CN116650097A - Tissue denaturation point detection method, device and electrosurgical instrument during sealing process - Google Patents

Abstract

The present application discloses a method, device and electrosurgical instrument for detecting tissue degeneration points in the sealing process. The method includes: obtaining the real-time electrical signal fed back by the tissue at the current moment based on the input energy of the electrode; Whether the denaturation point is reached; wherein, the set time period before the current moment is taken as the historical time period, and the electrical signal fed back by the tissue based on the input energy of the electrodes acquired within the historical time period is the historical time period electrical signal. The detection results of tissue degeneration points obtained in this scheme can reduce the error of detection results caused by accidental factors such as jumps in tissue signal values. Compared with the single-point comparison scheme of the prior art, this scheme has higher accuracy.

Description

Tissue denaturation point detection method, device and electrosurgical instrument during sealing process

technical field

The present application relates to the technical field of electrosurgical instruments, in particular to a method and device for detecting tissue degeneration points during a sealing process, and an electrosurgical instrument.

Background technique

The electrosurgical instrument includes a handle assembly, a rotating head assembly, an elongated body assembly and an end effector assembly sequentially connected from the proximal end to the distal end. The handle assembly is connected with a host capable of outputting high frequency and high voltage current. The elongated body assembly extends distally from the distal end of the rotatable head assembly, and manipulation of the rotatable head assembly by a user enables the elongated body assembly and the end effector assembly to collectively rotate about the longitudinal axis of the elongated body assembly. An end effector assembly is operably mounted to the distal end of the elongate body assembly for manipulating tissue to perform a particular surgical procedure.

The end effector is designed as a clamp-on structure. The clamp structure includes two clamping arms that cooperate with each other, at least one clamping arm is provided with an electrode (determined according to the unipolar type or the bipolar type). During the operation, when the two clamping arms clamp the tissue in the middle and close it tightly, the electrode is energized through the handle assembly and sends out a high-frequency alternating current signal. The electric signal passes through the tissue and heats the tissue. This process is preheating process. The nature of the tissue will change during the heating process, so the electrical signal passing through the tissue will also change. The tissue will transmit the value of the electrical signal to the host, and the host will judge whether the tissue has changed in nature according to the change of the electrical signal (hereinafter referred to as tissue Denaturation point), when the tissue reaches the denaturation point, hemostasis seals, the host changes the output electrical signal and then changes the output energy of the electrodes to complete tissue sealing. Based on the above described process, it is critical to accurately identify whether the tissue has reached the point of denaturation.

In some existing schemes, the reference curve of the impedance value changing with time under the ideal state during the heating process of the tissue is predetermined, and the node corresponding to the minimum value of the impedance value in the reference curve is used as the tissue denaturation point, and the actual tissue sealing , the impedance value calculated in real time is compared with the impedance value recorded by the reference curve. If the actual calculated impedance value is the same as the lowest point of the defined impedance value, it is considered that the tissue has reached the denaturation point at this time. However, for electrosurgical instruments, during the tissue sealing process, it is difficult to ensure that the impedance value can change according to the law defined by the reference curve, and the actual detected impedance value is prone to jumps, and it is only based on a certain moment of detection. The obtained impedance value is compared with the minimum value of the cured impedance to determine whether the tissue has reached the denaturation point, and it is easy to misjudgment. Therefore, there is a certain amount of error in the way in which the tissue reaches the denaturation point is determined in existing protocols.

Contents of the invention

The technical problem to be solved in this application is the poor accuracy of the detection results of the existing electrosurgical instruments on whether the tissue has reached the denaturation point during the tissue sealing process. Detection method, device and electrosurgical instrument.

For the above technical problems, the application provides the following technical solutions:

In the first aspect, the technical solution of the present application provides a method for detecting tissue denaturation points during the sealing process, including:

Obtain the real-time electric signal fed back by the tissue based on the input energy of the electrodes at the current moment;

Judging whether the tissue has reached the denaturation point according to the real-time electrical signal and the electrical signal of the historical period; wherein, the set period before the current moment is used as the historical period, and the tissue acquired within the historical period is based on electrode input The electrical signal fed back by the energy is the electrical signal of the historical period.

In the second aspect, the technical solution of the present application provides a device for detecting tissue degeneration points during the sealing process, including:

The sampling module is configured to acquire the real-time electric signal fed back by the tissue based on the input energy of the electrode at the current moment;

The denaturation point judging module is configured to judge whether the tissue has reached the denaturation point according to the real-time electrical signal and the historical period electric signal; wherein, the set period before the current moment is used as the historical period, and within the historical period The obtained electric signal fed back by the tissue based on the input energy of the electrodes is the electric signal of the historical period.

In the third aspect, the technical solution of the present application provides a computer-readable storage medium, in which program information is stored, and the computer executes the organization in the sealing process described in the first aspect after calling the program instructions Denaturation point detection method.

In a fourth aspect, the technical solution of the present application provides an electronic device, the electronic device includes at least one processor and at least one memory, at least one of the memory stores program information, and at least one of the processors calls the program After the instruction, execute the method for detecting tissue denaturation points during the sealing process described in the first aspect.

In the fifth aspect, the technical solution of the present application provides an electrosurgical instrument, the host of the electrosurgical instrument is equipped with the device for detecting tissue denaturation points during the sealing process described in the second aspect or the computer-readable device described in the third aspect. A storage medium or the electronic device described in the fourth aspect.

Compared with the prior art, the technical solution of the present application has the following technical effects:

The technical solution of the present application provides a method, device, and electrosurgical instrument for detecting tissue denaturation points during the sealing process. During the tissue sealing process, real-time electrical signals fed back by the tissue based on the input energy of the electrodes are always detected. When judging whether the tissue has reached the denaturation point, it is different from the method in the prior art that simply judges based on the comparison result of the "single point value" of the real-time electrical signal and a certain "cured value", but combines the electrical signal of the historical period , the judgment result can only be obtained after an overall logical judgment is made on the real-time electric signal and the electric signal in the historical period. Therefore, the detection results of tissue degeneration points obtained in this scheme can reduce the error of detection results caused by accidental factors such as jumps in tissue signal values. Compared with the single-point comparison scheme of the prior art, this scheme has a higher Accuracy, and can more realistically feedback the actual characteristics or changing laws of tissue electrical signals.

Description of drawings

Preferred embodiments of the application will be described in detail below through the accompanying drawings, which will help to understand the purpose and advantages of the application, wherein:

Fig. 1 is the structural representation of the electrosurgical instrument involved in the scheme of the present application;

Fig. 2 is a flowchart of a method for detecting tissue denaturation points during the sealing process according to an embodiment of the present application;

Fig. 3 is a flowchart of a method for detecting tissue denaturation points during the sealing process according to another embodiment of the present application;

Fig. 4 is a flowchart of a method for detecting tissue denaturation points during the sealing process according to another embodiment of the present application;

Fig. 5 is a schematic structural diagram of a neuron in a multilayer perceptron according to an embodiment of the present application;

Fig. 6 is a structural block diagram of a device for detecting tissue denaturation points during the sealing process according to an embodiment of the present application;

Fig. 7 is a schematic diagram of the hardware connection relationship of the electronic equipment implementing the method for detecting tissue denaturation points during the sealing process according to an embodiment of the present application.

Detailed ways

The technical solutions of the present application will be clearly and completely described below in conjunction with the accompanying drawings. Apparently, the described embodiments are some of the embodiments of the present application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the scope of protection of this application.

In the description of this application, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer" etc. The indicated orientation or positional relationship is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the application and simplification of the description, rather than indicating or implying that the referred device or element must have a specific orientation, use a specific orientation construction and operation, therefore should not be construed as limiting the application. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and should not be construed as indicating or implying relative importance.

