Patch clamp electrophysiological data processing method and system

Abstract

The present invention discloses a patch clamp electrophysiological data processing method and system, which obtains ion channel current data by collecting patch clamp microelectrodes; removes baseline drift from the ion channel current data by an adaptive quadratic linear fitting function to obtain baseline-corrected current data; discretizes and performs high-frequency filtering on the baseline-corrected current data based on a preset sampling frequency to obtain a filtered current signal sequence; calculates an optimal embedding dimension by a false nearest neighbor method, calculates an optimal delay time based on a mean shift method, and performs second phase space reconstruction using the filtered current signal sequence, the optimal embedding dimension, and the optimal delay time to obtain an optimal vector set and a phase space reconstruction matrix; calculates a principal component vector set and the information entropy corresponding to each vector by a singular value decomposition algorithm and the Shannon formula, obtains a principal vector sequence by discrimination using a preset information entropy threshold, and obtains electrophysiological characteristic data by singular matrix reconstruction.

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] Bioelectric currents generated by cells play 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, materials technology, and integrated circuit technology, patch clamp technology has been able to accurately measure the current magnitude of each ion channel in cells using fiber microelectrodes, obtaining a large amount of electrophysiological data with high research value. This is of great significance in determining the functional characteristics of ion channels, studying intercellular electrical signal transmission, and verifying the mechanism of drug action. However, with the improvement of the accuracy of intercellular bioelectric current measurement, the collected electrophysiological data is mixed with a large amount of current signals not related to the research object and the biological activity noise of the cells themselves, in addition to the electronic noise generated by the hardware system and environmental electromagnetic interference. Therefore, developing 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. The application includes: collecting first data and second data from a patch clamp electrophysiological system, the first data being data generated after the configured carbon fiber electrode applies multiple scanning voltages to the cell, and the second data being data generated after the configured stimulation electrode applies multiple scanning voltages to the cell after the configured carbon fiber electrode applies electrical stimulation to the cell, extracting target current data from the first data, and generating a first curve based on 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 based on the initial current data and sampling time of the 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 solution 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 traditional solution 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 calculation 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 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 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 performing high-frequency filtering to obtain a filtered current signal sequence;

[0010] Step S4, calculating the optimal embedding dimension by the false nearest neighbor method, performing a first phase space reconstruction based on the optimal embedding dimension to obtain a first vector set, calculating the optimal delay time based on the optimal embedding dimension, the first vector set, and the mean shift method, performing a second phase space reconstruction by filtering the current signal sequence, the optimal embedding dimension, and the optimal delay time to obtain an optimal vector set, and vectorizing the optimal vector set to obtain a 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 main vector sequence through discrimination through the preset information entropy threshold, and obtain the electrophysiological feature data through singular matrix reconstruction and matrix vectorization processing.

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

[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 stimulation operation in the ion channel current data is removed to obtain basic current data, the basic current data is uniformly sampled by correcting the sampling interval to obtain corrected sampling point data, an adaptive quadratic linear fitting function of undetermined coefficients is established based on the corrected sampling point data, a least squares objective function is established by least squares method and the adaptive quadratic linear fitting function of the undetermined coefficients, partial derivatives of the least squares objective function are respectively taken based on the undetermined coefficients to obtain a partial derivative equation group, the partial derivative equation group is solved to obtain 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 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 i-th 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, 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 an FIR low-pass filter with a preset cutoff frequency to obtain a filtered current signal sequence.

[0021] As a preferred embodiment of the patch clamp electrophysiological data processing method of the present invention, the method further comprises: performing signal decomposition processing on the filtered current signal sequence using an improved singular value decomposition algorithm, calculating an optimal embedding dimension using the filtered current signal sequence and a false nearest neighbor method, and performing a first phase space reconstruction on the filtered 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 mean 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-padded 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 collect ion channel current data, apply negative pressure to the cell membrane of the target cell to form a high-resistance seal, apply voltage stimulation to the target cell based on a preset electrical signal stimulation scheme, and obtain ion channel current data through patch clamp microelectrodes;

