusted to the oxygen delivery state according to the oxygen supply frequency adjustment instruction; if the patient's next breathing state is the inhalation state, the oxygen supply state is adjusted to the oxygen delivery stop state or the reverse oxygen delivery state according to the oxygen supply frequency adjustment instruction, so that the oxygen delivery frequency is matched with the patient's next breathing state, reducing the discomfort caused to the patient by the incompatibility between the oxygen delivery state and the patient's actual breathing state.

[0114] In order to better meet the patient's oxygen demand and improve the fit between the patient's actual blood oxygen concentration and actual oxygen inhalation, Figure 6 As shown, step S40 also includes:

[0115] S501: Obtain the estimated oxygen content in the next breathing state.

[0116] Specifically, according to the oxygen supply frequency adjustment instruction, the oxygen flow rate that needs to be delivered to the patient's nasal cavity and the corresponding oxygen supply time in the next breathing state are obtained, and the expected oxygen supply content of the patient in the next breathing state is obtained according to the product of the oxygen flow rate and the oxygen supply time. For example, if the oxygen flow rate is set to 5 liters per minute, the time for each breathing state conversion is 5 seconds, that is, the oxygen supply time is 5 seconds each time, then the expected oxygen supply content in the next breathing state is approximately 0.41 liters.

[0117] S502: Calculate the blood oxygen concentration for the expected oxygen delivery content to obtain expected blood oxygen concentration data that meets the expected oxygen delivery content.

[0118] Specifically, based on the patient's historical blood glucose concentration changes and historical respiratory data, a blood oxygen conversion rate that is consistent with the patient's blood oxygen conversion is calculated. For example, if the historical inhaled oxygen content is 0.5 liters and the blood oxygen concentration change difference is 5 mmHg, the blood oxygen conversion rate is 1%. The expected blood oxygen concentration change value of the expected oxygen delivery content of 0.41 liters is 4.1 mmHg. The blood oxygen concentration data obtained in the patient's current respiratory state is 85 mmHg, and the expected blood oxygen concentration value for the next respiratory state is 89.1 mmHg.

[0119] S503: Acquire the patient's actual blood oxygen concentration data in real time.

[0120] Specifically, the actual blood oxygen concentration data of the patient is obtained through the blood oxygen sensor. For example, when the patient's breathing state switches to the next breathing state, the actual blood oxygen concentration data of the patient is obtained through the blood oxygen sensor, such as the actual blood oxygen concentration value is 80 mmHg.

[0121] S504: Compare the actual blood oxygen concentration data with the expected blood oxygen concentration data to obtain a blood oxygen concentration comparison difference.

[0122] Specifically, the actual blood oxygen concentration data is compared with the expected blood oxygen concentration data. For example, if the actual blood oxygen concentration value is 80 mmHg and the expected blood oxygen concentration value is 89.1 mmHg, the blood oxygen concentration comparison difference is 9.1 mmHg.

[0123] S505: Generate an oxygen supply pressure adjustment instruction corresponding to the blood oxygen concentration comparison difference and used to adjust the oxygen supply pressure according to the blood oxygen concentration comparison difference.

[0124] Specifically, according to the blood oxygen concentration comparison difference, the oxygen supply pressure of the next breathing state is adjusted through the oxygen supply pressure regulation instruction, so that the oxygen content inhaled into the patient's lungs can meet the expected blood oxygen concentration requirement by adjusting the oxygen supply pressure, thereby reducing the gap between the actual blood oxygen concentration and the expected blood oxygen concentration.

[0125] In this embodiment, since it takes a certain amount of time for oxygen to enter the lungs and then combine with hemoglobin, the change in blood oxygen concentration usually lags behind the change in the respiratory signal. Therefore, in order to better detect the patient's blood oxygen concentration accurately, a certain threshold is set for the change in blood oxygen concentration to reduce the situation where the blood oxygen concentration changes too much and there is no time for oxygen delivery, such as Figure 7 As shown, step S505 also includes:

[0126] S601: Compare the blood oxygen concentration comparison difference with a preset blood oxygen concentration change threshold to obtain a blood oxygen concentration change result.

