Methods and systems for investigating the presence of traumatic brain injury

Abstract

Methods and systems for investigating the brain health of patients are described. In certain forms, the presence of traumatic brain injury (TBI), such as mild traumatic brain injury (mTBI), is investigated in patients by analyzing data representing the patient's eye movements.

Description

[Technical Field]

[0001] [Statement regarding the corresponding application] This application is based on Australian Patent Application No. 2023902472, the entirety of which is incorporated herein by reference.

[0002] This technology relates to the field of brain health surveys, and in particular to the investigation of the presence of traumatic brain injury (TBI), such as mild TBI (mTBI), commonly known as concussion. [Background technology]

[0003] The human eye is a complex and delicate organ used to perceive the world around us. The eye captures light and forms an image on the retina. This generates electrical signals, which are sent to the brain and interpreted as visual information. Scientists and medical professionals are increasingly recognizing the importance of tracking eye movements in the diagnosis and treatment of various medical conditions. In particular, eye movements have been found to be a useful indicator of brain pathology, including the presence of certain medical conditions.

[0004] One example of a condition that can be assessed using eye movements is traumatic brain injury, such as mild traumatic brain injury (mTBI). Mild traumatic brain injury is a complex neurobehavioral phenomenon caused by deformation of brain tissue due to mechanical forces from direct impact to the skull or indirect forces such as acceleration / deceleration. It can cause a variety of symptoms, including headache, dizziness, fatigue, depression, anxiety, irritability, loss of consciousness, and cognitive impairment, and these symptoms can last from days to years because the brain network is disrupted due to damage to the ultrastructure of axons and changes in neurometabolism. The effects of a concussion can affect the brain's ability to control eye movements, potentially causing symptoms such as double vision, blurred vision, and impaired coordination.

[0005] Although our knowledge of the biomechanics and pathophysiology of concussions is increasing, there are no standardized biomarkers (clinically or serologically). Diagnosis of concussion is based on a combination of self-reported symptoms and physical and neurological examinations. Self-reported symptoms may not be fully disclosed to the physician and are subjective. One classic investigation method involves the physician moving their finger while observing how the patient's eyes follow the movement. Because such methods are prone to subjectivity and error, a clinical environment suitable for careful examination is ideal.

[0006] There are many situations outside of clinical settings where it may be necessary to quickly and accurately assess the possibility of a concussion. These situations include during contact sports such as football (NFL, soccer, Australian Rules Football), rugby, boxing, and combat sports, as well as at the scene of injuries such as road traffic accidents.

[0007] Brain imaging diagnostics such as CT scans and MRI scans require expensive and bulky equipment, are not portable, and rarely possess sufficient sensitivity to diagnose mTBI, making them unsuitable for on-site diagnosis in the situations described above.

[0008] One existing system is Oculogica's EyeBox. This system relies on binocular movement (a measure of how well both eyes move synchronously with each other) to measure the likelihood of concussion, yielding sensitivity and specificity of 80.4% and 66.1% in mTBI detection (Samadani, U., Spinner, RJ, Dynkowski, G., Kirelik, S., Schaaf, T., Wall, SP, & Huang, P. (2022), Eye tracking for classification of concussion in adults and pediatrics, Frontiers in neurology, 13. doi:10.3389 / fneur.2022.1039955). This sensitivity may be insufficient for use as a diagnostic tool to immediately determine whether a patient can participate in mTBI-risk activities such as contact sports. Furthermore, the operating protocol used by EyeBox (binocular movement) requires a large desktop with a stable platform. Due to inaccuracies in EyeBox readings and physical characteristics, EyeBox is unsuitable for use in sports field and field settings to provide an indicator of concussion around the time of injury.

[0009] The Neuroalign system uses binocular movements, saccades, and reaction time, with a frame rate of only 100Hz. A frame rate of only 100Hz increases the likelihood of missing more subtle eye-gazing events (such as microsaccades and other gazing measures, which will be discussed later in the context of scientific and technological forms).

[0010] Analysis of tracking eye movements is an existing area of ​​research in mTBI. Michael Kelly (commercial devices are not available) developed a portable device that displays 10-second tracking eye movement images-8 protocol, which has been used in 849 athletes (12-18 years old) and 98 mTBI patients. mTBI patients showed substantially asymmetric tracking movements based on z-scores from standard data (Kelly, M. (2017), Technical Report of the Use of a Novel Eye Tracking System to Measure Impairment Associated with Mild Traumatic Brain Injury, Cureus, 9(5), e1251. doi:10.7759 / cureus.1251). Similarly, Neuroflex (Saccade Analystics) and Righteye Vision System both utilize tracking eye movements (their sensitivity to mTBI detection has not been reported), but only measure the amplitude of the error. RightEye does not contribute to calibration error, which is a significant confounding factor in the accuracy of tracking eye movements.Maruta et al., the authors of EyeSync (NeuroSync), also rely on tracking eye movement errors in their protocol (Maruta, J., Heaton, KJ, Kryskow, EM, Maule, AL, & Ghajar, J. (2013), Dynamic visuomotor synchronization: quantification of predictive timing, Behav Res Methods, 45(1), 289-300. doi:10.3758 / s13428-012-0248-3; Maruta, J., Heaton, KJ, Maule, AL, & Ghajar, J. (2014), Predictive visual tracking: specificity in mild traumatic brain injury and sleep deprivation, Mil Med, 179(6), 619-625. doi:10.7205 / MILMED-D-13-00420; Maruta, J., Spielman, LA, Rajashekhar, U., & Ghajar, J. (2018), Association of Visual Tracking Metrics With Post-concussion Symptomatology, Frontiers in neurology, 9. doi:10.3389 / fneur.2018.00611; Maruta, J., Suh, M., Niogi, SN, Mukherjee, P., & Ghajar, J. (2010), Visual tracking synchronization as a metric for concussion screening, J Head Trauma Rehabil, 25(4), 293-305. doi:10.1097 / HTR.0b013e3181e67936). The inventors believe that this measure can be improved using the means described herein.

[0011] The RightEye Vision System uses an metric known as the Bivariate Contour Ellipse Area (BCEA) to estimate gaze stability. BCEA measures the variance of eyes tracking coordinates on an elliptical region on a graph, particularly how much falls within the 68% distribution (Snegireva, N., Derman, W., Patricios, J., & Welman, K. (2021), Eye tracking to assess concussions: an intra-rater reliability study with healthy youth and adult athletes of selected contact and collision team sports, Experimental Brain Research, 239(11), 3289-3302. doi:10.1007 / s00221-021-06205-6). These gaze procedures involved presenting a stimulus for only two seconds, requiring participants to move their heads simultaneously with attempting to gaze. Such short stimulus presentations do not elicit many eye movements that are usefully analyzable. In another study, the RightEye procedure involved displaying an object for 7 seconds (BCEA as an outcome measure). This yielded a significant difference between concussion-affected and non-concussion-affected subjects and improved when combined with a statistical model including binocular movements (Hunfalvay, M., Murray, NP, & Carrick, FR (2021), Fixation stability as a biomarker for differentiating mild traumatic brain injury from age-matched controls in pediatrics, Brain Inj, 35(2), 209-214, doi:10.1080 / 02699052.2020.1865566). However, this still only yielded a sensitivity of 65% and a specificity of 70% in mTBI diagnosis. Leonard and colleagues similarly investigated gaze eye movements specifically in mTBI.In this group, a 30 Hz scanning laser eyescope (a large desktop machine used in clinics) was used to track fixation events at 480 Hz. These fixation measures were assessed using directional histograms, specifically by examining BCEA, fixation saccadic velocity, acceleration, amplitude, and drift. During the study protocol, only the measures of maximum fixation saccadic velocity, acceleration, and amplitude were proven to be significantly different between patients who had recently suffered a concussion and healthy patients (Leonard, BT, Kontos, AP, Marchetti, GF, Zhang, M., Eagle, SR, Reecher, HM, ···Rossi, EA (2021), Fixational eye movements following concussion, J Vis, 21(13), 11. doi:10.1167 / jov.21.13.11).

