Remote control method of ECMO equipment

Abstract

The invention provides a remote control method for ECMO equipment, and relates to the technical field of medical instruments, and the method comprises the steps: collecting sensor data of the ECMO equipment in real time; encrypting sensor data acquired in real time, and remotely transmitting the sensor data to a central data device for extracting characteristic parameters; the feature parameters extracted in the second step are obtained, and a first feature parameter and a second feature parameter related to the oxyhemoglobin saturation are obtained by calculating a cross correlation coefficient and a level coefficient; the central processing unit constructs a parameter relation model according to the first characteristic parameters and the second characteristic parameters obtained through calculation in the third step, and the new flow needing to be adjusted of the blood pump is calculated; and according to the calculated new flow needing to be adjusted of the blood pump, a mapping control algorithm is utilized to obtain the rotating speed adjustment amount of the motor.

Description

Technical Field

[0001] The present invention provides a remote control method for ECMO equipment, which relates to the technical field of medical devices. Background Art

[0002] Modern ECMO equipment is becoming more and more intelligent and automated, with precise sensors and advanced control systems that can monitor and adjust various parameters in real time, such as blood flow, oxygen concentration, temperature, etc. This provides a basis for remote control, allowing remote operators to accurately grasp the operating status of the equipment and effectively control it.

[0003] With the widespread coverage of high-speed wireless networks such as 4G and 5G and fiber optic networks, the data transmission speed and stability have been greatly improved, making it possible to transmit a large amount of data in real time for remote control of ECMO equipment. The development of Internet of Things technology enables various devices to be interconnected through the network. ECMO equipment can be used as a terminal device in the Internet of Things, connected to the remote control platform through sensors and communication modules, to achieve real-time monitoring of equipment status and transmission of remote control instructions.

[0004] Remote control can reduce the need for on-site operation by professionals, and reduce labor costs and transportation costs. At the same time, remote monitoring can also detect equipment failures in a timely manner and perform remote maintenance, reducing equipment downtime, improving equipment utilization, and further reducing medical costs. Remote control technology helps to integrate ECMO equipment resources in the region and realize centralized management and deployment of equipment. According to patient needs, equipment can be remotely dispatched to different medical institutions to improve equipment utilization efficiency and optimize medical resource allocation. Summary of the invention

[0005] In order to solve the above technical problems, the present invention proposes a remote control method for ECMO equipment, comprising the following steps:

[0006] Step 1: Collect sensor data of ECMO equipment in real time;

[0007] Step 2: Encrypt the sensor data collected in real time and transmit it remotely to the central data device for feature parameter extraction;

[0008] Step 3: Obtain the characteristic parameters extracted in step 2, and obtain the first characteristic parameter and the second characteristic parameter related to the blood oxygen saturation by calculating the cross-correlation coefficient and the level coefficient;

[0009] Step 4: The central processing unit constructs a parameter relationship model according to the first characteristic parameter and the second characteristic parameter calculated in step 3, and calculates a new flow rate that the blood pump needs to adjust;

[0010] Step 5: Obtain the motor speed adjustment amount by using the calculated new flow rate that the blood pump needs to adjust and utilizing a mapping control algorithm.

[0011] In the preferred embodiment, in step 3, the characteristic parameters extracted in step 2 are obtained, and the characteristic parameter to be calculated in time period t is set as T I (t), the blood oxygen saturation signal is V(t), and its discrete sequences are T I [i] and V[i](i=1,2,…,N),

[0012] Calculate the mean of the discrete sequence of characteristic parameters and blood oxygen saturation signals and

[0013]

[0014] The Ith characteristic parameter to be calculated is T I The cross-correlation coefficient C of (t) and the blood oxygen saturation signal V(t) I The calculation formula is:

[0015]

[0016] Select the cross-correlation coefficient C I The characteristic parameter corresponding to the maximum is used as the first characteristic parameter related to blood oxygen saturation.

