Patch clamp electrophysiological data processing method and system

Abstract

The invention discloses a patch clamp electrophysiological data processing method and a patch clamp electrophysiological data processing system. The method comprises the following steps: acquiring ion channel current data through a patch clamp microelectrode; baseline drift removal processing is carried out on the ion channel current data through an adaptive quadratic linear fitting function to obtain baseline correction current data; performing discretization processing and high-frequency filtering processing on the baseline correction current data based on a preset sampling frequency to obtain a filtering current signal sequence; performing calculation through a false nearest neighbor method to obtain an optimal embedding dimension, performing calculation based on an average displacement method to obtain optimal delay time, and performing second phase space reconstruction through the filtering current signal sequence, the optimal embedding dimension and the optimal delay time to obtain an optimal vector set and a phase space reconstruction matrix; calculating through a singular value decomposition algorithm and a Shannon formula to obtain a principal component vector set and an information entropy corresponding to each vector, judging through a preset information entropy threshold to obtain a principal vector sequence, and reconstructing through a singular matrix to obtain electrophysiological feature data.

Description

Technical Field

[0001] The present invention relates to the technical field of electrophysiological data processing, and in particular to a patch clamp electrophysiological data processing method and system. Background Art

[0002] The bioelectric current generated by cells plays an important role in many life activities. Researchers use patch clamp technology to collect bioelectric currents in various cells, thereby conducting research on weak currents at the cellular scale. In recent years, thanks to innovative breakthroughs in sensor technology, automation technology, material technology, and integrated circuit technology, patch clamp technology has been able to accurately measure the current size of each ion channel in cells through fiber microelectrodes, and obtain a large amount of electrophysiological data with high research value. It is of great significance in the determination of ion channel functional characteristics, the study of intercellular electrical signal transmission, and the verification of drug action mechanisms. However, with the improvement of the accuracy of intercellular bioelectric current measurement, in addition to the electronic noise generated by the hardware system and the environmental electromagnetic interference, the collected electrophysiological data is also mixed with a large number of non-research object current signals and the biological activity noise of the cells themselves. Therefore, the development of improved processing methods for patch clamp electrophysiological data is a promising research direction.

[0003] At present, the Chinese invention patent application with application number CN202211477691.5 discloses a data processing method and related equipment, which includes: collecting first data and second data from a patch clamp electrophysiological system, the first data is data generated after the configured carbon fiber electrode applies multiple scanning voltages to the cell, the second data is data generated after the configured stimulation electrode applies electrical stimulation to the cell, and the configured carbon fiber electrode applies multiple scanning voltages to the cell, extracting target current data from the first data, and generating a first curve according to the target current data and the second data, sampling a single scanning voltage to obtain a target sampling point, and generating a corresponding second curve according to the initial current data and sampling time of multiple target sampling points in the first data after sampling or the second data after sampling, so as to obtain the original current and voltage data and convert the original data into an intuitive and effective curve. However, this scheme directly draws the current-time curve of the electrophysiological data collected by the patch clamp, lacks the processing of the electrophysiological data, and increases the computational complexity and error possibility of subsequent data research. Summary of the invention

[0004] The technical problem solved by the present invention is that the conventional solution directly draws a current-time curve from the electrophysiological data collected by the patch clamp, lacks processing of the electrophysiological data, and increases the computational complexity and error possibility of subsequent data research.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] A patch clamp electrophysiological data processing method, comprising:

[0007] Step S1, locating the target cell, applying negative pressure to the cell membrane of the target cell through a patch clamp microelectrode to form a high-resistance seal, applying voltage stimulation to the target cell based on a preset electrical signal stimulation scheme, and acquiring ion channel current data through the patch clamp microelectrode;

[0008] Step S2, removing the baseline drift from the ion channel current data, segmenting and uniformly sampling the ion channel current data to obtain corrected sampling point data, calculating the undetermined coefficients of the adaptive quadratic linear fitting function based on the corrected sampling point data, and performing a difference calculation between the ion channel current data and the baseline fitting value to obtain the baseline corrected current data;

