CN116473557B - Detection method suitable for dynamic characteristic index of schizophrenic patient - Google Patents
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Abstract
The invention relates to a detection method suitable for dynamic characteristic indexes of patients with schizophrenia, which relates to the technical field of dynamic characteristic index detection, and comprises the following steps: in a dark room, a subject continuously looks at a cross shape presented in the center of a display screen; after the cross shape disappears, the subject observes the visual stimulation pattern presented at the center of the display screen, judges the drifting direction of the visual stimulation pattern, and records and counts the judging result of the subject and the presentation time of the visual stimulation pattern; the visual stimulus pattern is a randomly occurring small or large edge-blurred sinusoidal grating; judging 160 times altogether; and carrying out data processing according to the judging result of the small visual stimulation pattern and the presentation time of the visual stimulation pattern by the subject to obtain the dynamic characteristic index of the schizophrenic patient. The invention provides a method for measuring dynamic characteristics of a schizophrenic patient in a visual movement perception paradigm, which fills the blank of the prior art.
Description
Technical Field
The application relates to the technical field of dynamic characteristic index detection, in particular to a detection method suitable for dynamic characteristic indexes of schizophrenic patients.
Background
Schizophrenia is a serious mental disorder, the main symptoms of which are positive symptoms, negative symptoms, cognitive dysfunction and the like. The disease usually occurs in late teenagers or early adulthood, has a significant disability rate, morbidity and places a serious economic burden on the patient and his home.
Currently, existing visual perception measures for schizophrenic patients include contour integration capability, visual perception capability, peripheral inhibition strength and the like, but no method for obtaining dynamic characteristic indexes of schizophrenic patients in a visual movement perception paradigm has been reported so far.
Disclosure of Invention
The application provides a detection method suitable for dynamic characteristic indexes of schizophrenic patients, which can calculate 5 indexes of test dynamic average value (Temporal Dynamic Mean, recorded as TD-Mean), test dynamic standard deviation (Temporal Dynamic Standard Deviation, recorded as TD-SD), test dynamic variation coefficient (Temporal Dynamic Coefficient of Variation, recorded as TD-CV), sample Entropy (recorded as Sample Entropy) and Lempel-Ziv Complexity (recorded as LZC), and fills the gap of the detection of the visual perception dynamic characteristic indexes of schizophrenic patients in the prior art.
The application provides a detection method suitable for dynamic characteristic indexes of a schizophrenic patient, wherein the dynamic characteristic indexes of the schizophrenic patient are dynamic characteristic indexes of the schizophrenic patient in a visual movement perception model, and the dynamic characteristic indexes comprise TD-Mean, TD-SD, TD-CV, sampEn and LZC; the detection method comprises the following steps:
in a dark room, a subject continuously looks at a cross shape presented in the center of a display screen;
after the cross shape disappears, the subject observes the visual stimulation pattern presented at the center of the display screen, judges the drifting direction of the visual stimulation pattern, and records and counts the judging result of the subject and the presentation time of the visual stimulation pattern; the visual stimulus pattern is a randomly occurring small or large sinusoidal grating with blurred edges, which occurs 160 times in total;
and carrying out data processing according to the judging result of the small visual stimulation pattern and the presentation time of the visual stimulation pattern by the subject to obtain the dynamic characteristic index of the schizophrenic patient.
Further, the parameters of the display screen include: the screen is corrected by linearization, and the background brightness value of the screen except for the visual stimulus pattern is 56cd/m 2 。
Further, the head of the subject and the display screen are maintained on the same horizontal line, and the distance between the eyes of the subject and the display screen is 47 cm.
Further, the visual stimulus pattern is a sinusoidal grating pattern, and parameters of the sinusoidal grating pattern include: the contrast is 50%, the spatial frequency is 1cycle/°, the motion direction is left or right, the diameter size of the small visual stimulation pattern is 2 degrees, the diameter size of the large visual stimulation pattern is 10 degrees, the edge of the sinusoidal grating pattern is subjected to fuzzy processing by adopting a Gaussian function, and the fuzzy width is 30%.
Further, the visual stimulus pattern was run using the psychophysical toolbox psychatolbox of MATLAB software.
Further, the time for the subject to continue looking at the cross presented in the center of the display screen is 500 milliseconds.
