Method and system for real-time monitoring of carbon monoxide during extracorporeal circulation

Abstract

The application discloses a method and system for monitoring carbon monoxide in real time during extracorporeal circulation, and relates to the technical field of medical monitoring. The method comprises the following specific steps: light path construction and gas sampling: presetting distributed monitoring nodes on an extracorporeal circulation pipeline, integrating corresponding laser light sources and photodetectors in each probe, and synchronously collecting sample gas at each node of the body through a microfluidic chip and a micro-flow gas pump array; the application realizes multi-node synchronous detection by adopting tunable diode laser absorption spectroscopy technology through the construction of distributed monitoring nodes, realizes multi-position synchronous gas sampling in combination with a microfluidic chip and a micro-flow gas pump array, solves the problems that the prior art cannot realize real-time and non-invasive monitoring and lacks space-time resolution capability, can realize real-time acquisition of spatial distribution differences of carbon monoxide concentration at different parts of the extracorporeal circulation pipeline, and analyzes dynamic rules of changes of the carbon monoxide concentration with time, thereby providing more comprehensive and accurate data support for medical evaluation.

Description

Technical Field

[0001] This invention relates to the field of medical monitoring technology, specifically to a method and system for real-time monitoring of carbon monoxide during extracorporeal circulation. Background Technology

[0002] Extracorporeal circulation technology plays a vital role in modern medicine, and is widely used in complex medical scenarios such as cardiac surgery and organ transplantation. During extracorporeal circulation, in order to ensure the patient's life safety and the smooth progress of the operation, it is necessary to monitor a number of physiological parameters of the patient in real time and accurately. As an endogenous gaseous neurotransmitter, carbon monoxide plays a unique role in the physiological and pathological processes of the human body. Therefore, real-time and accurate monitoring of carbon monoxide levels during extracorporeal circulation is of great significance for medical personnel to comprehensively assess the patient's physiological status, detect potential complications in a timely manner, formulate reasonable treatment plans, and improve the patient's prognosis.

[0003] Currently, existing carbon monoxide monitoring methods have many limitations and cannot meet the actual needs during extracorporeal circulation. Traditional detection methods are difficult to achieve real-time, non-invasive monitoring during extracorporeal circulation, and the operation process is complex and cumbersome. This not only increases the workload of medical staff, but may also affect the reliability of monitoring results due to untimely or inaccurate operation, thus delaying timely intervention for patients. Optical gas detection methods have weak anti-interference ability in the complex environment of extracorporeal circulation and are easily affected by other factors, resulting in insufficient detection accuracy. At the same time, this method cannot adapt well to the complex fluid environment in extracorporeal circulation tubing, further affecting the accuracy of monitoring data. In addition, existing technologies can usually only monitor carbon monoxide concentration at a single location, and cannot obtain the spatial distribution differences of carbon monoxide concentration in different parts of the tubing, nor can they analyze its dynamic changes over time. This makes it difficult for doctors to locate the source of abnormal carbon monoxide and cannot accurately assess the relevant performance of key components in the extracorporeal circulation system, thus adversely affecting medical judgment and decision-making. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies by providing a method and system for real-time monitoring of carbon monoxide during extracorporeal circulation. This system achieves simultaneous multi-node detection by constructing distributed monitoring nodes and employing tunable diode laser absorption spectroscopy technology. Combined with microfluidic chips, it enables simultaneous gas sampling at multiple locations, effectively solving the problem of existing methods being unable to provide real-time, non-invasive monitoring. Furthermore, by integrating multiple auxiliary laser sources for interference gas compensation and using a multiple linear regression algorithm to establish an interference compensation model, the accuracy and anti-interference capability of detection are improved. In addition, by performing temporal, spatial, and spatiotemporal correlation analysis on multi-node concentration data, the dynamic changes and spatial distribution differences of carbon monoxide concentration are obtained, allowing for the identification of abnormal sources and evaluation of key component performance. Through the establishment of multi-level alarm mechanisms and data storage and retrospective analysis functions, comprehensive and accurate monitoring information is provided to medical personnel, contributing to improved safety and success rates in extracorporeal circulation surgery.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: A method for real-time monitoring of carbon monoxide during extracorporeal circulation, comprising the following specific steps: Optical path construction and gas sampling: Distributed monitoring nodes are preset in the extracorporeal circulation pipeline. Each probe integrates a corresponding laser source and photodetector. Sample gas is collected from each node of the body synchronously through microfluidic chip and micro-flow gas pump array. Multi-node synchronous detection: Each monitoring node adopts a distributed feedback tunable diode laser, which achieves mode-skipping scanning through independent temperature control and current drive, and synchronously acquires the attenuation signal of the laser after passing through the sample gas and converts it into an electrical signal output; Interference gas compensation and concentration inversion: Each probe integrates an auxiliary laser source, and a compensation model is established to compensate the carbon monoxide detection signal in real time, while inverting the real-time carbon monoxide concentration at each node; Spatiotemporal resolution analysis and data feedback: Through time series analysis, multi-node concentration data is processed to identify concentration abrupt change points, calculate the concentration difference and gradient of key nodes and the performance parameters of key components, and visualize the concentration distribution. Combined with blood flow velocity, abnormal locations are located, data is transmitted synchronously and alarms are triggered. Data storage and retrospective analysis: Monitoring data is stored in a medical standard format, covering the entire process of extracorporeal circulation and the preset duration after surgery, which can be adjusted. Data can be retrieved and analyzed by keywords.

