Eye movement signal-based nystagmus fast and slow phase identification system and identification method thereof

Abstract

The invention relates to the technical field of nystagmus fast and slow phase detection, in particular to a nystagmus fast and slow phase recognition system based on eye movement signals and a phase recognition method thereof, and the phase recognition system comprises a data acquisition module, a signal processing module, a recognition module and an output module. The data acquisition module acquires an eyeball movement image sequence through an infrared camera; the signal processing module preprocesses each frame of image in the eyeball movement image sequence, including ROI drawing, image binaryzation, connected domain area calculation and eyeball positioning; the recognition module is used for extracting nystagmus features from the preprocessed eye movement signals and performing fast and slow phase recognition; and the output module is used for outputting identification and calculation results in a visual form. The system has the advantages that the system comprises the data acquisition module, the signal processing module, the identification module and the output module; therefore, automatic identification of the nystagmus fast and slow phase is realized, manual intervention is reduced, the diagnosis time is shortened, and the diagnosis efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of nystagmus fast and slow phase detection, and in particular to a nystagmus fast and slow phase identification system and identification method based on eye movement signals. Background Art

[0002] Nystagmus is a common clinical symptom in patients with vertigo, and identifying its fast and slow phases is crucial for the diagnosis of vestibular disorders. Currently, nystagmus detection primarily relies on electronystagmography (ENG) or videonystagmography (VNG). However, these methods require specialized equipment and are complex to operate, making them difficult to disseminate in primary healthcare settings. Furthermore, existing technologies for automatically identifying the fast and slow phases of nystagmus lack accuracy and rely on manual analysis, resulting in low efficiency. Therefore, an efficient and accurate method for identifying the fast and slow phases of nystagmus is urgently needed.

[0003] Existing technologies (such as electronystagmography (ENG) and videonystagmography (VNG)) require expensive specialized equipment and are complex to operate, making them difficult to popularize in primary healthcare settings. Most methods rely on manual analysis of nystagmus signals by doctors or professionals, which is inefficient and susceptible to subjective factors. Automatic identification of fast and slow nystagmus phases has limited accuracy, especially in complex eye movement signals, prone to misjudgment or omission. Existing methods are often designed for specific devices or scenarios and are difficult to adapt to a variety of eye movement signal acquisition devices (such as standard cameras and mobile phone cameras). The output is often raw data or simple waveforms, lacking intuitive visualization, hindering doctors' rapid diagnosis. Summary of the Invention

[0004] In view of this, the present invention aims to provide a system and method for identifying the fast and slow phases of nystagmus based on eye movement signals. Through a data acquisition module, a signal processing module, an identification module and an output module, the fast and slow phases of nystagmus can be automatically and accurately identified, thereby reducing dependence on professional equipment and improving diagnostic efficiency.

[0005] To achieve the above-mentioned objectives, the technical solution created by the present invention is implemented as follows: a system for identifying fast and slow phases of nystagmus based on eye movement signals, comprising: a data acquisition module, a signal processing module, a recognition module, and an output module; the data acquisition module uses an infrared camera to collect a sequence of eye movement images; the signal processing module preprocesses each frame of the eye movement image sequence to obtain an eye movement signal and the coordinates of the eye center; the recognition module is used to extract nystagmus features from the eye movement signal and perform fast and slow phase identification; and the output module is used to output the identification and calculation results in a visual form.

[0006] Further, the signal processing module includes: an ROI drawing unit for presetting an ROI area in the image; an image binarization unit for converting the image within the ROI into a grayscale image and binarizing the grayscale image; a connected component analysis unit for performing connected component analysis on the black areas in the binarized image and calculating the area of each connected region; and an eyeball positioning unit for determining the eyeball position based on an area threshold and circularity and outputting the eyeball center.

[0007] Further, the recognition module includes: a nystagmus feature extraction unit for extracting the features of the nystagmus signal, including frequency, amplitude, and direction, through filtering methods and time-frequency analysis methods; and a slow / fast phase recognition unit for determining the slow phase of nystagmus and the slow phase value of nystagmus based on the velocity of the coordinates of the eyeball center.