[0017] In a preferred embodiment, the second characteristic parameter with the greatest correlation with the first characteristic parameter is calculated. Assume that the first characteristic parameter is X and the alternative characteristic parameter is Y. The J measurements with the first characteristic parameter X and the alternative characteristic parameter Y are sorted in ascending order, and the difference d of each pair of measurements with sequence number j is calculated. j ,

[0018] d j =|X j -Y j |;

[0019] Among them, X j , Y j are respectively the measured value of the first characteristic parameter and the measured value of the alternative characteristic parameter at the position with sequence number j;

[0020] Calculate the level coefficient r with the first characteristic parameter X and the alternative characteristic parameter Y according to the formula s :

[0021]

[0022] Among them, α is the adjustment parameter, choose r s The largest candidate characteristic parameter is used as the second characteristic parameter.

[0023] In a preferred embodiment, in step 4, the pulse pressure is used as the first characteristic parameter and the blood pump flow rate is used as the second characteristic parameter, and the venous pressure P v , arterial pressure P a The pulse pressure-flow relationship model between Q and the blood pump flow rate is:

[0024] Q=k1-k2P v -k3P a ;

[0025] Among them, k1 is the basic flow coefficient, k2 and k3 are the adjustment coefficients of the pulse pressure-flow relationship model;

[0026] When venous pressure increases ΔP v When the blood pump first flow adjustment amount ΔQ v for:

[0027] ΔQ v =-k2ΔP v

[0028] Adjusted blood pump first new flow Q n-v for:

[0029] Q n-v =Q-ΔQ v = k1-k2(P v +ΔP v )-k3P a

[0030] When arterial pressure increases ΔP a When the blood pump second flow adjustment amount ΔQ a for:

[0031] ΔQ a =-k3ΔP a

[0032] Adjusted blood pump second new flow Q n-a for:

[0033] Q n-a =Q-ΔQ a =k1-k2P v -k3(P a +ΔP a )

[0034] Considering the simultaneous changes in venous pressure and arterial pressure, the total flow adjustment of the blood pump ΔQ is:

[0035] ΔQ=-k2ΔP v -k3ΔP a

[0036] The new flow rate Q that the blood pump needs to adjustn for:

[0037] Q n = k1-k2(P v +ΔP v )-k3(P a +ΔP a ).

[0038] In the preferred embodiment, in step 5, the blood pump flow deviation in period t is assumed to be Q′(t), the motor speed adjustment amount is Y, and Q1, Q2, and Q3 are upper, middle, and lower boundary values ​​of the deviation determined according to actual conditions:

[0039]

[0040] Among them, a is the lower limit of the motor speed, b is the upper limit of the motor speed, and n represents the motor grade parameter.

[0041] In a preferred embodiment, in step 5, the new flow rate Q to be adjusted by the blood pump is calculated. n , calculate the valve opening rate L of the blood pump:

[0042]

[0043] Among them, L is the valve opening rate, ρ is the blood flow density, q is the blood pump flow coefficient, P1 is the blood flow pressure value inside the blood pump pipeline, P m It is the blood flow pressure value at the outlet of the blood pump valve.

[0044] In a preferred embodiment, in step 2, the ECMO device transmitter uses a hash algorithm to perform a hash operation on the sensor data set collected in real time to generate a hash value of a fixed length, and transmits the data set to be transmitted and the generated hash value to the central processor through the network. The central processor uses the same hash algorithm as the ECMO device transmitter to perform a hash operation on the received data set again to generate a new hash value. If it is exactly the same as the hash value sent by the ECMO device transmitter, it means that the data set has not been tampered with during the transmission process.

[0045] In a preferred embodiment, in step 2, the central data device performs noise filtering on the received data set. For low-frequency blood flow pseudo-signals, an adaptive filtering algorithm is used. For intermediate frequency noise generated by equipment operation, a Butterworth band-stop filter is used to remove it. High-frequency electromagnetic interference is intercepted by a low-pass filter.

[0046] Compared with the prior art, the present invention has the following beneficial technical effects:

[0047] Precise monitoring and adjustment: By collecting sensor data from ECMO equipment in real time, encrypting transmission and extracting characteristic parameters, the operating status of the equipment and the patient's physiological information can be more comprehensively and timely understood. On this basis, the characteristic parameters most relevant to blood oxygen saturation are accurately calculated, and the new flow rate and motor speed adjustment required for the blood pump are obtained, so as to achieve precise control of the equipment, which helps maintain the blood oxygen balance in the patient's body, reduce the risk of complications caused by insufficient oxygen supply or excessive perfusion, and improve the safety and effectiveness of patient treatment.