[0006] Furthermore, in the optical path construction and gas sampling steps, monitoring probes are set at preset positions in the extracorporeal circulation pipeline to form distributed monitoring nodes. Each monitoring probe integrates a set of tunable diode laser sources and a set of photodetectors. The center wavelength of the tunable diode laser source corresponds to the characteristic absorption wavelength of carbon monoxide. Multi-location synchronous gas sampling is achieved through a microfluidic chip array. The microfluidic chip includes independent sampling channels, gas pretreatment channels, and detection channels. The gas pretreatment channel has a built-in hydrophilic membrane, and each sampling channel corresponds to one monitoring node. The sampling flow rate is controlled by a micro-flow pump array. Each sampling channel in the micro-flow pump array is equipped with a micro-pump to synchronously introduce the sample gas from each monitoring node into the corresponding micro-gas absorption pool. The micro-gas absorption pool is integrated into the detection channel of the microfluidic chip and adopts a multi-reflection structure. The detection optical path of each monitoring node is independent and works synchronously.

[0007] Furthermore, in the multi-node synchronous detection step, the laser source of each monitoring node adopts a distributed feedback tunable diode laser, and its center wavelength is calibrated to the characteristic absorption wavelength of carbon monoxide; each laser source is equipped with an independent temperature control module and current drive module to achieve mode-skipping scanning; after the scanning laser passes through the sample gas in the corresponding micro gas absorption cell, the photodetectors of each node synchronously collect the attenuated laser signal and convert the optical signal into an electrical signal to be output to the data processing module.

[0008] Furthermore, in the interfering gas compensation and concentration inversion step, each monitoring probe integrates multiple sets of auxiliary laser sources, with the center wavelength of each set of auxiliary laser sources corresponding to the characteristic absorption wavelength of a preset interfering gas. The absorption intensity of each interfering gas at the corresponding wavelength is detected by the auxiliary laser sources. Combined with a preset database of interfering gas absorption coefficients, an interference compensation model for each monitoring node is established. This interference compensation model uses a multiple linear regression algorithm to compensate for the carbon monoxide detection signal in real time, and its formula is: ,in, To compensate for the effective absorption intensity of carbon monoxide, This represents the original absorption intensity of carbon monoxide. To determine the types and quantities of interfering gases, For the first Correction factor for the absorption coefficient of interfering gases. For the first Measurement of the absorption intensity of interfering gases, For the first The actual partial pressure of the interfering gas As a reference voltage divider, The actual temperature of the sample gas. For reference temperature, This is a correction item for environmental interference.

[0009] Furthermore, in the interference gas compensation and concentration inversion step, the real-time carbon monoxide concentration at each node is inverted. Specifically, based on Beer-Lambert's law, a continuous two-segment curve fitting method is used for concentration inversion: the carbon monoxide concentration is divided into two intervals, namely... The low concentration range This is the high concentration range. As the concentration segmentation threshold, for the low concentration range, the concentration inversion formula is: For the high concentration range, the concentration inversion formula is: ,in, Real-time carbon monoxide concentration in the low / high concentration range , , These are the fitting coefficients for the low concentration range. , , These are the fitting coefficients for the high concentration range. It is the sampling flow rate correction factor. This is the actual sampled flow rate of the microfluidic chip. This is the standard sampling flow rate. It is the blood flow velocity coupling coefficient. It is the actual blood flow velocity within the extracorporeal circulation tubing. It is the standard blood flow velocity, the boundary value of the concentration range. Based on clinical data analysis, the concentration of each monitoring node was determined by performing concentration inversion on the compensated signals of each monitoring node.