[0009] Step S3, discretizing the baseline corrected current data based on a preset sampling frequency to obtain an initial current signal sequence, and obtaining a filtered current signal sequence through high-frequency filtering;

[0010] Step S4, calculating the best embedding dimension by the false nearest neighbor method, reconstructing the first phase space based on the best embedding dimension to obtain the first vector set, calculating the best delay time based on the best embedding dimension, the first vector set and the average shift method, reconstructing the second phase space by filtering the current signal sequence, the best embedding dimension and the best delay time to obtain the best vector set, and vectorizing the best vector set to obtain the phase space reconstruction matrix;

[0011] Step S5, perform singular value decomposition on the phase space reconstruction matrix through the singular value decomposition algorithm to obtain the principal component vector set, calculate the information entropy corresponding to each vector in the principal component vector set through the Shannon formula, obtain the principal vector sequence through discrimination through the preset information entropy threshold, and obtain the electrophysiological feature data through singular matrix reconstruction and matrix-vector processing.

[0012] As a preferred solution of the patch clamp electrophysiological data processing method described in the present invention, the determination of high-resistance sealing includes: obtaining sealing resistance data between the microelectrode and the target cell through the patch clamp microelectrode, and when the sealing resistance data is less than a preset resistance threshold, determining that a high-resistance seal is not formed;

[0013] When the sealing resistance is greater than or equal to the preset resistance threshold, it is determined that a high-resistance seal is formed;

[0014] A stimulation voltage is applied to the target cells through a preset electrical signal stimulation scheme and ion channel current data is collected. The preset electrical signal stimulation scheme includes step voltage stimulation, ramp voltage stimulation, square wave pulse stimulation and simulated ventricular voltage stimulation.

[0015] As a preferred solution of the patch clamp electrophysiological data processing method described in the present invention, the ion channel current data is subjected to baseline drift removal processing, the ion channel current data is subjected to data segmentation processing based on the time corresponding to the application of the stimulation voltage to the target cell, the time period data corresponding to the voltage application stimulation operation in the ion channel current data is removed to obtain the basic current data, the basic current data is uniformly sampled by correcting the sampling interval to obtain the corrected sampling point data, an adaptive quadratic linear fitting function of the undetermined coefficient is established based on the corrected sampling point data, a least squares objective function is established by the least squares method and the adaptive quadratic linear fitting function of the undetermined coefficient, partial derivatives of the least squares objective function are respectively obtained based on the undetermined coefficients to obtain a partial derivative equation group, the partial derivative equation group is solved to obtain the specific values ​​of the undetermined coefficients, a baseline fitting value is calculated based on the adaptive quadratic linear fitting function of the determined coefficients, and the ion channel current data and the baseline fitting value are subtracted to obtain the baseline corrected current data;

[0016] Its calculation expression is:

[0017] f(x i )=k1X i 2 +k2x i +k3

[0018]

[0019] Among them, f(x i ) represents the adaptive quadratic linear fitting function, i represents the correction sampling point number, x i represents the horizontal coordinate of the ith correction sampling point, k1 represents the quadratic term coefficient, k2 represents the linear term coefficient, k3 represents the constant coefficient, n represents the total number of correction sampling points, S represents the least squares objective function, y i Represents the ion channel current value corresponding to the i-th correction sampling point.

[0020] As a preferred solution of the patch clamp electrophysiological data processing method described in the present invention, wherein: the baseline corrected current data is discretized based on a preset sampling frequency to obtain an initial current signal sequence, and the initial current sequence is subjected to high-frequency noise filtering by a FIR low-pass filter with a preset cutoff frequency to obtain a filtered current signal sequence.