[0008] Further, the output module includes a visualization unit for generating a nystagmus waveform diagram, slow phase markers, and parameter statistics.

[0009] A method for recognizing the slow and fast phases of nystagmus based on eye movement signals is implemented using the above-mentioned system for recognizing the slow and fast phases of nystagmus based on eye movement signals, and includes the following steps: S1: Use the data acquisition module to collect a sequence of eye movement images of the eyeball through an infrared camera.

[0010] S2: Use the signal processing module to preprocess each frame of the eye movement image sequence to obtain an eye movement signal and the coordinates of the eyeball center.

[0011] S3: Use the recognition module to extract nystagmus features from the eye movement signal and perform slow / fast phase recognition.

[0012] S4: Use the output module to output the recognition and calculation results in a visual form.

[0013] Further, step S2 includes the following steps: S21: The ROI drawing unit of the signal processing module presets an ROI in the image.

[0014] S22: The image binarization unit of the signal processing module converts the image within the ROI into a grayscale image and binarizes the image using the Otsu algorithm to obtain a black and white image.

[0015] S23: The connected component analysis unit of the signal processing module performs connected component analysis on the black areas in the black and white image in step S22, marks all connected regions, and calculates the area of each connected region.

[0016] S24: The eye location unit of the signal processing module filters out the connected regions smaller and larger than the set area threshold, retains the connected regions that meet the threshold conditions as the candidate regions of the eye, determines the eye center position according to the circularity of the candidate regions and the relative position between the center of the candidate region and the center of the eye movement image, and outputs the coordinates of the eye center.

[0017] Further, the connected component analysis in step S23 adopts a two-pass scanning method; the specific steps are as follows: The first pass of scanning: Traverse the image, assign a temporary label to each pixel, and record the equivalence relationship; The second pass of scanning: Merge the temporary labels according to the recorded equivalence relationship, and assign a unique label to each connected region.

[0018] Further, in step S3, the recognition module is used to determine the slow phase of nystagmus and the slow phase value of nystagmus according to the speed of the coordinates of the eye center.

[0019] Further, the process of determining the slow phase of nystagmus and the slow phase value of nystagmus includes the following steps: S31: Obtain the set of eye center coordinates, a = { (x1, y1), (x2, y2)....}; Among them, a represents the set of eye center coordinates, x represents time, and y represents displacement.

[0020] S32: Take three consecutive eye center coordinates as a group, denoted as a n = (x n , y n ), a n+1 = (x n+1 , y n+1 ), a n+2 = (x n+2 , y n+2 ); Among them, a n represents the nth point in the set a, x n represents the time of the nth point, and y n represents the displacement of the nth point.

[0021] S33: Calculate the speed of the first two points s1 = (y n+1 - y n ) / (x n+1 - x n ), and the speed of the last two points s2 = (y n+2 - y n+1 ) / (x n+2 - x n+1 ).

[0022] S34: If s1 and s2 have the same sign, introduce a new label p nAs a base point, it is used to record the starting point of the current movement trend; set the initial value of p n to the value of the current point a n , update a n to the value of a n+1 , and obtain the next two eye center coordinates, which are respectively recorded as a n+1 and a n+2 . Get the new a n , a n+1 , a n+2 and the base point p n .

[0023] S35: Repeat steps S32 - S34 until s1 and s2 have different signs.

[0024] S36: If s1 and s2 have different signs, introduce a new marker p n+1 as a base point to record the starting point of the current movement trend; set the initial value of p n+1 to the value of the current point a n+1 , replace a n with the value of a n+1 , continue to obtain the next two eye center coordinates, which are respectively recorded as a n+1 and a n+2 . Get the new a n、 a n+1 , a n+2 and the base point p n+1 .