In the description of this application, it should be noted that unless otherwise specified and limited, the terms "installation", "connection", and "connection" should be understood in a broad sense, for example, it can be a fixed connection or a detachable connection. Connected, or integrally connected; it can be directly connected, or indirectly connected through an intermediary, and it can be the internal communication of two elements. Those of ordinary skill in the art can understand the specific meanings of the above terms in this application in specific situations.

In addition, the technical features involved in the different embodiments of the present application described below may be combined as long as they do not constitute a conflict with each other.

The present application generally relates to a scheme for detecting tissue degeneration points during the tissue sealing process of a medical device, and particularly relates to a detection method, device and electrosurgical instrument for tissue degeneration points during the sealing process. An electrosurgical instrument 100 as shown in FIG. 1 may be used to cut tissue, coagulate (cautery and seal) tissue, and/or clamp tissue during a surgical procedure. The electrosurgical instrument 100 is operable to transmit electrosurgical energy to the end effector assembly 30 to act on tissue to achieve cutting, coagulation (caulting and sealing) tissue, and the electrosurgical instrument 100 can also provide electrosurgical energy to The end effector assembly 30 is used for gripping and manipulating tissue. The jaw structure of the end effector assembly 30 can be selectively opened or closed, so that the end effector assembly 30 clamps the tissue and applies electrosurgical energy to the tissue.

As shown in FIG. 1 , the electrosurgical instrument 100 provided in the present application is usually connected to a host that outputs electrosurgical energy. It can be understood that the same electrosurgical instrument 100 can be used in cooperation with various hosts through the external cable 40 . The electrosurgical instrument 100 includes a handle assembly 10 , an elongated body assembly 20 and an end effector assembly 30 sequentially connected from a proximal end to a distal end. The proximal part of the handle assembly 10 is provided with a power supply connection part for realizing electrical connection with the host. The power supply connection part is shaped as a socket or a plug, and is configured to be easily connected to an output terminal or an output connection seat of the host. The power supply connection part can also be configured in the form of an electric slip ring to provide a reliable electrical connection when the actuator rotates. The end effector assembly 30 is used for manipulating tissue to perform a specific surgical operation, such as clamping, coagulating, cutting, etc. the tissue.

Referring to FIG. 1 , the end effector assembly 30 includes a first clamping portion 31 and a second clamping portion 32 that are pivotally connected, and the first clamping portion 31 and the second clamping portion 32 approach each other. The first clamping part 31 and the second clamping part 32 pivot to a direction away from each other so as to release the tissue. Or, in an alternative embodiment, the first gripping portion 31 of the end effector assembly 30 can be operatively pivoted toward the second gripping portion 32 until the jaws of the end effector assembly 30 are closed to grip Tissue; the first clamping part 31 pivots away from the second clamping part 32 until the jaws of the end effector assembly 30 are opened to release the tissue, and vice versa. Further, at least one clamping part of the first clamping part 31 and the second clamping part 32 is provided with an electrode in contact with the tissue, so as to transmit electrosurgical energy to the tissue to realize the sealing operation.

As shown in FIG. 1 , at least a portion of the handle assembly 10 is gripped by a user to facilitate manipulation of the surgical instrument by the operator. The handle assembly 10 is operable to provide actuation forces, eg, a closing actuation force and a cutting actuation force, to the end effector assembly 30 . The handle assembly 10 includes a grip body 11 capable of being gripped by a user and a closure trigger 12 pivotally connected to the grip body 11 . The user operates the end effector assembly 30 to perform a closing or opening action by operating the closing trigger 12 . The holding body 11 is generally T-shaped as a whole, and an accommodating cavity is formed inside it for accommodating the driving mechanism and the excitation circuit.

Elongated body assembly 20 includes a plurality of longitudinal members that operably couple end effector assembly 30 to a plurality of actuators housed by handle assembly 10 . Elongated body assembly 20 includes an outer sleeve that defines the outer surface of the elongated body and accommodates movement of other components therethrough. For example, the outer sleeve is shaped for longitudinal movement relative to an inner actuation member axially received within the outer sleeve. The inner actuation member may be a rod, shaft, stamped metal or other suitable metal part.

1, the closing trigger 12 is operatively sleeved on the proximal portion of the elongated body assembly 20, and the handle assembly 10 also includes a cutting trigger 13 pivotally connected to the holding body 11. The user A knife actuation member within end effector assembly 30 is operated to perform a cutting action by operating cutting trigger 13 . Cables for transmitting electrosurgical energy are also arranged in the outer sleeve of the slender body assembly 20, and the distal ends of the cables are connected to the first clamping portion 31 and/or the second clamping portion of the end effector assembly 30 respectively. 32, so as to transmit high-frequency electrosurgical energy to the electrodes on the first clamping part 31 and/or the second clamping part 32.

A knob 50 is also provided at the distal end of the handle assembly 10 , and the user can operate the dial knob 50 to realize the overall rotation of the elongated body assembly 20 and the end effector assembly 30 around the longitudinal axis. The side of the closing trigger 12 away from the elongated body assembly 20 extends to the main body of the gripping main body 11 . The grip portion of the grip body 11 has an opening 110 on a side opposite to the closing trigger 12 , and the closing trigger 12 can slide along the opening 110 to partially slide into or partially out of the accommodating cavity. When the area where the closing trigger 12 enters the accommodating chamber increases, the angle between the first clamping portion 31 and/or the second clamping portion 32 of the end effector assembly 30 decreases gradually until the When the closing trigger 12 is pivoted to the closed position, the end effector assembly 30 is in the closed state. When the closing trigger 12 is further operated to pivot toward the gripping main body 11, the area where the closing trigger 12 enters the accommodating cavity further increases, and when the closing trigger 12 pivots to the firing position, the excitation circuit is turned on, Electrosurgical energy is provided to end effector assembly 30 . When the closing trigger 12 is operated to pivot from the closing position to the firing position, the operator can easily distinguish between the closing operation and the firing operation according to the difference in grip tactile sensation.

After the activation operation is completed, when the closing trigger 12 is operated to pivot away from the holding body 11, the area where the closing trigger 12 enters the receiving cavity gradually decreases, and the first end effector 30 The angle between the first clamping part 31 and/or the second clamping part 32 gradually increases, and the end effector assembly 30 is gradually opened until the closing trigger 12 is pivoted to the open position, and the end effector assembly 30 at maximum open. In some solutions, when the closing trigger 12 is pivoted to the open position, at least a partial area of the closing trigger 12 is located in the receiving chamber. It can be understood that the user can also choose to switch between the closing operation and the opening operation, so as to realize the clamping and opening of the target tissue without performing the process of activating the circuit.

Based on the above described process, when the closing trigger 12 pivots to the activation position, the activation circuit is switched to the ON state, and the electrosurgical energy output by the host is output to the end effector assembly 30 through the power supply connection part (that is, the electrical The surgical energy is transmitted to the electrodes of the first clamping part 31 and/or the second clamping part 32 ), so as to apply electrosurgical energy to the tissue on the end effector assembly 30 to achieve tissue sealing. During the tissue sealing process, the temperature of the tissue increases due to the injection of electrosurgical energy, which affects the impedance value of the tissue, the current value flowing through the tissue and the electrode voltage acting on the tissue will change, so the reaction to the end The current value or voltage value of the external actuator component 30 will change accordingly, and the change can be fed back to the host through the external cable 40 for the host to detect the temperature of the tissue or whether the tissue has reached the denaturation point. Usually, when the tissue reaches the denaturation point, it can be considered that the tissue has completed the preheating process or the interstitial fluid has reached the boiling point. At this time, the heating operation of the tissue can be ended, and the host can change the output electrosurgical energy to continue to complete the follow-up process of tissue sealing. Based on the above process, it is very critical to determine whether the tissue has reached the denaturation point. If the heating operation ends prematurely and enters the subsequent process, the sealing effect may be affected because the nature of the tissue does not meet the sealing requirements. If the tissue continues to heat after reaching the denaturation point, it may It will lead to the adverse consequences of serious tissue damage or even failure to complete the sealing under the action of high temperature. The method for detecting the tissue denaturation point during the sealing process provided by the proposal of the present application can be applied to the host of the above-mentioned electrosurgical instrument to accurately judge whether the tissue has reached the denaturation point.