[0021] As a preferred solution of the patch clamp electrophysiological data processing method described in the present invention, the filter current signal sequence is subjected to signal decomposition processing by using an improved singular value decomposition algorithm, an optimal embedding dimension is calculated by using the filter current signal sequence and a false nearest neighbor method, and a first phase space reconstruction is performed on the filter current signal sequence based on the optimal embedding dimension and Takens theorem to obtain a first vector set;

[0022] The optimal delay time is calculated based on the optimal embedding dimension, the first vector set and the average shift method. The filtered current signal sequence is reconstructed in the second phase space by using the filtered current signal sequence, the optimal embedding dimension, the optimal delay time and Takens theorem to obtain the optimal vector set. The optimal vector set is vectorized and zero-filled to obtain the phase space reconstruction matrix.

[0023] As a preferred solution of the patch clamp electrophysiological data processing method described in the present invention, the phase space reconstruction matrix is ​​subjected to singular value decomposition by a singular value decomposition algorithm to obtain a left singular matrix, a right singular matrix and a diagonal matrix, and the column vectors in the right singular matrix are extracted to obtain a principal component vector set.

[0024] As a preferred solution of the patch clamp electrophysiological data processing method described in the present invention, the information entropy corresponding to each vector in the principal component vector set is calculated by the Shannon formula, the information entropy corresponding to each vector in the principal component vector set is normalized and calculated to obtain the standard information entropy, the standard information entropy is judged by a preset information entropy threshold, when the standard information entropy is greater than or equal to the preset information entropy threshold, the vector corresponding to the standard information entropy is judged to be a principal vector sequence, the matrix element values ​​corresponding to the principal vector sequence in the left singular matrix, the right singular matrix and the diagonal matrix are extracted to obtain an updated left singular matrix, an updated right singular matrix and an updated diagonal matrix, the updated left singular matrix, the updated right singular matrix and the updated diagonal matrix are singularly reconstructed by a singular value decomposition algorithm to obtain a characteristic matrix, and the characteristic matrix is ​​matrix-vectorized to obtain electrophysiological characteristic data.

[0025] A patch clamp electrophysiological data processing system, comprising: an electrophysiological data acquisition module, a data preprocessing module, a feature extraction module and a terminal interaction module;

[0026] The electrophysiological data acquisition module is used to acquire ion channel current data, target cells and apply negative pressure to the cell membrane of the target cells to form a high-resistance seal, apply voltage stimulation to the target cells based on a preset electrical signal stimulation scheme, and acquire ion channel current data through patch clamp microelectrodes;

[0027] The data preprocessing module is used to remove the baseline drift of the ion channel current data, segment and uniformly sample the ion channel current data to obtain the corrected sampling point data, calculate the undetermined coefficients of the adaptive quadratic linear fitting function based on the corrected sampling point data, and perform difference calculation on the ion channel current data and the baseline fitting value to obtain the baseline corrected current data;

[0028] The feature extraction module is used to perform feature extraction on the baseline corrected current data to obtain a phase space reconstruction matrix, discretize the baseline corrected current data based on a preset sampling frequency to obtain an initial current signal sequence, obtain a filtered current signal sequence through high-frequency filtering, calculate the optimal embedding dimension through a false nearest neighbor method, perform a first phase space reconstruction based on the optimal embedding dimension to obtain a first vector set, calculate the optimal delay time based on the optimal embedding dimension, the first vector set and the average shift method, perform a second phase space reconstruction through the filtered current signal sequence, the optimal embedding dimension and the optimal delay time to obtain an optimal vector set, vectorize the optimal vector set to obtain a phase space reconstruction matrix, perform singular value decomposition on the phase space reconstruction matrix through a singular value decomposition algorithm to obtain a principal component vector set, calculate the information entropy corresponding to each vector in the principal component vector set through the Shannon formula, obtain a principal vector sequence through discrimination through a preset information entropy threshold, and obtain electrophysiological feature data through singular matrix reconstruction and matrix vectorization processing;

[0029] The terminal interaction module is used to provide an operation interface to the user, receive user operation instructions and provide visual feedback, wherein the user operation instructions include system switch instructions, system parameter setting instructions and historical record data query instructions;

[0030] The system parameter setting instructions include: a preset resistance threshold, a preset electrical signal stimulation scheme, a preset sampling frequency, a preset cutoff frequency and a preset information entropy threshold;

[0031] The preset electrical signal stimulation scheme includes step voltage stimulation, ramp voltage stimulation, square wave pulse stimulation and simulated ventricular voltage stimulation.