[0025] S37: Repeat step S33 to calculate s1 and s2. If s1 and s2 have the same sign, repeat steps S32 - S34; if s1 and s2 have different signs, calculate the speed s from the base point p n to the base point p n+1 , and the speed s' from the base point p n+1 to the point a n+2 .

[0026] S38: If the absolute value of s is greater than the absolute value of s', the eye center displacement between the base point p n and the base point p n+1 is recorded as the slow phase, and its speed is the slow phase value; if the absolute value of s' is greater than s, the eye center displacement between the base point p n+1 and the point a n+2 is recorded as the slow phase, and its speed is the slow phase value.

[0027] S39: Replace a n with the value of a n+2 , and continue to obtain the next two eye center coordinates to replace a n+1 and a n+2 respectively.

[0028] S310: Repeat steps S32 - S39 until the displacement data of the entire eye center is calculated.

[0029] The present invention can achieve the following beneficial effects: 1) Through the data acquisition module, signal processing module, recognition module, and output module, the present invention realizes the automatic recognition of the fast and slow phases of nystagmus, reduces manual intervention, shortens the diagnosis time, and improves the diagnosis efficiency.

[0030] 2) The present invention adopts a signal processing module to extract the characteristics of the nystagmus signal through Otsu algorithm for image binarization, connected component analysis, and time - frequency analysis methods, significantly improving the recognition accuracy of the fast and slow phases of nystagmus. By accurately calculating the speed and direction of the eye center coordinates, the fast and slow phases of nystagmus can be accurately distinguished.

[0031] 3) The present invention does not rely on a specific eye movement signal acquisition device, but can realize the acquisition of eye movement signals through common devices such as infrared cameras. This universality reduces the dependence on professional equipment, enabling the present invention to be applied in a wider range of medical environments.

[0032] 4) The present invention provides an intuitive result display method, including nystagmus waveform diagrams, slow - phase markings, and parameter statistics, etc. These visual results facilitate doctors to quickly understand the characteristics of nystagmus for accurate diagnosis and treatment. At the same time, the visual results also provide rich data support for researchers, helping to deeply explore the relationship between nystagmus and vertigo - related diseases. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings that form a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments and descriptions thereof of the present invention are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings: Figure 1 is a flowchart of a method for identifying the fast and slow phases of nystagmus based on eye movement signals according to an embodiment of the present invention; Figure 2 is a schematic diagram of an example of a nystagmus signal waveform diagram and slow - phase marking according to an embodiment of the present invention; Figure 3 is a flowchart of an algorithm for nystagmus slow - phase points according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0034] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and do not constitute a limitation to the present invention.

[0035] It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments may be combined with each other.

[0036] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "center", "longitudinal", "transverse", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation to the present invention. In addition, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first", "second", etc. may explicitly or implicitly include one or more of such features. In the description of the present invention, unless otherwise stated, the meaning of "a plurality of" is two or more.

[0037] In the description of the present invention, it should be noted that unless otherwise clearly specified and defined, the terms "installation", "connection", and "connection" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood through specific situations.

[0038] The present invention will be described in detail below in conjunction with embodiments.

[0039] An eye movement nystagmus slow and fast phase recognition system based on eye movement signals provided by an embodiment of the present invention includes: a data acquisition module, a signal processing module, an identification module, and an output module.

[0040] The data acquisition module acquires an eye movement image sequence of the eyeball through an infrared camera. In this embodiment, the sampling frequency of the infrared camera is 120 Hz, and the resolution of each frame is 640×480.

[0041] The signal processing module preprocesses each frame of the eye movement image sequence. The preprocessing includes drawing ROI (region of interest), image binarization, calculating the area of the connected domain, and locating the eyeball.

[0042] The signal processing module includes: an ROI drawing unit, an image binarization unit, a connected domain analysis unit, and an eyeball positioning unit.