The embodiment of the present application provides a method for detecting tissue degeneration points during the sealing process, as shown in Figure 2, the method includes the following steps:

S10: Obtain the real-time electrical signal fed back by the tissue based on the input energy of the electrodes at the current moment.

In conjunction with the electrosurgical instrument shown in FIG. 1 , when using it to perform tissue sealing, the first clamping part 31 and the second clamping part 32 of the end effector assembly 30 clamp the tissue, and the first clamping part 31 and/or Or the electrode on the second clamping part 32 is in direct contact with the tissue, and the electrode injects the electrosurgical energy output by the host into the tissue to heat the tissue. When the tissue temperature changes, its impedance value will also change, that is, the impedance value between the first clamping part 31 and the second clamping part 32 changes, so the first clamping part 31 and/or the second clamping part 32 The current or voltage of the upper electrode will change. The electrodes feed back the real-time changing current or voltage to the host through the external cable 40. In this step, the host uses the value of the current or voltage transmitted by the external cable 40 as the real-time electrical signal. Alternatively, the host uses an "analog-to-digital converter" to convert the current or voltage in the form of an analog signal into a digital signal as the real-time electrical signal. It can be understood that in the follow-up solutions of the present application, the form of the electrical signal can be artificially set or selected, as long as it is ensured that the electrical signals used have the same form during the entire detection process (for example, all are analog signals or all are digital signals) .

S20: Judging whether the tissue has reached a denaturation point according to the real-time electrical signal and the electrical signal of the historical period; wherein, the set period before the current moment is used as the historical period, and the tissue acquired in the historical period is based on The electrical signal fed back by the electrode input energy is the electrical signal of the historical period.

Wherein, the selection of the set time period may be determined after comprehensive consideration of the signal processing efficiency of the host computer and the detection cycle. The higher the signal processing efficiency of the host, the greater the amount of data that the host can process. In specific applications, the amount of data that can be processed in the detection cycle can be used as the amount of data that needs to be processed in the detection cycle. The amount of data that needs to be processed in the detection cycle includes real-time electrical signals and electrical signals in historical periods, so that historical periods can be determined. The signal quantity of the electrical signal determines the length of the set period.

The judgment logic method can be pre-stored in the host, and the preset judgment logic can be a pre-completed application program, which can be called and executed after being placed in the host. In the preset judgment logic, it is possible to judge whether the tissue has reached the point of denaturation by using the combination of historical period electric signals and real-time electric signals.

In this solution, according to the real-time electrical signal and the historical period electrical signal combined with the preset judgment logic to judge whether the tissue has reached the denaturation point, it is different from the "single point value" and a certain "cured value" in the prior art based solely on the real-time electrical signal. Instead, it is necessary to combine the electrical signals of the historical period and use the preset judgment logic to make an overall logical judgment on the real-time electrical signal and the electrical signal of the historical period before the judgment result can be obtained. Therefore, the detection results of tissue degeneration points obtained in this scheme can reduce the detection result error caused by accidental factors such as jumps in tissue signal values and highlight the characteristics of tissue electrical signals. Compared with the single-point comparison of the prior art scheme, this scheme has higher accuracy.

As shown in Figure 3, the preset judgment logic in some solutions includes:

S21: Obtain a real-time feature value corresponding to the real-time electrical signal according to the real-time electrical signal and the historical period electrical signal.

In this step, the real-time eigenvalues are obtained by combining the real-time electrical signal at the current moment and the electrical signal in the historical period. The purpose is to use the electrical signal in the historical period to smooth, sharpen, edge, and Gaussian blur the real-time electrical signal at the current moment. , to avoid the impact on the detection accuracy when the real-time electrical signal jumps and so on. Therefore, this scheme can highlight the characteristics of the tissue electrical signal.

S22: Obtain a reference feature value according to the real-time feature value and historical feature value. The historical characteristic value is determined according to the corresponding electrical signal deviation when the tissue reaches a denaturation point during the historical sealing process.

Specifically, the historical sealing process may be the tissue sealing process involved in performing animal experiments or historical clinical trials by using the host. The host can record the data in each sealing process to form a historical data set. In this solution, the data with the same attributes as the real-time feature value can be directly called from the historical data set for use. The historical eigenvalues can be regarded as the data for adjusting the deviation of the reference eigenvalues, that is, during the history sealing process, when the reference eigenvalues initially set by the host are inaccurate, the operators will adjust them. The amount of one adjustment is the "electrical signal deviation". If it needs to be adjusted up, it can be indicated by "+", if it needs to be adjusted down, it can be indicated by "-".

S23: Determine whether the real-time electric signal is greater than or equal to the reference characteristic value, if yes, perform step S24, otherwise return to step S10.

In this solution, the magnitude relationship between the two can be determined by directly comparing the real-time electrical signal with the reference characteristic value calculated in step S22.

S24: It is determined that the tissue has reached the denaturation point.

In this scheme, the benchmark eigenvalue is determined on the basis of the historical eigenvalue combined with the real-time eigenvalue, which can make the benchmark eigenvalue not only have the regularity after historical verification, but also be able to combine the actual feedback of the organization in the current sealing process. The signal detection results are adjusted to ensure that the reference characteristic value meets the changing requirements of the actual scene of the current sealing process, and to ensure the accuracy of the detection results when the tissue reaches the denaturation point.

And if the real-time characteristic value is smaller than the reference characteristic value, return to step S10 to the step of acquiring the real-time electrical signal fed back by the tissue based on the input energy of the electrodes at the current moment. That is, if the tissue has not reached the denaturation point, the host will continue to perform the warm-up operation on the tissue, and at the same time check whether the tissue has reached the denaturation point cyclically until the real-time characteristic value is greater than or equal to the reference characteristic value, that is, the tissue reaches the denaturation point. Analyzing conditions.

In the above step S21, the real-time electrical signal and the historical period electrical signal can be processed in various ways, so as to obtain the real-time characteristic value corresponding to the real-time electrical signal. In the embodiment of this application, three implementation schemes are provided. Among the three schemes, the real-time electrical signal and the historical period electrical signal are illustrated by taking the impedance value fed back by the tissue as an example. It can be understood that practical application does not need to be limited to the following three specific schemes provided in this embodiment.

Solution 1: Obtain the average electrical signal of the real-time electrical signal corresponding to each moment and the electrical signal of the historical period; use the smallest average electrical signal as the real-time characteristic value at the current moment.

In this scheme, the electrical signal is the impedance value fed back by the tissue, and the average electrical signal refers to the average value of the impedance value Z in the continuous time period. The impedance value in the continuous time period includes both the real-time impedance value and the historical time period Impedance value. Specifically, take the current moment as the moment corresponding to the Tth sampling period as an example. The host obtains the impedance values detected in T consecutive sampling periods in total, including (T-1) historical period impedance values in addition to the impedance value at the current moment. The process of obtaining real-time eigenvalues in this scheme is as follows:

At the initial moment, a very large average value of the minimum impedance value M _min is pre-stored in the host. In the detection process of real-time electrical signals, every time a real-time impedance value is obtained, a new average value of impedance value M _t will be calculated. If M _t < M _min , the new average value of impedance value M _t will be used as the new minimum value The average value of impedance, ie M _min is refreshed by M _t . Wherein, the frequency of the real-time electrical signal for detecting tissue feedback can be preset, so the sampling period is a known quantity.

During the sealing process, the host computer can calculate a new average impedance value M _t in each sampling cycle. As long as there is a smaller average impedance value, it will use it to refresh the average impedance value obtained before and It serves as the real-time eigenvalue corresponding to the current sampling period, so the minimum average value is always used as the real-time eigenvalue, and its formula is expressed as:

M _t =mean(Z _t , . . . , Z _tT ).

