A detection method and detection device for inducing nystagmus through skull vibration

Abstract

The application provides a detection method and detection equipment for inducing nystagmus through skull vibration, and belongs to the technical field of medical instruments. The method comprises the following steps: wearing goggles and calibrating the head under dark light, setting an offset threshold; collecting baseline images for more than 10 seconds under no load, extracting the pupil and baseline data through an improved Otsu algorithm; applying controlled vibration to the mastoid or the top of the skull according to three types of standardized modes; collecting images in real time during the vibration; collecting images at a frame rate for not less than 30 seconds after the vibration stops, and the abnormality can be prolonged; image interpolation and pseudo-image conversion into eye movement signals; automatically splitting the fast and slow phases and calculating various nystagmus indexes; data mapping, scoring and grading, and issuing a detection report. The application adopts the above-mentioned detection method and detection equipment for inducing nystagmus through skull vibration, and solves the problems of disordered SVINT detection parameters, difficult quantification and inability to systematically analyze the residual nystagmus.

Description

Technical Field

[0001] This invention relates to the field of medical device technology, and in particular to a method and device for detecting nystagmus induced by skull vibration. Background Technology

[0002] Nystagmus is an important indicator reflecting the physiological state of the vestibular system. By collecting and recording parameters such as the direction, frequency, intensity, and duration of nystagmus, a reference basis can be provided for the evaluation of vestibular-related physiological indicators. Video Nystagmography (VNG) is a commonly used eye movement acquisition device in the industry. The subject wears a special video goggles, and an infrared camera device collects images of eye movements. Combined with data processing, nystagmus-related parameters are obtained, and it is widely used in the routine testing of vestibular-related indicators.

[0003] The phenomenon of skull vibration-induced nystagmus was discovered in 1973 and gradually applied to related testing scenarios in 1999. In 2006, at the International Society of Otorhinolaryngology in France, this type of test was designated as an independent vestibular-related test. Skull vibration-induced nystagmus (SVINT) relies on bone-conducted vibration acting on vestibular-related sensory structures in the skull to detect indicators of bilateral vestibular physiological symmetry. Existing research data shows that applying 100Hz bone-conducted vibration to the mastoid region can induce nystagmus signals with a predominantly horizontal component in subjects with unilateral vestibular differences; after applying vibration stimulation to individuals with specific skull structures, the nystagmus exhibits specific directional characteristics. This testing method, due to its advantages of simplicity, non-invasiveness, and short testing time, has become a commonly used means of screening vestibular symmetry.

[0004] Existing skull vibration-induced nystagmus detection methods have several shortcomings in practical applications: Firstly, there are no unified standards for stimulation parameters such as vibration frequency, location of application, stimulation duration, and contact pressure, leading to significant differences in parameters used in different tests and resulting in poor consistency and repeatability of test results. Secondly, existing detection methods mostly rely on manual visual assessment of nystagmus performance, and conventional video nystagmus viewing equipment lacks targeted automated analysis modules, making it difficult to automatically and accurately quantify multi-dimensional parameters such as slow-phase angular velocity, nystagmus direction, and frequency. Furthermore, existing equipment cannot automatically capture and quantitatively analyze residual nystagmus data after vibration termination, which is not conducive to the comprehensive evaluation of vestibular-related indicators.

[0005] Therefore, developing a standardized stimulation parameter system, an automatic quantification and analysis system for nystagmus data, and an integrated system for vibration stimulation and eye movement acquisition that combines skull vibration-induced nystagmus detection with supporting equipment has high application value. Summary of the Invention

[0006] The purpose of this invention is to provide a method and device for detecting nystagmus induced by skull vibration, which solves the problems of chaotic SVINT detection parameters, difficulty in quantification, and inability to systematically analyze residual nystagmus in existing methods.

[0007] To achieve the above objectives, the present invention provides a method for detecting nystagmus induced by skull vibration, comprising the following steps: S1. Subjects wear video nystagmography eyepieces and complete head posture calibration in low light environment, and limit the head offset threshold. S2. Under no vibration stimulation, continuously acquire baseline eye movement images for no less than 10 seconds, process the images using the improved Otsu algorithm, extract pupil coordinates and calculate baseline fluctuation data; S3. The vibration control module controls the bone conduction vibration unit to apply bone conduction vibration to the mastoid process and / or cranial vault with standardized parameters, and presets three stimulation modes: standard, enhanced, and top stimulation. S4. During the vibration application, the binocular infrared camera unit acquires eye movement images in real time; S5. After the vibration stops, continue to collect eye-tracking images for no less than 30 seconds, set the sampling frame rate in segments with different settings, and extend the collection time as needed. S6. Preprocess all images, use interpolation algorithms to remove blink artifacts, and convert pixel coordinates into eyeball deflection angles to generate nystagmus signals. S7. Automatically distinguish between fast and slow phases based on nystagmus signals and calculate nystagmus parameters; S8. Parameter visualization plot output, combined with data confidence score to generate test report.

