CN112869744A - Schizophrenia auxiliary diagnosis method, system and storage medium - Google Patents
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Abstract
The invention provides an auxiliary diagnosis method, a system and a storage medium for schizophrenia, which are based on eye movement data collected by a free view experiment, have the characteristics of simplicity and easiness in operation and small manual workload, and provide five interpretable eye movement characteristics based on eye movement tracking data, successfully introduce a machine learning method into the field of schizophrenia diagnosis, and provide objective quantitative indexes and prediction results based on a classifier for doctors to diagnose schizophrenia.
Description
Technical Field
The invention relates to the technical field of psychosis diagnosis, in particular to a method, a system and a storage medium for auxiliary diagnosis of schizophrenia.
Background
Schizophrenia is a clinical syndrome of extremely complex polygene and genetic character, and has the characteristics of early onset age, long course of disease, serious functional damage and great social hazard. At present, the diagnosis of the disease is mainly judged by the clinical inquiry of doctors, and the disease lacks of good biological indexes, so that the conditions of high misdiagnosis rate, delayed treatment course of the disease and the like exist.
In recent years, eye tracking technology based on iris reflection and video image capture has matured, and the accuracy and convenience of acquisition of eye movement data have gradually increased. Brain mechanism studies of eye movements indicate that the complete eye movement process requires both sensory-motor transduction and cognitive function guidance. The sensorimotor transformation converts visual signals received by the retina into eye movements, and higher eye movement processes, such as inhibitory eye movement control, prediction of target movement velocity, etc., require the brain to initiate cognitive functions for regulation. Thus, the eyeball activity index can be used as a behavioral measurement index for exploring relevant high-level cognitive processes in the cerebral cortex and the subcortical layer of the human being.
Disclosure of Invention
The invention aims to provide a series of eye movement indexes with obvious abnormality aiming at the abnormal eye movement behavior pattern of the existing schizophrenia, and the machine learning method is utilized to predict the risk of the schizophrenia to be tested by taking the indexes as characteristics.
In order to achieve the above object, one aspect of the present invention provides a method for aiding diagnosis of schizophrenia, comprising the steps of:
s1, carrying out free view experiments on volunteers and patients, collecting eye movement tracking data, and establishing a database;
s2, establishing a sample feature matrix based on the feature calculation formula;
s3, training a classification model of the support vector machine;
s4, performing a free view experiment on the testee, collecting eye movement tracking data, and calculating a sample characteristic matrix;
and S5, inputting the sample feature matrix of the testee into the classification model trained in the step S3, calculating the risk of illness according to the classification result, and outputting a diagnosis conclusion.
Further, the step S1 further includes:
s101, recording eye movement tracking data of a subject by adopting eye movement tracking equipment, wherein the eye movement tracking data comprises an eyeball fixation position and a saccadic path;
s102, establishing a relational database by taking the serial number of the subject and the picture name as main keys, wherein each record in the database as a sample comprises eye tracking data corresponding to the main keys.
Further, the step S2 further includes:
s201, calculating average apparent jump speed
The calculation process is as follows:
wherein n represents the number of saccades, AmpiThe saccade intensity, t, representing the ith saccadeiThe duration of the ith jump vision is represented, the speed of the ith jump vision is obtained by dividing the duration of the ith jump vision by the duration of the ith jump vision, and the average value of the speeds of the n jump vision is obtained
Further, the step S2 further includes:
s202, calculating the dynamic range of the pupil, wherein the calculation process is as follows:
wherein d ismaxAnd dminRespectively represent the maximum pupil diameter andthe diameter of the smallest of the pupils is,
indicating the mean diameter of the pupil.
Further, the step S2 further includes:
s203, calculating the maximum mean ratio MMRPS of the pupil, wherein the calculation process comprises the following steps:
further, the step S2 further includes:
s204, calculating the viewpoint skewness SF, wherein the calculation process is as follows:
wherein x is0And y0Respectively the abscissa and ordinate, x, of the geometric centre of the imageiAnd yiThe horizontal and vertical coordinates of the ith fixation point are shown, and n is the number of fixation points.