[0032] The beneficial effects of the present invention are as follows: a variety of preset electrical signal stimulation schemes are set, which is conducive to studying the electrophysiological data of different types of cells. By introducing an adaptive quadratic linear fitting function to remove the baseline drift of ion channel current data, the baseline drift problem caused by experimental environment and equipment factors is effectively reduced, making subsequent analysis more accurate. Phase space reconstruction and principal component extraction based on information entropy are introduced into the singular value decomposition algorithm, and electrophysiological characteristic data are obtained through feature extraction, which is conducive to accurate analysis of trace currents, allowing researchers to carry out research in different directions based on electrophysiological characteristic data. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 A basic flow chart of a patch clamp electrophysiological data processing method provided by one embodiment of the present invention;

[0034] Figure 2 A schematic diagram of the basic framework of a patch clamp electrophysiological data processing system provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0035] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0036] Example 1, reference Figure 1 , as an embodiment of the present invention, provides a patch clamp electrophysiological data processing method, comprising:

[0037] Step S1, locating the target cell, applying negative pressure to the cell membrane of the target cell through a patch clamp microelectrode to form a high-resistance seal, applying voltage stimulation to the target cell based on a preset electrical signal stimulation scheme, and acquiring ion channel current data through the patch clamp microelectrode;

[0038] Step S2, removing the baseline drift from the ion channel current data, segmenting and uniformly sampling the ion channel current data to obtain corrected sampling point data, calculating the undetermined coefficients of the adaptive quadratic linear fitting function based on the corrected sampling point data, and performing a difference calculation between the ion channel current data and the baseline fitting value to obtain the baseline corrected current data;

[0039] Step S3, discretizing the baseline corrected current data based on a preset sampling frequency to obtain an initial current signal sequence, and obtaining a filtered current signal sequence through high-frequency filtering;

[0040] Step S4, calculating the best embedding dimension by the false nearest neighbor method, reconstructing the first phase space based on the best embedding dimension to obtain the first vector set, calculating the best delay time based on the best embedding dimension, the first vector set and the average shift method, reconstructing the second phase space by filtering the current signal sequence, the best embedding dimension and the best delay time to obtain the best vector set, and vectorizing the best vector set to obtain the phase space reconstruction matrix;

[0041] Step S5, perform singular value decomposition on the phase space reconstruction matrix through the singular value decomposition algorithm to obtain the principal component vector set, calculate the information entropy corresponding to each vector in the principal component vector set through the Shannon formula, obtain the principal vector sequence through discrimination through the preset information entropy threshold, and obtain the electrophysiological feature data through singular matrix reconstruction and matrix-vector processing.

[0042] In this embodiment, the determination of the high-resistance seal includes: obtaining the sealing resistance data between the microelectrode and the target cell through the patch clamp microelectrode, and when the sealing resistance data is less than a preset resistance threshold, determining that the high-resistance seal is not formed;

[0043] When the sealing resistance is greater than or equal to the preset resistance threshold, it is determined that a high-resistance seal is formed;

[0044] A stimulation voltage is applied to the target cells through a preset electrical signal stimulation scheme and ion channel current data is collected. The preset electrical signal stimulation scheme includes step voltage stimulation, ramp voltage stimulation, square wave pulse stimulation and simulated ventricular voltage stimulation.