[0010] Furthermore, in the spatiotemporal resolution analysis and data feedback step, multi-node concentration data is processed through time series analysis, specifically: Time dimension analysis: Time series of concentration data for each monitoring node is established by timestamp, the concentration change rate is calculated by sliding window method, and concentration change points are identified based on preset change rate judgment threshold; Spatial dimension analysis: Calculate the concentration difference and concentration gradient between preset key nodes. The concentration gradient is the ratio of the concentration difference to the corresponding pipeline length. Calculate the clearance efficiency of the oxygenator in the extracorporeal circulation system based on the concentration difference between key nodes. At the same time, visualize the concentration distribution of each monitoring node through heat map. Spatiotemporal correlation analysis: The cross-correlation algorithm is used to analyze the time delay of concentration changes at different nodes, combined with blood flow velocity data in the extracorporeal circulation tubing. The blood flow velocity data is synchronously collected by blood flow sensors. The distance between potential abnormal locations and monitoring nodes is calculated based on the correlation between time delay and blood flow velocity.

[0011] Furthermore, in the spatiotemporal resolution analysis and data feedback step, the sliding window method is used to calculate the concentration change rate, and concentration abrupt change points are identified based on a preset change rate judgment threshold. The calculation formula is as follows: ,in, yes The rate of change of carbon monoxide concentration at a certain monitoring node at a given time. yes The carbon monoxide concentration at the monitoring node should be monitored at all times. yes The carbon monoxide concentration at the monitoring node should be monitored at all times. It is the concentration sampling time interval. It is the coefficient of influence of temperature fluctuation. yes Time and Temperature interpolation at time, yes The threshold for determining concentration mutations at the monitoring node at any given time. It is the threshold correction coefficient. It is the number of sampling points within the sliding window. yes to The average concentration at any given time.

[0012] Furthermore, in the spatiotemporal resolution analysis and data feedback step, the clearance efficiency of the oxygenator in the extracorporeal circulation system is calculated based on the concentration difference between key nodes. The calculation formula is as follows: ,in, For oxygenator scavenging efficiency. This refers to the oxygenator outlet concentration. This refers to the oxygenator inlet concentration. This is the background concentration correction value.

[0013] Furthermore, in the spatiotemporal resolution analysis and data feedback step, a cross-correlation algorithm is used to analyze the time delay of concentration changes at different nodes. By combining blood flow velocity data within the extracorporeal circulation tubing, the distance between potential abnormal locations and monitoring nodes is calculated using the following formula: ,in, The distance between the abnormal location and the monitoring node. Mean blood flow velocity, This is the pressure influence coefficient. Due to pipeline pressure difference, Standard atmospheric pressure.

[0014] On the other hand, a real-time carbon monoxide monitoring system during extracorporeal circulation, the system comprising: Sensor module: It includes a distributed sensor array consisting of multiple medical-grade monitoring probes with main / auxiliary laser light sources and photodetectors, a microfluidic chip array with independent sampling units, and a synchronous control unit that uses a logic control chip to synchronize the laser scanning timing of each node with the working status of the sampling pump. The monitoring probes are connected to the extracorporeal circulation pipeline through a quick-release clamp with a flexible protective pad, and the microfluidic chip is connected to the pipeline through a sterile puncture sampling port and the outlet is connected to a waste gas collection device. Data processing module: It adopts a dual-core architecture circuit board with logic control chip + processor. The logic control chip module is responsible for laser signal synchronous demodulation, data filtering and buffering. The processor module runs a dedicated system and integrates time series analysis, concentration inversion and threshold judgment algorithm library and interfaces with hospital information system. The sensor interface expansion unit includes blood flow velocity and temperature / pressure sensor interfaces corresponding to monitoring nodes for collecting pipeline related data. Display and alarm module: including a high-definition touch screen that supports split-screen display, an alarm device with sound and light alarm and wireless early warning module and graded alarm, and a data export unit that supports exporting standard monitoring reports and spatiotemporal analysis reports containing concentration curves, heat maps and alarm records. Gas sampling and processing module: includes a microfluidic pump array composed of micropumps with adjustable flow rate and closed-loop stable control, matching the number of monitoring nodes; a gas pretreatment unit with a microfluidic chip-embedded hydrophilic membrane and pump inlet filter; and a waste gas treatment unit with a dedicated collection device that collects exhaust gas from each node and processes waste according to specifications.