Further, the step S2 further includes:
s205, calculating the effective observation time, wherein the calculation process is as follows:
wherein n and m respectively represent the number of fixation points and the number of saccades,
indicating the gaze duration of the ith gaze point,
the duration of the jump vision of the jth jump vision is represented, the calculation result is effective observation time which is marked as tvalid。
Further, the step S2 further includes:
and S206, combining the five calculated characteristic values with the corresponding test subject serial numbers and the corresponding picture names to form a sample characteristic matrix, wherein the dimension of the sample characteristic matrix is Nx 5, and N is the total number of samples.
Further, the step S3 further includes:
s301, respectively taking samples of a patient and a volunteer in a database as a positive sample and a negative sample, and training a support vector machine model;
s302, selecting a Gaussian radial basis function kernel, mapping the low-dimensional features to a high-dimensional feature space, and calculating the distance between two samples;
s303, training the support vector machine by adopting a sequential minimization optimization method, wherein a loss function of the support vector machine is set as an average error function.
Further, the distance between the samples is calculated as follows:
where x and x' represent the feature vectors of the two samples, respectively, and σ is the scaling factor of the kernel function.
Further, in step S5, the calculating process of the diagnosis result includes:
s501, calculating a prediction result according to the following formula:
wherein, p is defined as the number of positive samples divided by the total number of samples in the classification result, and Th is a preset threshold value;
s502, calculating confidence:
Conf(p)＝2*[sigmoid(10*|p-0.5|)-0.5]
wherein sigmoid is an activation function and is defined as:
in another aspect, the present invention also provides a diagnosis system for schizophrenia, comprising:
the data acquisition module is used for carrying out free view experiments on volunteers and patients, collecting eye movement tracking data and establishing a database;
the characteristic extraction module is used for establishing a sample characteristic matrix based on a characteristic calculation formula;
the model building module is used for training a support vector machine classification model;
and the diagnosis output module is used for inputting the sample characteristic matrix of the testee into the trained classification model, calculating the risk of illness according to the classification result and outputting a diagnosis conclusion.
In another aspect, the invention also provides a computer-readable storage medium, in which a computer program is stored, the computer program comprising program instructions which, when executed by a processor, cause the processor to carry out the steps of the method as claimed above.
The invention has the following beneficial effects:
the invention collects eye movement data based on free view experiments, has the characteristics of simple and easy operation and small manual workload, provides five interpretable eye movement characteristics based on eye movement tracking data, successfully introduces a machine learning method into the field of schizophrenia diagnosis, and provides objective quantitative indexes and prediction results based on classifiers for doctors to diagnose schizophrenia.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flowchart of a method for aiding diagnosis of schizophrenia in accordance with the present invention.
FIG. 2 is a diagram illustrating a confidence function of a predicted result according to the present invention.
Fig. 3 is a system block diagram of a diagnosis system for schizophrenia.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The eyeball activity index can be used as a behavioral measurement index for exploring relevant high-level cognitive processes of the cerebral cortex and the subcortical space of the human being. For schizophrenic patients with cognitive dysfunction, the ocular movement abnormality value relative to normal can be used as a biological index for characterizing the mental inhibition state of the schizophrenic patients. Therefore, the eye movement difference of the schizophrenia patient can be detected through a specific experimental paradigm, a series of eye movement indexes with remarkable abnormality are provided, and the indexes are used as characteristics to predict the risk of the schizophrenia to be tested by using a machine learning method.
In order to better understand the above technical solutions, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Fig. 1 is a flowchart of a method for auxiliary diagnosis of schizophrenia in accordance with an embodiment of the present invention. As shown in fig. 1, a method for auxiliary diagnosis of schizophrenia according to an embodiment of the present invention includes the following steps:
and S1, carrying out free-view experiments on the volunteers and the patients, collecting eye tracking data, and establishing a database.
Specifically, during the experiment, each volunteer or patient (collectively referred to as a subject) is asked to sit in front of the experimental device, to freely watch the pictures appearing on the screen, and meanwhile, the professional eye movement tracking device is used for recording the information such as the eyeball fixation position and the saccadic path.
After the experiment is finished, the eye movement tracking data is processed, a relational database is established by taking the tested serial number-picture name as a main key, namely, each tested serial number-picture name and the corresponding eye movement tracking data serve as a sample.
And S2, establishing a sample feature matrix based on the feature calculation formula.