[0012] Cifu and colleagues used a different large desktop application (EyeLink II at 500 frames per second) in a group of military personnel with a history of TBI (at least 8.5 months post-injury) (Cifu, DX, Wares, JR, Hoke, KW, Wetzel, PA, Gitchel, G., & Carne, W. (2015), Differential eye movements in mild traumatic brain injury versus normal controls, J Head Trauma Rehabil, 30(1), 21-28. doi:10.1097 / htr.0000000000000036). This gaze scale included positional variance and root mean square of eye velocity, along with the mean and absolute mean eye velocity during gaze. This gaze scale also included BCEA as a further measure of the geographical distribution of eye tracking coordinates. This did not yield any significant results using the present method.

[0013] In a pilot study using Nintendo® Wii devices and a 120Hz head-fixed eye-tracking system, researchers measured the percentage of time gaze was fixed on the center of the game screen (percentage of time at the center) and the number of gaze deviations (eye movements) away from the center of the screen during play (gaze deviation). Significant differences were observed between groups for both measures (Murray, NG, Ambati, VN, Contreras, MM, Salvatore, AP, & Reed-Jones, RJ (2014), Assessment of oculomotor control and balance post-concussion: a preliminary study for a novel approach to concussion management, Brain Inj, 28(4), 496-503. doi:10.3109 / 02699052.2014.887144).

[0014] Another study recently evaluated 86 concussion patients within 50 days of injury using an all-in-one virtual reality headset and eye tracker (HTC Vive with 250Hz eye tracker), measuring "the number of saccadic saccades generated, the size and velocity of microsaccadic saccades, the area covered, and the ratio of the vertical-horizontal components of fixation eye movements" (Mortazavi, M., Thirunagari, P., Sarva, S., & Pita, M. (2022), Microsaccadic Fixational Eye Movements as an Oculomotor Marker for Concussion, Neurology, 98(1 Supplement 1), S5-S5. doi:10.1212 / 01.wnl.0000801776.06317.1f). These groups were found to have a larger mean microsaccadic saccade size compared to the concussion patient cohort. Furthermore, microsaccadic saccades and drift covered a greater vertical area during gazing.

[0015] There is a further need for a tool that values convenience, objectivity, and / or accuracy more than existing diagnostic tools and can evaluate the medical condition through eye movements. SUMMARY OF THE INVENTION PROBLEMS TO BE SOLVED BY THE INVENTION

[0016] The object of the present technology is to provide an improved method, system, and / or device for investigating the health of a patient's brain, for example, for investigating the presence of traumatic brain injury (TBI) in a patient. Alternatively, the object of the present technology is to provide at least a useful option for the public. MEANS FOR SOLVING THE PROBLEM

[0017] According to one aspect of the present technology, a method for investigating the health of a patient's brain is provided. In a particular form, the method may include investigating the presence of traumatic brain injury (TBI) in a patient, such as mild traumatic brain injury (mTBI). The method may include analyzing data representing the patient's eye movements.

[0018] According to one aspect of the present technology, a computer-implemented method for investigating the presence of TBI in a patient, the method comprising: receiving eye data representing the patient's eye movements; analyzing the eye data and measuring an indicator of the presence of TBI in the patient; outputting the indicator, is provided.

[0019] In certain forms, eye data representing eye movements may include eye movements of the fixation eye, i.e., eye movements during a fixation task. More specifically, eye data may represent eye movements when a patient gazes at a fixed object. The fixed object may be displayed at an eccentric position relative to the patient. In some forms, eye data may represent eye movements when a patient gazes at multiple fixed objects that appear consecutively, for example, at random time intervals and / or over a random period of time. The multiple fixed objects may be displayed at different positions in the eye's field of vision.

[0020] Furthermore, or alternatively, eye data representing eye movements may include eye movements in tracking eye movements, i.e., eye data representing eye movements during a tracking eye movement task. More specifically, eye data may represent eye movements as a patient gazes at a moving object. The moving object may trace a certain shape, for example, it may repeatedly trace that shape.

[0021] In certain forms, the method may include measuring the gaze direction by detecting the position of the iris of the eye. Detecting the iris position may include providing eye data representing eye movement with an object detection algorithm, such as a one-shot object detector, like the YOLO (You Only Look Once) v7 real-time object detector. From this, the curvature of the iris can be calculated, and from this, the position of the pupil can be accurately estimated, even when the pupil is closed by the eyelid.

[0022] In certain forms, the method may involve classifying eye data representing eye movements using a transformer model, such as a bidirectional transformer model.

[0023] In a particular form, the method further includes receiving object data representing the position of an object when eye data representing eye movement is captured. The method may further include analyzing the object data to measure an indicator of the presence of TBI in the patient.

[0024] In certain forms, the analysis steps of eye data and, optionally, object data may include measuring one or more measures of eye movement and measuring an indicator of the presence of TBI in the patient based on one or more measures.

[0025] In certain forms, one or more measures are, a) A measure of the mean error between the position of a fixed object and the gaze position of a patient during a fixation task, for example, the measure of mean error is the root mean square error (RMSE), and the measure of mean error is... b) A measure of the frequency of microsaccades during the fixation task, c) A measure of one or more features of one or more square wave eye movements (SWJs) during a fixation task, d) A measure of weakness in tracking eye movements for moving objects may be included. For example, a measure of weakness in tracking eye movements may be the time before weakness occurs in a tracking eye movement task. Weakness may occur when the accuracy of the eyes in tracking a moving object falls below a threshold.

[0026] In certain forms, the data analysis step may include measuring one or more combinations of measures a), b), c), and d).

[0027] In certain forms, the data analysis step may include measuring any combination of two or more of the scales a), b), c), and d), and the metric measurement step may include combining two or more scales. For example, two or more scales may be combined as a weighted mean.

[0028] In certain forms, measuring an index may involve comparing one or more scales, and / or combinations of two or more scales, to one or more predetermined thresholds. In some forms, one or more predetermined thresholds may be measured from similar scales measured from other patients. In other forms, one or more predetermined thresholds may be measured from one or more similar scales measured from the patient at earlier time points.

[0029] In some forms, one or more measures may include a measure of the fluidity of eye movements tracking a moving object. In some forms, the measure of fluidity may be measured in combination with measure d).

[0030] In certain forms, the method involves outputting an index as a quantitative and / or qualitative assessment of the risk, danger, or severity of a patient having TBI.

[0031] In certain embodiments of this technology, the method further includes controlling a display screen to show an object to a patient. In some embodiments, the display screen may be controlled to show a stationary object. The stationary object may be displayed eccentrically to the patient. In some embodiments, the display screen may be controlled to show multiple stationary objects appearing consecutively, for example, at random time intervals and / or over a random period of time. The multiple stationary objects may be displayed at different positions in the field of view of the eye. In some embodiments, the display screen may be controlled to show a moving object. The moving object may trace a certain shape, for example, repeatedly trace that shape. The method may further include generating data representing the position of the object.

[0032] In a particular form, the method further includes controlling a camera to capture an image when the eyes are gazing at an object. The method may further include generating data representing eye movements from the images captured by the camera.

[0033] According to one aspect of this technology, a computer-implemented method for investigating the presence of TBI in a patient is provided. The method may include receiving eye data representing the fixation eye movements of the patient's eyes during a fixation task in which the patient gazes at a fixed object. The method may further include measuring one or more measures of eye movements from the eye data. One or more measures may include measures of one or more features of one or more square wave eye movements (SWJs). The method may further include measuring an index of the presence of TBI in the patient based on one or more measures. The method may further include outputting the index.