[0048] Reduce human errors: Traditional manual adjustments often rely on the experience and regular observation of medical staff, which is subjective and lags behind. This method automatically adjusts based on real-time data and precise calculations, reducing errors in human judgment and operation, ensuring that patients are always in a relatively ideal treatment state.

[0049] Optimizing blood pump flow control: This method calculates the new blood pump flow based on the characteristic parameters and parameter relationship model most relevant to blood oxygen saturation, making the blood pump flow adjustment more scientific and reasonable, avoiding the inefficiency caused by the traditional fixed flow mode or blind adjustment, and improving the accuracy and effectiveness of the blood pump's support for the patient's blood circulation, thereby improving the overall operating efficiency of the ECMO device.

[0050] Improve the automation level of the equipment: The motor speed adjustment amount is obtained by using the mapping control algorithm, which realizes the automation process from data collection to the final equipment parameter adjustment, reduces the manual intervention link, improves the response speed and automation level of the equipment, and enables the ECMO equipment to adapt to changes in the patient's physiological state more quickly, thereby improving the working efficiency of the equipment.

[0051] Data encryption transmission: Encryption processing is performed during the process of remotely transmitting sensor data to the central data device, effectively preventing the data from being stolen, tampered with or leaked during transmission, ensuring the privacy of patients and the security of equipment operation data, and meeting the strict requirements of medical data security.

[0052] Decision-making basis based on big data analysis: Through the collection of a large amount of sensor data and the extraction of characteristic parameters, as well as data analysis methods such as the calculation of cross-correlation coefficients, a more scientific and reliable decision-making basis is provided for the adjustment of blood pump flow and motor speed, avoiding the limitations of a single data point or empirical judgment, and improving the accuracy and reliability of control decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0054] Figure 1 A flow chart of a remote control method for an ECMO device of the present invention;

[0055] Figure 2 Schematic diagram of blood pump flow monitoring of the present invention;

[0056] Figure 3 A comparison diagram of the flow rate controlled after the mapping control algorithm of the present invention and the flow rate control of actual operation. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0058] In the drawings of the specific embodiments of the present invention, in order to better and more clearly describe the working principles of the various components in the system, the connection relationship of the various parts in the device is shown, which only clearly distinguishes the relative position relationship between the various components, and cannot constitute a limitation on the signal transmission direction, connection sequence and size, dimensions and shape of the components or structures.

[0059] like Figure 1 As shown, it is a flow chart of the remote control method of the ECMO device of the present invention, and the remote control method of the ECMO device comprises the following steps:

[0060] Step 1: Collect sensor data from ECMO equipment in real time.

[0061] The sensors are high-precision, high-sensitivity sensors that can adapt to the complex environment of the extracorporeal membrane oxygenation (ECMO) pipeline. For example, for blood flow measurement, an ultrasonic Doppler flow sensor is used and installed near the inlet and outlet blood vessels of the extracorporeal membrane oxygenation to ensure that changes in blood flow velocity can be accurately captured. The pressure sensor uses a semiconductor piezoresistive sensor, which is installed before and after the blood pump and at key positions inside the oxygenator to reflect the blood flow pressure in real time. The temperature sensor uses a thermistor sensor, which is evenly distributed on the surface of the pipeline and the oxygenator, close to the blood flow area, to accurately obtain blood temperature information.

[0062] Preferably, each sensor is calibrated, tested in a simulated ECMO circulation device using a simulated blood sample with a known standard value, and the sensor parameters are adjusted to ensure that the measurement error is controlled within a very small range.

[0063] When the ECMO device starts running, the sensor acquisition program is activated synchronously. The data acquisition frequency is set. To ensure the real-time data without excessively increasing the system burden, the sensor data is collected every 20 milliseconds. Preferably, the collected data is immediately marked with a timestamp so that it can be processed and analyzed in sequence later to ensure the timeliness and consistency of the data.

[0064] Step 2: Encrypt the sensor data collected in real time and transmit it remotely to the central data device for feature parameter extraction.

