Thoracic cavity negative pressure monitoring and alarming system after thoracic cavity operation

Abstract

The invention provides a thoracic cavity negative pressure monitoring and alarming system after a thoracic cavity operation. The thoracic cavity negative pressure monitoring and alarming system comprises a device structure; the device structurally comprises an implantable miniature pressure sensing array and an intelligent signal processing system, the system framework comprises the following aspects: a wireless transmission module, a central monitoring platform and a mobile terminal, and relates to the technical field of thoracic cavity negative pressure monitoring and alarming, the implantable miniature pressure sensing array is directly arranged on the surface of the wall layer pleura of the costal sinus area, so that the interference of a drainage tube is avoided, the thoracic cavity negative pressure can be accurately measured; and the 3 * 3 MEMS piezoresistive sensor array design is adopted, so that multi-point pressure data can be obtained, the measurement precision can be further improved, accurate and reliable thoracic cavity negative pressure information can be provided for doctors, and the condition can be accurately judged.

Description

Technical Field

[0001] The present invention relates to the technical field of thoracic negative pressure monitoring and alarm, and particularly to a thoracic negative pressure monitoring and alarm system after thoracic surgery. Background Art

[0002] Thoracic surgery is an important means for treating various chest diseases. Closed thoracic drainage after surgery is a routine operation, aiming to drain the gas and liquid in the thoracic cavity, maintain the thoracic negative pressure, and promote lung re-expansion. However, there are many drawbacks in the current closed thoracic drainage in terms of thoracic negative pressure monitoring.

[0003] Firstly, it is difficult for the drainage situation of the thoracic drainage tube to accurately reflect the thoracic negative pressure. When the position of the chest tube is not well placed, the drainage of gas and liquid is unsmooth. Even if the thoracic negative pressure is abnormal, it cannot be reflected from the drainage performance. Poor drainage effect, such as too slow or too fast drainage speed, cannot truly reflect the pressure state in the thoracic cavity. In addition, the blockage of the drainage tube will completely block the drainage, making it meaningless to judge the thoracic negative pressure through drainage observation.

[0004] Secondly, the current monitoring methods overly rely on doctors' experience. Doctors comprehensively judge the thoracic negative pressure by observing the volume, color, nature of the drainage fluid and the symptoms of the patient, etc. However, this method is highly subjective, and there may be differences in the judgments of different doctors. Moreover, doctors cannot always pay attention to the patients, making it difficult to detect the subtle changes in thoracic negative pressure in a timely manner.

[0005] Furthermore, the existing pressure monitoring means are simple. Most can only measure the numerical value of the pressure in the thoracic cavity and lack the real-time monitoring function. It is impossible to continuously and dynamically observe the changes in thoracic negative pressure, and it is easy to miss the key pressure fluctuation information. At the same time, the simple pressure monitoring lacks the intelligent analysis ability, cannot deeply process the pressure data, is difficult to accurately judge the cause of abnormal thoracic negative pressure, and cannot provide effective decision-making support for doctors.

[0006] In summary, there are many deficiencies in the existing thoracic negative pressure monitoring methods. There is an urgent need for a system that can monitor the thoracic negative pressure in real time and accurately, and has the functions of intelligent analysis and alarm, so as to improve the nursing quality and safety of patients after thoracic surgery. Summary of the Invention

[0007] In order to overcome the existing problems, the embodiments of the present application provide a thoracic negative pressure monitoring and alarm system after thoracic surgery. The implantable micro pressure sensing array is directly placed on the surface of the parietal pleura in the costophrenic sinus area, avoiding the interference of the drainage tube, and can accurately measure the thoracic negative pressure. The 3x3 MEMS piezoresistive sensor array design is used to obtain multi-point pressure data, further improving the measurement accuracy, providing accurate and reliable thoracic negative pressure information for doctors, and helping to accurately judge the condition.