[0034] In certain forms, one or more features may include one or more atypical features of a SWJ. We have identified that SWJs (plural) in patients experiencing TBI can be morphologically diverse in the sense that they include atypical movements that result in a shape different from those expected in the patient in a healthy state. For example, this morphological diversity may be expressed in the occurrence of different types or sequences of movements, or in measures such as duration, shape, amplitude, and / or velocity. SWJs exhibiting these atypical traits may be considered malformed compared to typical SWJs and may be referred to herein as “malformed” SWJs.

[0035] In a particular form, at least one of the one or more SWJs may contain two or more phases. In the examples, at least one SWJ may contain three or more phases. An SWJ having two phases may be called a biphasic SWJ, and an SWJ having two or more phases may be called a polyphasic SWJ.

[0036] The reference to a phase in a SWJ should be understood as referring to a specific event within the SWJ, which includes a single movement or a series of movements that can be distinguished from other events within the SWJ. For example, a typical SWJ in a healthy patient includes a primary intermittent deviation away from the object, a coasting portion, and a precise intermittent recovery back to the object, which together are considered a monophase (i.e., a monophasic SWJ). Events outside this morphology may be considered additional phases, whether they are additional events (e.g., additional deviations or recovery) or a combination thereof.

[0037] In certain forms, measuring one or more measures may involve the classification of two or more phases. In examples, phases may be classified as two or more of the following: primary discontinuous bias, secondary discontinuous bias, discontinuous recovery, gradual recovery, discontinuous spike, and coastal phase.

[0038] In certain forms, measuring one or more scales may involve quantifying one or more features of each of two or more phases.

[0039] In certain forms, one or more features of primary discontinuous bias and / or secondary discontinuous bias may include one or more of the discontinuous bias number, discontinuous velocity, discontinuous amplitude, and peak bias.

[0040] In certain forms, one or more features of intermittent and / or progressive recovery may include one or more of the following: intermittent or progressive recovery number, intermittent or progressive rate, intermittent or progressive amplitude, peak bias, and classification (e.g., accurate, hyperopic, or myopic).

[0041] In certain forms, one or more features of intermittent spikes may include one or more of the number of intermittent spikes and the intermittent amplitude.

[0042] In certain forms, one or more features of the coastal phase may include one or more of the following: duration, fibrillation, and inclination (e.g., flat angle, positive with respect to recovery, or negative with respect to recovery).

[0043] In certain forms, an index of variability in gaze stability between SWJ events can be measured. In certain forms, one or more features of the variability in gaze stability between SWJ events may include one or more of fibrillation and drift (e.g., sustained movement away from the object).

[0044] In certain embodiments, measuring an indicator of the presence of TBI in a patient may be based, at least in part, on the accumulated total number of SWJ occurrences. In some embodiments, a biphasic or polyphasic SWJ may be counted as a single SWJ occurrence. In other embodiments, each phase of a biphasic or polyphasic SWJ may be counted as a single SWJ occurrence.

[0045] In certain forms, SWJs with more phases may result in greater weighting. In certain forms, hierarchical weighting may be applied, at least partially, based on the complexity of the SWJ. In certain forms, weighting may be based, at least partially, on the number of phases of the SWJ. For example, greater weighting may result in an SWJ with more phases.

[0046] For example, hierarchical weighting can be applied, among which, 1. A typical SWJ is given as weighted W1, 2. A deformed SWJ is given as weighted W2, 3. A polyphasic SWJ[2] (i.e., measured as having two phases) is given as a weighted W3, 4. A polyphasic SWJ[3] is given as W4, 5. A polyphasic SWJ[4] is given as a weighted W5, and 6. Polyphasic SWJ[n] is W n+1 Given as, Here, W1 <W2<W3…<W n+1 That is the case.

[0047] In certain embodiments, the weighting can be adjusted based on one or more metrics of one or more features of the associated phase. For example, the weighting can be biased based on features such as the amplitude of the peak bias.

[0048] For example, the algorithm that implements this weighting can be Indicator of the presence of TBI = (typical SWJ 合計 *W1) + (malformed SWJ 合計 *W2) + (polymorphic SWJ[2] 合計 *W3) + … + (polymorphic SWJ[n] 合計 *W n+1 ) and may include.

[0049] In certain embodiments, each phase of a biphasic or polymorphic SWJ can be counted as one occurrence of an SWJ.

[0050] For example, the algorithm that implements this weighting can be Indicator of the presence of TBI = (typical SWJ 合計 *W1) + (malformed SWJ 合計 *W2) + (polymorphic SWJ[2] phase 合計 *W3) + … + (polymorphic SWJ[n] phase 合計 *W n+1 ) and may include.

[0051] In certain embodiments, additional eye data can be received, and the additional eye data represents the movement of the patient's fixated eye during a second fixation task. Measurement of a second indicator of the presence of TBI in a patient can be based on the additional eye data. The progression of the patient's neurological recovery can be measured based on a comparison between the second indicator of the presence of TBI in the patient and a previous indicator of the presence of TBI in the patient.

[0052] According to one aspect of the present technology, a system for investigating the presence of TBI in a patient is provided, the system including a processor configured to implement a computer-implemented method according to another aspect of the present technology.

[0053] According to one aspect of the present technology, a computer-readable medium is provided which stores instructions for performing a computer implementation method for investigating the presence of TBI in a patient, according to another aspect of the present technology.

[0054] Further embodiments of this technology should be considered in all its novel forms, and will become apparent to those skilled in the art by reading the following description which illustrates at least one example of a practical application of this technology.

[0055] Hereinafter, with reference to the drawings, one or more embodiments of this technology will be described for illustrative purposes only and without the intention of limitation. [Brief explanation of the drawing]

[0056] [Figure 1A] This is a frontal view of the human eye. [Figure 1B] Figure 1A is a cross-sectional view of eye 101 in the sagittal plane. [Figure 2A] This is a diagram illustrating typical eye behavior that shows microsaccades. [Figure 2B] This is a diagram illustrating typical eye behavior exhibiting subtle tremors. [Figure 2C] This is a diagram illustrating typical eye behavior that indicates drifting. [Figure 2D] This is a diagram illustrating typical eye behavior, showing rectangular wave eye movements. [Figure 2E] This is a plot of exemplary eye behavior exhibiting rectangular wave eye movements. [Figure 2F] This is a plot of exemplary eye behavior exhibiting abnormal rectangular wave eye movements. [Figure 2G] This is a plot of exemplary eye behavior exhibiting biphasic malformed rectangular wave eye movements. [Figure 2H] This is a plot of exemplary eye behavior exhibiting polyphasic malformed rectangular wave eye movements. [Figure 2I] This is a plot of exemplary eye behavior exhibiting abnormal rectangular wave eye movements. [Figure 2J] This is a plot of exemplary eye behavior exhibiting polyphasic malformed rectangular wave eye movements. [Figure 2K] This is a plot of exemplary eye behavior exhibiting abnormal rectangular wave eye movements. [Figure 2L] This is a plot illustrating exemplary eye behavior. [Figure 2M] This is an exemplary plot of eye behavior showing intermittent spikes in malformed rectangular wave eye movements. [Figure 3] This is a schematic diagram of a device and / or system for analyzing eye movements, following one representative form of this technology. [Figure 4] This is a schematic diagram of an eye-tracking system following one exemplary form of this technology. [Figure 5] This is a schematic diagram of an exemplary data analysis system that follows one form of this technology. [Figure 6] This is a flowchart illustrating an exemplary method for investigating the presence of TBI in a patient, according to one form of this technology. [Figure 7A] This is a diagram illustrating a display screen that shows the movement path of an object, in accordance with one form of this technology. [Figure 7B] Another illustration of the display screen in Figure 7A also shows the projection of the object's movement path over time along the X and Y axes. [Figure 8A] This is a diagram illustrating radially deformed eye tracking data in tracking eye movements, according to one form of this technology. [Figure 8B] This is a diagram illustrating radially deformed eye tracking data in tracking eye movements, according to one form of this technology. [Figure 8C] This is a diagram illustrating radially deformed eye tracking data in tracking eye movements, according to one form of this technology. [Figure 8D] This is a diagram illustrating radially deformed eye tracking data in tracking eye movements, according to one form of this technology. [Figure 9A] This is an illustration of eye tracking data in a tracking eye movement task, following another form of this technology. [Figure 9B] This is an illustration of eye tracking data in a tracking eye movement task, following another form of this technology. [Figure 9C] This is an illustration of eye tracking data in a tracking eye movement task, following another form of this technology. [Figure 9D] This is an illustration of eye tracking data in a tracking eye movement task, following another form of this technology. [Modes for carrying out the invention]