[0008] Preferably, the standardized stimulation parameters in S3 are: vibration frequency 100Hz±5Hz, stimulation duration 10~20s, contact pressure 10N±1N, vibration acceleration 0.1g~2.0g; the bilateral mastoids are stimulated in a staggered manner with a stimulation interval of at least 3s between the left and right mastoids.

[0009] Preferably, S2 uses the improved Otsu binarization expression as follows: ; in, To correct the threshold, The original threshold, k For correction factor, k The value ranges from 0.85 to 1.15, and is adaptively selected based on the average gray level of the image. Then, the effective pupil area is filtered through dual constraints of connected component area and circularity.

[0010] Preferred, the S5 segmented acquisition rule is as follows: 120fps high-frequency acquisition is used from 0 to 15 seconds after vibration stops, and 60fps acquisition is used from 15 to 30 seconds. If the instantaneous SPV is greater than 2° / s, it is determined that there is residual nystagmus. The longest acquisition time is extended to 60 seconds.

[0011] Preferably, according to S7 A threshold of 100° / s was used to distinguish between fast and slow phases of nystagmus; an exponential decay model was employed. The residual nystagmus decay time constant τ was obtained by fitting the solution; through Calculate the bilateral oculomotor direction dominance index; in, Where ω is the instantaneous angular velocity, and M is the number of effective slow-phase sampling points. For the first Slow phase angular velocity, To stimulate the left eye with a slow phase velocity, The right eye is stimulated with a slow phase angular velocity. This is the directional advantage index.

[0012] Preferably, S8 adopts Calculate the confidence level of the accounting data, among which The weighting coefficients for the three indicators are used, and a retest is prompted when the score is below 80. The device has an embedded improved multi-head attention neural network that automatically distinguishes between horizontal, vertical and compound nystagmus.

[0013] A detection device for nystagmus induced by skull vibration includes a video nystagmus viewing eyepiece, a binocular infrared camera unit, a bone conduction vibration unit, a vibration control module, an image acquisition and control module, a data storage module, a data processing and analysis module, and an output module. The binocular infrared camera unit is located in the left and right eyepiece tubes of the video nystagmus viewing eyepiece and is used to acquire binocular eye movement images. The bone conduction vibration unit is integrated on the video nystagmus viewing eyepiece and includes a left vibrator, a right vibrator, and a central vibrator. The left and right vibrators are positioned corresponding to the mastoid process of the human body, and the central vibrator is positioned corresponding to the top of the skull. The vibration control module is connected to the bone conduction vibration unit and is used to adjust the vibration output according to preset standard parameters. The image acquisition and control module is connected to the binocular infrared camera unit. The data storage module is connected to both the image acquisition and control module and the data processing and analysis module. The output module is connected to the data processing and analysis module.

[0014] Preferably, the bone conduction vibration unit is an electromagnetic bone conduction oscillator with a vibration frequency range of 30Hz to 200Hz. A flexible silicone pad is provided at the end of the oscillator that contacts the skin. The oscillator is equipped with a pressure sensor, and the pressure sensor and the vibration control module form a PID closed-loop pressure control system.

[0015] Preferably, the video oculomotor view eyepiece has a built-in nine-axis gyroscope and accelerometer for real-time acquisition of head pitch, roll and yaw data. When the head deviation exceeds a preset threshold, the corresponding frame data is marked as invalid. The eyepiece is also equipped with an integrated light shield and an adjustable power 850nm infrared light source.

[0016] Preferably, the binocular infrared camera unit has a sampling rate of ≥120 frames / second, an image resolution of not less than 320×240 pixels, and automatically increases the sampling frame rate when the image is blurry.

[0017] Therefore, the present invention employs the above-mentioned method and equipment for detecting nystagmus induced by skull vibration, and the technical effects are as follows: 1. Standardize stimulation parameters and improve detection repeatability: Integrate left and right mastoid and cranial vibrators into the detection eyepiece, and rely on PID closed-loop algorithm to control vibration pressure in real time, limiting fixed stimulation parameters such as 100Hz±5Hz and 10N±1N, and dividing into three standardized stimulation modes: standard, enhanced, and cranial. This eliminates the randomness of parameters caused by traditional manual hand pressure application, eliminates human operation bias, and significantly improves the horizontal comparability of detection data from different subjects and at different times.