[0008] The technical solution adopted by the embodiments of the present application to solve its technical problems is: A thoracic negative pressure monitoring and alarming system after thoracic surgery, comprising: Device structure; The device structure includes the following aspects: Implantable micro pressure sensing array: It consists of a 3×3 MEMS piezoresistive sensor array with precise dimensions of 5×5×1 mm³ and a flexible PCB substrate made of biocompatible polyimide material. It is accurately placed on the parietal pleura surface of the costophrenic sinus area through a Trocar cannula and cleverly avoids the drainage tube area, and is used to highly sensitively sense the thoracic negative pressure and stably output electrical signals; Intelligent signal processing system: Dynamic noise suppression module: Through an advanced wavelet transform algorithm, it effectively separates the respiratory baseline (0.1 - 0.3 Hz) and pathological signals (>1 Hz), and comprehensively suppresses the noise of the electrical signals output by the implantable micro pressure sensing array to obtain pure pressure signals; Pressure trend prediction module: Based on the deep LSTM neural network architecture, with multi-dimensional parameters such as the current pressure value, pressure change rate, respiratory frequency, and pressure fluctuation amplitude within a preset time period as inputs, through learning and training with a large amount of historical data, it accurately predicts the change trend of the thoracic negative pressure within a specific future time period; Abnormality determination module: According to the strictly set abnormality determination criteria, that is, the level I warning is that the negative pressure is continuously > -18 cmH2O or > -3 cmH2O for more than 30 minutes, and the level II alarm is that the instantaneous pressure change rate > 5 cmH2O / s. Combining with the pressure trend prediction result, it intelligently determines whether the thoracic negative pressure is abnormal; Abnormality determination criteria: Level I warning: Triggered when the thoracic negative pressure is continuously lower than -18 cmH2O or higher than -3 cmH2O for more than 30 minutes, indicating that the thoracic negative pressure may be in a dangerous range and requires close attention.

[0009] Level II alarm: Triggered when the instantaneous pressure change rate is greater than 5 cmH2O / s, indicating that there is a sharp change in the thoracic cavity pressure, and there may be an emergency pathological condition that requires immediate treatment.

[0010] Multi-modal alarm system: Closely connected to the abnormality determination module of the intelligent signal processing system. When it is determined as a level I warning, the devices in the ward emit soft and highly recognizable sound and light prompts. At the same time, the medical staff's mobile terminals vibrate at a specific frequency and a detailed prompt box pops up, displaying warning information, real-time pressure data, and a pressure change trend chart; when it is determined as a level II alarm, the devices in the ward emit strong and warning sound and light alarms, the mobile terminals continuously vibrate at a higher frequency and emit high-decibel alarm sounds, and at the same time comprehensively display the current pressure value, pressure change trend chart, trigger alarm reason, and information on recommended preliminary measures; Multi-modal alarm system Alarm Trigger Mechanism: When the intelligent signal processing system determines that an abnormal situation has occurred, the multimodal alarm system is immediately activated. For level I early warning, the alarm devices in the ward emit soft audible and visual prompts. At the same time, the mobile terminals of medical staff vibrate and a prompt box pops up, displaying the early warning information and relevant pressure data. For level II alarm, the alarm devices in the ward emit strong audible and visual alarms, the mobile terminals continuously vibrate and emit high-decibel alarm sounds to ensure that medical staff can quickly notice.

[0011] Alarm Information Display: Regardless of the level of the alarm, information such as the current pressure value, pressure change trend graph, and the reason for triggering the alarm will be displayed in detail on the mobile terminal to help medical staff quickly understand the patient's situation.

[0012] And the System Framework: The said system framework includes the following aspects: Wireless Transmission Module: Adopting low-power Bluetooth or ZigBee technology and equipped with a dedicated signal enhancement and error correction module, it efficiently transmits the pressure data processed by the intelligent signal processing system, including real-time pressure values, signals after noise suppression, prediction trends, and abnormal determination results, in the form of encrypted data packets to a wireless access point, and then the wireless access point securely transmits it to the central monitoring platform through the hospital's internal network; Wireless Transmission Transmission Settings: The device integrating the implantable micro pressure sensor array and the intelligent signal processing system is equipped with a low-power Bluetooth or ZigBee module. In the hospital's internal network environment, corresponding wireless access points are set up to ensure a stable connection between the device and the central monitoring platform.

[0013] Data Transmission Process: The pressure data processed by the intelligent signal processing system, including real-time pressure values, signals after noise suppression, prediction trends, and abnormal determination results, etc., are sent in the form of data packets through the wireless module. After receiving the data packets, the wireless access point transmits them to the hospital's internal network and finally delivers them to the central monitoring platform.

[0014] Central Monitoring Platform: Stably receives thoracic negative pressure data from multiple patients through the hospital's internal network, stores the data in an optimized database using a high-performance server cluster and large-capacity distributed storage devices, and uses advanced big data analysis technologies such as clustering analysis and association analysis to deeply integrate and analyze the data, and presents the analysis results in an intuitive and easy-to-understand visual chart form to provide comprehensive and accurate diagnostic support for doctors; Central Monitoring Platform Data reception and storage: The central monitoring platform receives thoracic negative pressure data transmitted from multiple patient devices through the hospital's internal network. High-performance servers and large-capacity storage devices are used to store data in the form of a database, such as using MySQL or PostgreSQL databases. The data of each patient is stored in an orderly manner according to the time series and parameter categories, facilitating subsequent query and analysis.