Further, the duration of the visual stimulus pattern presentation is adaptively adjusted using a 3-down-1-up-step method.
Further, the subject judges the drift direction of the visual stimulus pattern through the keyboard keys, and if the judgment is wrong, a sound prompt of 'dripping' is heard, and if the judgment is correct, no sound is generated.
Further, the step of obtaining dynamic characteristic indexes TD-Mean, TD-SD and TD-CV of the schizophrenic patient by performing data processing according to the judgment result of the small visual stimulus pattern and the presentation time of the visual stimulus pattern by the subject includes:
calculating the difference value of the time sequence value of the last test time minus the time sequence value of the previous test time by the time sequence of the presentation of all the tests of the subject aiming at the small visual stimulus pattern, and discarding the difference value obtained by the first test time and the second test time to obtain a group of difference values;
and according to the difference, calculating the average value, standard deviation and variation coefficient of the sequence to be used as TD-Mean, TD-SD and TD-CV respectively.
For a time series of presentation of all trials of the subject for a small visual stimulus pattern, the SampEn is calculated according to equation one:
the formula one:where m is expressed as a reconstructed vector length (1), r is expressed as an allowable deviation (0.2), N is expressed as a trial number (80), a is expressed as a ratio of the number of vector pairs to the total number of vector pairs within a range satisfying the allowable deviation r when the vector length is m+1, and B is expressed as a ratio of the number of vector pairs to the total number of vector pairs within a range satisfying the allowable deviation r when the vector length is m.
The LZC is calculated according to a formula II:
the formula II:where c is represented as the number of different substrings and N is represented as the number of trials (taken 80).
Compared with the prior art, the technical scheme provided by the embodiment of the application has at least the following advantages:
the embodiment of the application provides a detection method suitable for dynamic characteristic indexes of a schizophrenic patient, provides a method for measuring dynamic characteristics of a schizophrenic patient in a visual movement perception paradigm, can calculate 5 indexes of TD-Mean, TD-SD, TD-CV, sampEn and LZC, can calculate complexity and dynamic characteristics of visual perception of a subject from a very short (10-minute) paradigm, and is verified in the schizophrenic patient, thereby filling the gap in the detection of the visual perception dynamic characteristic indexes of the schizophrenic patient in the prior art.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the description of the embodiments or the prior art will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
Fig. 1 is a flow chart of a method for detecting dynamic characteristic indexes of a patient with schizophrenia according to an embodiment of the present application.
Fig. 2 is a flowchart of a visual motion sensing paradigm in an embodiment of the present application.
Fig. 3 is an example of a presentation time series of small visual stimulus patterns for schizophrenic patients and healthy subjects and a comparison of dynamic indicators TD-Mean, TD-SD, TD-CV, sampEn and LZC in an embodiment of the present application.
FIG. 4 shows the correlation between the dynamic indexes TD-CV and LZC and the score of the schizophrenia PANSS scale in the embodiment of the application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present application based on the embodiments herein.
Unless specifically indicated otherwise, the various raw materials, reagents, instruments, equipment, and the like used in this application are commercially available or may be prepared by existing methods.
The application provides a detection method suitable for dynamic characteristic indexes of a schizophrenic patient, as shown in a figure 1, wherein the dynamic characteristic indexes of the schizophrenic patient are dynamic characteristic indexes of the schizophrenic patient in a visual movement perception model, and the dynamic characteristic indexes comprise TD-Mean, TD-SD, TD-CV, sampEn and LZC; the detection method comprises the following steps:
in a dark room, a subject continuously looks at a cross shape presented in the center of a display screen;
after the cross shape disappears, the subject observes the visual stimulation pattern presented at the center of the display screen, judges the drifting direction of the visual stimulation pattern, and records and counts the judging result of the subject and the presentation time of the visual stimulation pattern; the visual stimulus pattern is a randomly occurring small or large edge-blurred sinusoidal grating;
and carrying out data processing according to the judging result of the small visual stimulation pattern and the presentation time of the visual stimulation pattern by the subject to obtain the dynamic characteristic index of the schizophrenic patient.
The embodiment of the application provides a detection method suitable for dynamic characteristic indexes of a schizophrenic patient, provides a method for measuring dynamic characteristics of the schizophrenic patient in a visual movement perception paradigm, can calculate 5 indexes of TD-Mean, TD-SD, TD-CV, sampEn and LZC, can calculate complexity and dynamic characteristics of visual perception of a subject from a paradigm which takes a very short time (10 minutes), is verified in the schizophrenic patient, and fills the gap of the detection of the visual perception dynamic characteristic indexes of the schizophrenic patient in the prior art.