[0043] The ROI drawing unit is used to preset an ROI in the image. The center of the ROI area coincides with the center of the image, and only the pixels within the ROI area are processed, while the pixels outside the ROI area are directly ignored. In this embodiment, the ROI area is a rectangular area, and the size of the ROI area is 640×480 (width: 640 pixels, height: 480 pixels).

[0044] Purpose of drawing the ROI: To reduce the computational amount, focus on the eye area, and avoid interference from irrelevant areas.

[0045] The image binarization unit is used to convert the image within the ROI into a grayscale image, and use the Otsu algorithm (Otsu threshold segmentation method), with a threshold T, to binarize the image and obtain a black-and-white image.

[0046] Otsu algorithm: Assume that the image contains L gray levels, the number of pixels with gray level i is ni, the total number of pixels is N = Σni, and the probability of gray level i is pi = ni / N.

[0047] Traverse all possible thresholds k to divide the image into two categories, C0 and C1. C0 contains all pixels with gray levels less than k, and C1 contains all pixels with gray levels greater than k.

[0048] Calculate the between-class variance of the two categories C0 and C1: g = w0(u0 - uT) 2 + w1(u1 - uT) 2 ; where u0 and u1 are the average gray levels of C0 and C1. w0 and w1 are the probabilities of u0 and u1 respectively. uT is the overall average gray level. T is the global threshold, which is the threshold k with the maximum between-class variance g.

[0049] Purpose of image binarization: To highlight the eyeball area for subsequent processing.

[0050] The connected component analysis unit is used to perform connected component analysis on the black areas in the binarized image and calculate the area of each connected region. The connected component analysis uses the Two-Pass Algorithm. First pass: Traverse the image, assign a temporary label to each pixel, and at the same time check the pixels above and to the left of it. If both of these adjacent pixels are the background (unlabeled), then assign a new unique label; if one or both of the adjacent pixels have been labeled, then use one of their labels to label the current pixel and record the equivalence relationship between different labels. This step ensures that the pixels within the same connected region will eventually be assigned the same label.

[0051] Second scan: Merge tags according to the equivalence relationship to ensure that all pixels belonging to the same connected component have the same final tag. During this process, the area of each connected component can be calculated simultaneously, that is, count the number of occurrences of each tag (i.e., the number of pixels contained in this connected component). By counting the number of pixels corresponding to each tag, the area of each connected component can be obtained.

[0052] Purpose of calculating the area of the connected component: Screen out the eyeball area by area.

[0053] The eyeball positioning unit is used to determine the position of the eyeball through the area threshold and circularity, and output the center of the eyeball. Specifically, according to the set area threshold, filter out connected areas that are too small or too large (such as interference from eyelashes, eyelids, etc.). Retain the connected areas that meet the threshold conditions as candidate areas for the eyeball. According to the circularity of each candidate area and the relative position of the center of the candidate area to the center of the eye movement image, further determine the position of the eyeball and output the coordinates of the center of the eyeball. Arrange the coordinates of the center of the eyeball in each frame of the image in chronological order to obtain the eye movement signal.

[0054] Purpose of outputting the coordinates of the center of the eyeball: Accurately locate the eyeball and provide a basis for subsequent extraction of eye movement signals.

[0055] The recognition module is used to extract nystagmus features from the preprocessed eye movement signal and perform fast and slow phase recognition. The recognition module includes: a nystagmus feature extraction unit and a fast and slow phase recognition unit. The nystagmus feature extraction unit uses filtering to filter out blink waves, and extracts the features of the nystagmus signal from the time series of the eye movement signal through time-frequency analysis methods, including nystagmus frequency, nystagmus amplitude, and nystagmus direction.

[0056] Nystagmus amplitude: Calculate the peak or average value of the nystagmus waveform.

[0057] Nystagmus frequency: Calculate the number of nystagmus times per unit time.

[0058] The fast and slow phase recognition unit determines the slow phase interval and its speed value according to the calculation of the speed of the center coordinates of the eyeball.

[0059] Fast phase of nystagmus: The direction of the rapid movement of the eyeball.