Among them, Z _t is the impedance value corresponding to the tth sampling period.

In the scheme of obtaining the average value of impedance value, not only the unstable detection result of impedance value can be filtered out, but also the noise in the signal detection process can be reduced, because the memory of the host only needs to store a minimum average value of impedance value M _min and T consecutive The detection result of the impedance value is enough, so in this solution, the host only needs to perform a small amount of calculation and provide a small storage space to realize it.

Solution 2: Obtain the weighted sum of the real-time electrical signal corresponding to each moment and the electrical signal of the historical period according to the preset weight value; use the smallest weighted sum as the real-time feature value at the current moment.

In this solution, the real-time feature value is obtained by calculating the weighted sum of the impedance values Z detected in consecutive T sampling periods. The process of obtaining real-time eigenvalues in this scheme is as follows:

Each impedance value Z detected in T sampling periods is multiplied by its preset weight value, and the weighted sum B _t is obtained by adding up the results of multiplying each impedance value by the weight value. In this solution, real-time impedance values are processed by weight value smoothing, sharpening, marginalization, Gaussian blurring and other processing methods to obtain new real-time eigenvalues. Taking T continuous impedance values as an example, there are also T preset weight values. When T=3, the mathematical expression of the weighted sum is:

B _t ＝W _-1 Z _t-1 +W ₀ Z _t +W ₁ Z _t-1 ;

Among them, W is the preset weight value, and the impedance value with the same subscript has a corresponding relationship with the weight value, and they are multiplied during calculation. The preset weight value can be determined by the electrical signal at the initial stage of different automatic tissue types, or it can be manually preset by the operator according to the demand, and the preset weight value is determined according to the kernel type selected by the operator, such as applying sharpening Kernel weighting factor that increases the difference between impedance values. Applying the average kernel weighting factor will average T consecutive impedance values. Here, the average kernel weight factor if all are , the result will be the same as that of Scheme 1. In the above process, the detected impedance value is multiplied and added to the preset weight value and the result obtained is stored in the host computer, and the minimum value of the weighted value B _t can be selected for the real-time feature value.

It can be understood that after the preset weight value in the above scheme is changed, the obtained real-time feature value will change, and the benchmark feature value will also change accordingly. Therefore, various benchmark feature values can be realized by adjusting the preset weight value. acquisition methods to meet the needs of different scenarios.

Solution 3: Obtain the predicted value of the electrical signal at the current moment according to the change rule of the electrical signal of the electrical signal in the historical period; if the difference between the real-time electrical signal and the predicted value of the electrical signal is less than the difference threshold, then The real-time electrical signal is used as the real-time feature value, otherwise, the predicted value of the electrical signal is used as the real-time feature value. The smaller the difference threshold, the more accurate the corresponding real-time feature value result. In the following embodiments, the difference threshold is directly described as zero, that is, ideally, the real-time electrical signal should be equal to the predicted value of the electrical signal.

Taking the historical period electrical signal as an example of the impedance value obtained within the set period, there is more than one impedance value, and the change law of the impedance value can be deduced according to the multiple impedance values, so that the impedance corresponding to the next sampling period can be predicted predicted value. In the specific implementation, the first order differential of the impedance value obtained by T consecutive sampling periods is used to obtain the predicted value of the impedance value: among the T impedance values, according to the order of the acquisition time of the impedance value, the impedance value of the next sampling period is used Z _t subtracts the impedance value Z _t-1 of the previous sampling period to get the signal difference between them, so T impedance values can get (T-1) signal difference; the (T-1) signal The difference is added and divided by (T-1) to obtain the average value of these signal differences as the average value of the historical signal; the average value of the historical signal is added to the recently obtained impedance value Z _t to obtain the predicted value A _t of the impedance value That is, the impedance value Z _t+1 , its formula can be expressed as:

This scheme mainly predicts the current impedance value through the historical impedance value before the current moment and the change trend of the historical impedance value. This scheme smoothes the fluctuation of the impedance value in T sampling periods and makes more effective use of the change trend prediction of the historical impedance value. Get the impedance value at the current moment. This solution is especially applicable to scenarios where the impedance value continues to drop or rise continuously. In the tissue preheating stage during the sealing process, the impedance value of the tissue will show a trend of steady change. For example, the five impedance values detected continuously may be: [6, 5.5, 4, 3, 2]. In the above sequence, 5.5 is a fluctuating value, and 2 is the most recently collected impedance value. The first-order differential method can be used to directly It is predicted that the next impedance value should be: 2-(1+1+1.5+0.5)/4=1. But if the next impedance value actually detected is 1.5, then the difference between the actual obtained impedance value and the predicted impedance value is too large, then directly replace the actually obtained impedance value with the predicted result 1 as the sixth impedance value.

Through the above solution, the host only needs to store a minimum historical signal average value A _min and impedance values detected in T consecutive sampling periods. Similar to Scheme 1, at the initial moment, the minimum historical signal average A _min is set to a very large value, and in the subsequent detection process, a new historical signal average A _t can be obtained every time a new impedance value is detected, If A _t < A _min , then A _min will be refreshed by A _t .

In the above scheme, the next electrical signal is predicted by using the change law of the electrical signal in the historical period. In actual application, the impedance value may not continue to decrease or increase continuously. At this time, the electrical signal prediction can be obtained through the following deformation method Value: According to the electric signal curve obtained by fitting in the historical period, the electric signal curve takes time as the abscissa and the electric signal value as the ordinate; the abscissa on the electric signal curve is the The ordinate corresponding to the current moment is used as the predicted value of the electric signal. In practical applications, after the impedance values of T consecutive sampling periods have been obtained, the change curve of the impedance value can be obtained by using the existing curve fitting method, the abscissa of the change curve selects time, and the ordinate selects the electrical signal. For the signal value, the impedance value predicted at any point in the future can be predicted by using the change curve. This solution can accurately predict the impedance value even in scenarios where the impedance value does not decrease or increase continuously.

In the above solution, the reference feature value can be obtained according to the real-time feature value and the historical feature value. Preferably, the reference feature value is determined in the following way: reference feature value=real-time feature value+historical feature value. In this solution, the historical characteristic value is obtained according to the deviation of the electrical signal when the tissue reaches the denaturation point during the historical sealing process. As mentioned earlier, the deviation can be positive or negative, so the historical feature value may be negative, and the baseline feature value may be larger than the real-time feature value or smaller than the implementation feature value.

In the above scheme provided by this application, the reference characteristic value not only considers the deviation of the reference characteristic value in the historical sealing process, but also considers the real-time characteristic value obtained in the current sealing process, and truly obtains the judgment standard in line with the current sealing process. In this way, it is more accurate to judge whether the tissue has reached the denaturation point.

In some embodiments, as shown in FIG. 4, in the above method, before step S10, the following steps are further included:

S00: Obtain the tissue electrical signal at the initial stage of the historical sealing process and the corresponding tissue electrical signal interval when the tissue reaches the denaturation point; the tissue electrical signal is the feedback of the tissue based on the input energy of the electrode at the initial stage electric signal.

In this step, use the data training model in the initial stage of the historical clinical experience in the sealing process. The initial stage refers to a short period of time after the sealing starts. There will be multiple, the purpose is to be able to collect a series of changing electrical signals. The duration of the initial stage can be obtained through calibration experiments.

When the tissue reaches the denaturation point, it corresponds to a point value of the electrical signal of the tissue. In order to ensure the accuracy of the test results of the denaturation point and avoid misjudgment caused by occasional jumps, this scheme can increase or decrease a certain amount on the basis of the point value. The tissue electrical signal interval is obtained after the error range, for example, the allowable error is 0.1%, and the corresponding tissue electrical signal is R when the tissue reaches the denaturation point, then the tissue electrical signal interval can be [0.99×R, 1.01×R]. It will be appreciated that the electrical signal may be chosen to be voltage, current, impedance value, energy, or the like.