The term "small or large edge-blurred sinusoidal grating" as used herein is understood to mean two edge-blurred sinusoidal gratings, one of which has a larger pattern size (denoted as large visual stimulus pattern) and the other of which has a smaller pattern size (denoted as small visual stimulus pattern).
In some embodiments, the parameters of the display screen include: the screen is corrected by linearization, and the background brightness value of the screen except for the visual stimulus pattern is 56cd/m 2 。
In some embodiments, the subject's head is maintained on the same horizontal line as the display screen and the subject's eyes are at a distance of 47 centimeters from the display screen.
In some specific embodiments, the visual stimulus pattern is a sinusoidal grating pattern, the parameters of the sinusoidal grating pattern comprising: the contrast is 50%, the spatial frequency is 1cycle/°, the motion direction is left or right, the diameter size of the small visual stimulation pattern is 2 degrees, the diameter size of the large visual stimulation pattern is 10 degrees, the edge of the sinusoidal grating pattern is subjected to fuzzy processing by adopting a Gaussian function, and the fuzzy width is 30%.
In some embodiments, the visual stimulus pattern is run using the psychophysical toolbox psychroox of MATLAB software.
In some embodiments, the time that the subject continues to look at the "cross" presented in the center of the display screen is 500 milliseconds.
In some embodiments, the duration of the presentation of the visual stimulus pattern is adaptively adjusted using a 3-down-1-up-staircase method. The "step-down-1-up-3" means that the presentation time of the visual stimulus pattern is shortened if 3 consecutive test runs are all judged to be correct, and the presentation time of the visual stimulus pattern is lengthened if 1 test run is judged to be incorrect.
In some embodiments, the subject determines the drift direction of the visual stimulus pattern by key presses of the keyboard, and if the determination is wrong, a "drop" audible cue is heard, and if the determination is correct, no sound is produced.
In some embodiments, the step of obtaining the dynamic characteristic index of the schizophrenic patient according to the data processing of the judgment result of the small visual stimulus pattern and the presentation time of the visual stimulus pattern by the subject includes:
calculating the difference value of the time sequence value of the last test time minus the time sequence value of the previous test time by the time sequence of the presentation of all the tests of the subject aiming at the small visual stimulus pattern, and discarding the difference value obtained by the first test time and the second test time to obtain a group of difference values;
according to the difference, calculating the average value, standard deviation and variation coefficient of the obtained sequence, respectively serving as TD-Mean, TD-SD and TD-CV, and adopting the 3 indexes to represent the dynamic variation condition of the tested person in the whole judging process.
In some embodiments, the sampenn is calculated according to equation one:
the formula one:where m is expressed as a reconstructed vector length (1), r is expressed as an allowable deviation (0.2), N is expressed as a trial number (80), a is expressed as a ratio of the number of vector pairs to the total number of vector pairs within a range satisfying the allowable deviation r when the vector length is m+1, and B is expressed as a ratio of the number of vector pairs to the total number of vector pairs within a range satisfying the allowable deviation r when the vector length is m.
The LZC is calculated according to a formula II:
the formula II:where c is represented as the number of different substrings and N is represented as the number of trials (taken 80).
The sample entropy sampenn can measure the complexity of the time sequence through the probability of the occurrence of the new mode, and can be applied to evaluating the complexity of the physiological signal time sequence, and the specific calculation process is as follows:
let the sequence of raster presentation times be x= { X 1 ，x 2 ，x 3 ，…，x N -a }; wherein N is the number of trials, and 80 is taken.
Spatially reconstructing the sequence X to form (N-m+1) m-dimensional vectors X m (i)＝[x(i)；x(i+1)；x(i+2)；…；x(i+m-1)]The method comprises the steps of carrying out a first treatment on the surface of the Wherein m is expressed as the length of the reconstructed vector, i is expressed as the sequence number of the vector, and 1 is less than or equal to i is less than or equal to (N-m+1).