[0045] In this embodiment, the ion channel current data is processed to remove the baseline drift, the ion channel current data is processed based on the time corresponding to the application of the stimulation voltage to the target cell, the ion channel current data is processed by data segmentation, the time period data corresponding to the voltage stimulation operation in the ion channel current data is removed to obtain the basic current data, the basic current data is uniformly sampled by correcting the sampling interval to obtain the corrected sampling point data, an adaptive quadratic linear fitting function of the undetermined coefficients is established based on the corrected sampling point data, a least squares objective function is established by the least squares method and the adaptive quadratic linear fitting function of the undetermined coefficients, the least squares objective function is partially derived based on the undetermined coefficients to obtain a partial derivative equation group, the partial derivative equation group is solved to obtain the specific values ​​of the undetermined coefficients, the baseline fitting value is calculated based on the adaptive quadratic linear fitting function of the determined coefficients, and the ion channel current data and the baseline fitting value are subtracted to obtain the baseline correction current data;

[0046] Its calculation expression is:

[0047] f(x i )=k1x i 2 +k2x i +k3

[0048]

[0049] Among them, f(x i ) represents the adaptive quadratic linear fitting function, i represents the correction sampling point number, x i represents the horizontal coordinate of the ith correction sampling point, k1 represents the quadratic term coefficient, k2 represents the linear term coefficient, k3 represents the constant coefficient, n represents the total number of correction sampling points, S represents the least squares objective function, y i Represents the ion channel current value corresponding to the i-th correction sampling point.

[0050] In this embodiment, the baseline corrected current data is discretized based on a preset sampling frequency to obtain an initial current signal sequence, and the initial current sequence is subjected to high-frequency noise filtering by a FIR low-pass filter with a preset cutoff frequency to obtain a filtered current signal sequence.

[0051] In this embodiment, the signal decomposition processing is performed on the filtered current signal sequence by using an improved singular value decomposition algorithm, the optimal embedding dimension is calculated by using the filtered current signal sequence and the false nearest neighbor method, and the first phase space reconstruction of the filtered current signal sequence is performed based on the optimal embedding dimension and Takens theorem to obtain a first vector set;

[0052] The optimal delay time is calculated based on the optimal embedding dimension, the first vector set and the average shift method. The filtered current signal sequence is reconstructed in the second phase space by using the filtered current signal sequence, the optimal embedding dimension, the optimal delay time and Takens theorem to obtain the optimal vector set. The optimal vector set is vectorized and zero-filled to obtain the phase space reconstruction matrix.

[0053] The calculation logic of obtaining the optimal embedding dimension by using the false nearest neighbor method includes: assigning an initial value M to the embedding dimension, calculating the distance between each point in the phase space and its nearest neighbor point to obtain a first Euclidean distance set;

[0054] Then, the embedding dimension is assigned a value of M+1 again, and the distance between each point in the phase space and its nearest neighbor is calculated to obtain the second Euclidean distance set. The absolute difference between the distances between the nearest neighbor points corresponding to each point in the first Euclidean distance set and the second Euclidean distance set is calculated, and the first change rate is obtained by comparing the absolute difference with the value of the corresponding point in the first Euclidean distance set. The first change rate is compared with a preset change rate threshold. If the first change rate is greater than the preset change rate threshold, the point is judged to be a false nearest neighbor.

[0055] The embedding dimensions are assigned values ​​of M+2, M+3...M+N in turn and iteratively calculated until the proportion of false nearest neighbor points to all points is less than or equal to the preset false nearest neighbor point proportion threshold. The iterative calculation is stopped and the corresponding embedding dimension value is determined as the optimal embedding dimension.

[0056] In this embodiment, the phase space reconstruction matrix is ​​subjected to singular value decomposition by a singular value decomposition algorithm to obtain a left singular matrix, a right singular matrix and a diagonal matrix, and the column vectors in the right singular matrix are extracted to obtain a principal component vector set.