S01: Using the tissue electrical signal in the initial stage of the history sealing process as an input sample, using the tissue electrical signal interval as an output sample to train a learning algorithm, and using the trained learning algorithm as a tissue denaturation point prediction model.

In this solution, an existing learning algorithm in the prior art may be selected for implementation, such as a deep learning algorithm. The difference between the input sample and the output sample will lead to the difference of the characteristic value of the judgment standard obtained by training. The embodiment of the present application can provide the following training methods for different input samples and output samples: input samples are voltage values and current values, or voltage values, current values, and tissue size values; output samples are voltage value intervals and current value intervals, or voltage The two-dimensional matrix obtained by the value sequence and the current value sequence, or the tissue impedance value interval, or the tissue impedance sequence. Correspondingly, the tissue electrical signal, the real-time electrical signal and the historical period electrical signal are: within a set period of time, the tissue is fed back based on the electrode input energy and has a corresponding relationship with the continuous voltage value and current value; or, within a set period of time, the tissue is fed back based on the electrode input energy and has a corresponding relationship with the continuous voltage value, current value and tissue size value; the tissue electrical signal interval is: the tissue reaches denaturation The voltage value interval and current value interval corresponding to the point; or, the corresponding two-dimensional matrix obtained based on the voltage value sequence and the current value sequence when the tissue reaches the denaturation point; or, the corresponding tissue impedance when the tissue reaches the denaturation point value interval, or the corresponding tissue impedance sequence when the tissue reaches the denaturation point. In practical applications, it needs to be ensured that the input samples and output samples selected during the training process are consistent with the input signals and output results selected during actual detection.

Described step S20 comprises:

S2A: Use the end time of the initial stage in the current sealing process as the current time, and use the initial stage in the current sealing process as the set time period. The real-time electrical signal and the historical period electrical signal are input into the tissue degeneration point prediction model as input signals, and the output result of the tissue degeneration point prediction model is used as the predicted electrical signal interval segment of the tissue degeneration point.

The initial stage in this step is the same as that in step S00. When the electrosurgical instrument gives a part of the initial electrosurgical energy to the tissue, the real-time measured tissue electrical signals of T consecutive sampling periods in the initial stage will be obtained. The real-time measured tissue electrical signals of T consecutive sampling periods are input into the tissue denaturation point prediction model that has been trained in step S01. The tissue denaturation point prediction model has received training related to tissue denaturation point experiments, and outputs the possible tissue electrical signal range when the tissue reaches the denaturation point, so the output of the tissue denaturation point prediction model should be the current T tissue electrical signal corresponding The predicted electrical signal interval segment.

S2B: Determine a real-time electric signal interval corresponding to a tissue denaturation point in the current sealing process according to the predicted electric signal interval. According to the different types of organizational electrical signals, the methods of using the predicted electrical signal intervals to determine the real-time electrical signal intervals are also different.

Specifically, if the tissue electrical signal interval is the corresponding voltage value interval and current value interval when the tissue reaches the denaturation point, then use the voltage value interval or current value interval as the real-time electrical signal interval segment; if the The electrical signal interval of the tissue is a two-dimensional matrix obtained based on the voltage value sequence and the current value sequence corresponding to when the tissue reaches the denaturation point, then the average voltage value obtained by combining the average voltage value obtained according to the voltage value sequence with the error range The interval or the average current value interval obtained by combining the average current value obtained by the current value sequence with the error range is used as the real-time electrical signal interval segment; if the tissue electrical signal interval is the corresponding tissue impedance when the tissue reaches the denaturation point value interval, then the tissue impedance value interval is used as the real-time electrical signal interval segment; if the tissue electrical signal interval is the corresponding tissue impedance sequence when the tissue reaches the denaturation point, then the The average impedance value interval obtained by combining the average impedance value with the error range is used as the real-time electrical signal interval segment.

Above, the tissue size value is a parameter used to represent the size of the tissue, which is judged from the collected voltage and current. In actual scenarios, since the host computer can detect changes in electrical signals such as current value and voltage value due to changes in tissue properties, the size of the tissue can be obtained by using a conventional derivation algorithm. Impedance values and other parameters. As mentioned above, the sequence is a series of values with a limited length, that is, it includes multiple values, and the average value calculation result can be used as the actual electrical signal value to be obtained. For example, the impedance value sequence is a series of impedance values with a limited length. The average value of all impedance values in the sequence is used as the actual corresponding impedance value, and the impedance value interval can be obtained by using the pre-specified error range, and the real-time Electrical signal interval segment.

S2C: Judging whether the real-time electrical signal is located in the real-time electrical signal interval, if so, execute step S2D, otherwise return to step S10.

That is, in the subsequent detection process, it is only necessary to determine whether the real-time electrical signal falls within the interval of the real-time electrical signal to determine whether the tissue has reached the denaturation point.

S2D: Determine that the tissue has reached the point of denaturation.

Through this solution, the real-time electrical signal interval in the sealing process can be predicted at the initial stage of the sealing process, and the real-time electrical signal fed back by the organization can be detected in real time afterwards, as long as the real-time electrical signal falls into the above-mentioned real-time electrical signal interval It is then possible to determine that the tissue has reached the point of denaturation. Through training and learning algorithm, the process of data detection and calculation is simplified, and the process of real-time tracking of electrical signals is made, so that the judgment of denaturation point is faster, and the earlier the judgment of tissue denaturation, the easier it is to cooperate with the control electrode system so as not to It is easy to lose the tracking and control of the nature of the tissue, and judging the denaturation point based on the training model can get more accurate results.

In some schemes, the learning algorithm is a multi-layer perceptron algorithm, and the function of the i-th layer perceptron in the multi-layer perceptron algorithm is expressed as: y _i =f(w _i-1 x _i-1 +b) .

Among them, if i>1, then x _i-1 is the output of the (i-1) layer perceptron, if i=1, then x _i-1 is the obtained tissue electrical signal; w _i-1 is the perceptron The weight matrix composed of the preset weight factors in , b is the preset bias in the perceptron; f() is the preset nonlinear function.

As shown in Figure 5, it is a graph of a neuron in a perceptron. In each layer of the perceptron, there will be a finite number of parallel neurons, each with its own learnable weights and biases, using the same input. The output of these neurons will form the input of neurons in the next layer. In order to reduce the pressure on the hardware, the number of neurons in each layer will be changed, and the neurons in the last layer will output electrical signals.

In this scheme, the multi-layer perceptron algorithm is adopted. When using sample data for learning and training, the sample data can be selected according to needs. Obviously, different input samples and output samples can be obtained after the final training. Each weight value and deviation will also be different. As mentioned earlier, the input data selected during actual detection should be consistent with the input samples during training.

In addition, in the above solutions, the importance of the input samples can also be sorted in advance or, for example, the weight of the input samples with high importance can be increased. It is equivalent to pre-configuring the weight value of each input sample before entering the learning algorithm, and the input sample with a high weight value has a higher impact on the output result.

In the above embodiments of the present application, due to the combination of learning and training, the judgment result of the tissue degeneration point can have higher accuracy, and the characteristics of the tissue electrical signal can be more prominent. In addition, this solution only needs to input the electrical signal in the initial stage of the sealing process as an input signal into the tissue degeneration point prediction model, and can directly judge the real-time electrical signal interval when predicting tissue degeneration, and quickly obtain the tissue degeneration point prediction model. The conclusion of the degeneration situation can judge the tissue degeneration situation earlier and can more accurately cooperate with the control host of the electrode so as not to lose the tracking and control of the tissue situation.