[0065] S21: Encrypt the sensor data collected in real time and transmit it remotely to the central processor.

[0066] ECMO device transmitter operation:

[0067] Sensor data preparation: On the ECMO device side, collect sensor data to be transmitted, including vital signs data, device operating parameters and other key data, and organize these key data into a complete data set.

[0068] Select a hash algorithm: According to data security requirements and system support, select a suitable hash algorithm, such as MD5, SHA-1, SHA-256, etc. Preferably, SHA-256 is used as the hash algorithm, which has higher security.

[0069] Generate hash value: Use the selected hash algorithm to perform hash operation on the complete data set to generate a hash value of fixed length. This hash value is the unique identifier of the data and represents the security characteristics of the data in the complete data set.

[0070] Transmitting data set and hash value: Transmitting the data set to be transmitted and the generated hash value together to the central processor through the network. Preferably, the hash value is added to the data message as part of the data set, or a field is specially set in the data transmission protocol to transmit the hash value.

[0071] CPU Operation:

[0072] The central processing unit receives the data set and the hash value. After receiving the data set and the hash value transmitted from the sending end of the ECMO device, the central processing unit separates them and obtains the received data set and hash value respectively.

[0073] Re-hashing: Use the same hashing algorithm as the sending end of the ECMO device to re-hash the received data set to generate a new hash value.

[0074] Compare hash values: Compare the hash value regenerated by the central processor with the hash value sent by the ECMO device transmitter. If the two hash values ​​are exactly the same, it means that the data set has not been tampered with during transmission, the data set is complete and reliable, and subsequent processing and control calculations can be performed.

[0075] S22. The central data device performs signal filtering, enhancement and feature signal extraction on the data.

[0076] The fast Fourier transform (FFT) algorithm is used to perform spectrum analysis on the collected original signal to identify noise components such as pseudo signals (irregular low-frequency fluctuations) caused by equipment operation, electromagnetic interference, and unstable blood flow itself. For noise in different frequency bands, corresponding filtering methods are used to filter noise. For low-frequency blood flow pseudo signals, an adaptive filtering algorithm is used to dynamically adjust the filter parameters according to real-time blood flow characteristics; the intermediate frequency noise generated by equipment operation is accurately removed using a Butterworth band-stop filter; high-frequency electromagnetic interference is intercepted by a low-pass filter. The filtered signal must undergo residual analysis to ensure that the residual noise is lower than the set threshold, such as less than 5% of the total signal strength.

[0077] The filtered signal is decomposed into multiple scales through wavelet transform to amplify weak but critical characteristic signals, such as subtle fluctuations in pressure signals caused by small changes in blood flow. Among the decomposed scale components, characteristic parameters potentially associated with blood oxygen saturation are extracted, such as pulse pressure signals, blood pump flow signals, temperature signals, blood flow velocity, etc., to prepare sufficient and high-quality data feature sets for subsequent modeling.

[0078] Step three: Obtain the characteristic parameters extracted in step two, and obtain the first characteristic parameter and the second characteristic parameter related to the blood oxygen saturation by calculating the cross-correlation coefficient and the level coefficient.

[0079] Get the feature parameters extracted in step 2, and set the first feature parameter to be calculated as T I (t), the blood oxygen saturation signal is V(t), and its discrete sequences are T I [i] and V[i] (i=1,2,…,N), where N is a discrete number.

[0080] First calculate T I The mean of [i] and V[i]:

[0081]

[0082] The Ith characteristic parameter to be calculated is T IThe cross-correlation coefficient C of (t) and the blood oxygen saturation signal V(t) I The calculation formula is:

[0083]

[0084] Select the cross-correlation coefficient C I The characteristic parameter corresponding to the maximum is used as the first characteristic parameter of blood oxygen saturation.

[0085] Calculate the second characteristic parameter with the greatest correlation with the first characteristic parameter. Let the first characteristic parameter be X and the alternative characteristic parameter be Y. Sort the J measurements with the first characteristic parameter X and the alternative characteristic parameter Y in ascending order, obtain the corresponding sorting number j, and pair them. Calculate the difference d of each pair of measurements in the order j. j :

[0086] d j =|X j -Y j |;

[0087] Among them, X j , Y j They are respectively the measured value of the first characteristic parameter and the measured value of the alternative characteristic parameter at position j.