[0057] In this embodiment, the information entropy corresponding to each vector in the principal component vector set is calculated by the Shannon formula, the information entropy corresponding to each vector in the principal component vector set is normalized and calculated to obtain the standard information entropy, the standard information entropy is judged by a preset information entropy threshold, when the standard information entropy is greater than or equal to the preset information entropy threshold, the vector corresponding to the standard information entropy is judged to be a principal vector sequence, the matrix element values ​​corresponding to the main vector sequence in the left singular matrix, the right singular matrix and the diagonal matrix are extracted to obtain an updated left singular matrix, an updated right singular matrix and an updated diagonal matrix, the updated left singular matrix, the updated right singular matrix and the updated diagonal matrix are reconstructed by a singular value decomposition algorithm to obtain a characteristic matrix, and the characteristic matrix is ​​matrix-vectorized to obtain electrophysiological characteristic data.

[0058] Example 2, reference Figure 2 , as one embodiment of the present invention, provides a patch clamp electrophysiological data processing system, including: an electrophysiological data acquisition module, a data preprocessing module, a feature extraction module and a terminal interaction module;

[0059] The electrophysiological data acquisition module is used to acquire ion channel current data, target cells and apply negative pressure to the cell membrane of the target cells to form a high-resistance seal, apply voltage stimulation to the target cells based on a preset electrical signal stimulation scheme, and acquire ion channel current data through patch clamp microelectrodes;

[0060] The data preprocessing module is used to remove the baseline drift of the ion channel current data, segment and uniformly sample the ion channel current data to obtain the corrected sampling point data, calculate the undetermined coefficients of the adaptive quadratic linear fitting function based on the corrected sampling point data, and perform difference calculation on the ion channel current data and the baseline fitting value to obtain the baseline corrected current data;

[0061] The feature extraction module is used to perform feature extraction on the baseline corrected current data to obtain a phase space reconstruction matrix, discretize the baseline corrected current data based on a preset sampling frequency to obtain an initial current signal sequence, obtain a filtered current signal sequence through high-frequency filtering, calculate the optimal embedding dimension through a false nearest neighbor method, perform a first phase space reconstruction based on the optimal embedding dimension to obtain a first vector set, calculate the optimal delay time based on the optimal embedding dimension, the first vector set and the average shift method, perform a second phase space reconstruction through the filtered current signal sequence, the optimal embedding dimension and the optimal delay time to obtain an optimal vector set, vectorize the optimal vector set to obtain a phase space reconstruction matrix, perform singular value decomposition on the phase space reconstruction matrix through a singular value decomposition algorithm to obtain a principal component vector set, calculate the information entropy corresponding to each vector in the principal component vector set through the Shannon formula, obtain a principal vector sequence through discrimination through a preset information entropy threshold, and obtain electrophysiological feature data through singular matrix reconstruction and matrix vectorization processing;

[0062] The terminal interaction module is used to provide an operation interface to the user, receive user operation instructions and provide visual feedback, wherein the user operation instructions include system switch instructions, system parameter setting instructions and historical record data query instructions;

[0063] The system parameter setting instructions include: a preset resistance threshold, a preset electrical signal stimulation scheme, a preset sampling frequency, a preset cutoff frequency and a preset information entropy threshold;

[0064] The preset electrical signal stimulation scheme includes step voltage stimulation, ramp voltage stimulation, square wave pulse stimulation and simulated ventricular voltage stimulation.

[0065] It should be understood by those skilled in the art that embodiments of the present invention may provide methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program codes. Among them, the storage medium may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, disk or optical disk. These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0066] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A patch clamp electrophysiological data processing method, characterized in that: include: Step S1, locating the target cell, applying negative pressure to the cell membrane of the target cell through a patch clamp microelectrode to form a high-resistance seal, applying voltage stimulation to the target cell based on a preset electrical signal stimulation scheme, and acquiring ion channel current data through the patch clamp microelectrode; Step S2, removing the baseline drift from the ion channel current data, segmenting and uniformly sampling the ion channel current data to obtain corrected sampling point data, calculating the undetermined coefficients of the adaptive quadratic linear fitting function based on the corrected sampling point data, and performing a difference calculation between the ion channel current data and the baseline fitting value to obtain the baseline corrected current data; Step S3, discretizing the baseline corrected current data based on a preset sampling frequency to obtain an initial current signal sequence, and obtaining a filtered current signal sequence through high-frequency filtering; Step S4, calculating the best embedding dimension by the false nearest neighbor method, reconstructing the first phase space based on the best embedding dimension to obtain the first vector set, calculating the best delay time based on the best embedding dimension, the first vector set and the average shift method, reconstructing the second phase space by filtering the current signal sequence, the best embedding dimension and the best delay time to obtain the best vector set, and vectorizing the best vector set to obtain the phase space reconstruction matrix; Step S5, perform singular value decomposition on the phase space reconstruction matrix through the singular value decomposition algorithm to obtain the principal component vector set, calculate the information entropy corresponding to each vector in the principal component vector set through the Shannon formula, obtain the principal vector sequence through discrimination through the preset information entropy threshold, and obtain the electrophysiological feature data through singular matrix reconstruction and matrix-vector processing.