An embodiment of the present application provides a device for detecting tissue degeneration points during the sealing process, as shown in FIG. 6 , including: a sampling module 610 configured to acquire real-time electrical signals fed back by the tissue based on electrode input energy at the current moment. The denaturation point judging module 620 is configured to judge whether the tissue has reached the denaturation point according to the real-time electrical signal and the electrical signal in the historical period; wherein, the set period before the current moment is used as the historical period, and in the historical period The electrical signal fed back by the tissue based on the input energy of the electrodes acquired internally is the electrical signal of the historical period. Wherein, the selection of the set time period may be determined after comprehensive consideration of the signal processing efficiency of the host computer and the detection cycle. In this solution, according to the real-time electrical signal and the historical period electrical signal combined with the preset judgment logic to judge whether the tissue has reached the denaturation point, it is different from the "single point value" and a certain "cured value" in the prior art based solely on the real-time electrical signal. Instead, it is necessary to combine the electrical signals of the historical period and use the preset judgment logic to make an overall logical judgment on the real-time electrical signal and the electrical signal of the historical period before the judgment result can be obtained. Therefore, the detection results of tissue degeneration points obtained in this scheme can reduce the error of detection results caused by accidental factors such as jumps in tissue signal values, and this scheme has higher accuracy.

In some schemes, the denaturation point judging module 620 is also used to obtain the real-time characteristic value corresponding to the real-time electric signal according to the real-time electric signal and the historical period electric signal; obtain the real-time characteristic value and the historical characteristic value according to the real-time characteristic value Baseline eigenvalues. The historical feature value is determined according to the corresponding electrical signal deviation when the tissue reaches the denaturation point during the historical sealing process; it is judged whether the real-time electrical signal is greater than or equal to the reference feature value, and if so, it is determined that the tissue has reached the denaturation point. In this scheme, the real-time eigenvalues are obtained by combining the real-time electrical signal at the current moment and the electrical signal at the historical period. The purpose is to use the electrical signal at the historical period to smooth, sharpen, edge, and Gaussian blur the real-time electrical signal at the current moment. , to avoid the impact on the detection accuracy when the real-time electrical signal jumps and so on. Therefore, this scheme can highlight the characteristics of the tissue electrical signal. The historical sealing process may be the tissue sealing process involved in animal experiments or historical clinical trials performed by the host computer. The historical feature value can be regarded as the data for adjusting the deviation of the reference feature value. In this scheme, the benchmark eigenvalue is determined on the basis of the historical eigenvalue combined with the real-time eigenvalue, which can make the benchmark eigenvalue not only have the regularity after historical verification, but also be able to combine the actual feedback of the organization in the current sealing process. The signal detection results are adjusted to ensure that the reference characteristic value meets the changing requirements of the actual scene of the current sealing process, and to ensure the accuracy of the detection results when the tissue reaches the denaturation point.

In the solution above, the denaturation point judging module 620 is further configured to return to the sampling module 610 when the real-time characteristic value is smaller than the reference characteristic value. That is, if the tissue has not reached the denaturation point, the host will continue to perform the warm-up operation on the tissue, and at the same time check whether the tissue has reached the denaturation point cyclically until the real-time characteristic value is greater than or equal to the reference characteristic value, that is, the tissue reaches the denaturation point. Analyzing conditions.

Further, the denaturation point judging module 620 is configured to process the real-time electrical signal and the historical period electrical signal in various ways, so as to obtain the real-time characteristic value corresponding to the real-time electrical signal. In the embodiment of the present application Three implementation schemes are provided, and in the three schemes, the real-time electric signal and the electric signal in the historical period are illustrated by taking the impedance value fed back by the tissue as an example. specifically:

The denaturation point judging module 620 is configured to obtain the average electrical signal of the real-time electrical signal and the electrical signal of the historical period corresponding to each moment; and use the smallest average electrical signal as the real-time characteristic value at the current moment. In this solution, the method of obtaining the average value of impedance value can not only filter out the unstable detection results of impedance value, but also reduce the noise in the signal detection process, because the memory of the host only needs to store a minimum average value of impedance value M _min and T consecutive impedance value detection results are enough, so in this solution, the host only needs to perform a small amount of calculation and provide a small storage space to realize it.

Alternatively, the denaturation point judging module 620 is configured to obtain the weighted sum of the real-time electrical signal corresponding to each moment and the electrical signal of the historical period according to a preset weight value; the smallest weighted sum is used as the weighted sum of the current moment Real-time eigenvalues. In this solution, the real-time feature value is obtained by calculating the weighted sum of the impedance values Z detected in consecutive T sampling periods. In this solution, various acquisition methods of reference feature values can be realized by adjusting preset weight values to meet the needs of different scenarios.

Alternatively, the denaturation point judging module 620 is configured to obtain the electrical signal prediction value at the current moment according to the electrical signal variation law of the electrical signal in the historical period; if the real-time electrical signal is consistent with the electrical signal prediction value If the difference between them is smaller than the difference threshold, the real-time electrical signal is used as the real-time feature value; otherwise, the predicted value of the electrical signal is used as the real-time feature value. This scheme smoothes the fluctuation of the impedance value in T sampling periods and more effectively uses the change trend of the historical impedance value to predict the impedance value at the current moment. This solution is especially applicable to scenarios where the impedance value continues to drop or rise continuously.

In some schemes, the denaturation point judging module 620 is configured to fit the obtained electrical signals in the historical period to obtain an electrical signal curve, where the electrical signal curve takes time as the abscissa and the electrical signal value as the ordinate ; Taking the abscissa on the electrical signal curve as the ordinate corresponding to the current moment as the predicted value of the electrical signal. This solution uses the change curve to predict the predicted impedance value at any point in the future, and can accurately predict the impedance value in scenarios where the impedance value does not continue to decrease or increase continuously.

In some solutions, in the denaturation point judging module 620, the reference feature value is determined in the following manner: reference feature value=real-time feature value+historical feature value. In the above scheme provided by this application, the reference characteristic value not only considers the deviation of the reference characteristic value in the historical sealing process, but also considers the real-time characteristic value obtained in the current sealing process, and truly obtains the judgment standard in line with the current sealing process. In this way, it is more accurate to judge whether the tissue has reached the denaturation point.

In some solutions, the device also includes:

The sample acquisition module is configured to acquire the tissue electrical signal at the initial stage in the historical sealing process and the corresponding tissue electrical signal interval when the tissue reaches the denaturation point; the tissue electrical signal is at the initial stage, the tissue is based on the electrode The electrical signal to which the input energy is fed back. Use the data of the initial stage of the historical clinical experience in the sealing process to train the model. The initial stage refers to a short period of time after the start of sealing. Select the tissue electrical signals collected in a stage, and the number of signals will include multiple . When the tissue reaches the denaturation point, it corresponds to a point value of the electrical signal of the tissue. In order to ensure the accuracy of the test results of the denaturation point and avoid misjudgment caused by occasional jumps, this scheme can increase or decrease a certain amount on the basis of the point value. The tissue electrical signal interval is obtained after the error range.

The model training module uses the tissue electrical signal in the initial stage of the history sealing process as an input sample, uses the tissue electrical signal interval as an output sample to train the learning algorithm, and uses the trained learning algorithm as a tissue denaturation point prediction model . An existing learning algorithm in the prior art may be selected to be implemented, such as a deep learning algorithm. The difference between the input sample and the output sample will lead to the difference of the characteristic value of the judgment standard obtained by training. The embodiment of the present application can provide the following training methods for different input samples and output samples: input samples are voltage values and current values, or voltage values, current values, and tissue size values; output samples are voltage value intervals and current value intervals, or voltage The two-dimensional matrix obtained by the value sequence and the current value sequence, or the tissue impedance value interval, or the tissue impedance sequence. Correspondingly, the tissue electrical signal, the real-time electrical signal and the historical period electrical signal are: within a set period of time, the tissue is fed back based on the electrode input energy and has a corresponding relationship with the continuous voltage value and current value; or, within a set period of time, the tissue is fed back based on the electrode input energy and has a corresponding relationship with the continuous voltage value, current value and tissue size value; the tissue electrical signal interval is: the tissue reaches denaturation The voltage value interval and current value interval corresponding to the point; or, the corresponding two-dimensional matrix obtained based on the voltage value sequence and the current value sequence when the tissue reaches the denaturation point; or, the corresponding tissue impedance when the tissue reaches the denaturation point value interval, or the corresponding tissue impedance sequence when the tissue reaches the denaturation point. In practical applications, it needs to be ensured that the input samples and output samples selected during the training process are consistent with the input signals and output results selected during actual detection.