Vector X m (i) And X m (j) The maximum difference in the corresponding elements is defined as the distance between the vectors, denoted d [ X ] m (i)，X m (j)]The method comprises the steps of carrying out a first treatment on the surface of the Wherein d is expressed as the distance of vector pairs, i is not less than 1 and not more than (N-m+1), j is not less than 1 and not more than (N-m+1), and i is not equal to j.
The distance between the record vectors satisfies d [ X ] m (i)，X m (j)]The number < r, denoted N m (i) The method comprises the steps of carrying out a first treatment on the surface of the Where r represents a measurement value of "similarity", i.e., an allowable deviation, and 0.2 is taken.
N is obtained m (i) And contain vector X m (i) The ratio of vector to total number (N-m), i.eWherein B is i Expressed as an inclusion vector X within a range satisfying the allowable deviation r when the vector length is m m (i) Vector pair number of (a) and vector X included m (i) Is a ratio of vector to total number.
Solving forAverage value of>Wherein B is expressed as the ratio of the number of vector pairs to the total number of vector pairs in the range satisfying the allowable deviation r when the vector length is m, and 1.ltoreq.i.ltoreq.N-m+1.
Setting the vector length to be m+1, and repeating the steps to obtainWherein A is expressed as the ratio of the number of vector pairs to the total number of vector pairs within the range satisfying the allowable deviation r when the vector length is m+1, and 1.ltoreq.i.ltoreq.N-m+1.
Defining sample entropy asWherein, N is the number of trials, 80 is taken, so N is a limited value, and the calculation of sampEn according to the formula I is finally obtained.
The LZC is a complexity algorithm for evaluating a sequence with a specific length, reflects the occurrence speed of a new mode in a time sequence, and comprises the following specific calculation processes:
let the sequence of raster presentation times be x= { X 1 ，x 2 ，x 3 ，…，x N -a }; wherein N is the number of trials, and 80 is taken.
Binarizing the sequence X with the median as a boundary to convert the sequence into a sequence P= { s containing only 0 and 1 1 ，s 2 ，s 3 ，…，s N If the value of a point is equal to or greater than the median of the time series, the point is assigned a value of 1, and conversely, a value of 0.
Then searching the new character string in the binarization sequence from left to right, wherein the new character string needs to have the characteristics of uniqueness and continuity. For the sequence P to be analyzed, it is assumed that the partial string of the sequence P is s= { S 1 ，s 2 ，s 3 ，…，s r R=2, 3, …, N-1), the next character being q=s r+1 SQ is the combination of S and Q in series, i.e. sq= { S, Q }; SQpi represents the string from which SQ prunes the last character, i.e., SQpi= { s 1 ，s 2 ，s 3 ，…，s r -a }; v (sqpi) represents a set of substrings of sqpi and c represents the number of different substrings. If Q belongs to V (sqpi), Q is not a new substring, S remains unchanged at this time, let q=s r+1 s r+2 Then the relationship between Q and V (sqpi) is analyzed continuously, and the process is repeated until q=s r+1 s r+2 …s r+i (1. Ltoreq.i. Ltoreq.N-r) does not belong to V (SQpi), where Q is a new string, let c=c+1, SQ= { S, Q } = { S 1 ，s 2 ，s 3 ，…，s r+i }，SQπ＝{s 1 ，s 2 ，s 3 ，…，s r+i-1 }。
Repeating the previous step until the last character of the sequence P is retrieved, and finally obtaining the LZC calculated according to the formula II.
The present application is further illustrated below in conjunction with specific examples. It should be understood that these examples are illustrative only of the present application and are not intended to limit the scope of the present application. The experimental procedures, which are not specified in the following examples, are generally determined according to national standards. If the corresponding national standard does not exist, the method is carried out according to the general international standard, the conventional condition or the condition recommended by the manufacturer.