[0018] 2. Full-process automated algorithm processing, eliminating subjective human interpretation: The algorithm employs improved Otsu adaptive binarization, connected component filtering, third-order spline interpolation to remove blink artifacts, and five-point filtering for noise reduction to accurately extract pupil coordinates. It also quantifies eye position based on pixel-angle conversion formulas. By automatically distinguishing between fast and slow phases of nystagmus through velocity thresholds, it automatically calculates multiple parameters such as slow phase angular velocity, nystagmus frequency, latency, and directional dominance index in batches. This avoids the subjective defects of traditional visual observation and semi-quantitative manual methods, significantly improving detection efficiency and data accuracy.

[0019] 3. Segmented acquisition for complete capture of residual nystagmus, improving detection dimensions: A high-frequency acquisition mechanism is set up during vibration and a graded differentiated sampling mechanism after vibration stops. After the vibration ends, continuous acquisition is carried out for no less than 30 seconds, and can be extended to 60 seconds to capture residual nystagmus. The decay time constant is obtained by fitting with an exponential decay model, realizing automatic capture and quantitative analysis of residual nystagmus for the first time, making up for the technical shortcomings of traditional equipment that cannot quantify residual nystagmus, and enriching the evaluation basis of vestibular indicators. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the structure of the skull vibration-induced nystagmus detection device provided in an embodiment of the present invention; Figure 2 A schematic diagram of the bone conduction vibration unit of the skull vibration-induced nystagmus detection device provided in an embodiment of the present invention; (a) is the central vibrator; (b) is the right vibrator; (c) is the left vibrator; Figure 3This is a schematic diagram of the cross-sectional structure of the video nystagmus view eyepiece provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of a video nystagmus view eyepiece system module provided in an embodiment of the present invention; Figure 5 A flowchart illustrating the detection method provided in an embodiment of the present invention; Figure 6 This is a flowchart of eye-tracking image preprocessing and nystagmus parameter analysis provided in an embodiment of the present invention; Figure 7 This is a schematic diagram of the visualization output interface for detection results provided in an embodiment of the present invention; Figure 8 This is a nystagmus waveform diagram of a patient with unilateral vestibular dysfunction in an embodiment of the present invention; Figure 9 This is a schematic diagram of the duration and attenuation curve of residual nystagmus in an embodiment of the present invention.

[0021] Figure Labels 1. Camera; 2. Eyepiece and goggles; 3. Sponge pad; 4. Elastic strap; 100. Video oculomotor view eyepiece; 200. Binocular infrared camera unit; 300. Bone conduction vibration unit; 400. Vibration control module; 500. Image acquisition and control module; 600. Data storage module; 700. Data processing and analysis module; 800. Output module. Detailed Implementation

[0022] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0023] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0024] Example 1 like Figures 1-4 As shown, this invention provides a detection device for nystagmus induced by skull vibration, mainly comprising a video nystagmus viewing eyepiece 100, a bone conduction vibration unit 300, and a vibration control module 400. The video nystagmus viewing eyepiece 100 includes an eyepiece eyecup 2 mounted on a frame, with a left and right eyepiece tube mounted on the eyepiece eyecup 2. An elastic fixing strap 4 is provided on the frame to secure the eyepiece to the subject's head. A sponge pad 3 is also provided at the end of the frame near the subject's head for conforming to the human eye. The eyepiece has an overall goggle-like structure and is secured to the subject's head by the elastic fixing strap 4. A flexible silicone pad is provided on the inner side of the eyepiece where it contacts the subject's face to ensure comfortable wear and block ambient light.

[0025] The left and right eyepiece tubes are equipped with binocular infrared camera units 200. Each eyepiece tube contains at least one infrared camera 1 and at least one infrared LED. The infrared light emitted by the LEDs illuminates the subject's eyes, and the infrared camera 1 captures the infrared light image reflected from the eyes to generate an eye-tracking image sequence. The image sampling rate of the binocular infrared camera unit 200 is no less than 120 frames per second, and the image resolution is no less than 320×240 pixels.

[0026] The bone conduction vibration unit 300, with its independent vibration unit in contact with at least one stimulation site on the subject's skull, includes a left vibrator and a right vibrator, respectively positioned in the left and right eyepiece tubes corresponding to the subject's mastoid process; it also includes a central vibrator positioned at the top of the eyepiece corresponding to the top of the subject's skull. The vibration frequency range of the vibrators in the bone conduction vibration unit 300 is 50Hz to 150Hz, and the maximum output force equivalent value at 100Hz is not less than 130dB peFL.