Wherein, the denaturation point judging module 620 is further configured to use the end time of the initial stage in the current sealing process as the current time, and use the initial stage in the current sealing process as the set time period. The real-time electrical signal and the historical period electrical signal are input into the tissue degeneration point prediction model as input signals, and the output result of the tissue degeneration point prediction model is used as the predicted electrical signal interval segment of the tissue degeneration point. A real-time electric signal interval corresponding to a tissue denaturation point in the current sealing process is determined according to the predicted electric signal interval. It is judged whether the real-time electrical signal is located in the interval of the real-time electrical signal, and if so, it is determined that the tissue has reached a denaturation point. In the above solution, if the tissue electrical signal interval is the corresponding voltage value interval and current value interval when the tissue reaches the denaturation point, then the voltage value interval or current value interval is used as the real-time electrical signal interval; if The tissue electric signal interval is a two-dimensional matrix obtained based on the voltage value sequence and the current value sequence corresponding to when the tissue reaches the denaturation point, and the average voltage obtained by combining the average voltage value obtained according to the voltage value sequence with the error range value interval or the average current value interval obtained by combining the average current value obtained from the current value sequence with the error range as the real-time electrical signal interval segment; if the tissue electrical signal interval is the corresponding tissue when the tissue reaches the denaturation point Impedance value interval, then use the tissue impedance value interval as the real-time electrical signal interval; if the tissue electrical signal interval is the corresponding tissue impedance sequence when the tissue reaches the denaturation point, then the tissue impedance sequence will The average impedance value interval obtained by combining the obtained average impedance value with the error range is used as the real-time electrical signal interval segment.

Through this solution, the real-time electrical signal interval in the sealing process can be predicted at the initial stage of the sealing process, and the real-time electrical signal fed back by the organization can be detected in real time afterwards, as long as the real-time electrical signal falls into the above-mentioned real-time electrical signal interval It is then possible to determine that the tissue has reached the point of denaturation. Through training and learning algorithm, the process of data detection and calculation is simplified, and the process of real-time tracking of electrical signals is made, so that the judgment of denaturation point is faster, and the earlier the judgment of tissue denaturation, the easier it is to cooperate with the control electrode system so as not to It is easy to lose the tracking and control of the nature of the tissue, and judging the denaturation point based on the training model can get more accurate results.

In some schemes, in the model training module, the learning algorithm is a multi-layer perceptron algorithm, and the function of the i-th layer perceptron in the multi-layer perceptron algorithm is expressed as: y _i =f(w _{i- 1} x _i-1 +b). Among them, if i>1, then x _i-1 is the output of the (i-1) layer perceptron, if i=1, then x _i-1 is the obtained tissue electrical signal; w _i-1 is the perceptron The weight matrix composed of the preset weight factors in , b is the preset bias in the perceptron; f() is the preset nonlinear function. This program adopts the multi-layer perceptron algorithm. When using sample data for learning and training, the sample data can be selected according to needs. Different input samples and output samples will get different weight values and deviations after the final training is completed. . As mentioned earlier, the input data selected during actual detection should be consistent with the input samples during training. Due to the combination of learning and training methods, the judgment result of the tissue degeneration point can have higher accuracy, and the characteristics of the tissue electrical signal can be more prominent. In addition, this solution only needs to input the electrical signal in the initial stage of the sealing process as an input signal into the tissue degeneration point prediction model, and can directly judge the real-time electrical signal interval when predicting tissue degeneration, and quickly obtain the tissue degeneration point prediction model. The conclusion of the degeneration situation can judge the tissue degeneration situation earlier and can more accurately cooperate with the control host of the electrode, and it is not easy to lose the tracking and control of the tissue situation.

The embodiment of the present application also provides a computer-readable storage medium, the computer-readable storage medium stores program information, and the computer executes the sealing process described in any one of the above method embodiments after calling the program instructions A method for the detection of tissue denaturation points.

The embodiment of the present application also provides an electronic device. As shown in FIG. After reading the program information, the device 710 executes the method for detecting tissue denaturation points during the sealing process described in any one of the above method embodiments. The device may also include: an input device 730 and an output device 740 . Processor 710, memory 720, input device 730, and output device 740 may be communicatively coupled. The memory 720, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs and modules. The processor 710 executes various functional applications and data processing by running the non-volatile software programs, instructions and modules stored in the memory 720, that is, to realize the method for detecting tissue denaturation points during the sealing process provided by any of the above solutions. The memory 720 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application program required by at least one function; data etc. In addition, the memory 720 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage devices. In some embodiments, the memory 720 may optionally include a memory that is remotely located relative to the processor 710, and these remote memories may be connected to the device that performs the method for detecting tissue denaturation points during the sealing process through a network. Examples of the aforementioned networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof. The input device 730 can receive user clicks and generate signal inputs related to user settings and function control of the method for detecting tissue denaturation points during the sealing process. The output device 740 may include a display device such as a display screen. The one or more modules are stored in the memory 720 , and when executed by the one or more processors 710 , execute the method for detecting tissue denaturation points during sealing in any of the above method embodiments.

The embodiment of the present application also provides an electrosurgical instrument, wherein the host of the electrosurgical instrument is configured with the device for detecting tissue denaturation points during the sealing process described in the above embodiments, or a computer-readable storage medium or electronic equipment.

Apparently, the above-mentioned embodiments are only examples for clear description, rather than limiting the implementation. For those of ordinary skill in the art, other changes or changes in different forms can be made on the basis of the above description. It is not necessary and impossible to exhaustively list all the implementation manners here. However, the obvious changes or changes derived therefrom are still within the scope of protection of the present application.

Claims (18)