Example 1
The embodiment provides a detection method suitable for dynamic characteristic indexes of a schizophrenic patient, wherein the dynamic characteristic indexes of the schizophrenic patient are dynamic characteristic indexes of the schizophrenic patient in a visual movement perception model, and the dynamic characteristic indexes comprise TD-Mean, TD-SD, TD-CV, sampEn and LZC; the detection method comprises the following steps: in a dark room, a subject continuously looks at a cross shape presented in the center of a display screen; after the cross shape disappears, the subject observes the visual stimulation pattern presented at the center of the display screen, judges the drifting direction of the visual stimulation pattern, and records and counts the judging result of the subject and the presentation time of the visual stimulation pattern; the visual stimulus pattern is a randomly occurring small or large edge-blurred sinusoidal grating; and carrying out data processing according to the judging result of the small visual stimulation pattern and the presentation time of the visual stimulation pattern by the subject to obtain the dynamic characteristic index of the schizophrenic patient. The specific process is as follows:
1.1 subject
In 2020 through 2022, data was collected for male schizophrenic patients at the department of psychiatric hospitalization in the seventh people hospital in Hangzhou, with specialized physicians responsible for subject recruitment and control of the group criteria. Male healthy subjects of the control group were also enrolled in the university of Zhejiang and society. The positive and negative symptoms scale (Positive and Negative Syndrome Scale, PANSS) was used to evaluate the psychotic symptoms in schizophrenic patients.
After screening for age and medication, 60 male schizophrenic patients and 44 male healthy subjects were included in the statistical analysis. All participants fully understood and signed informed consent.
1.2 test procedure
Each tested position completes two tests on the portable notebook computer, the test duration depends on the judging speed of the tested position, and each test is generally about 10 minutes.
Before the test starts, the notebook display is subjected to linearization screen correction, and the actual brightness value of the gray background of the notebook display is consistent with that of a laboratory standard display, which is 56cd/m 2 After which the display parameters of the notebook display remain unchanged.
Testing was performed in a windowless consulting room in a seventh people hospital in Hangzhou, where other light sources in the room except for the notebook display were turned off. The head of the tested person is fixed by using the mandibular rest, so that the tested person can look straight at the center of the screen of the notebook horizontally, and the distance between eyes and the screen is kept to be 47 cm.
As shown in fig. 2, the notebook screen presents a stimulation mode of a horizontal drifting sinusoidal grating, the contrast is 50%, and the spatial frequency is 1cycle/°; according to the requirements, the grating movement speeds of the two tests are respectively 2 degrees/s and 4 degrees/s; the edge of the grating is subjected to fuzzy processing by adopting a Gaussian function, and the fuzzy width is 30%; there are two directions of motion (left and right) and two magnitudes (2 ° and 10 °) in the grating, so that 4 different visual stimulus patterns are produced in combination in each test. The visual stimulus program was run based on the psychophysical toolbox psychatolbox of MATLAB software.
As shown in fig. 2, the visual motion perception paradigm was divided into 160 trials in each test, with 80 trials for large and small visual stimulus patterns each, occurring randomly. After each test is started, the center of the screen presents a cross, and the subject needs to continuously watch the cross position. After 500 milliseconds, the cross disappears and the center of the screen randomly presents a stimulus pattern. The grating drifts leftwards or rightwards, the participant needs to judge the drift direction of the grating through the key of the keyboard, and if the judgment error can hear a prompt of 'drop', the judgment is correct, no sound is generated. The duration of the grating presentation is adaptively adjusted using a 3-down 1-up staircase method.
Because of the presence of peripheral inhibition, the subject may have more difficulty detecting the direction of motion of the large grating than the small grating, so to exclude the effect of peripheral inhibition, we calculate the dynamic index and perform statistical analysis for the small stimulus.
1.3 visual perception dynamic characteristic index calculation
Calculating the difference value of the time sequence value of the last test time minus the time sequence value of the previous test time by the time sequence of the presentation of all the tests of the subject aiming at the small visual stimulus pattern, and discarding the difference value obtained by the first test time and the second test time to obtain a group of difference values; according to the difference, calculating the average value, standard deviation and variation coefficient of the obtained sequence, respectively serving as TD-Mean, TD-SD and TD-CV, and adopting the 3 indexes to represent the dynamic variation condition of the tested person in the whole judging process.
The sample entropy sampenn can measure the complexity of the time sequence through the probability of the occurrence of the new mode, and can be applied to evaluating the complexity of the physiological signal time sequence, and the specific calculation process is as follows:
let the sequence of raster presentation times be x= { X 1 ，x 2 ，x 3 ，…，x N -a }; wherein N is the number of trials, and 80 is taken.
Spatially reconstructing the sequence X to form (N-m+1) m-dimensional vectors X m (i)＝[x(i)；x(i+1)；x(i+2)；...；x(i+m-1)]The method comprises the steps of carrying out a first treatment on the surface of the Wherein m is expressed as the length of the reconstructed vector, i is expressed as the number of vectors, and i is more than or equal to 1 and less than or equal to (N-m+1).