Specifically, the step S2 further includes:
s201, calculating average apparent jump speed
The calculation process is as follows:
wherein n represents the number of saccades, AmpiThe saccade intensity, t, representing the ith saccadeiThe duration of the ith jump vision is represented, the speed of the ith jump vision is obtained by dividing the duration of the ith jump vision by the duration of the ith jump vision, and the average value of the speeds of the n jump vision is obtained
S202, calculating the dynamic range of the pupil, wherein the calculation process is as follows:
wherein d ismaxAnd dminRespectively representing a maximum pupil diameter and a minimum pupil diameter,
indicating the mean diameter of the pupil.
S203, calculating the maximum mean ratio MMRPS of the pupil, wherein the calculation process comprises the following steps:
s204, calculating the viewpoint skewness SF, wherein the calculation process is as follows:
wherein x is0And y0Respectively the abscissa and ordinate, x, of the geometric centre of the imageiAnd yiThe horizontal and vertical coordinates of the ith fixation point are shown, and n is the number of fixation points.
S205, calculating the effective observation time, wherein the calculation process is as follows:
wherein n and m respectively represent the number of fixation points and the number of saccades,
indicating the gaze duration of the ith gaze point,
the duration of the jump vision of the jth jump vision is represented, the calculation result is effective observation time which is marked as tvalid。
And S206, combining the five calculated characteristic values with the corresponding test subject serial numbers and the corresponding picture names to form a sample characteristic matrix, wherein the dimension of the sample characteristic matrix is Nx 5, and N is the total number of samples.
And S3, training a classification model of the support vector machine.
Specifically, step S3 further includes:
s301, taking samples of the patient and the volunteer in the database as a positive sample and a negative sample respectively, and training a support vector machine model.
S302, selecting a Gaussian radial basis function kernel, mapping the low-dimensional features to a high-dimensional feature space, and calculating the distance between two samples.
Wherein, the calculation process of the distance of the sample is as follows:
where x and x' represent the feature vectors of the two samples, respectively, and σ is the scaling factor of the kernel function.
S303, training the support vector machine by adopting a sequential minimization optimization method, wherein a loss function of the support vector machine is set as an average error function.
And S4, performing free view experiments on the testee, collecting eye movement tracking data, and calculating a sample characteristic matrix.
And S5, inputting the sample feature matrix of the testee into the classification model trained in the step S3, calculating the risk of illness according to the classification result, and outputting a diagnosis conclusion.
Specifically, in step S5, the calculating process of the diagnosis result includes:
s501, calculating a prediction result according to the following formula:
wherein, p is defined as the number of positive samples classified in the classification result divided by the total number of samples, and Th is a preset threshold value.
S502, calculating a confidence, where fig. 2 is a schematic diagram of a confidence function of the prediction result of the present invention, and as shown in fig. 2, the confidence calculation function is:
Conf(p)＝2*[sigmoid(10*|p-0.5|)-0.5]
wherein sigmoid is an activation function and is defined as:
fig. 3 is a system block diagram of a diagnosis system for schizophrenia. As shown in fig. 3, an auxiliary diagnosis system for schizophrenia of the present invention comprises:
and the data acquisition module 1 is used for carrying out free view experiments on volunteers and patients, collecting eye movement tracking data and establishing a database.
And the characteristic extraction module 2 is used for establishing a sample characteristic matrix based on a characteristic calculation formula.
And the model establishing module 3 trains a classification model of the support vector machine.
And the diagnosis output module 4 is used for inputting the sample characteristic matrix of the testee into the trained classification model, calculating the risk of illness according to the classification result and outputting a diagnosis conclusion.
In another embodiment of the invention also provides a computer readable storage medium having stored thereon a computer program comprising program instructions which, when executed by a processor, cause the processor to carry out the steps of the above-mentioned method.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or terminal. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (13)
1. An auxiliary diagnosis method for schizophrenia is characterized by comprising the following specific steps:
s1, carrying out free view experiments on volunteers and patients, collecting eye movement tracking data, and establishing a database;
s2, establishing a sample feature matrix based on the feature calculation formula;
s3, training a classification model of the support vector machine;
s4, performing a free view experiment on the testee, collecting eye movement tracking data, and calculating a sample characteristic matrix;
and S5, inputting the sample feature matrix of the testee into the classification model trained in the step S3, calculating the risk of illness according to the classification result, and outputting a diagnosis conclusion.