1. A method for detecting tissue degeneration points in a sealing process, characterized in that, comprising: Obtain the real-time electric signal fed back by the tissue based on the input energy of the electrodes at the current moment; Judging whether the tissue has reached the denaturation point according to the real-time electrical signal and the electrical signal of the historical period; wherein, the set period before the current moment is used as the historical period, and the tissue acquired within the historical period is based on electrode input The electrical signal fed back by the energy is the electrical signal of the historical period. 2. The method for detecting a denaturation point of tissue during the sealing process according to claim 1, wherein said judging whether said tissue has reached a denaturation point according to said real-time electrical signal and an electrical signal of a historical period comprises: Obtaining a real-time characteristic value corresponding to the real-time electrical signal according to the real-time electrical signal and the historical period electrical signal; Obtaining the reference characteristic value according to the real-time characteristic value and the historical characteristic value; the historical characteristic value is determined according to the corresponding electrical signal deviation when the tissue reaches the denaturation point during the historical sealing process; If the real-time electrical signal is greater than or equal to the reference characteristic value, it is determined that the tissue has reached a denaturation point. 3. the tissue denaturation point detection method in the sealing process according to claim 2, is characterized in that, in said obtaining reference characteristic value according to described real-time characteristic value and historical characteristic value: The sum of the real-time feature value and the historical feature value is used as the reference feature value. 4. The method for detecting tissue denaturation points in the sealing process according to claim 2, wherein said judging whether said tissue has reached denaturation points according to said real-time electrical signals and historical period electrical signals further comprises: If the real-time electrical signal is smaller than the reference characteristic value, return to the step of acquiring the real-time electrical signal fed back by the tissue based on the input energy of the electrodes at the current moment. 5. The method for detecting tissue denaturation points in the sealing process according to claim 2, wherein the real-time characteristic value corresponding to the real-time electrical signal is obtained according to the real-time electrical signal and the historical period electrical signal, comprising : Obtain the average electrical signal of the real-time electrical signal corresponding to each moment and the electrical signal of the historical period; The smallest average electrical signal is used as the real-time feature value at the current moment. 6. The method for detecting tissue denaturation points in the sealing process according to claim 2, wherein the real-time characteristic value corresponding to the real-time electrical signal is obtained according to the real-time electrical signal and the historical period electrical signal, comprising : Obtain the weighted sum of the real-time electrical signal and the historical period electrical signal corresponding to each moment according to the preset weight value; The smallest weighted sum is used as the real-time feature value at the current moment. 7. The method for detecting tissue denaturation points in the sealing process according to claim 2, wherein the real-time characteristic value corresponding to the real-time electrical signal is obtained according to the real-time electrical signal and the historical period electrical signal, comprising : Obtaining the predicted value of the electrical signal at the current moment according to the electrical signal variation law of the electrical signal in the historical period; If the difference between the real-time electrical signal and the predicted value of the electrical signal is less than the difference threshold, then use the real-time electrical signal as the real-time feature value, otherwise use the predicted value of the electrical signal as the real-time feature value. 8. The method for detecting tissue denaturation points in the sealing process according to claim 7, wherein said obtaining the predicted value of the electrical signal at the current moment according to the electrical signal variation law of the electrical signal in the historical period comprises: Acquiring the signal difference of every two adjacent acquired electrical signals in the historical period; Obtain the average value of all signal differences as the historical signal average; The sum of the latest electrical signal acquired in the historical period and the average value of the historical signal is used as the predicted value of the electrical signal. 9. The method for detecting tissue denaturation points during the sealing process according to claim 7, characterized in that said obtaining the predicted value of the electrical signal at the current moment according to the electrical signal variation law of the electrical signal in the historical period comprises: Fitting an electrical signal curve according to the acquired electrical signals in the historical period, the electrical signal curve takes time as the abscissa and the electrical signal value as the ordinate; Taking the abscissa on the electrical signal curve as the ordinate corresponding to the current moment as the predicted value of the electrical signal. 10. The method for detecting tissue degeneration points in the sealing process according to any one of claims 1-9, characterized in that: The electrical signal fed back by the tissue based on the input energy of the electrodes is the tissue impedance value. 11. The method for detecting tissue degeneration points in the sealing process according to claim 1, characterized in that: Before obtaining the real-time electric signal fed back by the tissue based on the input energy of the electrodes at the current moment, the method further includes: Obtain the tissue electrical signal at the initial stage of the historical sealing process and the corresponding tissue electrical signal interval when the tissue reaches the denaturation point; the tissue electrical signal is the electrical signal fed back by the tissue based on the input energy of the electrodes at the initial stage ; Using the tissue electrical signal in the initial stage of the history sealing process as an input sample, using the tissue electrical signal interval as an output sample to train a learning algorithm, and using the trained learning algorithm as a tissue denaturation point prediction model; The judging whether the tissue reaches a denaturation point according to the real-time electrical signal and the historical period electrical signal includes: Taking the end moment of the initial stage in the current sealing process as the current moment, and taking the initial stage in the current sealing process as the set time period; Inputting the real-time electrical signal in the initial stage and the electrical signal in the historical period as input signals into the tissue degeneration point prediction model, and using the output result of the tissue degeneration point prediction model as the predicted electrical signal interval segment of the tissue degeneration point ; determining the real-time electrical signal interval corresponding to the tissue denaturation point in the current sealing process according to the predicted electrical signal interval; If the real-time electrical signal is located in the real-time electrical signal interval, it is determined that the tissue has reached the denaturation point; otherwise, return to the step of acquiring the real-time electrical signal fed back by the tissue based on the input energy of the electrodes at the current moment. 12. The method for detecting tissue degeneration points in the sealing process according to claim 11, characterized in that: The tissue electrical signal, the real-time electrical signal and the historical period electrical signal are: within a set period of time, the tissue feeds back continuous voltage values and current values with corresponding relationships based on the input energy of the electrodes; or , the continuous corresponding voltage value, current value and tissue size value fed back by the tissue based on the electrode input energy within a set period of time; The tissue electrical signal interval is: the corresponding voltage value interval and current value interval when the tissue reaches the denaturation point; or, the corresponding two-dimensional matrix obtained based on the voltage value sequence and the current value sequence when the tissue reaches the denaturation point; Or, the tissue impedance value range corresponding to when the tissue reaches the denaturation point, or the tissue impedance sequence corresponding to the tissue reaching the denaturation point. 13. The method for detecting tissue denaturation points during the sealing process according to claim 12, wherein the real-time electrical signal corresponding to the tissue denaturation points in the current sealing process is determined according to the predicted electrical signal interval segment. Signal interval segments, including: If the tissue electrical signal interval is the corresponding voltage value interval and current value interval when the tissue reaches a denaturation point, then use the voltage value interval or current value interval as the real-time electrical signal interval; If the tissue electrical signal interval is a two-dimensional matrix obtained based on the voltage value sequence and current value sequence corresponding to when the tissue reaches the denaturation point, the average voltage value obtained according to the voltage value sequence combined with the error range is the average The voltage value interval or the average current value interval obtained according to the average current value obtained by the current value sequence combined with the error range is used as the real-time electrical signal interval segment; If the tissue electrical signal interval is the corresponding tissue impedance value interval when the tissue reaches a denaturation point, then use the tissue impedance value interval as the real-time electrical signal interval; If the tissue electrical signal interval is the tissue impedance sequence corresponding to when the tissue reaches the denaturation point, the average impedance value interval obtained from the average impedance value obtained according to the tissue impedance sequence combined with the error range is used as the real-time electrical signal interval part. 14. The method for detecting tissue denaturation points during the sealing process according to claim 11, characterized in that, the tissue electrical signal in the initial stage of the historical sealing process is used as an input sample, and the tissue electrical signal interval As the output sample, the learning algorithm is trained, and the trained learning algorithm is used as the tissue denaturation point prediction model: Described learning algorithm is multi-layer perceptron algorithm; In described multi-layer perceptron algorithm, the function expression of the i-th layer perceptron is: y _i = f(w _i-1 x _i-1 + b); Among them, if i>1, then x _i-1 is the output of the (i-1) layer perceptron, if i=1, then x _i-1 is the obtained tissue electrical signal; w _i-1 is the perceptron The weight matrix composed of the preset weight factors in , b is the preset bias in the perceptron; f() is the preset nonlinear function. 15. A device for detecting tissue degeneration points during the sealing process, characterized in that it comprises: The sampling module is configured to acquire the real-time electric signal fed back by the tissue based on the input energy of the electrode at the current moment; The denaturation point judging module is configured to judge whether the tissue has reached the denaturation point according to the real-time electrical signal and the historical period electric signal; wherein, the set period before the current moment is used as the historical period, and within the historical period The obtained electric signal fed back by the tissue based on the input energy of the electrodes is the electric signal of the historical period. 16. A computer-readable storage medium, characterized in that, program information is stored in the computer-readable storage medium, and the computer executes the sealing process according to any one of claims 1-14 after calling the program instructions Detection method for tissue denaturation point. 17. An electronic device, characterized in that the electronic device includes at least one processor and at least one memory, at least one of the memory stores program information, and at least one of the processors calls the program instructions to execute The method for detecting tissue degeneration points during the sealing process according to any one of claims 1-14. 18. An electrosurgical instrument, characterized in that the host of the electrosurgical instrument is equipped with the device for detecting tissue denaturation points during the sealing process according to claim 15 or the computer-readable storage medium according to claim 16 or The electronic device of claim 17.