Vector X m (i) And X m (j) The maximum difference in the corresponding elements is defined as the distance between the vectors, denoted d [ X ] m (i)，X m (j)]The method comprises the steps of carrying out a first treatment on the surface of the Wherein d is expressed as the distance of vector pairs, i is not less than 1 and not more than (N-m+1), j is not less than 1 and not more than (N-m+1), and i is not equal to j.
The distance between the record vectors satisfies d [ X ] m (i)，X m (j)]The number < r, denoted N m (i) The method comprises the steps of carrying out a first treatment on the surface of the Where r represents a measurement value of "similarity", i.e., an allowable deviation, and 0.2 is taken.
N is obtained m (i) And contain vector X m (i) The ratio of vector to total number (N-m), i.eWherein B is i Expressed as an inclusion vector X within a range satisfying the allowable deviation r when the vector length is m m (i) Vector pair number of (a) and vector X included m (i) Is a ratio of vector to total number.
Solving forAverage value of>Wherein B is represented as the number of vector pairs and the total number of vector pairs within a range satisfying the tolerance r when the vector length is mThe ratio of i to (N-m+1) is more than or equal to 1.
Setting the vector length to be m+1, and repeating the steps to obtainWherein A is expressed as the ratio of the number of vector pairs to the total number of vector pairs within the range satisfying the allowable deviation r when the vector length is m+1, and 1.ltoreq.i.ltoreq.N-m+1.
Defining sample entropy asWherein, N is the number of trials, 80 is taken, so N is a limited value, and the calculation of sampEn according to the formula I is finally obtained.
The formula one:where m is expressed as a reconstructed vector length (1), r is expressed as an allowable deviation (0.2), N is expressed as a trial number (80), a is expressed as a ratio of the number of vector pairs to the total number of vector pairs within a range satisfying the allowable deviation r when the vector length is m+1, and B is expressed as a ratio of the number of vector pairs to the total number of vector pairs within a range satisfying the allowable deviation r when the vector length is m.
The LZC is a complexity algorithm for evaluating a sequence with a specific length, reflects the occurrence speed of a new mode in a time sequence, and comprises the following specific calculation processes:
let the sequence of raster presentation times be x= { X 1 ，x 2 ，x 3 ，…，x N -a }; wherein N is the number of trials, and 80 is taken.
Binarizing the sequence X with the median as a boundary to convert the sequence into a sequence P= { s containing only 0 and 1 1 ，s 2 ，s 3 ，…，s N If the value of a point is equal to or greater than the median of the time series, the point is assigned a value of 1, and conversely, a value of 0.
Then searching the new character string in the binarization sequence from left to right, wherein the new character string needs to have the characteristics of uniqueness and continuity. For to-be-separatedThe sequence P is analyzed, assuming that the partial string of the sequence P is s= { S 1 ，s 2 ，s 3 ，…，s r R=2, 3, …, N-1), the next character being q=s r+1 SQ is the combination of S and Q in series, i.e. sq= { S, Q }; SQpi represents the string from which SQ prunes the last character, i.e., SQpi= { s 1 ，s 2 ，s 3 ，…，s r -a }; v (sqpi) represents a set of substrings of sqpi and c represents the number of different substrings. If Q belongs to V (sqpi), Q is not a new substring, S remains unchanged at this time, let q=s r+1 s T+2 Then the relationship between Q and V (sqpi) is analyzed continuously, and the process is repeated until q=s r+1 s r+2 …s r+i (1. Ltoreq.i. Ltoreq.N-r) does not belong to V (SQpi), where Q is a new string, let c=c+1, SQ= { S, Q } = { S 1 ，s 2 ，s 3 ，…，s r+i }，SQπ＝{s 1 ，s 2 ，s 3 ，…，s r+i-1 }。
Repeating the previous step until the last character of the sequence P is retrieved, and finally obtaining the LZC calculated according to the formula II.
The formula II:where c is represented as the number of different substrings and N is represented as the number of trials (taken 80).