[0015] Data analysis and mining: Big data analysis techniques are used to integrate and analyze the large amount of stored thoracic negative pressure data of patients. For example, through cluster analysis, the commonalities and differences in the thoracic negative pressure changes of patients with different surgical types are discovered; through association analysis, the potential relationships between thoracic negative pressure and other physiological indicators (such as heart rate, blood oxygen saturation) are found. The analysis results are presented to doctors in the form of visual charts, providing comprehensive and intuitive diagnostic support for doctors.

[0016] Mobile terminal: An APP specially developed for the needs of medical staff is installed, with a simple and easy-to-use operation interface. It establishes a stable connection with the central monitoring platform through the hospital's internal network or a secure Internet channel. Medical staff can view the thoracic negative pressure data, pressure trend prediction results, and alarm information of patients in real time through this APP, and can flexibly set personalized alarm thresholds, parameters, and reminder methods according to the individual conditions of patients.

[0017] Mobile terminal APP function implementation: Medical staff install a specially developed APP on mobile terminals (such as mobile phones, tablets). The APP establishes a connection with the central monitoring platform through the hospital's internal network or the Internet. On the APP interface, medical staff can view the thoracic negative pressure data, pressure trend prediction results, alarm information, etc. of patients in real time. At the same time, personalized alarm thresholds and parameters can be set in the APP, such as adjusting the triggering conditions of level I early warning and level II alarm for patients with different conditions.

[0018] Remote monitoring and management: Whether medical staff are inside or outside the hospital, as long as the mobile terminal is connected to the network, remote monitoring of patients can be achieved. For example, when a doctor is on an external consultation, through the mobile terminal, they can timely understand the changes in the thoracic negative pressure of patients. In case of an alarm, they can remotely guide nurses for preliminary treatment, improving medical efficiency and response speed.

[0019] The advantages of the embodiments of this application are: 1. The implantable micro pressure sensing array is directly placed on the parietal pleura surface of the costophrenic sinus area, avoiding the interference of the drainage tube, and can accurately measure the thoracic negative pressure. The 3x3 MEMS piezoresistive sensor array design can obtain multi-point pressure data, further improving the measurement accuracy, providing accurate and reliable thoracic negative pressure information for doctors, and helping to accurately judge the condition.

[0020] 2. The dynamic noise suppression algorithm separates the respiratory baseline and pathological signals through wavelet transform, effectively removing the noise interference generated by physiological activities such as breathing, making the monitored pressure signal purer, truly reflecting the pathological changes of intrathoracic negative pressure, and improving the effectiveness and analysis value of the data.

[0021] 3. The pressure trend prediction model based on the LSTM neural network can accurately predict the future change trend of intrathoracic negative pressure by learning a large amount of historical data, discover potential abnormalities in advance, enable doctors to take intervention measures at the initial stage of the disease development, avoid the deterioration of the condition, and strive for precious time for the treatment of patients.

[0022] 4. The clear I-level warning and II-level alarm abnormal judgment criteria, combined with the real-time monitoring of the intelligent signal processing system, can accurately judge the abnormal situation of intrathoracic negative pressure. Different levels of warnings and alarms provide clear indications of the severity of the condition for doctors, helping doctors make quick decisions and take corresponding treatment measures.

[0023] 5. The wireless real-time transmission is realized by using low-power Bluetooth or ZigBee technology, and the intrathoracic negative pressure data is transmitted to the central monitoring platform and mobile terminal in a timely manner. Medical staff can obtain the intrathoracic negative pressure information of patients in real time, keep track of the changes in the condition at any time, and achieve real-time monitoring.

[0024] 6. The multi-modal alarm system combines various alarm methods such as vision, hearing, and touch. No matter where the medical staff are, they can detect the alarm information in time. This all-round alarm method ensures the timeliness and effectiveness of the alarm, avoids missing key alarm information due to negligence, and guarantees the life safety of patients.

[0025] 7. The central monitoring platform uses big data analysis technology to integrate and analyze the intrathoracic negative pressure data of a large number of patients, mine the potential laws and correlations in the data, provide comprehensive and accurate diagnostic suggestions for doctors, assist doctors in formulating personalized treatment plans, and improve the scientificity and accuracy of medical decisions.

