Retina response prediction system based on machine learning

Abstract

The invention discloses a retinal response prediction system based on machine learning, and the system comprises a data extraction module which is used for extracting electroretinogram (ERG) data from a local data file, calculating related waveform parameters according to the ERG data, and sorting the related waveform parameters to obtain ERG analysis results classified according to frequency; the feature engineering module is used for merging ERG analysis results under different frequencies, adding light intensity information in a merged data set and cleaning data; the model training module is used for detecting and eliminating outliers in the data by using an isolated forest algorithm, and training according to the data by using different regression algorithms to obtain a forward prediction model and a reverse prediction model; and the feature prediction module is used for providing interactive operation through a graphical user interface (GUI), acquiring a data file according to a user instruction, and selecting a pre-trained machine learning model according to the instruction to execute forward or reverse prediction of the retina reaction, so that the efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of retinal research, and in particular to a retinal response prediction system based on machine learning. Background Art

[0002] Since biological retinas are difficult to obtain, especially in humans and other higher animals, this limits the ability to conduct direct experimental studies on retinal function, and obtaining sufficient numbers of retinal samples is a relatively cumbersome task for related research.

[0003] The complexity and fragility of the retina require great caution in experimental operations, and when studying the variables of light stimulation of the retina, external conditions such as light conditions and equipment technology will interfere with the accuracy and reliability of the experiment, which to a certain extent increases the difficulty of related research.

[0004] In current visual prosthesis research, researchers believe that reconstructing light stimulation images is a great challenge. Some current deep learning models can reconstruct some light stimulation images based on the retinal spike signals of organisms, but these models run slowly and it is difficult to obtain retinal spike signals, making it difficult to perform real-time light stimulation reconstruction.

[0005] Currently, most of the research on retinal signals focuses on spike signals, and very few studies are conducted on retinal ERG. Researchers of retinal ERG need to spend a lot of time manually organizing data, which greatly slows down the research progress. Summary of the invention

[0006] To achieve the above objectives and other related objectives, the present invention discloses a retinal response prediction system based on machine learning, comprising: The data extraction module is used to extract electroretinogram (ERG) data from the local data file, calculate relevant waveform parameters based on the ERG data, and sort the relevant waveform parameters to obtain ERG analysis results classified by frequency; Feature engineering module, used to merge ERG analysis results at different frequencies, add light intensity information to the merged data set, and clean the data; The model training module is used to detect and remove outliers in the data using the isolation forest algorithm, and to train the forward prediction model and the reverse prediction model based on the data by using different regression algorithms; The feature prediction module is used to provide interactive operations through a graphical user interface (GUI), obtain data files according to user instructions, and select a pre-trained machine learning model according to the instructions to perform forward or reverse prediction of retinal response.

[0007] Furthermore, the data extraction module includes: ERG information extraction module, used to extract electroretinogram (ERG) data from local data files, calculate latency, duration, amplitude, peak value of a wave, duration, amplitude, peak value of b wave, save the above parameters and generate parameter files; A format conversion module is used to read parameter files and convert the format of the parameter files; The frequency segmentation module is used to segment the data in the parameter file after format conversion according to frequency.

[0008] Furthermore, the feature engineering module includes: Added light intensity data module to supplement the light intensity information in the data; The zero value data deletion module is used to delete invalid data rows in which both the a-wave amplitude and the b-wave amplitude are zero; The average calculation module is used to read the cleaned data, group them according to frequency and light intensity, and calculate the average value of each group of data; A file merging module, used to merge the average value with the data after deleting invalid data rows; A file format conversion module, used to convert the merged file into a specified format; The standard deviation calculation module is used to calculate the standard deviation of data.