[0127] Specifically, the blood oxygen concentration change threshold is set according to the analysis of the patient's condition. For example, for patients with symptoms of respiratory failure, the blood oxygen concentration needs to be maintained above 90 mmHg, and the blood oxygen concentration fluctuation cannot exceed 10 mmHg. The blood oxygen concentration change threshold is set to 10 mmHg. If the blood oxygen concentration comparison difference under the actual breathing state is 9.1 mmHg, the blood oxygen concentration change result does not exceed the blood oxygen concentration change threshold that meets the patient's actual condition, and the patient's actual blood oxygen concentration fluctuation is within a controllable range.

[0128] S602: According to the blood oxygen concentration change result, determine whether the patient's actual oxygen intake content meets the patient's oxygen intake demand.

[0129] Specifically, based on the comparison result between the actual blood oxygen concentration comparison difference and the preset blood oxygen concentration change threshold, it is judged whether the patient's actual oxygen absorption content meets the patient's oxygen absorption demand. If the blood oxygen concentration change result is that the actual blood oxygen concentration comparison difference exceeds the preset blood oxygen concentration change threshold, it means that the patient's actual oxygen absorption content cannot meet the patient's oxygen absorption demand. For example, for patients with symptoms of respiratory failure, if the blood oxygen concentration change exceeds the preset change threshold such as 10 mmHg, it means that the blood oxygen concentration obtained by converting the patient's oxygen is insufficient to meet the human body's oxygen absorption needs, which is likely to affect the normal work of human organs; if the blood oxygen concentration change result is that the actual blood oxygen concentration comparison difference does not exceed the preset blood oxygen concentration change threshold, it means that the patient's actual oxygen absorption content can meet the patient's oxygen absorption demand.

[0130] S603: If not, then call the oxygen supply pressure adjustment instruction corresponding to the blood oxygen concentration comparison difference, so as to adjust the patient's actual blood oxygen concentration to meet the expected blood oxygen concentration requirements.

[0131] Specifically, if the patient's actual oxygen uptake content cannot meet the patient's oxygen uptake needs, which may easily have an adverse effect on the normal operation of the patient's organs, the oxygen supply pressure regulation instruction calculated by the blood oxygen concentration comparison difference will be called in time to adjust the oxygen delivery pressure in time according to the patient's actual blood oxygen concentration absorption. The external force of pressure regulation is used to increase the oxygen content inhaled into the lungs, thereby reducing the large fluctuations in the blood oxygen concentration comparison difference.

[0132] S604: If yes, continue to monitor the changes in blood oxygen concentration under adjacent breathing states.

[0133] Specifically, by continuously monitoring the difference between the actual blood oxygen concentration and the expected blood oxygen concentration, when the blood oxygen concentration comparison difference exceeds the preset blood oxygen concentration change threshold, the oxygen supply pressure adjustment instruction can be called in time to adjust the oxygen supply pressure.

[0134] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0135] In one embodiment, a breathing signal detection system is provided, and the breathing signal detection system corresponds to the breathing signal detection method in the above embodiment. Figure 8 As shown, the respiratory signal detection system includes a data acquisition module, a data processing module, a respiratory state prediction module and an instruction generation module. The detailed description of each functional module is as follows:

[0136] The data acquisition module is used to acquire the respiratory pressure signal sent by the respiratory airflow sensor in real time.

[0137] The data processing module is used to process the respiratory pressure signal to obtain an effective respiratory pressure signal that meets the patient's actual respiratory condition.

[0138] The respiratory state prediction module is used to perform data calculation on the effective respiratory pressure signal to obtain respiratory frequency data, and to predict the respiratory state based on the respiratory frequency data to obtain the patient's next respiratory state.