[0057] 7.1. Eye The present invention relates to devices, systems, and methods for analyzing data representing eye movements, such as the ability of the eye to track an object. Several relevant aspects relating to the anatomy and movement of the eye are described below. While the present invention primarily concerns the analysis of human eye movements, the eyes of other animals may be analyzed in other forms.

[0058] 7.1.1. Anatomy of the eye Figure 1A is a front view of a human eye 101, including the eye 104 and pupil 106. The movement of the eye 101 can be characterized by the movement of the eye 104 and / or pupil 106 along two mutually orthogonal axes, for example, the lateral axis relative to the body (i.e., the horizontal direction when the body is upright), indicated by the x-axis 107 in Figure 1A, and the vertical axis relative to the body (i.e., the vertical direction when the body is upright), indicated by the y-axis 108 in Figure 1A. These axes are also shown in Figure 1B, which is a cross-section of the eye 101 in Figure 1A in the sagittal plane (the vertical plane when the body is upright).

[0059] 7.1.2. Eye Exercises The body has muscles 104 that control eye movement. The human eye 101 has extrinsic and medial eye muscles. There are six extrinsic eye muscles that control eye movement and alignment within the orbit, and seven muscles that control part of the upper eyelid. The intrinsic eye muscles control the movement of the lens and the dilation / contraction of the pupil, which allows us to focus on nearby objects and control the amount of light entering the eye.

[0060] Eye movements are characterized by several types, including the following:

[0061] A saccade is a rapid, ballistic eye movement that shifts the gaze abruptly. The amplitude of a saccade varies from small movements, such as those seen when reading, to much larger movements, such as those seen when scanning a room. Saccades can be spontaneously induced, but they also occur reflexively when the eyes are open, even when fixating on an object.

[0062] Microsaccades are a type of fixation eye movement. They are small, twitchy, involuntary, minute eye movements, similar to miniature versions of voluntary saccades. They usually occur during prolonged fixation to prevent fading.

[0063] • A pro saccadic saccadic motion is a saccadic movement directed toward an object, and usually occurs reflexively.

[0064] An anti-saccade is a saccade movement away from an object, and is usually performed intentionally.

[0065] • Eye drift is a slower, more gradual movement that occurs between microsaccades during fixation, although the brain mechanisms behind eye drift are not fully understood.

[0066] Tremors are small, high-frequency fluctuations that occur between microsaccades.

[0067] • Fixation eye movements include, for example, microsaccades, square wave eye movements, tremors, and drift.

[0068] Fixation consists of slower, finer movements (fixation eye movements) that help to focus the eyes on an object and prevent perceptual fading. The duration can vary, for example, between 50 and 600 ms.

[0069] • Tracking eye movements are the tracking of much slower eye movements designed to keep a moving stimulus over the fovea. Such movements are under voluntary control in the sense that the viewer can choose whether or not to track a moving stimulus, and occur during saccades.

[0070] Rectangular wave eye movements are a type of eye movement that occurs within approximately 150–500 ms, moving away from or back to a point of fixation at a constant magnitude. Further discussion of rectangular wave eye movements in relation to eye movement disorders is provided below.

[0071] Figures 2A to 2D illustrate typical eye behavior, showing some of the eye movements described above. Figure 2A shows a plot of coordinates of eye fixation against time. The two small changes shown with respect to the direction of fixation can be characterized as microsaccades. Figure 2B shows a plot of horizontal (x) coordinates of eye fixation against time. The small high-frequency perturbation shown can be characterized as a fine tremor. Figure 2C shows plots of horizontal (x) and vertical (y) coordinates of eye fixation at various points in time, with the line indicating a continuity of points in the plot. The gradual shift of fixation over time shown in this plot can be characterized as drift. The movements shown in Figures 2A to 2C are typical fixation eye movements, i.e., small involuntary eye movements that can occur when a person attempts to fix their gaze on an object. Figure 2D shows a plot of coordinates of eye fixation against time. As shown in the figure, a rapid change in gaze direction from an object over a short period (e.g., several hundred milliseconds) and then a return to the object can be characterized as a square wave eye movement (SWJ). Since SWJs generally occur horizontally, the gaze position plotted on the vertical axis in Figure 2D can be the horizontal (x) coordinate of the eye gaze, at least for some embodiments of this application. For completeness, it should be understood that the discussion of motion in the horizontal direction is not intended to exclude instances in which SWJs occur in other directions (e.g., vertical).

[0072] This form of technology can be used to analyze one or more of the types of eye movements described above.

[0073] Oculomotor disorders occur when there is some abnormality or impairment in normal eye movement, such as saccadic or smooth pursuit movements being inaccurate, interrupted, or irregular in timing relative to an object. Saccadic oculomotor disorders are movement disorders in which the eyes move excessively or insufficiently relative to an object, accompanied by saccadic eye movements for correction. Specific indices can be used to quantify oculomotor disorders. For example, saccadic gain is the ratio of eye movement to target position, and stimulus delay is the delay in response from the presentation of a target-shaped stimulus to the initiation of a motor command.

[0074] 7.1.2.1.Square wave eye movement Figures 2E–2M show theoretical plots of the one-dimensional eye position 1000 (i.e., the horizontal coordinate of eye gazing) against time, compared to the fixation target position 1002, illustrating some more detailed features of the rectangular wave eye movements referred to herein. Figures 2F–2M show biphasic and polyphasic SWJs of malformation, illustrating various eye movement behaviors described herein.

[0075] As described above and briefly shown in Figure 2D, square wave eye movements (SWJs) are a form of eye movement that occurs within 150–500 ms and moves away from or back to the fixation point with approximately equal magnitude. Fixation eye movements (including microsaccades such as drift and tremors) are thought to improve visibility by interfering with neural adaptation to an unchanging stimulus. From the perspective of the importance of detecting square wave eye movements and characterizing several forms of this technique, the characterization of square wave eye movements in healthy and unhealthy patients is described in more detail here.

[0076] Referring to Figure 2E, a theoretical plot of the first-dimensional eye position 1000 (i.e., the horizontal coordinate of the eye's gaze) against time, compared to the fixation target position 1002, is shown. In healthy patients, the SWJ has a distinct and precise single bias from the object of fixation 1002 in the horizontal direction of less than 3°. This initial bias may be commonly referred to herein as the primary intermittent bias ("PSD") 1010.

[0077] PSD 1010 is followed by a coastal phase 1020 in which the eye position 1000 is maintained. Peak deviation ("PD") 1014 may occur between PSD 1010 and coastal phase 1020 (not shown in Figure 2E, see Figure 2F). The reference to peak deviation 1014 should be understood as meaning the initial overshoot of intermittent motion before returning to coastal phase 1020. In healthy patients, PD 1014 is small or negligible. In contrast, Figure 2F shows a malformed SWJ with a large PD 1014 following a late PSD 1010.