1.4 test results
FIG. 3 is a comparison of dynamic indicators and examples of small visual stimulus patterns presentation time series for schizophrenic patients and healthy subjects in an embodiment of the present application; wherein, in fig. 3: graph a represents an example of a presentation time series of small visual stimulus patterns for schizophrenic patients and healthy subjects, with the ordinate representing the presentation time of the small visual stimulus patterns and the abscissa representing the test times; b is expressed as a group comparison of dynamic indexes TD-Mean, the ordinate is expressed as TD-Mean, and the abscissa is expressed as grating movement speed; c is expressed as inter-group comparison of dynamic indexes TD-SD, the ordinate is expressed as TD-SD, and the abscissa is expressed as grating movement speed; d is expressed as an inter-group comparison of dynamic indexes TD-CV, the ordinate is expressed as TD-CV, and the abscissa is expressed as grating movement speed; e is represented as an inter-group comparison of the dynamic index LZC, the ordinate is represented as LZC, and the abscissa is represented as the grating movement speed; the f-chart is shown as an inter-group comparison of the dynamic index samplen, the ordinate is shown as samplen, and the abscissa is shown as the raster motion speed. As shown in fig. 3a, the time of the small-stimulus grating presentation was more fluctuated in schizophrenic patients than in healthy subjects, and using the mixed linear model and post-hoc test, it was found that the dynamic index TD-Mean (fig. 3b, adjusted p=0.003), TD-SD (fig. 3c, adjusted p=0.005) and LZC (fig. 3e, adjusted p=0.010) were significantly increased under fast (4 °/s) conditions, TD-CV was significantly decreased under both slow (2 °/s, adjusted p=0.024) and fast (4 °/s, adjusted P <0.001 x) conditions (fig. 3 d), sampEn was significantly increased under slow (2 °/s, adjusted p=0.009 x) and fast (4 °/s, adjusted P <0.0001 x) conditions), and the dynamic index of the present patent application showed that there was abnormal change in the patient under conditions.
FIG. 4 is a correlation of the dynamic index TD-CV, LZC with the total score of the schizophrenia PANSS scale (positive and negative symptom scale, a standardized rating scale for measuring the severity of symptoms in schizophrenic patients) in the examples of the present application; wherein, in fig. 4: graph a shows the correlation between TD-CV and the score of the PANSS scale of schizophrenia, the ordinate shows TD-CV, and the abscissa shows the total score of the PANSS scale; graph b shows the correlation between LZC and the total score of the schizophrenia PANSS scale, the ordinate shows LZC, and the abscissa shows the total score of the PANSS scale. As shown in fig. 4, using pearson correlation analysis, it was found that in schizophrenic patients, the dynamic index TD-CV (fig. 4a, r=0.269, p=0.038) and LZC (fig. 4b, r= -0.264, p=0.045) of the small visual stimulus pattern were significantly correlated with the PANSS scale total score under rapid (4 °/s) conditions, indicating that the dynamic index proposed in the present application was potentially linked to the clinical symptoms of schizophrenic patients.
In summary, the embodiment of the application provides a method for detecting dynamic characteristic indexes of a schizophrenic patient, provides a method for measuring dynamic characteristics of a schizophrenic patient in a visual movement perception paradigm, can calculate 5 indexes of TD-Mean, TD-SD, TD-CV, sampEn and LZC, can calculate complexity and dynamic characteristics of visual perception of a subject from a paradigm which takes a very short time (10 minutes), is verified in the schizophrenic patient, and fills the blank of the detection of the dynamic characteristic indexes of the visual perception of the schizophrenic patient in the prior art.
Various embodiments of the present application may exist in a range format; it should be understood that the description in a range format is merely for convenience and brevity and should not be interpreted as a rigid limitation on the scope of the application. It is therefore to be understood that the range description has specifically disclosed all possible sub-ranges and individual values within that range. For example, it should be considered that a description of a range from 1 to 6 has specifically disclosed sub-ranges, such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6, etc., as well as single numbers within the range, such as 1, 2,3, 4, 5, and 6, wherever applicable. In addition, whenever a numerical range is referred to herein, it is meant to include any reference number (fractional or integer) within the indicated range.