[0088] Calculate the level coefficient r with the first characteristic parameter X and the alternative characteristic parameter Y according to the formula s :

[0089]

[0090] Among them, α is the adjustment parameter, when r s When it is greater than the threshold, it indicates that there is a strong correlation between the first feature parameter X and the candidate feature parameter Y.

[0091] Select level coefficient r s The largest candidate characteristic parameter is used as the second characteristic parameter.

[0092] Step 4: The central processing unit constructs a parameter relationship model according to the first characteristic parameter and the second characteristic parameter calculated in step 3, and calculates a new flow rate that the blood pump needs to adjust.

[0093] In this embodiment, pulse pressure is used as the first characteristic parameter and blood pump flow rate is used as the second characteristic parameter as an example for description.

[0094] S31: Construct a pulse pressure-flow relationship model.

[0095] Venous pressure v , arterial pressure P a The pulse pressure-flow relationship model between Q and the blood pump flow Q is expressed as:

[0096] Q=k1-k2P v -k3P a ;

[0097] Among them, k1 is the basic flow coefficient, which depends on factors such as the patient's basic physiological needs and the initial settings of the ECMO device; k2 and k3 are the adjustment coefficients of the pulse pressure-flow relationship model, which are determined by fitting experimental data or based on clinical experience.

[0098] S32: Blood pump flow adjustment calculation

[0099] When venous pressure increases ΔP v In order to maintain the appropriate flow rate, the blood pump first flow adjustment amount ΔQ v Calculated according to the following formula:

[0100] ΔQ v =-k2ΔP v

[0101] Adjusted blood pump first new flow Q n-v for:

[0102] Q n-v =Q-ΔQ v = k1-k2(P v +ΔP v )-k3P a

[0103] Similarly, when arterial pressure increases by ΔP a When the blood pump second flow adjustment amount ΔQ a for:

[0104] ΔQ a =-k3ΔP a

[0105] Adjusted blood pump second new flow Q n-a for:

[0106] Q n-a =Q-ΔQ a =k1-k2P v -k3(P a +ΔP a )

[0107] Considering the simultaneous changes in venous pressure and arterial pressure, the total flow adjustment ΔQ is:

[0108] ΔQ=-k2ΔP v -k3ΔP a

[0109] The new flow rate Q that the blood pump needs to adjust n for:

[0110] Q n = k1-k2(P v +ΔP v )-k3(P a +ΔP a ).

[0111] like Figure 2 As shown, the blood pump flow is monitored. Once the venous pressure is detected to be increased, it means that the reflux is blocked, and the flow of the roller blood pump needs to be reduced to prevent venous congestion. If the arterial pressure increases, it indicates that the perfusion resistance increases, and the blood pump flow should also be appropriately reduced to avoid excessive pressure damaging blood vessels and organs.

[0112] Step 5: Obtain the motor speed adjustment amount by using the calculated new flow rate that the blood pump needs to adjust and utilizing a mapping control algorithm.

[0113] The central processor continuously compares the current actual flow rate (feedback from the built-in flow monitoring module of the blood pump, which is also connected to the processor through the analog input port) with the new flow rate. If there is a deviation, the mapping control algorithm is used to quickly calculate the compensation amount and drive the blood pump to adjust. If the actual flow rate is low, the motor power is immediately increased to increase the roller speed until the actual flow rate stabilizes near the target value, and the error is controlled within ±0.1L / min, achieving high-precision constant flow perfusion.

[0114] The mapping control algorithm specifically includes the following calculation steps:

[0115] Assume that the flow deviation in period t is Q′(t), the motor speed adjustment is Y; Q1, Q2, Q3 are the upper, middle and lower boundary values ​​of the deviation determined according to the actual situation:

[0116]

[0117] Among them, a is the lower limit of the motor speed, b is the upper limit of the motor speed, and n represents the motor grade parameter.

[0118] The mapping control algorithm has good adaptability to the nonlinearity and uncertainty of the system. Since the relationship between flow and various physiological parameters and equipment characteristics in the ECMO system is complex and may change over time, the mapping control algorithm can handle this situation well.