[0078] In healthy patients, the sacral phase 1020 is accompanied only by small fibrillation and can last for approximately 70 ms to 700 ms (on average, approximately 200 ms). In contrast, Figure 2F shows a malformed SWJ with a sacral phase 1020 with a negative inclination. Following the sacral phase 1020, an intermittent recovery ("SR") 1030 returns the eye position 1000 to the object of fixation 1002. In healthy patients, the SR 1030 is accurate.

[0079] In individuals with mTBI, including those with a progressive symptom load and those with mild and severe phenotypes, the brain's ocular system that manages square wave eye movements may be observed to have altered characteristics of square wave eye movements, enabling it to function reliably.

[0080] It has been established that rectangular wave eye movements can be biphasic in several patients with mTBI, meaning they overshoot and return to the fixation target by a third corrective saccade (which differs from the standard "tabletop" appearance of rectangular wave eye movements). See Figure 2G.

[0081] The inventors have identified that the movements can be morphologically diverse in further ways, and in severe cases, may include a series of intermittent movements in a multiphase manner. For example, some mTBI cases may show multiple deviations away from the object, with larger peak deviations than in healthy patients, and are unable to return to the object upon recovery. This may be both hyperopia (overshoot) and myopia (undershoot). Fibrillation and drift, observed in the coastal phase, may also be present. In describing the characteristics of square wave eye movements in detail, the inventors have further introduced new terminology to simplify the description of square wave eye movements as biphasic. In fact, complex multiphasic behaviors, including multi-stage attenuation in fixation movements, have been identified that are classifiable and quantifiable.

[0082] As will be discussed later, in certain forms of this technique, these characteristics can be measured and used to establish the severity of the condition. For example, intermittent motion can be quantified along with the subsequent coastal phase. In addition to the frequency and / or amplitude of SWJs, or as an alternative to these, failure of intermittent recovery and complexity of intermittent amplitude may be considered in the measurement of trauma.

[0083] Several examples of rectangular wave eye movements in patients with mTBI are shown in Figures 2F to 2K.

[0084] In one embodiment of this technique, SWJ may be measured including primary intermittent bias ("PSD") 10¹⁰. The PSD 10¹⁰ may include one or more of the following: intermittent velocity ("SV"), intermittent amplitude ("SA") 10¹⁰ (note that microsaccades are described in the literature as eye movements less than 3°, while saccades are greater than 3°), and peak bias ("PD") 10¹⁴.

[0085] In one embodiment of the present technology, SWJ may be measured after PSD 1010, including one or more additional intermittent biases, referred to herein as intermittent bias n ("SDn") 1016, i.e., additional "n" biases that move away from the object after PSD 1010. Measures of SDn 1016 may include one or more of the following: intermittent bias number (n) ("SDC"), intermittent velocity ("SV"), intermittent amplitude ("SA") 1012, and peak bias ("PD") 1014.

[0086] In one embodiment of this technology, SWJ may be measured as including a coastal phase 1020 between motions. The measure of coastal phase 1020 may include one or more of the following: duration (e.g., expected to be in the order of 100ms to 400ms) and slope (e.g., flat angle, positive with respect to recovery, or negative with respect to recovery). In the embodiment, fibrillation may be measured using the RMS error from the line, where a value of the standard deviation against a threshold (e.g., 1) indicates a malformation.

[0087] In one embodiment of this technology, SWJ may be measured as including one or more attempts during recovery, e.g., intermittent recovery ("SRn") 1030 and gradual recovery ("GRn") 1040 (where "n" is the number of attempts during recovery). Gradual recovery ("GRn") 1040 may be distinguished from intermittent recovery ("SRn") 1030 by the rate at which recovery occurs. For example, a typical microsaccade occurs in less than 10 ms, with most of the time consumed in a 200 ms coastal phase (appearing nearly vertical during tracking) prior to an equally rapid recovery. Gradual recovery may occur in a time frame of 50–60 ms and may be more oblique or curved compared to vertical. The scales for intermittent recovery ("Srn")1030 and / or progressive recovery ("GRn")1040 may include one or more of the following: intermittent / progressive recovery number (n) ("SRC" / "GRC"), intermittent / progressive velocity ("SV" / "GV"), intermittent amplitude ("SA"), peak deviation ("PD"), and recovery classification (e.g., as accurate, hyperopic, or myopic).

[0088] In one embodiment of this technology, and in particular with reference to Figure 2M, a SWJ may be measured as containing one or more intermittent spikes ("SS") 1050. The reference to intermittent spikes should be understood as meaning a malformation of the SWJ in which intermittent recovery is initiated but is unable to return to the biased state before attempting a more typical recovery. The measure of SS 1050 may include one or more of the number of intermittent spikes (n) ("SSC") and intermittent amplitudes ("SA") 1012.

[0089] 7.1.3. Eye Tracking The present invention relates to a device, system, and method for analyzing data representing the movement of eye 101. Such data can be obtained by "tracking" the movement of eye 101. Unless otherwise explicitly stated in the context, the term "tracking" means the act of identifying the movement of eye 101 over a period of time. Identifying the movement of eye 101 makes it possible to characterize and analyze that movement. In certain forms, the movement of eye 101 is tracked by visualizing the movement of pupil 106. The pupil 106 is the aperture through which light enters the eye 101, and its position indicates the direction of the eye's line of sight.

[0090] The eye's ability to track an object can be fixed or moving. A fixed object may be an object or display on a display screen that is held in a fixed position relative to the eye's field of vision for a certain length of time. A moving object may be an object or display on a display screen that moves relative to the eye's field of vision. For example, a display may move across a display screen presented to the eye. An object may be represented on a display screen by a dot-like object or image, such as a small image (e.g., a dot). Alternatively, an object may be a larger or more complex object or image. An object may also be called a visual stimulus.

[0091] Eye tracking may involve measuring and, optionally, characterizing any error in the eye's ability to track an object. For example, the error could be the difference in the eye's gaze direction compared to the object's position. The difference may be expressed by any appropriate parameter, such as physical distance, a scale equivalent to distance (e.g., pixels on a display screen), or an angle representing the difference in angle between the eye's gaze direction and the direction of the object from the eye.

[0092] 7.2. Eye Analysis Device / System A schematic diagram of a device and / or system 200 for analyzing eye movements, according to a specific form of this technology, is shown in Figure 3. Such a device / system may otherwise be referred to as an eye analysis device / system 200.

[0093] The eye analysis system 200 may include an eye tracking system 300 and a data analysis system 400. The eye tracking system 300 may be configured to track the movement of eye 101 and output data representing the movement of eye 101. This data may be provided to the data analysis system 400, which may analyze the data representing the movement of eye 101. The data analysis system 400 may output certain information obtained from the analysis process. In the following description, although described as separate functional systems, in some forms the eye tracking system 300 and the data analysis system 400 may be implemented in one or more of the same physical systems, such as a computer or computing network. In other forms, different physical systems may implement the functions provided by the eye tracking system 300 and the data analysis system 400, respectively. In some forms, each functional system may be implemented by multiple physical systems.

[0094] 7.2.1. Eye Tracking System In certain embodiments of this technology, the eye tracking system 300 may be any assembly of components configured to track the movement of the eye 101 and output data representing the movement of the eye 101. Any preferred eye tracking system 300 may be used, and exemplary embodiments are described with reference to Figure 4, which is a schematic diagram of the eye tracking system 300 according to one exemplary embodiment of this technology.