[0015] Compared with existing technologies, this method and system for real-time monitoring of carbon monoxide during extracorporeal circulation has the following advantages: I. This invention constructs distributed monitoring nodes and uses tunable diode laser absorption spectroscopy technology to achieve synchronous detection of multiple nodes. It combines microfluidic chips and microflow gas pump arrays to achieve synchronous gas sampling at multiple locations. This solves the problems of existing technologies that cannot perform real-time, non-invasive monitoring and lack spatiotemporal resolution. It can obtain the spatial distribution differences of carbon monoxide concentration in different parts of the extracorporeal circulation tubing in real time and analyze its dynamic changes over time, providing more comprehensive and accurate data support for medical assessment.

[0016] Second, this invention uses time series analysis algorithms to dynamically process multi-node concentration data, and deeply analyzes carbon monoxide concentration changes from three dimensions: time, space, and spatiotemporal correlation. It can not only identify concentration abrupt change points, calculate the performance parameters of key components, and generate concentration distribution heatmaps, but also locate potential abnormal locations by combining blood flow velocity data. In addition, the system supports data storage and retrospective analysis, which facilitates medical staff to retrieve data after surgery, providing comprehensive and detailed information for subsequent medical decisions and research, and helping to improve the overall level and safety of extracorporeal circulation medicine.

[0017] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0019] Figure 1 This is a schematic diagram of a real-time carbon monoxide monitoring system during extracorporeal circulation. Figure 2 This is a flowchart of a method for real-time monitoring of carbon monoxide during extracorporeal circulation. Detailed Implementation

[0020] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below. Example

[0021] This embodiment provides a real-time carbon monoxide monitoring system during extracorporeal circulation, such as... Figure 1 As shown, it includes a sensing module, a data processing module, a display and alarm module, and a gas sampling and processing module; The sensing module comprises a distributed sensor array, a microfluidic chip array, and a synchronization control unit. The distributed sensor array consists of multiple independent monitoring probes. Each probe contains a main laser source corresponding to the characteristic wavelength of carbon monoxide, multiple auxiliary laser sources corresponding to the characteristic wavelengths of interfering gases, and a photodetector adapted to the infrared band. The probes are connected to the extracorporeal circulation tubing via a medical-grade quick-release clamp with a flexible protective pad on the inner wall. The microfluidic chip array is fabricated using microfabrication technology. Each chip contains multiple integrated sampling channels, a water removal channel (with a built-in high-precision hydrophilic filter membrane), and a sampling unit with a micro gas absorption pool. The chip inlet is connected to the sterile puncture sampling port of the extracorporeal circulation tubing via a medical-grade tubing, and the outlet is connected to a waste gas collection device. The synchronization control unit is integrated into the sensing module and uses a logic control chip to synchronize the laser scanning timing of each probe with the operating status of the sampling pump.

[0022] The data processing module adopts a dual-core architecture circuit board with a logic control chip and a processor. The logic control chip module is responsible for multi-node laser signal synchronous demodulation (second harmonic detection), data filtering (anti-interference algorithm), and caching. The processor module integrates a time series analysis algorithm library including sliding window calculation, cross-correlation analysis, and heat map generation. It also includes a concentration inversion algorithm library with pre-stored high and low concentration segment fitting equations and interference compensation models, a threshold judgment module that supports custom alarm thresholds, and connects to the hospital information system through a network interface. The sensor interface expansion unit includes a blood flow velocity sensor interface connected to the blood flow detection device and temperature / pressure sensor interfaces corresponding to each monitoring node.

[0023] The display and alarm module includes a high-definition touch screen (supporting split-screen display: the first display area shows the concentration value and the change curve of the switchable time axis, the second display area shows the scalable pipeline concentration heat map, and the third display area shows the performance parameters of key components and the statistics of abrupt change points), an alarm device including an audible and visual alarm and a wireless early warning module (three-level alarm) installed in a preset area, and a data export unit that supports exporting standard reports and spatiotemporal analysis reports containing concentration curves, heat maps, and alarm records.