2. A patch clamp electrophysiological data processing method as claimed in claim 1, characterized in that: The determination of the high-resistance seal comprises: obtaining the sealing resistance data between the microelectrode and the target cell through the patch clamp microelectrode, and when the sealing resistance data is less than a preset resistance threshold, determining that the high-resistance seal is not formed; When the sealing resistance is greater than or equal to the preset resistance threshold, it is determined that a high-resistance seal is formed; A stimulation voltage is applied to the target cells through a preset electrical signal stimulation scheme and ion channel current data is collected. The preset electrical signal stimulation scheme includes step voltage stimulation, ramp voltage stimulation, square wave pulse stimulation and simulated ventricular voltage stimulation.

3. A patch clamp electrophysiological data processing method as claimed in claim 1, characterized in that: The ion channel current data is processed to remove the baseline drift, the ion channel current data is processed based on the time corresponding to the application of the stimulation voltage to the target cell, the data of the time period corresponding to the voltage application stimulation operation in the ion channel current data is removed to obtain the basic current data, the basic current data is uniformly sampled by correcting the sampling interval to obtain the corrected sampling point data, an adaptive quadratic linear fitting function of the undetermined coefficient is established based on the corrected sampling point data, a least squares objective function is established by using the least squares method and the adaptive quadratic linear fitting function of the undetermined coefficient, partial derivatives of the least squares objective function are respectively obtained based on the undetermined coefficients to obtain a partial derivative equation group, the partial derivative equation group is solved to obtain the specific values ​​of the undetermined coefficients, a baseline fitting value is calculated based on the adaptive quadratic linear fitting function of the determined coefficients, and the ion channel current data and the baseline fitting value are subtracted to obtain the baseline corrected current data; Its calculation expression is: f(x i )=k1x i 2 +k2x i +k3 Among them, f(x i ) represents the adaptive quadratic linear fitting function, i represents the correction sampling point number, x i represents the horizontal coordinate of the ith correction sampling point, k1 represents the quadratic term coefficient, k2 represents the linear term coefficient, k3 represents the constant coefficient, n represents the total number of correction sampling points, S represents the least squares objective function, y i Represents the ion channel current value corresponding to the i-th correction sampling point.

4. A patch clamp electrophysiological data processing method as claimed in claim 1, characterized in that: The baseline corrected current data is discretized based on a preset sampling frequency to obtain an initial current signal sequence, and the initial current sequence is subjected to high-frequency noise filtering by a FIR low-pass filter with a preset cutoff frequency to obtain a filtered current signal sequence.

5. A patch clamp electrophysiological data processing method as claimed in claim 1, characterized in that: The signal decomposition processing of the filtered current signal sequence is performed by using an improved singular value decomposition algorithm, the optimal embedding dimension is calculated by using the filtered current signal sequence and the false nearest neighbor method, and the first phase space of the filtered current signal sequence is reconstructed based on the optimal embedding dimension and Takens theorem to obtain the first vector set; The optimal delay time is calculated based on the optimal embedding dimension, the first vector set and the average shift method. The filtered current signal sequence is reconstructed in the second phase space by using the filtered current signal sequence, the optimal embedding dimension, the optimal delay time and Takens theorem to obtain the optimal vector set. The optimal vector set is vectorized and zero-filled to obtain the phase space reconstruction matrix.