[0026] 8. The mobile terminal APP enables medical staff to remotely view patient data, set alarm thresholds and parameters, and achieve remote monitoring and management. Whether medical staff are inside or outside the hospital, they can respond to the alarm information of patients in time, improve the medical efficiency and response speed, and optimize the allocation and utilization of medical resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a schematic diagram of the device structure process in the intrathoracic negative pressure monitoring and alarm system after thoracic surgery of the present invention; Figure 2 It is a schematic diagram of the system framework process in the intrathoracic negative pressure monitoring and alarm system after thoracic surgery of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0028] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention. In addition, for the convenience of description below, the "upper", "lower", "left", "right", etc. cited are consistent with the upper, lower, left, right, etc. of the accompanying drawings themselves. The "first", "second", etc. in the following text are for distinction in description and have no other special meanings.

[0029] Embodiments of the present application provide a thoracic negative pressure monitoring and alarm system after thoracic surgery to solve the problems in the prior art. The implantable micro pressure sensing array is directly placed on the parietal pleura surface of the costophrenic sinus area, avoiding the interference of the drainage tube, and can accurately measure the thoracic negative pressure. The 3x3 MEMS piezoresistive sensor array design can obtain multi-point pressure data, further improving the measurement accuracy, providing accurate and reliable thoracic negative pressure information for doctors, and helping to accurately judge the condition. The dynamic noise suppression algorithm separates the respiratory baseline and pathological signals through wavelet transform, effectively removing the noise interference generated by physiological activities such as breathing, making the monitored pressure signal purer, truly reflecting the pathological changes of the thoracic negative pressure, and improving the effectiveness and analysis value of the data. The pressure trend prediction model based on the LSTM neural network can accurately predict the future change trend of the thoracic negative pressure by learning a large amount of historical data, discover potential abnormalities in advance, enable doctors to take intervention measures at the initial stage of the disease development, avoid the deterioration of the condition, and strive for precious time for the treatment of patients. The clear I-level warning and II-level alarm abnormal judgment criteria, combined with the real-time monitoring of the intelligent signal processing system, can accurately judge the abnormal situation of the thoracic negative pressure. Different levels of warnings and alarms provide a clear indication of the severity of the condition for doctors, helping doctors make quick decisions and take corresponding treatment measures. The wireless real-time transmission is realized by using low-power Bluetooth or ZigBee technology, and the thoracic negative pressure data is transmitted to the central monitoring platform and mobile terminal in a timely manner. Medical staff can obtain the thoracic negative pressure information of patients in real time, keep track of the condition changes at any time, and achieve real-time monitoring. The multi-modal alarm system combines various alarm methods such as vision, hearing, and touch. No matter where the medical staff are, they can detect the alarm information in time. This all-round alarm method ensures the timeliness and effectiveness of the alarm, avoids missing key alarm information due to negligence, and guarantees the life safety of patients. The central monitoring platform uses big data analysis technology to integrate and analyze the thoracic negative pressure data of a large number of patients, excavate the potential laws and correlations in the data, provide comprehensive and accurate diagnostic suggestions for doctors, assist doctors in formulating personalized treatment plans, and improve the scientificity and accuracy of medical decisions. The mobile terminal APP enables medical staff to remotely view patient data, set alarm thresholds and parameters, and achieve remote monitoring and management. Whether the medical staff are in the hospital or outside the hospital, they can respond to the alarm information of patients in time, improve the medical efficiency and response speed, and optimize the allocation and utilization of medical resources.

[0030] The technical solutions in the embodiments of the present application are to solve the above problems, and the general idea is as follows: Embodiment

[0031] This embodiment provides a thoracic negative pressure monitoring and alarm system after thoracic surgery, as Figure 1-2 shown, including: Device structure; The device structure includes the following aspects: Implantable micro pressure sensor array: It consists of a 3×3 MEMS piezoresistive sensor array with a precise size of 5×5×1 mm³ and a flexible PCB substrate made of biocompatible polyimide material. It is precisely placed on the surface of the parietal pleura in the costophrenic sinus area through a Trocar sleeve and cleverly avoids the drainage tube area. It is used to highly sensitively sense the negative pressure in the chest cavity and stably output electrical signals. Intelligent signal processing system: Dynamic noise suppression module: Through advanced wavelet transform algorithm, it can effectively separate the respiratory baseline (0.1-0.3Hz) and pathological signals (>1Hz), and perform comprehensive noise suppression processing on the electrical signals output by the implantable micro pressure sensor array to obtain pure pressure signals; Pressure trend prediction module: Based on the deep LSTM neural network architecture, it takes the current pressure value, pressure change rate, respiratory rate and pressure fluctuation amplitude in the preset time period as input, and accurately predicts the change trend of chest negative pressure in a specific time period in the future through learning and training of a large amount of historical data; Abnormal judgment module: Based on the strictly set abnormal judgment standards, that is, the level I warning is the negative pressure <-18cmH2O or >-3cmH2O for more than 30 minutes, and the level II alarm is the instantaneous pressure change rate >5cmH2O / s. Combined with the pressure trend prediction results, it intelligently judges whether the chest negative pressure is abnormal; Abnormality determination criteria: Level I warning: Triggered when the negative chest pressure is lower than -18cmH2O or higher than -3cmH2O for more than 30 minutes, indicating that the negative chest pressure may be in a dangerous range and requires close attention.