[0027] The data preprocessing module is used to remove baseline drift from the ion channel current data, segment and uniformly sample the ion channel current data to obtain 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 between the ion channel current data and the baseline fitting value to obtain 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, perform discrimination through a preset information entropy threshold to obtain a principal vector sequence, 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. 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 present invention has the following beneficial effects: It provides multiple preset electrical signal stimulation schemes, facilitating the study of electrophysiological data from different cell types. By introducing an adaptive quadratic linear fitting function to remove baseline drift from ion channel current data, it effectively reduces baseline drift caused by experimental environment and equipment factors, making subsequent analysis more accurate. Phase space reconstruction and information entropy-based principal component extraction are introduced into the singular value decomposition algorithm. Electrophysiological characteristic data is obtained through feature extraction, facilitating the precise analysis of trace currents and enabling researchers to conduct diverse research based on electrophysiological characteristic data. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 A schematic diagram of the basic flow 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, with reference to Figure 1 , as one 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 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 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 performing high-frequency filtering to obtain a filtered current signal sequence;

[0040] Step S4, calculating the optimal embedding dimension by the false nearest neighbor method, performing a first phase space reconstruction based on the optimal embedding dimension to obtain a first vector set, calculating the optimal delay time based on the optimal embedding dimension, the first vector set, and the mean shift method, performing a second phase space reconstruction by filtering the current signal sequence, the optimal embedding dimension, and the optimal delay time to obtain an optimal vector set, and vectorizing the optimal vector set to obtain a 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 main vector sequence through discrimination through the preset information entropy threshold, and obtain the electrophysiological feature data through singular matrix reconstruction and matrix vectorization processing.

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

[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 segmented based on the time corresponding to the application of the stimulation voltage to the target cell, the time period data corresponding to the applied voltage stimulation operation in the ion channel current data is removed, 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, the 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 numerical value of the undetermined coefficient, the baseline fitting value is calculated based on the adaptive quadratic linear fitting function of the determined coefficient, and the ion channel current data and the baseline fitting value are subtracted to obtain the baseline corrected 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 i-th 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 filtered out of high-frequency noise using an FIR low-pass filter with a preset cutoff frequency to obtain a filtered current signal sequence.

[0051] In this embodiment, the filtered current signal sequence is decomposed using an improved singular value decomposition algorithm, the optimal embedding dimension is calculated 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 mean 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-padded to obtain the phase space reconstruction matrix.

[0053] The calculation logic for obtaining the optimal embedding dimension by 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 to obtain a first Euclidean distance set;

[0054] Then the embedding dimension is assigned to M+1 again, 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, the absolute difference is compared with the value of the corresponding point in the first Euclidean distance set to obtain a first change rate, the first change rate is compared with the preset change rate threshold, if the first change rate is greater than the preset change rate threshold, then the point is judged to be a false nearest neighbor point.

[0055] The embedding dimension is assigned values ​​of M+2, M+3...M+N in sequence 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 ratio 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 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 the singular value decomposition algorithm to obtain a characteristic matrix, and the characteristic matrix is ​​matrix-vectorized to obtain electrophysiological feature data.

[0058] Example 2, reference Figure 2 , as one embodiment of the present invention, provides 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;

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

[0060] The data preprocessing module is used to remove baseline drift from the ion channel current data, segment and uniformly sample the ion channel current data to obtain 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 between the ion channel current data and the baseline fitting value to obtain 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, perform discrimination through a preset information entropy threshold to obtain a principal vector sequence, 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. 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] Those skilled in the art will appreciate that embodiments of the present invention may provide methods, systems, or computer program products. Therefore, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, 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 code. 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 (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work 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 The 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 and are not intended to limit the present invention. 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 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 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 performing high-frequency filtering to obtain a filtered current signal sequence; Step S4, performing signal decomposition processing on the filtered current signal sequence using a singular value decomposition algorithm, calculating an optimal embedding dimension using the filtered current signal sequence and a false nearest neighbor method, and reconstructing the filtered current signal sequence into a first phase space based on the optimal embedding dimension and Takens' theorem to obtain a first vector set; The optimal delay time is calculated based on the optimal embedding dimension, the first vector set, and the mean shift method. The filtered current signal sequence is reconstructed in the second phase space 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. Step S5, perform singular value decomposition on the phase space reconstruction matrix through the singular value decomposition algorithm to obtain a left singular matrix, a right singular matrix and a diagonal matrix, extract the column vectors in the right singular matrix 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 the main vector sequence through discrimination through the preset information entropy threshold, and obtain electrophysiological feature data through singular matrix reconstruction and matrix vectorization processing.