[0009] Furthermore, the model training module includes: A forward ensemble model training module is used to identify and filter outliers in the data using an isolation forest algorithm, perform regression predictions by stacking random forest and multilayer perceptron neural network models, and use a custom accuracy calculation method to evaluate the prediction results, forming a model that predicts parameters in the electroretinogram based on the light frequency and light intensity of the stimulus light; The forward MLP model training module is used to detect and remove outliers in the training data using the isolation forest algorithm, build a pipeline containing two standard scalers and a multi-layer perceptron neural network regressor, and make predictions on the test set, calculate the prediction accuracy, and form a model that predicts the parameters in the electroretinogram according to the light frequency and light intensity of the stimulus light; The forward random forest model training module is used to use the isolation forest algorithm to identify and filter outliers in the data, and use grid search CV to optimize the hyperparameters of the polynomial features and the random forest regression model, make predictions on the test set, and calculate the accuracy, forming a model that predicts the parameters in the electroretinogram based on the light frequency and light intensity of the stimulus light; The reverse MLP model training module is used to use the isolation forest algorithm to identify and filter outliers in the data, and then establish a multi-layer perception regression model to form a model for predicting the light frequency and light intensity of the stimulus light based on various parameters of the electroretinogram.

[0010] Furthermore, the model training module also includes: The painting model training module is used to preprocess the features using Yeo-Johnson transformation and normalization, and train using a multi-output gradient boosting regression model to obtain a painting model that generates a waveform diagram based on the predicted retinal response parameters.

[0011] Furthermore, the feature prediction module includes: A graphical user interface module, used for receiving user instructions in a human-computer interaction manner; The retinal response reverse prediction module based on the MLP model is used to receive specific retinal response parameters, including latency, duration, amplitude, peak value of a wave, duration, amplitude, peak value of b wave, and predict through the pre-trained reverse MLP model to obtain the intensity and frequency of the stimulus light; The retinal response forward prediction program module based on the RF-MLP model is used to receive the frequency and intensity of the stimulus light, and predict the retinal response parameters through the pre-trained forward integration model; The retinal response forward prediction program module based on the random forest model is used to receive the frequency and intensity of the stimulus light, and predict the retinal response parameters through the pre-trained forward integration model; The retinal response forward prediction program module based on the MLP model is used to receive the frequency and intensity of the stimulus light, and predict it through the pre-trained forward MLP model to obtain the retinal response parameters.

[0012] Furthermore, the feature prediction module includes: The retinal response waveform reconstruction module is used to receive the frequency and intensity of the stimulus light, select the specified training model according to the user's instructions to obtain the retinal response parameters, and call the painting model to generate the retinal response waveform image.

[0013] Furthermore, the feature prediction module includes: The light stimulation reconstruction module is used to receive retinal response parameters, obtain the frequency and intensity of the stimulation light according to the retinal response reverse prediction module based on the MLP model, and generate a light stimulation restoration video according to the frequency and intensity of the stimulation light.

[0014] By adopting the above technical solution, the electroretinogram data is first extracted from the local data file through the data extraction module, and the relevant waveform parameters are calculated and sorted according to the electroretinogram data. Then, the light intensity information is added to the data set and the data is cleaned. Then, the machine learning-based model is trained according to the data. Then, the user instructions are received according to the graphical user interface, and the retinal response is forward or reversely predicted based on the user input data and the trained machine learning model, which speeds up the processing of ERG data and improves efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, among which:

[0016] Figure 1 A system block diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0018] Reference Figure 1 , an embodiment of the present invention provides a retinal response prediction system based on machine learning, comprising: The data extraction module is used to extract electroretinogram (ERG) data from the local data file, calculate relevant waveform parameters based on the ERG data, and sort the relevant waveform parameters to obtain ERG analysis results classified by frequency.

[0019] The data extraction module includes: The ERG information extraction module is used to extract electroretinogram (ERG) data from local data files, calculate the latency, duration, amplitude, peak value of the a wave, duration, amplitude, peak value of the b wave, save the above parameters and generate a parameter file.

[0020] Specifically, the preferred local data file format of this embodiment is a data file in HDF5 format, and the parameter file is also a file in HDF5 format.