[0139] The instruction generation module is used to generate an oxygen supply adjustment instruction corresponding to the next breathing state according to the next breathing state, so as to adjust the patient's oxygen supply state to match the next breathing state in time.

[0140] Optionally, the instruction generation module specifically includes:

[0141] The breathing time acquisition submodule is used to obtain the actual breathing time of the patient in the current breathing state in real time.

[0142] The respiratory change time prediction submodule is used to predict the expected respiratory change time for the patient to reach the next respiratory state based on the respiratory rate data.

[0143] The oxygen supply frequency adjustment instruction generation submodule is used to generate an oxygen supply frequency adjustment instruction that conforms to the respiratory frequency data according to the expected respiratory change time.

[0144] The oxygen supply frequency adjustment instruction calling submodule is used to call the oxygen supply frequency adjustment instruction to timely adjust the patient's oxygen supply frequency when the actual breathing time meets the expected breathing change time, so as to make the oxygen supply frequency meet the next breathing state.

[0145] Optionally, the instruction generation module further includes:

[0146] The estimated oxygen content acquisition submodule is used to obtain the estimated oxygen content of the next respiratory state.

[0147] The estimated blood oxygen concentration data calculation submodule is used to calculate the blood oxygen concentration of the estimated oxygen supply content to obtain the expected blood oxygen concentration data that meets the estimated oxygen supply content.

[0148] The actual blood oxygen concentration acquisition submodule is used to obtain the patient's actual blood oxygen concentration data in real time.

[0149] The blood oxygen concentration comparison submodule is used to compare the actual blood oxygen concentration data with the expected blood oxygen concentration data to obtain the blood oxygen concentration comparison difference.

[0150] The oxygen supply pressure regulation instruction generation submodule is used to generate an oxygen supply pressure regulation instruction corresponding to the blood oxygen concentration comparison difference and used to adjust the oxygen supply pressure according to the blood oxygen concentration comparison difference.

[0151] Optionally, the oxygen delivery pressure regulation instruction generation submodule further includes:

[0152] The blood oxygen concentration difference comparison submodule is used to compare the blood oxygen concentration comparison difference with a preset blood oxygen concentration change threshold to obtain a blood oxygen concentration change result.

[0153] The oxygen demand judgment submodule is used to judge whether the patient's actual oxygen content meets the patient's oxygen demand based on the change results of blood oxygen concentration.

[0154] The oxygen supply pressure adjustment instruction calling submodule is used to call the oxygen supply pressure adjustment instruction corresponding to the blood oxygen concentration comparison difference if not, so as to adjust the patient's actual blood oxygen concentration to meet the expected blood oxygen concentration requirements.

[0155] Optionally, the data processing module specifically includes:

[0156] The data filtering submodule is used to perform data filtering processing on the respiratory pressure signal to obtain respiratory pressure filtered data after eliminating respiratory interference data.

[0157] The data processing submodule is used to input the respiratory pressure filtering data into a pre-built respiratory change model to obtain an effective respiratory pressure signal that conforms to the patient's actual respiratory change situation.

[0158] The breathing change curve drawing submodule is used to draw the effective breathing pressure signal into a breathing change curve that conforms to the patient's actual breathing condition, so as to monitor the patient's actual breathing condition according to the breathing change curve.

[0159] Optionally, the data processing submodule specifically includes:

[0160] The historical breathing data acquisition unit is used to acquire the historical breathing data in a non-interference state.

[0161] The model building unit is used to perform data training on historical respiratory data to generate a respiratory change model that conforms to the patient's respiratory conditions in an undisturbed state.

[0162] The data comparison unit is used to input the respiratory pressure filtering data into the respiratory change model, compare the respiratory pressure filtering data under the same respiratory state with the historical respiratory data, and obtain the respiratory data comparison result.