[0119] In actual application, in some experimental ECMO flow control studies, mapping control algorithms are used to respond to patients' different flow requirements under different physiological conditions (such as blood pressure fluctuations, changes in blood viscosity, etc.). By continuously optimizing the upper, middle and lower boundary values ​​of the deviation, stable flow control can be achieved to a certain extent, reducing flow control errors caused by individual differences and complex physiological changes.

[0120] Example 2

[0121] The hydraulic performance of the blood pump is also affected by the valve opening rate. Increasing the number of blades can improve the pressure supply capacity of the blood pump, but when the number of blades is too large, it may increase the flow resistance of the fluid, resulting in a decrease in the efficiency of the blood pump. Therefore, the adjustment of the valve opening rate needs to balance the flow and efficiency to achieve the best hydraulic performance. Changes in the valve opening rate will also affect the hemodynamics inside the blood pump, including flow velocity distribution, pressure distribution, etc. These factors together determine the performance and safety of the blood pump.

[0122] Therefore, this embodiment focuses on the control of the valve opening rate. Based on the embodiment 1, in step 5, the new flow rate Q to be adjusted by the blood pump is calculated. n , the valve opening rate of the blood pump is calculated. The valve opening rate L of the blood pump can be specifically expressed by the following formula:

[0123]

[0124] Among them, L is the valve opening rate, ρ is the blood flow density, q is the blood pump flow coefficient, P1 is the blood flow pressure value inside the blood pump pipeline, P m It is the blood flow pressure value at the outlet of the blood pump valve.

[0125] In a preferred embodiment, in order to avoid excessively high or low pressure at the blood pump outlet, the valve opening rate should be adjusted as slowly as possible. Slow adjustment can gradually change the blood flow and reduce the possibility of pressure waves and sudden changes in pressure. For example, the valve opening can be gradually increased or decreased. After each adjustment, wait for a period of time to observe the change in the pressure in the pipeline, and make sure that the pressure is stable within a safe range before making the next adjustment.

[0126] In the process of adjusting the valve opening rate, it is necessary to monitor the pressure at key locations in the pipeline in real time. Real-time collection of pressure data can be achieved by installing pressure sensors. A feedback mechanism is established based on the pressure monitoring data. If the pressure approaches the alarm value, the valve opening rate is stopped or adjusted in the opposite direction in time to prevent the pressure from being too high or too low.

[0127] like Figure 3 As shown, a comparison diagram of the flow rate controlled by the mapping control algorithm of this embodiment and the flow rate control of actual operation is shown. As the number of feedback samples increases, the flow rate obtained by calculation matches the flow data of actual operation.

[0128] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0129] In one embodiment, a computer-readable storage medium is provided, storing a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0130] In one embodiment, a computer program product or computer program is provided, the computer program product or computer program includes computer instructions, the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps in the above-mentioned method embodiments.

[0131] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. The database involved in the embodiments provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited thereto. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, data processing logic devices based on quantum computing, etc., but are not limited thereto.

[0132] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0133] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A remote control method for ECMO equipment, characterized in that: The steps include: Step 1: Collect sensor data of ECMO equipment in real time; Step 2: Encrypt the sensor data collected in real time and transmit it remotely to the central data device for feature parameter extraction; Step 3: Obtain the characteristic parameters extracted in step 2, and obtain the first characteristic parameter and the second characteristic parameter related to the blood oxygen saturation by calculating the cross-correlation coefficient and the level coefficient; Step 4: The central processing unit constructs a parameter relationship model according to the first characteristic parameter and the second characteristic parameter calculated in step 3, and calculates a new flow rate that the blood pump needs to adjust; Step 5: Obtain the motor speed adjustment amount by using the calculated new flow rate that the blood pump needs to adjust and utilizing a mapping control algorithm.

2. The remote control method of ECMO equipment according to claim 1, characterized in that: In step 3, the characteristic parameters extracted in step 2 are obtained, and the characteristic parameter to be calculated in time period t is set as T I (t), the blood oxygen saturation signal is V(t), and its discrete sequences are T I [i] and V[i](i=1,2,…,N), Calculate the mean of the discrete sequence of characteristic parameters and blood oxygen saturation signals and The Ith characteristic parameter to be calculated is T I The cross-correlation coefficient C of (t) and the blood oxygen saturation signal V(t) I The calculation formula is: Select the cross-correlation coefficient C I The characteristic parameter corresponding to the maximum is used as the first characteristic parameter related to blood oxygen saturation.