[0024] The gas sampling and processing module includes a micro-flow pump array consisting of micro-pumps with adjustable flow rate and closed-loop stable control, matching the number of monitoring nodes; a gas pretreatment unit with a microfluidic chip-embedded hydrophilic membrane and pump inlet filter (to remove blood droplets and organic impurities); and a waste gas treatment unit with a dedicated collection device for collecting exhaust gas and treating waste according to regulations.

[0025] The photodetector of the sensing module is connected to the logic control chip module of the data processing module via a signal transmission line. The sampling pump of the microfluidic chip array is connected to the synchronization control unit via a control line. The synchronization control unit is connected to the data processing module via a data line. The processor module of the data processing module is connected to the display terminal, alarm device, and data export unit of the display and alarm module, and the micro-flow gas pump array of the gas sampling and processing module via data lines. The micro-flow gas pump array of the gas sampling and processing module is connected to the sampling channel inlet of the microfluidic chip array via a gas pipeline. Example

[0026] This embodiment is applied to the extracorporeal circulation process during adult heart valve replacement surgery. It is necessary to focus on monitoring the changes in carbon monoxide concentration in the oxygenator, arterial filter, and venous return segment in order to assess the patient's oxygen metabolism status and the stability of the extracorporeal circulation system. Monitoring probes are set at key locations in the extracorporeal circulation tubing to form distributed monitoring nodes. Each monitoring probe is fixed to the tubing by a quick-release clamp with a flexible protective pad to ensure that the laser inlet and outlet are perpendicular to the tubing axis and the sampling port faces the side of the tubing to avoid blood flow impact. The sampling channel inlet of the microfluidic chip array is connected to the sterile puncture sampling port of each monitoring node through medical-grade tubing, and the sampling channel outlet is connected to a dedicated waste gas collection device through tubing. The signal output lines of each monitoring probe are connected to the logic control chip module interface of the data processing module. The blood flow velocity sensor is installed at a preset tubing position, and the temperature / pressure sensors are respectively set on the outer wall of the tubing of each monitoring node and connected to the data processing module through the sensor interface expansion unit. The display terminal is placed next to the surgeon's operating table, the audible and visual alarm is fixed in a conspicuous position in the operating room, and the wireless early warning module is paired with the mobile terminal of the surgeon. like Figure 2 As shown, after the extracorporeal circulation machine starts, the system automatically activates all monitoring nodes. Under the control of the temperature control module and the current drive module, the distributed feedback tunable diode lasers of each node synchronously emit lasers corresponding to the characteristic absorption wavelength of carbon monoxide. After the laser passes through the miniature gas absorption cell in the microfluidic chip, the photodetector collects the attenuation signal and converts it into an electrical signal, which is then transmitted to the data processing module. The logic control chip module performs second harmonic demodulation on the electrical signal, extracts the characteristic signal peak, and uses an anti-interference filtering algorithm to remove high-frequency noise. At the same time, it combines the interference gas absorption signal collected by the auxiliary laser source with the interference compensation formula. Calculate the absorption intensity after compensation, where To compensate for the effective absorption intensity of carbon monoxide, This represents the original absorption intensity of carbon monoxide. To determine the types and quantities of interfering gases, For the first Correction factor for the absorption coefficient of interfering gases. For the first Measurement of the absorption intensity of interfering gases, For the first The actual partial pressure of the interfering gas As a reference voltage divider, The actual temperature of the sample gas. For reference temperature, This is a correction item for environmental interference.

[0027] The processor module of the data processing module calculates the real-time concentration of each node—the low-concentration range—based on the compensated absorption intensity and the concentration segmentation threshold, selecting the corresponding concentration fitting equation. ) through formula Calculations, high concentration range ( ) through formula Calculation, where This represents the carbon monoxide concentration for the corresponding range. , , These are the fitting coefficients for the low concentration range. , , These are the fitting coefficients for the high concentration range. It is the sampling flow rate correction factor. This is the actual sampled flow rate of the microfluidic chip. This is the standard sampling flow rate. The blood flow velocity coupling coefficient is... It is the actual blood flow velocity within the extracorporeal circulation tubing. It is the standard blood flow velocity. The concentration segment thresholds are used; the oxygenator scavenging efficiency formula is used. Calculate the cleaning efficiency, where For oxygenator scavenging efficiency. This refers to the oxygenator outlet concentration. This refers to the oxygenator inlet concentration. This is the background concentration correction value; if the clearance efficiency is lower than the alarm lower limit, the system triggers the corresponding level alarm. The surgeon views the concentration distribution heatmap on the display terminal, combines it with the data collected by the blood flow velocity sensor to determine the cause of the abnormality, and then adjusts the parameters of the extracorporeal circulation system.