[0021] The format conversion module is used to read the parameter file and convert the format of the parameter file.

[0022] Specifically, the format conversion module reads the data in the HDF5 file and converts it into a Pandas DataFrame object. The DataFrame object is then exported and saved as an Excel file for further data processing and analysis.

[0023] The frequency segmentation module is used to segment the data in the parameter file after format conversion according to frequency.

[0024] Specifically, the frequency segmentation module segments the columns in the Excel file containing all frequency data according to specific ranges, and saves the data of each frequency range into a separate Excel file, such as 1HZ.xlsx, 2HZ.xlsx, etc., for subsequent processing.

[0025] The feature engineering module is used to merge the ERG analysis results at different frequencies, add light intensity information to the merged data set, and clean the data.

[0026] Among them, the feature engineering module includes: Add a light intensity data module to supplement the light intensity information in the data.

[0027] As described above, the light intensity information in the experimental data is supplemented to facilitate subsequent learning based on the data. The light intensity information is supplemented by technicians in this field based on actual data during the experiment, and will not be repeated here.

[0028] The module for deleting zero-value data is used to delete invalid data rows in which both the a-wave amplitude and the b-wave amplitude are zero.

[0029] In the above, the invalid data rows whose a-wave amplitude and b-wave amplitude are both zero are deleted to eliminate the invalid data, reduce data redundancy and improve data quality.

[0030] The average calculation module is used to read the cleaned data, group them according to frequency and light intensity, and calculate the average value of each group of data; A file merging module, used to merge the average value with the data after deleting invalid data rows; The file format conversion module is used to convert the merged file into a specified format.

[0031] The above-mentioned method converts the merged Excel file into a CSV format file to improve the accessibility and compatibility of the data.

[0032] The standard deviation calculation module is used to calculate the standard deviation of data.

[0033] The model training module is used to detect and remove outliers in the data using the isolation forest algorithm, and to train the forward prediction model and the reverse prediction model based on the data by using different regression algorithms.

[0034] The model training module includes: The forward ensemble model training module is used to identify and filter outliers in the data using the isolation forest algorithm, perform regression prediction by stacking random forest and multilayer perceptron neural network models, and use a customized accuracy calculation method to evaluate the prediction results, forming a model that predicts the parameters in the electroretinogram based on the light frequency and light intensity of the stimulus light.

[0035] The accuracy calculation method described above is set by those skilled in the art and will not be described in detail here.

[0036] The forward MLP model training module is used to detect and remove outliers in the training data using the isolation forest algorithm, build a pipeline containing two standard scalers and a multi-layer perceptron neural network regressor, and make predictions on the test set, calculate the prediction accuracy, and form a model that predicts the parameters in the electroretinogram according to the light frequency and light intensity of the stimulus light; The forward random forest model training module is used to use the isolation forest algorithm to identify and filter outliers in the data, and use grid search CV to optimize the hyperparameters of the polynomial features and the random forest regression model, make predictions on the test set, and calculate the accuracy, forming a model that predicts the parameters in the electroretinogram based on the light frequency and light intensity of the stimulus light; The reverse MLP model training module is used to use the isolation forest algorithm to identify and filter outliers in the data, and then establish a multi-layer perception regression model to form a model for predicting the light frequency and light intensity of the stimulus light based on various parameters of the electroretinogram.

[0037] The painting model training module is used to preprocess the features using Yeo-Johnson transformation and normalization, and train using a multi-output gradient boosting regression model to obtain a painting model that generates a waveform diagram based on the predicted retinal response parameters.

[0038] The specific model training method mentioned above is a method well known to those skilled in the art and will not be described in detail here.

[0039] The feature prediction module is used to provide interactive operations through a graphical user interface (GUI), obtain data files according to user instructions, and select a pre-trained machine learning model according to the instructions to perform forward or reverse prediction of retinal response.

[0040] Among them, the feature prediction module includes: The graphical user interface module is used to receive user instructions in a human-computer interaction manner.