[0095] 7.2.1.1. Display Screen In the exemplary embodiment shown in Figure 4, the eye-tracking system 300 comprises a display screen 310. The display screen 310 may include any device configured to visually present information to the viewer. The information may be, for example, in the form of an image. The display screen 310 may be controllable to change the information displayed to the viewer. In particular, the display screen 310 may display an object 303 to the patient. For example, a stationary object or a moving object may be displayed to the observer. In the case of a moving object, the object 303 may move along a movement path 305. Since the range of eye movement may be important in detecting some medical conditions, in certain embodiments, the display is positioned to occupy a large portion of the field of view of the eye 101, for example, beyond 100° of the field of view. In other embodiments, the system may search for eye movements closer to the center of the field of view, in which case the display screen may be smaller. Since the object 303 may move on the display, this means that the viewer must move their gaze over a larger distance to track the movement of the object (e.g., up, down, left, and right).

[0096] In certain exemplary forms, the display screen 310 is an electronic display such as an LCD, LED, or OLED screen. In some forms, the display screen 310 may be displayed to an observer through a virtual reality (VR) or augmented reality (AR) display screen, while in other forms, a reflection of the display screen 310 may be displayed to an observer. The information displayed on the display screen 310 may be controllable by a processor included as part of the display screen 310, or may be configured to control the display screen 310 via a physical or wireless connection. In some forms, the display screen 310 is included as part of an electronic device such as a portable electronic device 350, such as a smartphone, tablet, or laptop computer.

[0097] In some forms, the display screen 310 may be self-emissive, for example, it may include light-emitting elements such as LEDs. In other forms, the display screen 310 may be non-self-emissive, for example, it may use electronic ink (e-ink) to display information. In such forms, a separate light source may be used to illuminate the display screen. It will be understood that the level of illumination must be sufficient for the camera settings, such as frame rate, exposure, and resolution, so that the image is sharp and not blurry.

[0098] In some forms, the eye tracking system 300 may be equipped with multiple display screens.

[0099] 7.2.1.2. Camera In certain embodiments of this technology, the eye-tracking system 300 comprises a camera 320. The camera 320 may include any optical device configured to capture and record visual images. An appropriate frame rate for the camera 320 can be guided by the Nyquist-Shannon sampling theorem, namely, that the sampling frequency must be at least twice the frequency of the motion being recorded. Since frequencies below approximately 100 Hz carry virtually no information about the motion of the fixating eye, in certain embodiments, the eye-tracking system 300 is thought to use a camera 320 with a minimum frame rate of approximately 200 Hz. For example, in one embodiment, the camera 320 has a frame rate of 240 Hz. This results in a time gap of 4.2 ms between frames, and consequently captures eye movements with timeframes longer than this timescale, including slight fixating movements such as SWJs and components of polyphasic SWJs, which may have durations as short as approximately 5 ms.

[0100] Furthermore, the resolution of camera 320 can be high enough to capture eye movements that are detected and analyzed in the analysis method. For example, microsaccades typically have a visual angle of less than 3°. In some forms, the camera may have, for example, a visual accuracy of approximately 0.01. In some forms, camera 320 may have sufficient resolution, and since camera 320 may be positioned relative to eye 101, the size of the image capturing eye 101 may be, for example, at least 500 × 500 pixels.

[0101] The captured image may be displayed on a display screen, in some forms this display screen may be a display screen 310 included as part of the eye-tracking device 200, and in other forms the camera may be configured to transmit the image to a display screen for displaying the image, or to another device which may include memory for storing the image for display elsewhere. The image may be transmitted via a wired or wireless connection to a display screen located, for example, away from the eye-tracking system 300. The image may be displayed on the display screen in real time, near real time, or at a time later than when it was captured by the camera. In some forms the camera 320 includes memory configured to store the visual image. In certain forms the camera 320 is a digital camera, and reference to an image may mean that the data recorded by the camera is representative of the image.

[0102] In certain exemplary forms, the camera 320 may be included as part of an electronic device such as a portable electronic device 350, such as a smartphone, tablet, or laptop computer. In such forms, the camera 320 may include a display screen 310 that can be configured to display images captured by the camera 320.

[0103] 7.2.1.3. Processor In certain forms of this technology, the eye tracking system 300 may comprise one or more processors. While multiple different processors may be used, the processes may be considered to operate together or as a functional unit. For the purposes of the following discussion, this specification will refer to a single processor configured to perform any one of the functions described, but it should be understood that multiple processors may be used in several forms.

[0104] The processor may be configured to generate data representing the movement of the eye 101 from image data captured by the camera 320. The processor may be configured as part of the same device as the camera, for example, a portable electronic device 350 in the form shown in Figure 4, or the processor may be located remotely from the camera 320 and receive image data from the camera 320, for example, via a wired or wireless connection.

[0105] The processor may control the display screen 310 and present information to the patient, for example, a stationary or moving object that the patient gazes at during eye tracking. The processor may be configured to generate data representing the position of the object 303. A preferred motion protocol may be provided to the processor, for example, from memory or via a preferred communication connection.

[0106] The processor may be further configured to output data representing the movement of eye 101. The data may be output by the processor by transmitting the information over a suitable communication network or by outputting the information through a main device, such as a display screen 310. Alternatively, the information may be stored in memory, such as the memory of a portable electronic device 350, for later output. The output data representing the movement of eye 101 may be an array of data representing the position and / or movement of eye 101, such as the pupil 106, at multiple point in time. The data may be output in any suitable format, such as a CSV file.

[0107] The processor may be further configured to output data representing the position of object 303 (which may include data representing the motion of object 303 if the object is in motion). This data may exist concurrently with data representing the motion of eye 101; that is, when eye 101 is gazing and eye movement is captured in eye movement data, the object data represents the motion of object 303. The data may be output by the processor by transmitting the information over a suitable communication network or by outputting the information through a main device, such as a display screen 310. Alternatively, the information may be stored in memory, such as the memory of a portable electronic device 350, for later output. The output data representing the motion of object 303 may be an array of data representing the position and / or motion of object 303 at multiple points in time. The data may be output in any suitable format, such as a CSV file.

[0108] In some configurations, the processor may output together, for example, data representing the movement of eye 101 and data representing the movement of object 303 in the same data file. The output data may be timestamped so that the position of eye 101 is recorded relative to the position of object 303 at each point in time.

[0109] 7.2.2. Data Analysis System Figure 5 is a schematic diagram of an exemplary data analysis system 400 according to one embodiment of the present technology. The data analysis system 400 may comprise a hardware platform 402 that manages the collection and processing of data from an eye-tracking system 300, for example, data representing the movement of the eye 101 and data representing the movement of an object 303. The hardware platform 402 may comprise a processor 404, memory 406, and other components typically present in such a computer device. The hardware platform 402 may reside locally with the eye-tracking system 300, or it may be remote from the eye-tracking system 300 and receive data via a preferred communication connection, such as a network 416. In the exemplary embodiment of the present technology described herein, the memory 406 stores information accessible by the processor 404, which includes instructions 408 that can be executed by the processor 404, and data 410 that can be read, manipulated, or stored by the processor 404. The memory 406 may be any suitable means known in the art that can store information in a manner accessible by the processor 404, including a computer-readable medium or other medium for storing data that can be read using an electronic device.

[0110] The processor 404 may be any suitable device known to those skilled in the art. Although the processor 404 and memory 406 are illustrated as being in a single unit, this is not intended to limit them, and it should be understood that each of the functionalities described herein may be performed by multiple processors and memories, which may be remote or not remote to each other or from the processing system 400. Instructions 408 may include any set of instructions suitable for execution by the processor 404. For example, instructions 408 may be stored as computer code on a computer-readable medium. Instructions may be stored in any suitable computer language or format. Data 410 may be read, stored or modified by the processor 404 in accordance with the instructions 410. Data 410 may also be formatted in any suitable computer-readable format. Again, although data is illustrated as being contained in a single location, this is not intended to limit it, and it should be understood that data may be stored in multiple memories or locations. Data 410 may also include a record 412 of control routines for an embodiment of the system 400.