[0028] During the core operational phase of valve replacement surgery, the system continuously performs spatiotemporal resolved analysis, using the concentration change rate formula in the time dimension. Calculate the concentration change rate at each node, where yes The rate of change of carbon monoxide concentration at a certain monitoring node at a given time. yes The carbon monoxide concentration at the monitoring node should be monitored at all times. yes The carbon monoxide concentration at the monitoring node should be monitored at all times. It is the concentration sampling time interval. It is the coefficient of influence of temperature fluctuation. yes Time and Temperature interpolation at any given time; and simultaneously using the abrupt change threshold formula. Calculate the dynamic threshold, its yes to If the average concentration at any given time does not exceed a threshold, there are no abrupt change points. Spatially, the concentration difference between the oxygenator inlet and outlet is calculated, and the scavenging efficiency is calculated using the scavenging efficiency formula. A concentration distribution heatmap shows the concentration gradient changes at each node. In the spatiotemporal correlation analysis, a cross-correlation algorithm is used to obtain the time delay of concentration changes at different nodes. Combined with mean blood flow velocity Pressure influence coefficient Pipeline pressure difference Through the anomaly location formula Calculate the distance to potential anomaly locations, where The distance between the abnormal location and the monitoring node. The pressure is set to standard atmospheres. The calculation results are used to determine whether there is an abnormal source.

[0029] If the concentration at a certain monitoring node exceeds the corresponding alarm threshold, the system will immediately trigger the corresponding level alarm and push the warning information (including the abnormal node, concentration value, and trend) to the preset mobile terminal. Medical staff can review the concentration curve within the preset time period through the display terminal and judge the cause of the abnormality in combination with the patient's clinical test results. If it is caused by temporary physiological metabolic changes, no special intervention is required and the alarm will be automatically deactivated after the concentration drops back to the normal range. During the phase of gradually reducing the flow rate on the extracorporeal circulation machine in preparation for evacuation, the system focuses on monitoring the concentration at key nodes and the oxygenator clearance efficiency. As the blood flow is adjusted, the real-time concentration at key nodes is calculated using the concentration inversion formula, the oxygenator clearance efficiency is calculated using the clearance efficiency formula, and the concentration change rate is calculated using the concentration change rate formula to confirm that there are no abnormal abrupt changes. The data processing module confirms, through spatiotemporal correlation analysis, that the concentration change trend at each node is synchronized with the blood flow adjustment and that there are no abnormal source signals. After the cardiopulmonary bypass is removed, the system continues to monitor for a preset duration after the operation, and then automatically stops monitoring. The data processing module stores the entire monitoring data, clears the efficiency, and alarm records in a standard medical data format. Medical staff can retrieve the monitoring data by entering the patient identification information through the search function of the display terminal, and export the monitoring data reports and spatiotemporal analysis reports in standard format for postoperative condition review and surgical effect evaluation.