[0163] The error compensation unit is used to perform error compensation on the respiratory pressure filtering data according to the respiratory data comparison result to obtain a valid respiratory pressure signal that conforms to the historical respiratory frequency.

[0164] Optionally, the respiratory change curve drawing submodule also includes:

[0165] The respiratory differential value calculation submodule is used to calculate the peak data of the respiratory change curves of adjacent respiratory states to obtain the respiratory differential values ​​of adjacent respiratory states.

[0166] The differential value comparison submodule is used to compare the respiratory differential value with a preset differential threshold to obtain a differential value comparison result.

[0167] The breathing abnormality judgment submodule is used to judge whether the patient's actual breathing condition has breathing abnormality according to the differential value comparison result.

[0168] The abnormal alarm instruction generation submodule is used to generate a breathing abnormality alarm instruction corresponding to the breathing differential value, so as to handle the abnormal breathing situation in time.

[0169] For the specific definition of the breathing signal detection system, please refer to the definition of the breathing signal detection method above, which will not be repeated here. Each module in the above-mentioned breathing signal detection system can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0170] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Fig. 9As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used for the detected respiratory signal data, and is stored in order according to the time sequence of the occurrence of the respiratory signal. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a respiratory signal detection method is implemented.

[0171] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the breathing signal detection method in the above embodiment are implemented.

[0172] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0173] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0174] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A breathing signal detection method, characterized in that: The breathing signal detection method comprises: Acquire the respiratory pressure signal sent by the respiratory airflow sensor in real time; Performing data processing on the respiratory pressure signal to obtain an effective respiratory pressure signal that conforms to the actual respiratory condition of the patient; Performing data calculation on the effective respiratory pressure signal to obtain respiratory frequency data, and predicting the respiratory state based on the respiratory frequency data to obtain the next respiratory state of the patient; According to the next respiratory state, generating an oxygen supply adjustment instruction corresponding to the next respiratory state, so as to timely adjust the patient's oxygen supply state to match the next respiratory state; Obtain the estimated oxygen content in the next breathing state; Calculating the blood oxygen concentration based on the predicted oxygen delivery content to obtain expected blood oxygen concentration data that meets the predicted oxygen delivery content; Obtain the patient's actual blood oxygen concentration data in real time; Comparing the actual blood oxygen concentration data with the expected blood oxygen concentration data to obtain a blood oxygen concentration comparison difference; According to the blood oxygen concentration comparison difference, an oxygen supply pressure adjustment instruction corresponding to the blood oxygen concentration comparison difference and used to adjust the oxygen supply pressure is generated.

2. The respiratory signal detection method according to claim 1, characterized in that: The step of generating an oxygen supply adjustment instruction corresponding to the next respiratory state according to the next respiratory state so as to timely adjust the patient's oxygen supply state to match the next respiratory state specifically includes: Obtain the actual breathing time of the patient in the current breathing state in real time; Predicting the expected respiratory change time for the patient to reach the next respiratory state based on the respiratory rate data; generating an oxygen supply frequency adjustment instruction that conforms to the respiratory frequency data according to the estimated respiratory change time; When the actual breathing time meets the expected breathing change time, the oxygen delivery frequency adjustment instruction is called to timely adjust the patient's oxygen delivery frequency so that the oxygen delivery frequency meets the next breathing state.

3. The respiratory signal detection method according to claim 1, characterized in that: The method of generating an oxygen supply pressure adjustment instruction corresponding to the blood oxygen concentration comparison difference and used to adjust the oxygen supply pressure according to the blood oxygen concentration comparison difference also includes: Comparing the blood oxygen concentration comparison difference with a preset blood oxygen concentration change threshold to obtain a blood oxygen concentration change result; According to the blood oxygen concentration change result, judging whether the actual oxygen content of the patient meets the patient's oxygen demand; If not, the oxygen supply pressure adjustment instruction corresponding to the blood oxygen concentration comparison difference is called to adjust the patient's actual blood oxygen concentration to meet the expected blood oxygen concentration requirement.