[0032] Level II alarm: Triggered when the instantaneous pressure change rate is greater than 5cmH2O / s, indicating that there is a sharp change in intrathoracic pressure, and there may be an emergency pathological condition that requires immediate treatment.

[0033] Multimodal alarm system: closely connected with the abnormality judgment module of the intelligent signal processing system, when it is judged as a level I warning, the equipment in the ward will issue a soft and highly recognizable sound and light prompt, and the medical staff's mobile terminal will vibrate at a specific frequency and pop up a detailed prompt box, displaying the warning information, real-time pressure data and pressure change trend chart; when it is judged as a level II alarm, the equipment in the ward will issue a strong and warning sound and light alarm, the mobile terminal will continue to vibrate at a higher frequency and issue a high-decibel alarm, and at the same time fully display the current pressure value, pressure change trend chart, the cause of the alarm triggering, and the recommended preliminary measures; Multi-modal alarm system Alarm Trigger Mechanism: When the intelligent signal processing system determines that an abnormal situation has occurred, the multi-modal alarm system is immediately activated. For level I early warning, the alarm devices in the ward emit soft sound and light prompts. At the same time, the mobile terminals of medical staff vibrate and a prompt box pops up, displaying the early warning information and relevant pressure data. For level II alarm, the alarm devices in the ward emit strong sound and light alarms, and the mobile terminals continuously vibrate and emit high-decibel alarm sounds to ensure that medical staff can quickly notice.

[0034] Alarm Information Display: Regardless of the level of the alarm, information such as the current pressure value, pressure change trend graph, and the reason for triggering the alarm will be displayed in detail on the mobile terminal to help medical staff quickly understand the patient's situation.

[0035] And System Framework: The system framework includes the following aspects: Wireless Transmission Module: Adopting low-power Bluetooth or ZigBee technology and equipped with a dedicated signal enhancement and error correction module, it efficiently transmits the pressure data processed by the intelligent signal processing system, including real-time pressure values, signals after noise suppression, prediction trends, and abnormal determination results, to the wireless access point in the form of encrypted data packets. Then, the wireless access point securely transmits it to the central monitoring platform through the hospital's internal network; Wireless Transmission Transmission Settings: The device integrating the implantable micro pressure sensor array and the intelligent signal processing system is equipped with a low-power Bluetooth or ZigBee module. In the hospital's internal network environment, corresponding wireless access points are set up to ensure a stable connection between the device and the central monitoring platform.

[0036] Data Transmission Process: The pressure data processed by the intelligent signal processing system, including real-time pressure values, signals after noise suppression, prediction trends, and abnormal determination results, etc., are sent in the form of data packets through the wireless module. After receiving the data packets, the wireless access point transmits them to the hospital's internal network and finally delivers them to the central monitoring platform.

[0037] Central Monitoring Platform: It stably receives the thoracic negative pressure data of multiple patients through the hospital's internal network, stores the data in the form of an optimized database using a high-performance server cluster and a large-capacity distributed storage device, and uses advanced big data analysis technologies such as clustering analysis and association analysis to deeply integrate and analyze the data, and presents the analysis results in the form of intuitive and easy-to-understand visualization charts to provide comprehensive and accurate diagnostic support for doctors; Central Monitoring Platform Data Reception and Storage: The central monitoring platform receives thoracic negative pressure data transmitted from multiple patient devices through the hospital's internal network. High-performance servers and large-capacity storage devices are used to store data in the form of a database, such as using MySQL or PostgreSQL databases. The data of each patient is stored in an orderly manner according to the time series and parameter categories, facilitating subsequent query and analysis.