[0060] Slow phase of nystagmus: The direction of the slow return of the eyeball.

[0061] The output module is used to output the recognition and calculation results in a visual form, including nystagmus waveform diagrams, slow phase markers, and parameter statistics.

[0062] The output module includes: a visualization unit and a storage unit. The visualization unit is used to generate nystagmus waveform diagrams, slow phase markers, and statistics of parameters (such as nystagmus frequency, nystagmus amplitude). The storage unit is used to store the recognition and calculation results.

[0063] Such asFigure 1 , Figure 2 As shown in Figure 2 , a method for identifying the slow and fast phases of nystagmus based on eye movement signals uses the above-mentioned system for identifying the slow and fast phases of nystagmus based on eye movement signals, and includes the following steps: S1: Data acquisition: Use the data acquisition module to collect a sequence of eye movement images of the eyeball through an infrared camera.

[0064] S2: Signal preprocessing: Use the signal processing module to preprocess each frame of the eye movement image sequence to obtain eye movement signals and the center coordinates of the eyeball. Specifically, it includes the following steps: S21: The ROI drawing unit presets an ROI in the image, and the center of the ROI area coincides with the center of the image; only the pixels within the ROI area are processed, and the pixels outside the ROI area are directly ignored.

[0065] S22: The image binarization unit of the signal processing module converts the image within the ROI area into a grayscale image, and uses the Otsu algorithm to binarize the image to obtain a black and white image.

[0066] S23: The connected component analysis unit of the signal processing module performs connected component analysis on the black areas in the black and white image obtained in step S22, marks all connected areas, and calculates the area of each connected area.

[0067] S24: The eyeball positioning unit of the signal processing module filters out the connected areas smaller than and larger than the area threshold through the set area threshold, retains the connected areas that meet the threshold conditions as candidate areas of the eyeball, and determines the eyeball position according to the circularity of the candidate area and the relative position of the area center and the image center, and outputs the center coordinates of the eyeball.

[0068] Furthermore, the connected component analysis adopts a two-pass scanning method, and the specific steps are as follows: The first pass of scanning: Traverse the image, assign temporary labels to each pixel, and record the equivalence relationship.

[0069] The second pass of scanning: Merge the temporary labels according to the recorded equivalence relationship, and assign a unique label to each connected area.

[0070] S3: Nystagmus feature extraction and slow and fast phase identification: Use the identification module to extract nystagmus features from the eye movement signals, and determine the slow phase of nystagmus and the slow phase value according to the speed of the center coordinates of the eyeball.

[0071] Furthermore, the algorithm flow of the slow phase points of nystagmus in step S3 includes the following steps: S31: Obtain a set of center coordinates of the eyeball, a = {(x1, y1), (x2, y2)....}; Among them, a represents the set of eye center coordinates, x represents time (in seconds), and y represents displacement (in degrees).

[0072] S32: Take three consecutive eye center coordinates as a group, denoted as a n = (x n , y n ), a n+1 = (x n+1 , y n+1 ), a n+2 = (x n+2 , y n+2 ).

[0073] Among them, a n represents the nth point in the set a, x n represents the time of the nth point, and y n represents the displacement of the nth point.

[0074] S33: Calculate the speed of the first two points s1 = (y n+1 - y n ) / (x n+1 - x n ), and the speed of the last two points s2 = (y n+2 - y n+1 ) / (x n+2 - x n+1 ).

[0075] S34: If s1 and s2 have the same sign, introduce a new marker p n as the base point to record the starting point of the current movement trend; set the initial value of p n to the value of the current point a n , update a n to the value of a n+1 , and obtain the next two eye center coordinates, denoted as a n+1 and a n+2 respectively, to get the new a n , a n+1 , a n+2 and the base point p n ; S35: Repeat steps S32 - S34 until s1 and s2 have different signs.