2. A patch clamp electrophysiological data processing method according to claim 1, characterized in that: The determination of the high-resistance seal comprises: obtaining sealing resistance data between the microelectrode and the target cell through a patch clamp microelectrode, and determining that a high-resistance seal is not formed when the sealing resistance data is less than a preset resistance threshold; 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 according to claim 1, characterized in that: The ion channel current data is processed to remove baseline drift, and the ion channel current data is segmented based on the time corresponding to the application of stimulation voltage to the target cell, and the time period data corresponding to the application of voltage stimulation operation in the ion channel current data is removed to obtain basic current data, and the basic current data is uniformly sampled by correcting the sampling interval to obtain corrected sampling point data, and an adaptive quadratic linear fitting function of undetermined coefficients is established based on the corrected sampling point data, and a least squares objective function is established by using the least squares method and the adaptive quadratic linear fitting function of the undetermined coefficients, and the partial derivatives of the least squares objective function are respectively calculated based on the undetermined coefficients to obtain a partial derivative equation group, and the partial derivative equation group is solved to obtain specific values ​​of the undetermined coefficients, and 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 baseline-corrected current data; Its calculation expression is: ； ； in, Represents the adaptive quadratic linear fitting function, i represents the correction sampling point number, represents the horizontal coordinate of the i-th correction sampling point, represents the coefficient of the quadratic term, represents the coefficient of the first-order term, represents the constant coefficient, n represents the total number of correction sampling points, S represents the least squares objective function, Represents the ion channel current value corresponding to the i-th correction sampling point.

4. A patch clamp electrophysiological data processing method according to 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 filtered out of high-frequency noise using an FIR low-pass filter with a preset cutoff frequency to obtain a filtered current signal sequence.

5. The patch clamp electrophysiological data processing method according to claim 1, wherein: 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. 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 the updated left singular matrix, the updated right singular matrix and the updated diagonal matrix. The updated left singular matrix, the updated right singular matrix and the updated diagonal matrix are reconstructed by the singular value decomposition algorithm to obtain the characteristic matrix, and the characteristic matrix is ​​matrix-vectorized to obtain the electrophysiological characteristic data.

6. 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 collect ion channel current data, apply negative pressure to the cell membrane of the target cell to form a high-resistance seal, apply voltage stimulation to the target cell based on a preset electrical signal stimulation scheme, and obtain ion channel current data through patch clamp microelectrodes; The data preprocessing module is used to remove baseline drift from the ion channel current data, segment and uniformly sample the ion channel current data to obtain 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 between the ion channel current data and the baseline fitting value to obtain baseline-corrected current data; The feature extraction module is used to extract features from 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, and obtain a filtered current signal sequence through high-frequency filtering. The filtered current signal sequence is decomposed using the singular value decomposition algorithm. The optimal embedding dimension is calculated using the filtered current signal sequence and the false nearest neighbor method. Based on the optimal embedding dimension and Takens' theorem, the first phase space of the filtered current signal sequence is reconstructed to obtain the first vector set. The optimal delay time is calculated based on the optimal embedding dimension, the first vector set, and the mean shift method. The filtered current signal sequence is reconstructed in the second phase space 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. The phase space reconstruction matrix is ​​decomposed into a left singular matrix, a right singular matrix and a diagonal matrix using a singular value decomposition algorithm. The column vectors in the right singular matrix are extracted to obtain a principal component vector set. The information entropy corresponding to each vector in the principal component vector set is calculated using the Shannon formula. The principal vector sequence is obtained by discrimination using a preset information entropy threshold. The electrophysiological characteristic data is obtained 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. 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.

7. 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 according to claim 6 is implemented.

8. 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 according to claim 6 is implemented.

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