[0027] The vibration control module 400 is connected to the vibration unit and is used to control the start, stop, vibration frequency, vibration duration and vibration intensity of the bone conduction vibration unit 300 according to preset standardized stimulation parameters.

[0028] The device also includes an image acquisition and control module 500, a data storage module 600, a data processing and analysis module 700, and an output module 800. The image acquisition and control module 500 is electrically connected to the binocular infrared camera unit 200 and is used to control the sampling rate and exposure parameters of the camera 1, and to acquire eye movement image sequences in real time. The data storage module 600 is used to store the acquired eye movement image sequences, preprocessed eye movement signals, and calculated nystagmus parameters. The data processing and analysis module 700 includes a processor and a memory. The memory stores executable computer program instructions, and the processor executes the computer program instructions to implement the steps in the aforementioned detection method. The output module 800 includes a display unit and a data output interface, used to output the detection results in a visual form, and can be connected to an external computer or printer.

[0029] The skull vibration unit employs an electromagnetic bone conduction oscillator with a vibration frequency range of 30Hz to 200Hz and a maximum output force equivalent value of no less than 130dB peFL. Its surface in contact with the subject's skin is equipped with a flexible silicone pad to buffer vibration and maintain stable contact pressure. The video nystagmus viewing eyepiece 100 incorporates a nine-axis gyroscope and accelerometer to monitor the subject's head position and movement in real time. Simultaneously, the eyepiece is equipped with a light shield to block ambient light during detection, ensuring clear eye movement images can be acquired even in low-light conditions. The data processing and analysis module 700 embeds a deep learning-based nystagmus classification model, which uses a neural network algorithm to extract features and classify nystagmus types from the preprocessed eye movement signals.

[0030] like Figures 5-6 As shown, based on the above-described device, this invention provides a method for detecting nystagmus induced by skull vibration, comprising the following steps: S1. Subject Preparation and Visual Fixation Suppression. Subjects are seated and wear a video nystagmus-viewing eyepiece 100. The eyepiece contains an infrared light source and a binocular infrared camera 1. Detection is performed under either dark or low-light conditions to eliminate the suppressive effect of visual fixation on nystagmus. The eyepiece is equipped with an integrated surround light shield to control the ambient illuminance to ≤5lx. An 850nm near-infrared light source is selected, and three levels of power adaptive dimming are set to avoid pupil overexposure caused by eye reflection. After wearing the device, the initial head posture is calibrated using the built-in nine-axis gyroscope in the eyepiece, and pitch is recorded. , roll ,yaw The reference Euler angles are used to set head offset thresholds: pitch / roll offset > 3° and yaw offset > 5° are considered abnormal head postures. The frame integrates a miniature distance sensor to calculate the distance between the camera and the eyes in real time and adaptively adjust the lens focal length to keep the pupils of both eyes stably positioned in the region of interest of the image.

[0031] S2. Automatic Calibration and Baseline Recording. The device self-test and calibration program is initiated. The AI ​​eye-tracking algorithm automatically identifies and tracks the subject's pupils, calculating the coordinates of the eye center. Without applying vibration stimulation, a continuous eye-tracking image sequence of at least 10 seconds is acquired, and baseline eye-tracking data is recorded to determine the presence of spontaneous nystagmus. If present, baseline nystagmus parameters are recorded.

[0032] Image processing employs an improved Otsu adaptive binarization algorithm. The standard Otsu algorithm iterates through grayscale values ​​to find the grayscale value that maximizes the inter-class variance between the foreground and background, using this value as the initial threshold. Segmentation deviations are easily caused by interference from eyelashes and infrared reflections. This solution introduces an adaptive correction coefficient to obtain a correction threshold. The value of k ranges from 0.85 to 1.15, based on the overall average gray level of the image. Dynamic values: When the image is too bright and the reflection is severe, k is set to 0.85 to 0.95 to reduce the segmentation threshold and prevent the pupil area from being hollowed out; when the image has a balanced gray level, k=1.0; when the overall image is too dark, k is set to 1.05 to 1.15 to increase the threshold and filter out skin and eyelash blemishes.

[0033] Pupil selection is based on connected component features, with the following selection criteria: Where S is the area of ​​the connected region, L is the perimeter of the connected region, and the coordinates of the pupil center are accurately output. Through the formula: ; Calculate the standard deviation of baseline eye position fluctuation. Baseline data is considered acceptable if the value is less than 1.5 pixels. Spontaneous nystagmus is marked if the slow phase velocity (SPV) during the baseline phase is greater than 2° / s. Baseline offset is automatically removed from subsequent detection parameters.