[0076] S36: If s1 and s2 have different signs, introduce a new marker p n+1 as the base point to record the starting point of the current movement trend; set the initial value of p n+1 to the value of the current point a n+1 , replace a n with the value of a n+1 , and continue to obtain the next two eye center coordinates, denoted as an+1 with a n+2 value to obtain a new a n、 a n+1 and a n+2 and the base point p n+1 .

[0077] S37: Repeat step S33 to calculate s1 and s2. If s1 and s2 have the same sign, repeat steps S32 - S34; if s1 and s2 have different signs, calculate the base point p n to the base point p n+1 speed s, and the base point p n+1 to the point a n+2 speed s'.

[0078] S38: If the absolute value of s is greater than the absolute value of s', the displacement of the eye center between the base point p n to the base point p n+1 is recorded as the slow phase, and its speed is the slow phase value; if the absolute value of s' is greater than s, the displacement of the eye center from the base point p n+1 to the point a n+2 is recorded as the slow phase, and its speed is the slow phase value.

[0079] S39: Replace a n with the value of a n+2 , and continue to obtain the next two eye center coordinates to replace a n+1 and a n+2 values respectively.

[0080] S310: Repeat steps S32 - S39 until all the eye center coordinate data is calculated.

[0081] S4: Result output: The output module outputs the slow phase interval determined in step S3 and its speed value in a visual form, including the nystagmus waveform diagram, slow phase markers, and statistics of parameters (such as nystagmus frequency, nystagmus amplitude).

[0082] The above specific embodiments do not limit the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub - combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. An optokinetic nystagmus slow and fast phase recognition system based on eye movement signals, characterized in that, Including: A data acquisition module, a signal processing module, an identification module, and an output module; The data acquisition module acquires a sequence of eye movement images of the eyeball through an infrared camera; The signal processing module preprocesses each frame of the eye movement image sequence to obtain an eye movement signal and the coordinates of the center of the eyeball; The identification module is used to extract nystagmus features from the eye movement signal and perform fast and slow phase identification; The output module is used to output the identification and calculation results in a visual form.

2. The nystagmus slow and fast phase recognition system based on eye movement signals according to claim 1, characterized in that, The signal processing module includes: An ROI drawing unit, which is used to preset an ROI area in the image; An image binarization unit, which is used to convert the image within the ROI into a grayscale image and binarize the grayscale image; A connected component analysis unit, which is used to perform connected component analysis on the black areas in the binarized image and calculate the area of each connected region; An eyeball positioning unit, which is used to determine the position of the eyeball through an area threshold and circularity and output the center of the eyeball.

3. The nystagmus slow and fast phase recognition system based on eye movement signals according to claim 1, characterized in that: The identification module includes: A nystagmus feature extraction unit, which is used to extract the features of the nystagmus signal, including frequency, amplitude, and direction, through a filtering method and a time-frequency analysis method; A fast and slow phase identification unit, which is used to determine the slow phase of nystagmus and the slow phase value of nystagmus according to the speed of the coordinates of the center of the eyeball.

4. The system for identifying fast and slow phases of nystagmus based on eye movement signals according to claim 1, characterized in that: The output module includes a visualization unit, which is used to generate a nystagmus waveform diagram, slow phase markers, and parameter statistics.

5. A method for identifying the slow and fast phases of nystagmus based on eye movement signals, which is implemented by using the system for identifying the slow and fast phases of nystagmus based on eye movement signals according to any one of claims 1-4, and is characterized in that: Including the following steps: S1: Use the data acquisition module to acquire a sequence of eye movement images of the eyeball through an infrared camera; S2: Use the signal processing module to preprocess each frame of the eye movement image sequence to obtain an eye movement signal and the coordinates of the center of the eyeball; S3: Use the identification module to extract nystagmus features from the eye movement signal and perform fast and slow phase identification; S4: Use the output module to output the identification and calculation results in a visual form.