[0038] Data Analysis and Mining: Big data analysis techniques are used to integrate and analyze a large amount of stored thoracic negative pressure data of patients. For example, through cluster analysis, the commonalities and differences in the changes of thoracic negative pressure of patients with different surgical types are discovered; through association analysis, the potential relationships between thoracic negative pressure and other physiological indicators (such as heart rate, blood oxygen saturation) are found. The analysis results are presented to doctors in the form of visual charts, providing comprehensive and intuitive diagnostic support for doctors.

[0039] Mobile Terminal: An APP specifically developed for the needs of medical staff is installed, with a simple and easy-to-use operation interface. It establishes a stable connection with the central monitoring platform through the hospital's internal network or a secure Internet channel. Medical staff can view the thoracic negative pressure data of patients, pressure trend prediction results, and alarm information in real time through this APP, and can flexibly set personalized alarm thresholds, parameters, and reminder methods according to the individual conditions of patients.

[0040] Mobile Terminal APP Function Realization: Medical staff install a specially developed APP on mobile terminals (such as mobile phones, tablets). The APP establishes a connection with the central monitoring platform through the hospital's internal network or the Internet. On the APP interface, medical staff can view the thoracic negative pressure data of patients, pressure trend prediction results, alarm information, etc. in real time. At the same time, personalized alarm thresholds and parameters can be set in the APP, such as adjusting the triggering conditions of level I early warning and level II alarm for patients with different conditions.

[0041] Remote Monitoring and Management: Whether medical staff are inside or outside the hospital, as long as the mobile terminal is connected to the network, remote monitoring of patients can be achieved. For example, when a doctor goes out for a consultation, through the mobile terminal, he can timely understand the changes in the thoracic negative pressure of the patient. In case of an alarm, he can remotely guide the nurse for preliminary treatment, improving medical efficiency and response speed.

[0042] The MEMS piezoresistive sensor utilizes the piezoresistive effect. When the thoracic pressure undergoes a slight change, the resistance value of the carefully designed piezoresistive resistor inside it changes accordingly, and is accurately converted into a voltage signal output through a high-precision external circuit, ensuring the accuracy of pressure perception.

[0043] The biocompatible polyimide material of the flexible PCB substrate undergoes special surface treatment to further improve its affinity with human tissues, reduce the inflammatory response and the risk of rejection. At the same time, it has good flexibility and electrical insulation properties to ensure the stable operation of the sensor array.

[0044] The wavelet transform algorithm of the dynamic noise suppression module is optimized, and the parameters are adjusted according to the characteristics of the thoracic pressure signal. It can more accurately separate the respiratory baseline and pathological signals in a complex physiological noise environment, effectively improving the signal quality.

[0045] The LSTM neural network of the pressure trend prediction module is trained with historical data of a large number of patients undergoing thoracic surgery, covering multi-factor data such as different surgical types, patient ages, and physical conditions, to improve the generalization ability and accuracy of the prediction model.

[0046] When determining the abnormality of the thoracic negative pressure, the abnormality determination module not only relies on the preset level I warning and level II alarm standards, but also combines the confidence analysis of the pressure trend prediction to provide doctors with more reliable abnormality judgment results.

[0047] For the multi-modal alarm system at different alarm levels, the frequency, color, and intensity of the audible and visual alarms can be adjusted according to the personalized settings of medical staff on the mobile terminal APP to meet different usage scenarios and personal preferences.

[0048] The wireless transmission module adopts an adaptive transmission rate adjustment mechanism to adjust the data transmission rate in real time according to the network environment, ensuring the stability and efficiency of data transmission. At the same time, it has data encryption and integrity verification functions.

[0049] The big data analysis technology of the central monitoring platform can compare and analyze the thoracic negative pressure data of patients at different times, in different departments, and with different conditions, excavate potential clinical rules and risk factors, and provide data support for the medical quality management of the hospital.

[0050] The mobile terminal APP has a data caching function. When the network connection is unstable or interrupted, it can temporarily store the thoracic negative pressure data of patients and automatically upload it to the central monitoring platform after the network is restored, ensuring the integrity and continuity of the data.

[0051] By adopting the above technical solutions: The patient underwent thoracic surgery due to lung tumor and was monitored using this thoracic negative pressure monitoring and alarm system after the operation.

[0052] After the operation, the doctor accurately placed the implantable micro pressure sensor array on the parietal pleura surface of the patient's costophrenic sinus area through the Trocar cannula, avoiding the drainage tube area. The sensor array began to sense the thoracic negative pressure in real time and convert the pressure change into an electrical signal and transmit it to the intelligent signal processing system.