[0034] S3. Apply standardized vibration stimulation. Apply bone-conducted vibration stimulation to at least one stimulation site on the subject's skull according to preset standardized stimulation parameters. The standardized stimulation parameters include: vibration frequency of 100Hz±5Hz, vibration duration of 10 to 20 seconds, and applied pressure of 10N±1N. The standardized stimulation parameters also include vibration acceleration amplitude, which is 0.1g to 2.0g.

[0035] For different types of subjects and testing purposes, multiple stimulation modes are preset, including: standard mode (100Hz, 10N, 10 seconds, mastoid stimulation), enhanced mode (100Hz, 10N, 20 seconds, mastoid stimulation), and top stimulation mode (100Hz, 10N, 15 seconds, cranial top stimulation). The stimulation sites include the mastoid region and the top of the skull. The bilateral mastoid regions are stimulated in a staggered manner, with the left mastoid stimulation ending after a 3-second interval before the right mastoid stimulation is started. The cranial top stimulation is carried out independently to avoid interference from the superposition of vibration signals.

[0036] A PID closed-loop pressure control algorithm is adopted: in, For pressure deviation, Set pressure on the goal. For real-time pressure measurement by pressure sensors, This is the pressure deviation value. The integral coefficient is... The differential coefficients are... It provides the output control quantity for the PID controller; real-time closed-loop voltage regulation; when the measured pressure value is lower than 8N, the device pauses stimulation and prompts a pop-up window to re-wear it; it synchronously monitors the output acceleration of the oscillator in real time, and automatically adjusts the drive voltage if it exceeds the range of 0.1g~2.0g.

[0037] S4. Real-time acquisition and recording of induced nystagmus data during the stimulation period. During the application of vibration stimulation, binocular infrared cameras continuously acquire binocular eye movement image sequences at a sampling rate of no less than 120 frames per second, recording induced nystagmus data in real time during the stimulation period. The default sampling frame rate is 120fps, which is automatically increased to 150fps when pupillary imaging is blurred. All image frames are bound to a unified timestamp, synchronously marking the start and stop times of vibration. Combining real-time head movement data from the gyroscope, image frames with head offset exceeding the threshold are automatically marked as invalid. Subsequently, interpolation algorithms are used to fill in the missing eye position data, and the images during the stimulation phase are stored independently in partitions.

[0038] S5. Continuous acquisition and recording of post-stimulation nystagmus data. After the vibration stimulus stops, continue to acquire eye movement image sequences for no less than 30 seconds, and record the duration and decay process of post-stimulation nystagmus. A segmented differentiated acquisition strategy is adopted: high-frequency acquisition at 120fps for 0-15s after vibration stops, and acquisition at 60fps for 15-30s. When an instantaneous SPV > 2° / s is detected, post-stimulation nystagmus is determined to exist. If it does not decay to the baseline, the acquisition time is automatically extended, with a maximum acquisition time of 60s.

[0039] S6. Eye-tracking image preprocessing and eye-tracking signal extraction. The acquired eye-tracking image sequence is preprocessed, including image binarization, connected component analysis, and pupil center localization. Time-series data of eyeball center coordinates are extracted to generate eye-tracking signals.

[0040] Third-order spline interpolation is used to handle blink artifacts: ; in, All are interpolation fitting coefficients; For the missing frame sequence number, Pupil coordinates are supplemented for interpolation; if the pupil area in a single frame drops by more than 60% compared to the adjacent frame, blinking is detected and the eye position coordinates are automatically completed.

[0041] The pixel coordinates are converted into eye rotation angles based on the calibration coefficients: ; in, For equipment calibration coefficients, The horizontal deflection angle of the eyeball. Baseline average pupil coordinates, The vertical deflection angle of the eyeball; a five-point moving average filter is used. Smooth the original angle signal, where, For the smoothed angle after filtering, For adjacent raw angle data, filter out random noise from the imaging.

[0042] S7. Quantitative Analysis and Evaluation of Nystagmus Parameters. Time-frequency analysis and feature extraction are performed on the eye movement signal to calculate at least one of the following nystagmus parameters: slow phase angular velocity, fast phase direction, nystagmus frequency, nystagmus intensity, onset latency of stimulus-period nystagmus, duration of afterimage nystagmus, decay time constant of afterimage nystagmus, and directional dominance index of nystagmus.

[0043] Based on the velocity vector of the eyeball center coordinates, the system automatically distinguishes between the fast and slow phases of nystagmus and calculates the angular velocity, direction, and duration of each phase.