In this application, unless otherwise indicated, terms of orientation such as "upper" and "lower" are used specifically to refer to the orientation of the drawing in the figures. In addition, in the description of the present application, the terms "include", "comprise", "comprising" and the like mean "including but not limited to". Relational terms such as "first" and "second", and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Herein, "and/or" describing an association relationship of an association object means that there may be three relationships, for example, a and/or B, may mean: a alone, a and B together, and B alone. Wherein A, B may be singular or plural. Herein, "at least one" means one or more, and "a plurality" means two or more. "at least one", "at least one" or the like refer to any combination of these items, including any combination of single item(s) or plural items(s). For example, "at least one (individual) of a, b, or c," or "at least one (individual) of a, b, and c," may each represent: a, b, c, a-b (i.e., a and b), a-c, b-c, or a-b-c, wherein a, b, c may be single or multiple, respectively.
The foregoing is merely a specific embodiment of the application to enable one skilled in the art to understand or practice the application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (7)
1. The method is characterized in that the dynamic characteristic index of the schizophrenic patient is the dynamic characteristic index of the schizophrenic patient in a visual movement perception model, and the dynamic characteristic index comprises a test time dynamic average value TD-Mean, a test time dynamic standard deviation TD-SD, a test time dynamic variation coefficient TD-CV, a sample entropy SampEn and a Lempel-Ziv complexity LZC; the detection method comprises the following steps:
in a dark room, a subject continuously looks at a cross shape presented in the center of a display screen;
after the cross shape disappears, the subject observes the visual stimulation pattern presented at the center of the display screen, judges the drifting direction of the visual stimulation pattern, and records and counts the judging result of the subject and the presentation time of the visual stimulation pattern; the visual stimulus pattern is a randomly occurring small or large edge-blurred sinusoidal grating;
performing data processing according to the judging result of the small visual stimulation pattern and the presentation time of the visual stimulation pattern by the subject to obtain the dynamic characteristic index of the schizophrenic patient;
the step of processing data according to the judging result of the small visual stimulation pattern and the presentation time of the visual stimulation pattern by the subject to obtain the dynamic characteristic index test dynamic average value TD-Mean, test dynamic standard deviation TD-SD and test dynamic variation coefficient TD-CV of the schizophrenic patient comprises the following steps:
calculating the difference value of the time sequence value of the last test time minus the time sequence value of the previous test time by the time sequence of the presentation of all the tests of the subject aiming at the small visual stimulus pattern, and discarding the difference value obtained by the first test time and the second test time to obtain a group of difference values;
according to the difference, calculating to obtain an average value, a standard deviation and a variation coefficient of the sequence, wherein the average value, the standard deviation and the variation coefficient are respectively used as a test time dynamic average value TD-Mean, a test time dynamic standard deviation TD-SD and a test time dynamic variation coefficient TD-CV;
the data processing is carried out according to the judging result of the subject on the small visual stimulation pattern and the presentation time of the visual stimulation pattern, so that the dynamic characteristic index sample entropy sampenn of the schizophrenic patient is obtained and calculated according to a formula I:
the formula one:the method comprises the steps of carrying out a first treatment on the surface of the Wherein m is represented as a reconstructed vector length, r is represented as a tolerance, N is represented as a number of trials, < >>Expressed as the ratio of the number of vector pairs to the total number of vector pairs in the range satisfying the tolerance r at a vector length of m+1, +.>Expressed as the ratio of the number of vector pairs to the total number of vector pairs in the range satisfying the allowable deviation r when the vector length is m;
and obtaining the Lempel-Ziv complexity LZC, and calculating according to a formula II:
the formula II:the method comprises the steps of carrying out a first treatment on the surface of the Wherein c is represented as the number of different substrings, and N is represented as the number of trials.
2. The method of claim 1, wherein the parameters of the display screen include: the screen is subjected to linearization correction, and the background brightness value of the screen except for the visual stimulus pattern is 56cd/m 2 。
3. The method of claim 1, wherein the subject's head is maintained on a horizontal line with the display screen and the subject's eyes are at a distance of 47 cm from the display screen.
4. The method of claim 1, wherein the visual stimulus pattern is run using a psychophysical toolbox psychroox of MATLAB software.
5. The method of claim 1, wherein the subject continues to look at the center of the display screen for a period of 500 milliseconds in a cross-shape.
6. The method of claim 1, wherein the duration of the presentation of the visual stimulus pattern is adaptively adjusted using a 3-down-1-up-step method.
7. The method of claim 1, wherein the subject determines the drift direction of the visual stimulus pattern by key presses of the keyboard, and if the determination is incorrect, a "drop" audible cue is heard, and if the determination is correct, no sound is produced.
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