[0041] Specifically, the system provides interactive operations through a graphical user interface (GUI), allowing users to upload data files in Excel or HDF5 format, select pre-trained machine learning models, and perform forward and reverse predictions of retinal responses.

[0042] The retinal response reverse prediction module based on the MLP model is used to receive specific retinal response parameters, including latency, duration, amplitude, and peak value of the a wave, and duration, amplitude, and peak value of the b wave, and predict them through the pre-trained reverse MLP model to obtain the intensity and frequency of the stimulus light.

[0043] When the prediction module obtains the retinal response parameters, the parameters can be manually input through the input box, or obtained by reading an Excel data file containing the retinal response parameters.

[0044] The retinal response forward prediction program module based on the RF-MLP model is used to receive the frequency and intensity of the stimulus light, and predict it through the pre-trained forward integration model to obtain the retinal response parameters.

[0045] The retinal response forward prediction program module based on the random forest model is used to receive the frequency and intensity of the stimulus light, and predicts through the pre-trained forward integration model to obtain the retinal response parameters.

[0046] The retinal response forward prediction program module based on the MLP model is used to receive the frequency and intensity of the stimulus light, and predict it through the pre-trained forward MLP model to obtain the retinal response parameters.

[0047] As mentioned above, each module can select a local stimulation mode and a global stimulation mode according to actual needs of the user, and the mode setting is set by technical personnel in this field according to actual needs.

[0048] The retinal response waveform reconstruction module is used to receive the frequency and intensity of the stimulus light, select the specified training model according to the user's instructions to obtain the retinal response parameters, and call the painting model to generate the retinal response waveform image.

[0049] As mentioned above, the frequency and light intensity of the stimulation light can be manually input by a person skilled in the art, or can be obtained by reading an Excel file pre-stored with light frequency and light intensity data.

[0050] The light stimulation reconstruction module is used to receive retinal response parameters, obtain the frequency and intensity of the stimulation light according to the retinal response reverse prediction module based on the MLP model, and generate a light stimulation restoration video according to the frequency and intensity of the stimulation light.

[0051] As mentioned above, the retinal reaction parameters can be manually input by a person skilled in the art, or can be obtained by reading an Excel file pre-stored with retinal reaction parameter data.

[0052] An embodiment of the present invention provides a retinal response prediction system based on machine learning. First, electroretinogram data is extracted from a local data file through a data extraction module, and relevant waveform parameters are calculated and sorted according to the electroretinogram data. Then, light intensity information is added to the data set and the data is cleaned. Then, a machine learning-based model is trained according to the data. Then, user instructions are received according to a graphical user interface, and retinal responses are forward or reversely predicted based on the user input data and the trained machine learning model, thereby speeding up the processing of ERG data and improving efficiency.

[0053] Those skilled in the art will appreciate that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined.

[0054] For the method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0055] It can be known from the description of the above implementation modes that those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be essentially or partly contributed to the prior art in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which can be a personal computer, a server or a network device, etc.) to execute the methods described in the various implementation modes of the present application or certain parts of the implementation modes.

[0056] Finally, 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A retinal response prediction system based on machine learning, characterized in that: include: The data extraction module is used to extract electroretinogram (ERG) data from the local data file, calculate relevant waveform parameters based on the ERG data, and sort the relevant waveform parameters to obtain ERG analysis results classified by frequency; Feature engineering module, which is used to merge the ERG analysis results at different frequencies, add light intensity information to the merged data set, and clean the data; The model training module is used to detect and remove outliers in the data using the isolation forest algorithm, and to train the forward prediction model and the reverse prediction model based on the data by using different regression algorithms; The feature prediction module is used to provide interactive operations through a graphical user interface (GUI), obtain data files according to user instructions, and select a pre-trained machine learning model according to the instructions to perform forward or reverse prediction of retinal response.