[0111] The hardware platform 402 may communicate with a display device 414 and display the results of data analysis. In some forms, the display device 414 may be a display screen 320 included as part of an eye-tracking system 300. The hardware platform 402 may communicate via a network 416 with one or more other devices (e.g., user devices such as a tablet computer 418a, a personal computer 418b, or a smartphone 418c, or other devices equipped with sensors), or with one or more server devices 420 having associated memory 422 for storage and processing data collected by the local hardware platform 402. It should be understood that the servers 420 and memory 422 may take any preferred form known in the art, for example, a “cloud-based” distributed server architecture. The network 416 may include a variety of configurations and protocols, whether wired or wireless, including the internet, intranets, virtual private networks, wide area networks, local networks, private networks using one or more enterprise-specific communication protocols, or a combination thereof.

[0112] After analyzing data representing the movement of eye 101, the data analysis system 400 may be configured to output certain information obtained from the analysis process, an example of which is described in more detail below. The information may be output by the data analysis system 400 by transmitting the information via the network 416, or by outputting the information through an output device, such as a display device 414, a tablet computer 418a, a personal computer 418, or a smartphone 418c. Alternatively, the information may be stored in memory, for example, one or both of the memories 406 or 422, for later output from the data analysis system 400.

[0113] In a particular form, the hardware platform 402 of the data analysis system 400 may include a computer device, such as a laptop or PC. In other forms, the hardware platform 402 may include multiple computer devices configured to operate collectively to perform data analysis / processing.

[0114] 7.3. Methods for analyzing eye tracking data In certain forms of this technology, one or more methods are provided for analyzing eye tracking data, i.e., data representing the movement of the eye 101. Unless otherwise stated, it should be understood that the analysis methods may be performed by a data analysis system 400, such as those described above, and in relation to Figure 5.

[0115] 7.3.1. Methods for investigating the presence of TBI Certain forms of this technology relate to methods, systems, and devices for investigating the presence of traumatic brain injury (TBI) in patients. In some forms, the investigation concerns the presence of mild traumatic brain injury (mTBI), which may alternatively be called concussion.

[0116] Figure 6 is a flowchart of an exemplary schematic method 600 for investigating the presence of TBI in a patient. Method 600 may be performed by a data analysis system 400, such as that shown in Figure 5, although in some forms other steps of the method, such as a preprocessing step, may be performed by the processor of an eye-tracking system 300. The method may include receiving eye data representing the movement of the patient's eyes 101, analyzing the eye data to measure an indicator of the presence of TBI in the patient, and outputting the indicator. Each of these steps is described in more detail below.

[0117] 7.3.1.1. Object Identification In a first step 601 of an exemplary method according to one embodiment of the present technology, the display screen 310 is controlled to display an object 303 to the patient for confirmation by the patient's eye 101. In a particular embodiment, one or both of two types of objects 303: a stationary object and / or a moving object may be displayed to the eye 101.

[0118] If the object 303 is a fixed object, this may be called a fixation task. That is, the object 303 is displayed on a display screen 310, and the patient is asked to gaze at the fixed object. The display screen 310 may be controlled to display the fixed object in an eccentric position relative to the patient. That is, this is a position in the field of view that is not directly in front of the patient, but where the eyes are directly facing forward. In some forms, the eccentric position may be thought to include an angle that defines up to 120° from central fixation. Considering the distance from the eyes 101, it will be understood that the display screen 310 needs to be large enough to allow the object 303 to be displayed in such a position.

[0119] In certain configurations, the display screen 310 can be controlled to display multiple fixed objects sequentially, that is, to display objects one after another. Each fixed object may be displayed for a random period, and / or the time interval between each fixed object being displayed may be random. The selection of random periods, and / or random time intervals, may be limited to certain maximum and minimum periods. Furthermore, or / or, multiple fixed objects may be displayed at different positions in the field of view of the eye. The selection of the display position of each fixed object on the display screen 310 may also be randomly selected from locations on the display screen 310 that have eccentric coordinates with respect to the eye 101.

[0120] If the object 303 is a moving object, it may be called a tracking eye movement task. That is, the moving object 303 is displayed on a display screen 310, and the patient is asked to gaze at the moving object and continue gazing at it as the moving object moves around the display screen 310. The display screen 310 may be controlled so that the moving object follows a predetermined path around the screen, and in some forms the moving object 303 may trace a specific shape, e.g., a circle or an ellipse, and the moving object 303 may repeatedly trace the same shape. In the example of the display screen 310 shown in Figures 7A and 7B, the object 303 may be any icon, e.g., a dot, and the object follows a movement path 305 on the display screen 310. The movement path 305 may be an ellipse, a circle, a sinusoid, a sawtooth, or any other movement path deemed suitable for testing the patient's eye tracking. The object 303 may move in either a clockwise or counterclockwise direction. In the exemplary display screen 210 in Figure 7B, the position of the object 303 is shown as a series of positions 303a, 303b, 303c, and 303d along its motion path 305 over time. The same figure also shows the projection of that path onto the X and Y axes over time. Due to the elliptical motion path 305 of the object 303 in this example, the eyes can move in both the X direction (corresponding to points 303a and 303c) and the Y direction (corresponding to points 303b and 303d) to train the range of eye movement. In some examples, the motion path 305 may be modified to be flat or off-axis to suit certain test conditions. The repeated movement of the object 303 along the motion path 305 smoothly trains the human eye-brain interface. In the illustrated example, the motion along the X and Y axes is sinusoidal, but in other examples, it can be modified to a sawtooth or square wave.

[0121] In some forms, the speed and / or amplitude of the movement of the object 303 on the display screen 210 can be changed over time. This increases the cognitive stress and physiological demands on the object during the tracking eye movement task. As will be explained later, increased cognitive stress increases the severity of symptoms, and it may be possible to identify a limit by speeding up the movement. In some forms, the subject is tested over a series of trials, and the movement speed of the object 303, or the acceleration rate of the object 303, may be gradually increased with each trial.

[0122] 7.3.1.2. Eye Tracking In step 602, which may occur simultaneously with step 601, the movement of the eye 101 is tracked while performing a fixation and / or tracking eye movement task. An eye tracking system 300, such as the one described above, may be used in this step, and the camera 320 may capture an image of the eye 101 during the task while the eye 101 is gazing at the object 303.

[0123] The eye tracking system 300 can generate data representing eye movement from images captured by the camera 320. This data can be provided to the data analysis system 400 using any preferred data communication protocol. The data representing eye movement from images captured by the camera 320 may include eye data representing eye movement and object data representing the position of the object.

[0124] In some forms, a calibration step may be performed in which the patient is asked to gaze at an object at one or more positions on the display screen 310. From the data generated in this step, a transformation may be measured to convert the detected eye position coordinates into the actual gaze coordinates. This transformation may be applied to eye movement data, as will be described later.

[0125] 7.3.1.3. Pre-processing In step 603, the data analysis system 400 receives eye data representing the movement of the eye 101 during the task. The data analysis system 400 may also receive object data representing the position of the object 303 during the task (if the data analysis system 400 does not already have this data), for example, this object data may be received from the processor of the eye tracking system 300.

[0126] In some forms, eye data representing the movement of eye 101 during the task, and / or object data representing the position of object 303 during the task, may need to be preprocessed in step 604 before further analysis can be performed. Examples of such preprocessing are described here. Although the data analysis system 400 is described as performing these preprocessing steps, it should be understood that in other forms, the eye tracking system 300 may perform some of these steps before providing the data to the data analysis system 400.