3. The remote control method of ECMO equipment according to claim 2, characterized in that: Calculate the second characteristic parameter with the greatest correlation with the first characteristic parameter. Let the first characteristic parameter be X and the alternative characteristic parameter be Y. Sort the J measurements with the first characteristic parameter X and the alternative characteristic parameter Y in ascending order, and calculate the difference d of each pair of measurements with sequence number j. j , d j =|X j -Y j |; Among them, X j , Y j are respectively the measured value of the first characteristic parameter and the measured value of the alternative characteristic parameter at the position with sequence number j; Calculate the level coefficient r with the first characteristic parameter X and the alternative characteristic parameter Y according to the formula s : Among them, α is the adjustment parameter, choose r s The largest candidate characteristic parameter is used as the second characteristic parameter.

4. The remote control method of ECMO equipment according to claim 1, characterized in that: In step 4, the pulse pressure is used as the first characteristic parameter and the blood pump flow rate is used as the second characteristic parameter. The venous pressure P v , arterial pressure P a The pulse pressure-flow relationship model between Q and the blood pump flow rate is: Q=k1-k2P v -k3P a ; Among them, k1 is the basic flow coefficient, k2 and k3 are the adjustment coefficients of the pulse pressure-flow relationship model; When venous pressure increases ΔP v When the blood pump first flow adjustment amount ΔQ v for: ΔQ v =-k2ΔP v Adjusted blood pump first new flow Q n-v for: Q n-v =Q-ΔQ v =k1-k2(P v +ΔP v )-k3P a When arterial pressure increases ΔP a When the blood pump second flow adjustment amount ΔQ a for: ΔQ a =-k3ΔP a Adjusted blood pump second new flow Q n-a for: Q n-a =Q-ΔQ a =k1-k2P v -k3(P a +ΔP a ) Considering the simultaneous changes in venous pressure and arterial pressure, the total flow adjustment of the blood pump ΔQ is: ΔQ=-k2ΔP v -k3ΔP a The new flow rate Q that the blood pump needs to adjust n for: Q n =k1-k2(P v +ΔP v )-k3(P a +ΔP a )。 5. The remote control method of ECMO equipment according to claim 3, characterized in that: In step 5, the blood pump flow deviation in period t is assumed to be Q′(t), the motor speed adjustment amount is Y, and Q1, Q2, and Q3 are upper, middle, and lower boundary values ​​of the deviation determined according to actual conditions: Among them, a is the lower limit of the motor speed, b is the upper limit of the motor speed, and n represents the motor grade parameter.

6. The remote control method of ECMO equipment according to claim 3, characterized in that: In step 5, the new flow rate Q to be adjusted by the blood pump is calculated. n , calculate the valve opening rate L of the blood pump: Among them, L is the valve opening rate, ρ is the blood flow density, q is the blood pump flow coefficient, P1 is the blood flow pressure value inside the blood pump pipeline, P m It is the blood flow pressure value at the outlet of the blood pump valve.

7. The remote control method of ECMO equipment according to claim 1, characterized in that: In the step 2, the ECMO device transmitter uses a hash algorithm to perform a hash operation on the sensor data set collected in real time, generates a hash value of a fixed length, and transmits the data set to be transmitted and the generated hash value to the central processor through the network. The central processor uses the same hash algorithm as the ECMO device transmitter to perform a hash operation on the received data set again to generate a new hash value. If it is exactly the same as the hash value sent by the ECMO device transmitter, it means that the data set has not been tampered with during the transmission process.

8. The remote control method of ECMO equipment according to claim 1, characterized in that: In the step 2, the central data device performs noise filtering on the received data set, uses an adaptive filtering algorithm for low-frequency blood flow pseudo signals, and uses a Butterworth band-stop filter to remove the intermediate frequency noise generated by the operation of the equipment; High-frequency electromagnetic interference is blocked by low-pass filters.

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