6. A patch clamp electrophysiological data processing method as claimed in claim 1, characterized in that: The singular value decomposition algorithm is used to perform singular value decomposition on the phase space reconstruction matrix to obtain a left singular matrix, a right singular matrix and a diagonal matrix, and the column vectors in the right singular matrix are extracted to obtain a principal component vector set.

7. A patch clamp electrophysiological data processing method as claimed in claim 1, characterized in that: The information entropy corresponding to each vector in the principal component vector set is calculated by the Shannon formula, and the information entropy corresponding to each vector in the principal component vector set is normalized to obtain the standard information entropy. The standard information entropy is judged by a preset information entropy threshold. When the standard information entropy is greater than or equal to the preset information entropy threshold, the vector corresponding to the standard information entropy is judged to be a principal vector sequence, and the matrix element values ​​corresponding to the principal vector sequence in the left singular matrix, the right singular matrix and the diagonal matrix are extracted to obtain an updated left singular matrix, an updated right singular matrix and an updated diagonal matrix. The updated left singular matrix, the updated right singular matrix and the updated diagonal matrix are reconstructed by a singular value decomposition algorithm to obtain a characteristic matrix, and the characteristic matrix is ​​matrix-vectorized to obtain electrophysiological characteristic data.

8. A patch clamp electrophysiological data processing system, characterized in that: include: Electrophysiological data acquisition module, data preprocessing module, feature extraction module and terminal interaction module; The electrophysiological data acquisition module is used to acquire ion channel current data, target cells and apply negative pressure to the cell membrane of the target cells to form a high-resistance seal, apply voltage stimulation to the target cells based on a preset electrical signal stimulation scheme, and acquire ion channel current data through patch clamp microelectrodes; The data preprocessing module is used to remove the baseline drift of the ion channel current data, segment and uniformly sample the ion channel current data to obtain the corrected sampling point data, calculate the undetermined coefficients of the adaptive quadratic linear fitting function based on the corrected sampling point data, and perform difference calculation on the ion channel current data and the baseline fitting value to obtain the baseline corrected current data; The feature extraction module is used to perform feature extraction on the baseline corrected current data to obtain a phase space reconstruction matrix, discretize the baseline corrected current data based on a preset sampling frequency to obtain an initial current signal sequence, obtain a filtered current signal sequence through high-frequency filtering, calculate the optimal embedding dimension through a false nearest neighbor method, perform a first phase space reconstruction based on the optimal embedding dimension to obtain a first vector set, calculate the optimal delay time based on the optimal embedding dimension, the first vector set and the average shift method, perform a second phase space reconstruction through the filtered current signal sequence, the optimal embedding dimension and the optimal delay time to obtain an optimal vector set, vectorize the optimal vector set to obtain a phase space reconstruction matrix, perform singular value decomposition on the phase space reconstruction matrix through a singular value decomposition algorithm to obtain a principal component vector set, calculate the information entropy corresponding to each vector in the principal component vector set through the Shannon formula, obtain a principal vector sequence through discrimination through a preset information entropy threshold, and obtain electrophysiological feature data through singular matrix reconstruction and matrix vectorization processing; The terminal interaction module is used to provide an operation interface to the user, receive user operation instructions and provide visual feedback, wherein the user operation instructions include system switch instructions, system parameter setting instructions and historical record data query instructions; The system parameter setting instructions include: a preset resistance threshold, a preset electrical signal stimulation scheme, a preset sampling frequency, a preset cutoff frequency and a preset information entropy threshold; The preset electrical signal stimulation scheme includes step voltage stimulation, ramp voltage stimulation, square wave pulse stimulation and simulated ventricular voltage stimulation.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the patch clamp electrophysiological data processing system of claim 8 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the patch clamp electrophysiological data processing system of claim 8 is implemented.

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