[0044] instantaneous angular velocity Set a threshold to distinguish between fast and slow phases. , It is determined to be a fast phase. Determined to be slow phase, slow phase angular velocity Where M is the number of effective slow phase sampling points, For the first Slow phase angular velocity. The dominant frequency is extracted as the nystagmus frequency from the power spectrum of the slow phase angular velocity sequence; initial latency. ;in, For the time corresponding to the first frame SPV > 2° / s, Nystagmus onset latency; Directional dominance index ,in To stimulate the left eye with a slow phase velocity, The right eye is stimulated with a slow phase angular velocity. As a directional advantage index, A score >30% indicates a high degree of bilateral vestibular asymmetry; residual nystagmus is assessed according to the exponential decay model. Nonlinear fitting to solve the decay time constant ,in The initial slow phase angular velocity of the residual nystagmus. Duration after vibration stops; addition of a comprehensive nystagmus intensity index. Quantifying the overall strength of nystagmus, among which The main frequency of nystagmus is used; the data processing and analysis module 700 embeds an improved multi-head attention neural network, which inputs SPV time-series signals to realize automatic classification and recognition of simple horizontal, simple vertical and compound nystagmus and output classification confidence scores.

[0045] S8. Output and Evaluation of Detection Results. The calculated nystagmus parameters are output in a visual format, including nystagmus waveforms, slow-phase markers, and parameter statistical reports. Simultaneously, the vestibular function status of the subject is evaluated based on a pre-set discriminant model or threshold, and the evaluation conclusion is output. Waveforms are plotted in sections, with baseline, stimulation period, and post-stimulation nystagmus curves displayed in layers, and fast and slow phase intervals marked with different background colors; a data confidence score is introduced. ,in The three indicators are weighted coefficients. When the score is less than 80, the credibility of the labeled data is insufficient and retesting is recommended. The system automatically generates a standardized PDF report containing waveform graphs and a full parameter table, and supports wired and wireless dual-channel data export.

[0046] Combining the testing equipment and the complete testing steps S1-S8 in this embodiment, Figure 7 This is a schematic diagram of the visualization output interface for the detection results in this embodiment, corresponding to the result output stage of step S8 in the detection method. After the detection is completed, the system automatically generates waveform maps for the left and right eyes. The left side of the interface shows the horizontal and vertical eye position change curves for the left eye, and the right side shows the horizontal and vertical eye position change curves for the right eye. The horizontal axis of the map represents the detection time in seconds, and the vertical axis represents the eyeball deflection angle. The entire waveform generation relies on the binocular infrared camera unit to acquire original eye images at a sampling rate of no less than 120 frames per second. The improved Otsu adaptive binarization algorithm in step S2 is used to complete the pupil region screening and positioning, and the frame-by-frame pupil coordinates are output. After further processing using S6 third-order spline interpolation to remove blink artifacts, pixel-angle formula conversion, and five-point moving mean filtering for smoothing, the discrete coordinate data is finally transformed into a continuous nystagmus variation curve. The example sample effectively acquired 190 frames throughout the entire process, with 100% pupil tracking coverage. The system uses the S7 fast / slow phase automatic discrimination algorithm, based on... Threshold The system distinguishes between fast and slow phases of nystagmus, automatically identifies rhythmic nystagmus in the horizontal direction, and calculates the nystagmus frequency (2.20 Hz), the total number of fast and slow phases, and the SPV values ​​of the slow phase in both the horizontal and vertical directions. The algorithm's classification confidence level is 99%. Simultaneously, the system calculates the confidence level according to the S8 data confidence formula: ; The system automatically assesses data quality; this sample showed no significant head movement or imaging artifacts, indicating good data stability. The system also provides a report with review suggestions. This interface integrates waveform display, key parameter summarization, and acquisition quality evaluation, replacing traditional manual recording methods and enabling visualized and digitally archived test data.

[0047] Figure 8This example shows the nystagmus waveform of a subject with unilateral vestibular asymmetry in this embodiment. The detection was performed using the S3 standard stimulation mode: vibration frequency 100Hz±5Hz, contact pressure closed-loop control 10N±1N, stimulation duration 10s, with targeted vibration stimulation applied to the mastoid process on one side only. Throughout the stimulation process, the device continuously acquired eye movement images at 120fps according to the S4 requirements. A gyroscope monitored head posture in real time; data with head deviation exceeding a threshold were automatically marked as invalid and subsequently supplemented using an interpolation algorithm. The original images were preprocessed and angles were converted to generate nystagmus curves showing the change with stimulation time. The horizontal axis of the graph represents the stimulation duration, and the vertical axis represents the eye movement angular velocity (° / s). The waveforms show that after stimulation, a regular nystagmus dominated by the horizontal component is rapidly induced. The system automatically separates the fast and slow phases and calculates the slow phase angular velocity (SPV). After measuring the left and right mastoid processes, the bilateral SPV values ​​are substituted into the directional dominance index calculation formula. ; Calculation A score >30% indicates bilateral vestibular physiological asymmetry. Compared to existing technologies that rely on the human eye to roughly determine nystagmus strength, this invention uses standardized stimulation and automated parameter quantification to transform the degree of vestibular asymmetry into specific numerical values, significantly improving the objectivity and repeatability of the assessment. This also directly demonstrates that this invention addresses the limitation of parameters not being quantitatively analyzeable.