[0030] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for real-time monitoring of carbon monoxide during extracorporeal circulation, characterized in that, The method includes: Optical path construction and gas sampling: Distributed monitoring nodes are preset in the extracorporeal circulation pipeline. Each probe integrates a corresponding laser source and photodetector. Sample gas is collected from each node synchronously through microfluidic chip and microflow gas pump array. Multi-node synchronous detection: Each monitoring node adopts a distributed feedback tunable diode laser, which achieves mode-skipping scanning through independent temperature control and current drive, and synchronously acquires the attenuation signal of the laser after passing through the sample gas and converts it into an electrical signal output; Interference gas compensation and concentration inversion: Each probe integrates an auxiliary laser source, and a compensation model is established to compensate the carbon monoxide detection signal in real time, while inverting the real-time carbon monoxide concentration at each node; Each monitoring probe integrates multiple sets of auxiliary laser sources, with the center wavelength of each set corresponding to the characteristic absorption wavelength of a preset interfering gas. The absorption intensity of each interfering gas at its corresponding wavelength is detected using these auxiliary laser sources. Combined with a preset database of interfering gas absorption coefficients, an interference compensation model is established for each monitoring node. This model uses a multiple linear regression algorithm to compensate for carbon monoxide detection signals in real time. The formula is as follows: ,in, To compensate for the effective absorption intensity of carbon monoxide, This represents the original absorption intensity of carbon monoxide. To determine the types and quantities of interfering gases, For the first Correction factor for the absorption coefficient of interfering gases. For the first Measurement of the absorption intensity of interfering gases, For the first The actual partial pressure of the interfering gas As a reference voltage divider, The actual temperature of the sample gas. For reference temperature, This is a correction term for environmental disturbances. The real-time carbon monoxide concentration at each node is retrieved specifically as follows: Based on Beer-Lambert's law, a continuous two-segment curve fitting method is used for concentration retrieval: the carbon monoxide concentration is divided into two intervals, namely... The low concentration range This is the high concentration range. As the concentration segmentation threshold, for the low concentration range, the concentration inversion formula is: For the high concentration range, the concentration inversion formula is: ,in, This is the real-time carbon monoxide concentration in the low / high concentration range. These are the fitting coefficients for the low concentration range. These are the fitting coefficients for the high concentration range. It is the sampling flow rate correction factor. This is the actual sampled flow rate of the microfluidic chip. This is the standard sampling flow rate. It is the blood flow velocity coupling coefficient. It is the actual blood flow velocity within the extracorporeal circulation tubing. It is the standard blood flow velocity, the boundary value of the concentration range. Based on clinical data analysis, the concentration of carbon monoxide at each monitoring node was determined by performing concentration inversion on the compensated signals of each monitoring node. Spatiotemporal resolution analysis and data feedback: Through time series analysis, multi-node concentration data is processed to identify concentration abrupt change points, calculate the concentration difference and gradient of key nodes and the performance parameters of key components, and visualize the concentration distribution. Combined with blood flow velocity, abnormal locations are located, data is transmitted synchronously and alarms are triggered. Data storage and retrospective analysis: Data for the entire monitoring period is stored in a medical standard format and supports keyword-based retrieval and retrospective analysis. The storage period is adjustable.

2. The method for real-time monitoring of carbon monoxide during extracorporeal circulation according to claim 1, characterized in that, In the optical path construction and gas sampling steps, monitoring probes are set at preset positions in the extracorporeal circulation tubing to form distributed monitoring nodes. Each monitoring probe integrates a set of tunable diode laser sources and a set of photodetectors. The center wavelength of the tunable diode laser source corresponds to the characteristic absorption wavelength of carbon monoxide. Multi-location synchronous gas sampling is achieved through a microfluidic chip array. The microfluidic chip includes independent sampling channels, gas pretreatment channels, and detection channels. The gas pretreatment channel has a built-in hydrophilic membrane, and each sampling channel corresponds to one monitoring node. The sampling flow rate is controlled by a micro-flow pump array. Each sampling channel in the micro-flow pump array is equipped with a micro-pump to synchronously introduce the sample gas from each monitoring node into the corresponding micro-gas absorption pool. The micro-gas absorption pool is integrated into the detection channel of the microfluidic chip and adopts a multi-reflection structure. The detection optical path of each monitoring node is independent and works synchronously.

3. The method for real-time monitoring of carbon monoxide during extracorporeal circulation according to claim 1, characterized in that, In the multi-node synchronous detection step, the laser source of each monitoring node adopts a distributed feedback tunable diode laser, and its center wavelength is calibrated to the characteristic absorption wavelength of carbon monoxide. Each laser source is equipped with an independent temperature control module and current drive module to achieve mode-skipping scanning. After the scanning laser passes through the sample gas in the corresponding micro gas absorption cell, the photodetectors of each node synchronously collect the attenuated laser signal and convert the optical signal into an electrical signal to be output to the data processing module.