6. The method for identifying fast and slow phases of nystagmus based on eye movement signals according to claim 5, wherein: Step S2 includes the following steps: S21: The ROI drawing unit of the signal processing module presets an ROI in the image; S22: The image binarization unit of the signal processing module converts the image within the ROI into a grayscale image and uses the Otsu algorithm to binarize the image to obtain a black and white image; S23: The connected component analysis unit of the signal processing module performs connected component analysis on the black areas in the black and white image obtained in step S22, marks all connected regions, and calculates the area of each connected region; S24: The eyeball positioning unit of the signal processing module filters out the connected regions smaller than and larger than the area threshold through the set area threshold, retains the connected regions that meet the threshold conditions as candidate regions of the eyeball, and determines the center position of the eyeball according to the circularity of the candidate region and the relative position between the center of the candidate region and the center of the eye movement image, and outputs the coordinates of the center of the eyeball.

7. The method for identifying fast and slow phases of nystagmus based on eye movement signals according to claim 6, wherein: The connected component analysis in step S23 adopts a two-pass scanning method; the specific steps are as follows: The first pass of scanning: Traverse the image, assign a temporary label to each pixel, and record the equivalence relationship; Second scan: Merge the temporary labels according to the recorded equivalence relations and assign unique labels to each connected region.

8. The method for identifying fast and slow phases of nystagmus based on eye movement signals according to claim 5, characterized in that: In step S3, use the recognition module to determine the slow phase of nystagmus and the slow-phase value of nystagmus according to the velocity of the eye center coordinates.

9. The method for identifying fast and slow phases of nystagmus based on eye movement signals according to claim 6, characterized in that: The process of determining the slow phase of nystagmus and the slow-phase value of nystagmus includes the following steps: S31: Obtain the set of eye center coordinates, a = {(x1, y1), (x2, y2)....}; where a represents the set of eye center coordinates, x represents time, and y represents displacement; S32: Take three consecutive eye center coordinates as a group, denoted as a n = (x n , y n ), a n+1 = (x n+1 , y n+1 ), a n+2 = (x n+2 , y n+2 ); Among them, a n represents the nth point in set a, x n represents the time of the nth point, y n represents the displacement of the nth point; S33: Calculate the speeds of the first two points \(s1=(y n+1 -y n ) / (x n+1 -x n ), and the speeds of the last two points \(s2=(y n+2 -y n+1 ) / (x n+2 -x n+1 ); S34: If s1 and s2 have the same sign, introduce a new marker p n As a base point, it is used to record the starting point of the current movement trend; n The initial value is set to the current point a n The value of a n Updated to a n+1 The value of , and get the coordinates of the next two eyeball centers and record them as a n+1 with a n+2 The value of a is obtained n 、a n+1 、a n+2 and the base point p n ; S35: Repeat steps S32 - S34 until s1 and s2 have different signs; S36: If s1 and s2 have different signs, introduce a new marker p n+1 as a base point for recording the starting point of the current movement trend; set the initial value of p n+1 to the value of the current point a n+1 ; replace a n with the value of a n+1 ; continue to obtain the next two eyeball center coordinates, denoted as a n+1 and a n+2 respectively, to get the new a n、 a n+1 and a n+2 , as well as the base point p n+1 ; S37: Repeat step S33 to calculate s1 and s2. If s1 and s2 have the same sign, repeat steps S32 - S34; if s1 and s2 have different signs, calculate the base point p n to the base point p n+1 the speed s, and from the base point p n+1 to the point a n+2 the speed s'; S38: If the absolute value of s is greater than the absolute value of s', then from the base point p n To base point p n+1 The eyeball center displacement between the base point p is recorded as the slow phase, and its speed is the slow phase value; if the absolute value of s' is greater than s, then the ... n+1 To point a n+2 The eyeball center displacement between the two phases is recorded as the slow phase, and its speed is the slow phase value; S39: Replace a n with the value of a n+2 , and continue to obtain the next two eyeball center coordinates to replace a n+1 and a n+2 respectively; S310: Repeat steps S32 - S39 until the entire set of eye center coordinate data is calculated.

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