[0048] Figure 9 This diagram illustrates the duration and decay curve of post-traumatic nystagmus obtained under the cranial stimulation mode in this embodiment. The parameters used were: S3 cranial stimulation mode parameters: 100Hz vibration frequency, 10N contact pressure, and 15s of continuous cranial vibration. Following the S5 acquisition specifications, the device continued continuous sampling for at least 30s after vibration stopped, employing a differentiated acquisition strategy: maintaining high-frequency acquisition at 120fps for 0-15s after vibration stoppage, reducing to 60fps for 15-30s, and automatically extending the acquisition period to 60s if the nystagmus did not return to baseline for an extended period. The vertical axis of the graph represents the slow phase velocity (SPV) of post-traumatic nystagmus, and the horizontal axis represents the duration of time after vibration cessation. The curve shows that the test samples did not immediately return to baseline after vibration ended; the nystagmus signal decayed slowly and gradually, exhibiting clear characteristics of post-traumatic nystagmus, meeting the criterion of SPV > 2° / s for determining post-traumatic nystagmus. The system relies on the S7 built-in exponential decay model. This invention employs a nonlinear least squares fitting method to solve for the decay time constant τ, and uses the specific value of τ to quantify the decay rate of residual nystagmus. Existing traditional detection equipment generally lacks the ability to acquire data for extended periods after vibration cessation, making it impossible to capture the changing patterns of residual nystagmus. This invention fills the technical gap in the automatic quantitative analysis of residual nystagmus by adding a post-acquisition timing sequence and a matching decay fitting algorithm, providing crucial data support for the evaluation of relevant indicators of special vestibular structures.

[0049] Figures 7-9 The invention demonstrates its three core innovations—integrated hardware architecture, standardized closed-loop stimulation control, and automatic quantification of the entire process algorithm—from three dimensions: report output, conventional mastoid stimulation nystagmus, and post-traumatic nystagmus attenuation. This fully verifies that the solution can unify stimulation standards, achieve automatic calculation of multiple nystagmus parameters, and completely collect and analyze post-traumatic nystagmus data, effectively overcoming several technical drawbacks of existing SVINT detection methods.

[0050] Therefore, this invention employs the aforementioned detection method and equipment for nystagmus induced by skull vibration. It achieves standardized control of stimulation parameters such as vibration pressure and frequency through PID closed-loop processing. It utilizes improved Otsu binary segmentation, interpolation artifact removal, and coordinate angle conversion to complete eye movement signal preprocessing. Furthermore, it combines a series of algorithms, including fast and slow phase threshold discrimination, directional dominance index, and aftereffect nystagmus index decay fitting, along with a segmented acquisition strategy during the stimulation period and after vibration cessation, and real-time head posture quality control. This fully automated quantification of multi-dimensional indicators such as SPV, nystagmus frequency, and decay time constant, and visualizes them, solving the pain points of inconsistent detection parameters, large errors in manual interpretation, and difficulty in quantitative analysis of aftereffect nystagmus in traditional methods. This improves the objectivity, repeatability, and completeness of vestibular-related indicator detection.

[0051] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for detecting nystagmus induced by skull vibration, characterized in that, Includes the following steps: S1. Subjects wear video nystagmography eyepieces and complete head posture calibration in low light environment, and limit the head offset threshold. S2. Under no vibration stimulation, continuously acquire baseline eye movement images for no less than 10 seconds, process the images using the improved Otsu algorithm, extract pupil coordinates and calculate baseline fluctuation data; S3. The vibration control module controls the bone conduction vibration unit to apply bone conduction vibration to the mastoid process and / or cranial vault with standardized parameters, and presets three stimulation modes: standard, enhanced, and top stimulation. S4. During the vibration application, the binocular infrared camera unit acquires eye movement images in real time; S5. After the vibration stops, continue to collect eye-tracking images for no less than 30 seconds, set the sampling frame rate in segments with different settings, and extend the collection time as needed. S6. Preprocess all images, use interpolation algorithms to remove blink artifacts, and convert pixel coordinates into eyeball deflection angles to generate nystagmus signals. S7. Automatically distinguish between fast and slow phases based on nystagmus signals and calculate nystagmus parameters; S8. Parameter visualization plot output, combined with data confidence score to generate test report.