2. The system according to claim 1, characterized in that The data extraction module comprises: ERG information extraction module, used to extract electroretinogram (ERG) data from local data files, calculate latency, duration, amplitude, peak value of a wave, duration, amplitude, peak value of b wave, save the above parameters and generate parameter files; A format conversion module is used to read parameter files and convert the format of the parameter files; The frequency segmentation module is used to segment the data in the parameter file after format conversion according to frequency.

3. The system according to claim 2, characterized in that The feature engineering module includes: Added light intensity data module to supplement the light intensity information in the data; The zero value data deletion module is used to delete invalid data rows in which both the a-wave amplitude and the b-wave amplitude are zero; The average calculation module is used to read the cleaned data, group them according to frequency and light intensity, and calculate the average value of each group of data; A file merging module, used to merge the average value with the data after deleting invalid data rows; A file format conversion module, used to convert the merged file into a specified format; The standard deviation calculation module is used to calculate the standard deviation of data.

4. The system according to claim 1, characterized in that The model training module includes: A forward ensemble model training module is used to identify and filter outliers in the data using an isolation forest algorithm, perform regression predictions by stacking random forest and multilayer perceptron neural network models, and use a custom accuracy calculation method to evaluate the prediction results, forming a model that predicts parameters in the electroretinogram based on the light frequency and light intensity of the stimulus light; The forward MLP model training module is used to detect and remove outliers in the training data using the isolation forest algorithm, build a pipeline containing two standard scalers and a multi-layer perceptron neural network regressor, and make predictions on the test set, calculate the prediction accuracy, and form a model that predicts the parameters in the electroretinogram according to the light frequency and light intensity of the stimulus light; The forward random forest model training module is used to use the isolation forest algorithm to identify and filter outliers in the data, and use grid search CV to optimize the hyperparameters of the polynomial features and the random forest regression model, make predictions on the test set, and calculate the accuracy, forming a model that predicts the parameters in the electroretinogram based on the light frequency and light intensity of the stimulus light; The reverse MLP model training module is used to use the isolation forest algorithm to identify and filter outliers in the data, and then establish a multi-layer perception regression model to form a model for predicting the light frequency and light intensity of the stimulus light based on various parameters of the electroretinogram.

5. The system according to claim 4, characterized in that The model training module also includes: The painting model training module is used to preprocess the features using Yeo-Johnson transformation and normalization, and train using a multi-output gradient boosting regression model to obtain a painting model that generates a waveform diagram based on the predicted retinal response parameters.

6. The system according to claim 1, characterized in that The feature prediction module comprises: A graphical user interface module, used for receiving user instructions in a human-computer interaction manner; The retinal response reverse prediction module based on the MLP model is used to receive specific retinal response parameters, including latency, duration, amplitude, peak value of a wave, duration, amplitude, peak value of b wave, and predict through the pre-trained reverse MLP model to obtain the intensity and frequency of the stimulus light; The retinal response forward prediction program module based on the RF-MLP model is used to receive the frequency and intensity of the stimulus light, and predict the retinal response parameters through the pre-trained forward integration model; The retinal response forward prediction program module based on the random forest model is used to receive the frequency and intensity of the stimulus light, and predict the retinal response parameters through the pre-trained forward integration model; The retinal response forward prediction program module based on the MLP model is used to receive the frequency and intensity of the stimulus light, and predict the retinal response parameters through the pre-trained forward MLP model.

7. The system according to claim 5, characterized in that The feature prediction module comprises: The retinal response waveform reconstruction module is used to receive the frequency and intensity of the stimulus light, select the specified training model according to the user's instructions to obtain the retinal response parameters, and call the painting model to generate the retinal response waveform image.

8. The system according to claim 6, characterized in that The feature prediction module comprises: The light stimulation reconstruction module is used to receive retinal response parameters, obtain the frequency and intensity of the stimulation light according to the retinal response reverse prediction module based on the MLP model, and generate a light stimulation restoration video according to the frequency and intensity of the stimulation light.

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