2. The method for aiding in the diagnosis of schizophrenia according to claim 1, wherein the step S1 further comprises:
s101, recording eye movement tracking data of a subject by adopting eye movement tracking equipment, wherein the eye movement tracking data comprises an eyeball fixation position and a saccadic path;
s102, establishing a relational database by taking the serial number of the subject and the picture name as main keys, wherein each record in the database as a sample comprises eye tracking data corresponding to the main keys.
3. The method for aiding diagnosis of schizophrenia according to claim 2, wherein the step S2 further comprises:
s201, calculating average apparent jump speed
The calculation process is as follows:
wherein n represents the number of saccades, AmpiThe saccade intensity, t, representing the ith saccadeiThe duration of the ith jump vision is represented, the speed of the ith jump vision is obtained by dividing the duration of the ith jump vision by the duration of the ith jump vision, and the average value of the speeds of the n jump vision is obtained
4. The method for aiding diagnosis of schizophrenia according to claim 3, wherein the step S2 further comprises:
s202, calculating the dynamic range of the pupil, wherein the calculation process is as follows:
wherein d ismaxAnd dminRespectively representing a maximum pupil diameter and a minimum pupil diameter,
indicating the mean diameter of the pupil.
5. The method for aided diagnosis of schizophrenia according to claim 4, wherein the step S2 further comprises:
s203, calculating the maximum mean ratio MMRPS of the pupil, wherein the calculation process comprises the following steps:
6. the method for aiding diagnosis of schizophrenia according to claim 5, wherein the step S2 further comprises:
s204, calculating the viewpoint skewness SF, wherein the calculation process is as follows:
wherein x is0And y0Respectively the abscissa and ordinate, x, of the geometric centre of the imageiAnd yiThe horizontal and vertical coordinates of the ith fixation point are shown, and n is the number of fixation points.
7. The method for aiding diagnosis of schizophrenia according to claim 5, wherein the step S2 further comprises:
s205, calculating the effective observation time, wherein the calculation process is as follows:
wherein n and m respectively represent the number of fixation points and the number of saccades,
indicating the gaze duration of the ith gaze point,
the duration of the jump vision of the jth jump vision is represented, the calculation result is effective observation time which is marked as tvalid。
8. The method for aiding diagnosis of schizophrenia according to any one of claims 3 to 7, wherein the step S2 further comprises:
and S206, combining the five calculated characteristic values with the corresponding test subject serial numbers and the corresponding picture names to form a sample characteristic matrix, wherein the dimension of the sample characteristic matrix is Nx 5, and N is the total number of samples.
9. The method for aiding in the diagnosis of schizophrenia according to claim 1, wherein the step S3 further comprises:
s301, respectively taking samples of a patient and a volunteer in a database as a positive sample and a negative sample, and training a support vector machine model;
s302, selecting a Gaussian radial basis function kernel, mapping the low-dimensional features to a high-dimensional feature space, and calculating the distance between two samples;
s303, training the support vector machine by adopting a sequential minimization optimization method, wherein a loss function of the support vector machine is set as an average error function.
10. The aided diagnosis method for schizophrenia according to claim 9, wherein the distance between samples is calculated by:
where x and x' represent the feature vectors of the two samples, respectively, and σ is the scaling factor of the kernel function.
11. The method for aided diagnosis of schizophrenia according to claim 1, wherein in step S5, the calculation of diagnosis result comprises:
s501, calculating a prediction result according to the following formula:
wherein, p is defined as the number of positive samples divided by the total number of samples in the classification result, and Th is a preset threshold value;
s502, calculating confidence:
Conf(p)＝2*[sigmoid(10*|p-0.5|)-0.5]
wherein sigmoid is an activation function and is defined as:
12. an auxiliary diagnostic system for schizophrenia, comprising:
the data acquisition module is used for carrying out free view experiments on volunteers and patients, collecting eye movement tracking data and establishing a database;
the characteristic extraction module is used for establishing a sample characteristic matrix based on a characteristic calculation formula;
the model building module is used for training a support vector machine classification model;
and the diagnosis output module is used for inputting the sample characteristic matrix of the testee into the trained classification model, calculating the risk of illness according to the classification result and outputting a diagnosis conclusion.
13. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program comprising program instructions which, when executed by a processor, cause the processor to carry out the steps of the method according to any one of claims 1 to 11.
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| Publication number | Priority date | Publication date | Assignee | Title |
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