[0053] The dynamic noise suppression module in the intelligent signal processing system uses the wavelet transform algorithm to effectively separate the respiratory baseline and pathological signals. For example, within the first hour after surgery, the pressure fluctuations caused by respiration are relatively obvious. However, through this algorithm, the respiratory noise is accurately removed, making the pressure signal better reflect the actual negative pressure in the thoracic cavity.

[0054] The pressure trend prediction module is based on the LSTM neural network. Combining parameters such as the patient's real-time pressure value, pressure change rate, and respiratory frequency, it predicts the negative pressure in the thoracic cavity. In the second hour after surgery, the prediction model shows that the negative pressure in the thoracic cavity may gradually decrease within the next 30 minutes, approaching the lower limit of the level I warning. The intelligent signal processing system continuously monitors the pressure data. When it detects that the negative pressure in the thoracic cavity remains > -3 cmH2O for 35 minutes, the abnormal determination module triggers a level I warning.

[0055] The multi-modal alarm system responds immediately. The alarm devices in the ward emit soft light and sound prompts. At the same time, the mobile terminals of the medical staff in charge of the patient vibrate and a prompt box pops up, displaying the warning information, real-time pressure data, and pressure change trend chart. After receiving the warning, the medical staff quickly view the detailed information on the mobile terminal and, in combination with the patient's overall condition, judge that it may be caused by a small amount of bloody effusion in the thoracic cavity blocking the drainage tube, resulting in a change in negative pressure. The doctor arranged a chest ultrasound examination, confirmed the presence of a small amount of fluid in the thoracic cavity, and restored the patency of the drainage tube by squeezing it, making the negative pressure in the thoracic cavity gradually return to normal.

[0056] During this period, the wireless transmission module uses low-power Bluetooth technology to transmit the patient's thoracic cavity negative pressure data to the central monitoring platform in real time and stably. The central monitoring platform stores and analyzes the data. By comparing with the data of other patients after lung tumor resection, it is found that the trend of negative pressure change in this patient is representative among similar patients, providing a reference for the treatment of subsequent similar cases. The medical staff can view the patient's thoracic cavity negative pressure data, pressure trend prediction results, and alarm information in real time through the mobile terminal APP. At the same time, they set more personalized alarm thresholds on the APP according to the patient's situation to more accurately monitor the patient's condition.

[0057] Finally, it should be noted that: Obviously, the above embodiments are only examples for clearly illustrating the present invention, and are not limitations on the implementation manners. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation manners here. And the obvious changes or modifications derived therefrom are still within the protection scope of the present invention.

Claims

1. A chest negative pressure monitoring and alarm system after thoracic surgery, characterized in that: include: Device structure; The device structure includes the following aspects: Implantable micro pressure sensor array: It consists of a 3×3 MEMS piezoresistive sensor array with a precise size of 5×5×1 mm³ and a flexible PCB substrate made of biocompatible polyimide material. It is precisely placed on the surface of the parietal pleura in the costophrenic sinus area through a Trocar sleeve and cleverly avoids the drainage tube area. It is used to highly sensitively sense the negative pressure in the chest cavity and stably output electrical signals. Intelligent signal processing system: Dynamic noise suppression module: Through advanced wavelet transform algorithm, it effectively separates the respiratory baseline (0.1-0.3Hz) and the pathological signal (>1Hz), and performs comprehensive noise suppression processing on the electrical signal output by the implantable micro pressure sensor array to obtain a pure pressure signal; Pressure trend prediction module: Based on the deep LSTM neural network architecture, it takes the current pressure value, pressure change rate, respiratory rate and pressure fluctuation amplitude in the preset time period as input, and accurately predicts the change trend of chest negative pressure in a specific time period in the future through learning and training of a large amount of historical data; Abnormal judgment module: Based on the strictly set abnormal judgment standards, that is, the level I warning is the negative pressure <-18cmH2O or >-3cmH2O for more than 30 minutes, and the level II alarm is the instantaneous pressure change rate >5cmH2O / s. Combined with the pressure trend prediction results, it intelligently judges whether the chest negative pressure is abnormal; Multimodal alarm system: closely connected with the abnormality determination module of the intelligent signal processing system. When it is determined to be a level I warning, the equipment in the ward will issue a soft and highly recognizable sound and light prompt. At the same time, the medical staff's mobile terminal will vibrate at a specific frequency and pop up a detailed prompt box, displaying warning information, real-time pressure data and pressure change trend chart; when it is determined to be a level II alarm, the equipment in the ward will issue a strong and warning sound and light alarm, the mobile terminal will continue to vibrate at a higher frequency and issue a high-decibel alarm, and at the same time fully display the current pressure value, pressure change trend chart, the cause of the alarm triggering, and the recommended preliminary measures; And the system framework: The system framework includes the following aspects: Wireless transmission module: adopts low-power Bluetooth or ZigBee technology, equipped with a special signal enhancement and error correction module, and efficiently transmits the pressure data processed by the intelligent signal processing system, including real-time pressure value, signal after noise suppression, predicted trend and abnormal judgment result, to the wireless access point in the form of encrypted data packets, and then the wireless access point transmits it to the central monitoring platform through the hospital's internal network security; Central monitoring platform: It stably receives chest negative pressure data from multiple patients through the hospital's internal network, uses high-performance server clusters and large-capacity distributed storage devices to store data in an optimized database format, and uses advanced big data analysis technologies such as cluster analysis and association analysis to deeply integrate and analyze data, and presents analysis results in the form of intuitive and easy-to-understand visual charts, providing doctors with comprehensive and accurate diagnostic support; Mobile terminal: Install an APP specially developed for the needs of medical staff, which has a simple and easy-to-use operating interface and establishes a stable connection with the central monitoring platform through the hospital's internal network or a secure Internet channel. Medical staff can use the APP to view the patient's chest negative pressure data, pressure trend prediction results, and alarm information in real time, and flexibly set personalized alarm thresholds, parameters, and reminder methods according to the patient's individual situation.