4. The method for real-time monitoring of carbon monoxide during extracorporeal circulation according to claim 1, characterized in that, In the spatiotemporal resolution analysis and data feedback step, multi-node concentration data is processed through time series analysis, specifically: Time dimension analysis: Time series of concentration data for each monitoring node is established by timestamp, the concentration change rate is calculated by sliding window method, and concentration change points are identified based on preset change rate judgment threshold; Spatial dimension analysis: Calculate the concentration difference and concentration gradient between preset key nodes. The concentration gradient is the ratio of the concentration difference to the corresponding pipeline length. Calculate the clearance efficiency of the oxygenator in the extracorporeal circulation system based on the concentration difference between key nodes. At the same time, visualize the concentration distribution of each monitoring node through heat map. Spatiotemporal correlation analysis: The cross-correlation algorithm is used to analyze the time delay of concentration changes at different nodes, combined with blood flow velocity data in the extracorporeal circulation tubing. The blood flow velocity data is synchronously collected by blood flow sensors. The distance between potential abnormal locations and monitoring nodes is calculated based on the correlation between time delay and blood flow velocity.

5. The method for real-time monitoring of carbon monoxide during extracorporeal circulation according to claim 1, characterized in that, In the spatiotemporal resolution analysis and data feedback step, the sliding window method is used to calculate the concentration change rate, and concentration abrupt change points are identified based on a preset change rate judgment threshold. The calculation formula is as follows: ,in, yes The rate of change of carbon monoxide concentration at a certain monitoring node at a given time. yes The carbon monoxide concentration at this monitoring node should be monitored at all times. yes The carbon monoxide concentration at this monitoring node should be monitored at all times. It is the concentration sampling time interval. It is the coefficient of influence of temperature fluctuation. yes Time and Temperature interpolation at time, yes The threshold for determining concentration mutations at the monitoring node at any given time. It is the threshold correction coefficient. It is the number of sampling points within the sliding window. to The average concentration at any given time.

6. The method for real-time monitoring of carbon monoxide during extracorporeal circulation according to claim 5, characterized in that, In the spatiotemporal resolution analysis and data feedback step, the clearance efficiency of the oxygenator in the extracorporeal circulation system is calculated based on the concentration difference between key nodes. The calculation formula is as follows: ,in, For oxygenator scavenging efficiency. This refers to the oxygenator outlet concentration. This refers to the oxygenator inlet concentration. This is the background concentration correction value.

7. The method for real-time monitoring of carbon monoxide during extracorporeal circulation according to claim 6, characterized in that, In the spatiotemporal resolution analysis and data feedback step, a cross-correlation algorithm is used to analyze the time delay of concentration changes at different nodes. By combining blood flow velocity data within the extracorporeal circulation tubing, the distance between potential abnormal locations and monitoring nodes is calculated using the following formula: ,in, The distance between the abnormal location and the monitoring node. Mean blood flow velocity, This is the pressure influence coefficient. Due to pipeline pressure difference, Standard atmospheric pressure.

8. A real-time carbon monoxide monitoring system during extracorporeal circulation, the system being applicable to the real-time carbon monoxide monitoring method during extracorporeal circulation as described in any one of claims 1-7, characterized in that, The system includes: Sensor module: It includes a distributed sensor array consisting of multiple medical-grade monitoring probes with main / auxiliary laser light sources and photodetectors, a microfluidic chip array with independent sampling units, and a synchronous control unit that uses a logic control chip to synchronize the laser scanning timing of each node with the working status of the sampling pump. The monitoring probes are connected to the extracorporeal circulation pipeline through a quick-release clamp with a flexible protective pad, and the microfluidic chip is connected to the pipeline through a sterile puncture sampling port and the outlet is connected to a waste gas collection device. Data processing module: It adopts a dual-core architecture circuit board with logic control chip + processor. The logic control chip module is responsible for laser signal synchronous demodulation, data filtering and buffering. The processor module runs a dedicated system and integrates time series analysis, concentration inversion and threshold judgment algorithm library and interfaces with hospital information system. The sensor interface expansion unit includes blood flow velocity and temperature / pressure sensor interfaces corresponding to monitoring nodes for collecting pipeline related data. Display and alarm module: including a high-definition touch screen that supports split-screen display, an alarm device with sound and light alarm and wireless early warning module and graded alarm, and a data export unit that supports exporting standard monitoring reports and spatiotemporal analysis reports containing concentration curves, heat maps and alarm records. Gas sampling and processing module: includes a microfluidic pump array composed of micropumps with adjustable flow rate and closed-loop stable control, matching the number of monitoring nodes; a gas pretreatment unit with a microfluidic chip-embedded hydrophilic membrane and pump inlet filter; and a waste gas treatment unit with a dedicated collection device that collects exhaust gas from each node and processes waste according to specifications.

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