4. The respiratory signal detection method according to claim 1, characterized in that: The data processing of the respiratory pressure signal to obtain an effective respiratory pressure signal that conforms to the actual respiratory condition of the patient specifically includes: Performing data filtering processing on the respiratory pressure signal to obtain respiratory pressure filtered data after eliminating respiratory interference data; Inputting the respiratory pressure filtered data into a pre-built respiratory variation model to obtain an effective respiratory pressure signal that conforms to the actual respiratory variation of the patient; The effective respiratory pressure signal is plotted into a respiratory change curve that conforms to the actual respiratory condition of the patient, so as to monitor the actual respiratory condition of the patient according to the respiratory change curve.

5. The respiratory signal detection method according to claim 4, characterized in that: The step of inputting the respiratory pressure filtering data into a pre-built respiratory variation model to obtain an effective respiratory pressure signal that conforms to the actual respiratory variation of the patient specifically includes: Obtain historical breathing data without interference; Performing data training on the historical respiratory data to generate a respiratory change model that conforms to the patient's respiratory condition in an undisturbed state; Inputting the respiratory pressure filtering data into the respiratory change model, performing data comparison between the respiratory pressure filtering data and the historical respiratory data under the same respiratory state, and obtaining a respiratory data comparison result; According to the respiratory data comparison result, error compensation is performed on the respiratory pressure filtering data to obtain a valid respiratory pressure signal that conforms to the historical respiratory frequency.

6. The respiratory signal detection method according to claim 4, characterized in that: The method of drawing the effective respiratory pressure signal into a respiratory change curve that conforms to the actual respiratory condition of the patient so as to monitor the actual respiratory condition of the patient according to the respiratory change curve further includes: Calculating the peak data of the respiratory change curves of adjacent respiratory states to obtain respiratory differential values ​​of adjacent respiratory states; Comparing the respiratory differential value with a preset differential threshold to obtain a differential value comparison result; According to the differential value comparison result, judging whether the actual breathing condition of the patient is abnormal; If so, a breathing abnormality alarm instruction corresponding to the breathing differential value is generated to facilitate timely processing of the breathing abnormality.

7. A breathing signal detection system, characterized in that: The breathing signal detection system comprises: A data acquisition module, used for acquiring the respiratory pressure signal sent by the respiratory airflow sensor in real time; A data processing module, used for processing the respiratory pressure signal to obtain an effective respiratory pressure signal that meets the actual respiratory condition of the patient; A respiratory state prediction module, used to perform data calculation on the effective respiratory pressure signal to obtain respiratory frequency data, and perform respiratory state prediction based on the respiratory frequency data to obtain the patient's next respiratory state; An instruction generation module is used to generate an oxygen supply adjustment instruction corresponding to the next respiratory state according to the next respiratory state, so as to timely adjust the patient's oxygen supply state to match the next respiratory state; The instruction generation module also includes: The estimated oxygen content acquisition submodule is used to obtain the estimated oxygen content of the next respiratory state; The estimated blood oxygen concentration data calculation submodule is used to calculate the blood oxygen concentration of the estimated oxygen delivery content to obtain the expected blood oxygen concentration data that meets the estimated oxygen delivery content; The actual blood oxygen concentration acquisition submodule is used to obtain the patient's actual blood oxygen concentration data in real time; The blood oxygen concentration comparison submodule is used to compare the actual blood oxygen concentration data with the expected blood oxygen concentration data to obtain the blood oxygen concentration comparison difference; The oxygen supply pressure regulation instruction generation submodule is used to generate an oxygen supply pressure regulation instruction corresponding to the blood oxygen concentration comparison difference and used to adjust the oxygen supply pressure according to the blood oxygen concentration comparison difference.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the breathing signal detection method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the breathing signal detection method according to any one of claims 1 to 6 are implemented.

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