2. A post-thoracic surgery chest negative pressure monitoring and alarm system as claimed in claim 1, characterized in that: The MEMS piezoresistive sensor uses the piezoresistive effect. When the chest pressure changes slightly, the resistance of the carefully designed piezoresistors inside it changes accordingly, which is accurately converted into a voltage signal output through a high-precision external circuit to ensure the accuracy of pressure perception.

3. The post-thoracic surgery chest negative pressure monitoring and alarm system according to claim 1, characterized in that: The biocompatible polyimide material of the flexible PCB substrate undergoes special surface treatment to further improve its affinity with human tissue, reduce the risk of inflammatory response and rejection, and at the same time has good flexibility and electrical insulation properties to ensure stable operation of the sensor array.

4. The post-thoracic surgery chest negative pressure monitoring and alarm system according to claim 1, characterized in that: The wavelet transform algorithm of the dynamic noise suppression module is optimized, and parameters are adjusted according to the characteristics of the chest pressure signal. It can more accurately separate the respiratory baseline and pathological signals in a complex physiological noise environment, and effectively improve the signal quality.

5. The post-thoracic surgery chest negative pressure monitoring and alarm system according to claim 1, characterized in that: The LSTM neural network of the pressure trend prediction module is trained with a large amount of historical data of thoracic surgery patients, covering multi-factor data of different surgery types, patient age, and physical condition, so as to improve the generalization ability and accuracy of the prediction model.

6. A post-thoracic surgery chest negative pressure monitoring and alarm system as claimed in claim 1, characterized in that: When determining that the chest negative pressure is abnormal, the abnormality determination module not only uses the preset level I warning and level II alarm standards, but also combines the confidence analysis of the pressure trend prediction to provide doctors with more reliable abnormality determination results.

7. A post-thoracic surgery chest negative pressure monitoring and alarm system as claimed in claim 1, characterized in that: The frequency, color and intensity of the sound and light alarms of the multimodal alarm system at different alarm levels can be adjusted according to the personalized settings of medical staff on the mobile terminal APP to meet different usage scenarios and personal preferences.

8. The post-thoracic surgery chest negative pressure monitoring and alarm system according to claim 1, characterized in that: The wireless transmission module adopts an adaptive transmission rate adjustment mechanism to adjust the data transmission rate in real time according to the network environment to ensure the stability and efficiency of data transmission, and at the same time has data encryption and integrity verification functions.

9. The post-thoracic surgery chest negative pressure monitoring and alarm system according to claim 1, characterized in that: The big data analysis technology of the central monitoring platform can compare and analyze the chest negative pressure data of patients in different time periods, different departments, and different conditions, explore potential clinical rules and risk factors, and provide data support for the hospital's medical quality management.

10. The post-thoracic surgery chest negative pressure monitoring and alarm system according to claim 1, characterized in that: The mobile terminal APP has a data caching function. When the network connection is unstable or interrupted, it can temporarily store the patient's chest negative pressure data and automatically upload it to the central monitoring platform after the network is restored to ensure the integrity and continuity of the data.

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