2. The method for detecting nystagmus induced by skull vibration according to claim 1, characterized in that, Standardized stimulation parameters in S3: vibration frequency 100Hz±5Hz, stimulation duration 10~20s, contact pressure 10N±1N, vibration acceleration 0.1g~2.0g; bilateral mastoids are stimulated in a staggered manner with a stimulation interval of at least 3s between the left and right mastoids.

3. The method for detecting nystagmus induced by skull vibration according to claim 1, characterized in that, S2 uses the improved Otsu binarization expression as follows: ； in, To correct the threshold, The original threshold, k For correction factor, k The value ranges from 0.85 to 1.15, and is adaptively selected based on the average gray level of the image. Then, the effective pupil area is filtered through dual constraints of connected component area and circularity.

4. The method for detecting nystagmus induced by skull vibration according to claim 1, characterized in that, S5 segmented acquisition rules: 120fps high-frequency acquisition is used from 0 to 15 seconds after vibration stops, and 60fps acquisition is used from 15 to 30 seconds. If the instantaneous SPV is greater than 2° / s, it is determined that there is residual nystagmus. The longest acquisition time is extended to 60 seconds.

5. The method for detecting nystagmus induced by skull vibration according to claim 1, characterized in that, S7 based on A threshold of 100° / s was used to distinguish between fast and slow phases of nystagmus; an exponential decay model was employed. The residual nystagmus decay time constant τ was obtained by fitting the solution; through Calculate the bilateral oculomotor direction dominance index; in, Where ω is the instantaneous angular velocity, and M is the number of effective slow-phase sampling points. For the first Slow phase angular velocity, To stimulate the left eye with a slow phase velocity, The right eye is stimulated with a slow phase angular velocity. This is the directional advantage index.

6. The method for detecting nystagmus induced by skull vibration according to claim 1, characterized in that, S8 adopts Calculate the confidence level of the accounting data, among which The weighting coefficients for the three indicators are used, and a retest is prompted when the score is below 80. The device has an embedded improved multi-head attention neural network that automatically distinguishes between horizontal, vertical and compound nystagmus.

7. A detection device for nystagmus induced by skull vibration, based on the detection method for nystagmus induced by skull vibration as described in any one of claims 1-6, characterized in that, The system includes a video oculomotor view eyepiece, a binocular infrared camera unit, a bone conduction vibration unit, a vibration control module, an image acquisition and control module, a data storage module, a data processing and analysis module, and an output module. The binocular infrared camera unit is located inside the left and right eyepiece tubes of the video oculomotor view eyepiece and is used to acquire images of eye movements in both eyes. The bone conduction vibration unit is integrated on the video oculomotor view eyepiece and includes a left vibrator, a right vibrator, and a central vibrator. The left and right vibrators are positioned corresponding to the mastoid process of the human body, and the central vibrator is positioned corresponding to the top of the skull. The vibration control module is connected to the bone conduction vibration unit and is used to adjust the vibration output according to preset standard parameters. The image acquisition and control module is connected to the binocular infrared camera unit. The data storage module is connected to both the image acquisition and control module and the data processing and analysis module. The output module is connected to the data processing and analysis module.

8. The detection device for nystagmus induced by skull vibration according to claim 7, characterized in that, The bone conduction vibration unit is an electromagnetic bone conduction oscillator with a vibration frequency range of 30Hz to 200Hz. A flexible silicone pad is set at the end of the oscillator that contacts the skin. The oscillator is equipped with a pressure sensor, and the pressure sensor and the vibration control module form a PID closed-loop pressure control system.

9. The detection device for nystagmus induced by skull vibration according to claim 7, characterized in that, The video nystagmus view eyepiece has a built-in nine-axis gyroscope and accelerometer to collect real-time head pitch, roll and yaw data. When the head deviation exceeds the preset threshold, the corresponding frame data is marked as invalid. The eyepiece is also equipped with an integrated light shield and an adjustable power 850nm infrared light source.

10. The detection device for nystagmus induced by skull vibration according to claim 7, characterized in that, The binocular infrared camera unit has a sampling rate of ≥120 frames / second and an image resolution of no less than 320×240 pixels. When the image is blurry, the sampling frame rate is automatically increased.

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