[0127] In an exemplary step, two datasets (i.e., one representing eye movements and another representing the position / movement of an object) are processed so that they can be easily compared and analyzed with respect to each other. For example, the datasets can be loaded, aligned, and curated by applying a primary extraction function. This function can also facilitate further analysis at each task of the protocol. More precisely, the data representing the position of object 303 in the eye-tracking protocol, which may consist of the object's position and timing on the display screen 310, is stored as annotations, which are loaded and sampled at a frame rate that matches the annotations for the data representing eye movements.

[0128] With respect to eye data representing eye movement, pupil tracking coordinates are loaded and resampled before being merged with simultaneously existing data representing the object's position. In certain exemplary forms of this technique, the gaze direction can be measured by detecting the position of the iris of the eye relative to the sclera. Detecting the iris position may be more tolerant of upper eyelid obstruction (which may occur during pupil tracking) and any occlusion due to reflections compared to detecting the pupil position. The radius of the iris is substantially fixed compared to the pupil, which changes size when expanded. Furthermore, excellent sampling is achieved by detecting the lateral curvature of the iris, which is greater than the curvature of the pupil. Thus, by using perspective-based curvature detection, the orientation of the eyeball in the orbit can be estimated with good accuracy. For example, in some forms of this method, the eye orientation can be measured to a sub-pixel estimate of approximately 0.25% of the pixels, which can be roughly equivalent to using a 2000 x 2000 pixel camera. For an average human adult eye with a width of 24 mm, this allows tracking the center of the eye at approximately 0.06°. This is smaller than the minimum amplitude of typical eye movements, thus improving the accuracy of the results. For example, the minimum amplitude of a SWJ can be approximately 0.2° to 1.5°. Since the components of a multiphasic SWJ can be as small as 0.1°, even smaller magnitudes than this can provide useful information from a quantitative perspective.

[0129] In some forms, this can be achieved by applying a pre-filter to the iris to normalize its color and texture, applying an object detection algorithm, such as a one-shot object detector, to the eye data to discover the iris boundary. An example of a one-shot object detector that can be applied to discover the iris boundary in certain forms is the YOLO (You Only Look Once) v7 real-time object detector. The one-shot object detector can first be trained with a suitable set of eye data in which all present irises are accurately labeled. A partial curvature detector can then be applied to estimate the iris's, fully elliptical shape, and consequently, the center of the iris and pupil, even if the pupil is closed by the eyelid. By measuring the center of the iris and pupil, the gaze direction can be derived using established techniques well known to those skilled in the art. In some forms, the method may include applying a secondary tracker to explore the curvature of the pupil and estimating the pupil's diameter, which may change in size during dilation.

[0130] In some forms, eye data can be processed to estimate the position of one or more angles of the eye 101. This can be achieved using existing techniques for detecting eye angles. If a change in the position of the eye angle(s) is detected, appropriate adjustments can be made to the measured pupil position and, therefore, the measured gaze direction.

[0131] In some forms, blink detection may be performed. In some forms, blink detection is performed to exclude eye data captured during blinking from further analysis. This ensures that the results are not affected by any abnormalities in eye movement that may occur during blinking. In other forms, blink detection may be used to measure blinking, e.g., the time between blinks and / or the frequency of blinking. In some forms, one or more of these may be useful measures to aid in the investigation of mTBI. In certain forms, blink detection may involve applying an object detection algorithm, e.g., a one-shot object detector, to the eye dataset. In certain forms, an example of a one-shot object detector that may be applied to an eye dataset for blink detection is the YOLO (You Only Look Once) v7 detector. Here again, the one-shot object detector may first be trained with a suitable set of eye data in which all present eyelids are accurately labeled. In some forms, the detection algorithm may further detect the eyelid eyelash position in the image data of eye 101. Since the eyelid position changes over time during blinking, the change in the vertical position of the eyelid may be used to measure the eyelid velocity / blinking velocity. In other forms, blinking may be detected by detecting the absence of a pupil and inferring that this absence is due to the eyelid being closed during blinking.

[0132] In another, optional preprocessing step, the coordinates of the eye position in the eye movement data can be transformed into gaze coordinates by applying any necessary transformations measured from the calibration step.

[0133] 7.3.1.4. Measurement of eye movement scale In step 604, the data analysis system 400 analyzes the data. As described, the analysis may measure the presence of TBI (e.g., mTBI) in the patient.

[0134] In certain forms, the analysis may involve measuring one or more scales of eye movement, and an indicator of the presence of TBI in a patient may be measured based on one or more scales. In different forms of this technique, the measurements may be based on each scale individually, or in other forms, the measurements may be based on a combination of two or more scales. Any combination of two or more scales may be used, and the forms of this technique are not limited to any particular combination of scales. It will be understood that the accuracy of the measurement may increase when more scales are used to measure the condition.

[0135] In general, measuring the presence of TBI can be based on comparing each scale to one or more thresholds. TBI may be measured as present if the corresponding scale is above or below the corresponding threshold. While TBI is not a condition diagnosed in a binary manner, it will be understood that thresholds may be selected to provide a specific level of confidence regarding whether the condition is present or not. In some forms, multiple thresholds may be used for each scale, and the thresholds provide a range of confidence levels regarding whether the condition is present or not. Such information may be carried as output, as will be described in more detail below.

[0136] The following chapters describe exemplary scales that can be measured from the data, methods for measuring them, and methods for using them to measure an indicator of the presence of TBI. While exemplary methods for measuring scales have been described, it should be understood that scales may be measurable using other methods in other forms of this technology. For example, in a particular form, the data analysis system 400 may perform computer analysis of eye movement data based on an approach that involves the application of algorithms, such as extracting features of eye movement data, classifying features of eye movement data, deriving values ​​for eye movement data on scales, and deriving and outputting an indicator of the presence of TBI in the patient.

[0137] Furthermore, machine learning methods may be used to analyze the large amount of data generated by the eye-tracking process and to measure a metric from this data. For example, in a particular form, a machine learning model(s) may be trained to measure an indicator of the presence of TBI in a patient. Such a machine learning model(s) may include one or more models of one or more machine learning algorithms (e.g., deep learning models using artificial neural networks). During training, the machine learning algorithm(s) may be trained by providing training data as input using learning techniques commonly known in the field of machine learning. In an embodiment, the training data may include eye-movement data described herein, including examples from healthy patients and patients experiencing TBI. In an embodiment, a machine learning model(s) may be trained to identify a metric within the eye-movement data described herein, from which an indicator of the presence of TBI in a patient may be measured. In an alternative embodiment, a machine learning model(s) may be trained to infer an indicator of the presence of TBI in a patient directly from eye-movement data.

[0138] 7.3.1.5. Microsaccade Frequency In an exemplary form, eye data representing eye movements during a fixation task are analyzed to measure the frequency of microsaccades during the fixation task.

[0139] In certain forms, saccades are filtered from eye movement data using any preferred form of computer algorithm, such as the previously described velocity and distance-based Hidden Markov Model (HMM) (Salvucci, DD, & Goldberg, JH, 2000, Identifying fixations and saccades in eye-tracking protocols, paper presented at the Proceedings of the 2000 symposium on Eye tracking research, Palm Beach Gardens, Florida, USA, https: / / doi.org / 10.1145 / 355017.355028). This process can cluster saccades into two groups depending on the distance of eye movement between the start and end of each saccade: small saccades and large saccades. In some forms, the model may apply two states, one state being fixation and the other state being any other eye movement. Alternatively, in another form, the model could offer three states: one state being fixation, another being a clipped value (after high-pass filtering), and yet another being any other eye movement.

[0140] In some forms, this approach is used to automatically label training data provided to a transformer model, i.e., a neural network that learns context and therefore meaning by tracking relationships in sequential data. The transformer model can be constructed by coding eye movements per frame as a sequence of linear vectors. These movements can be labeled into different classes as part of the training data. These different classes may be, for example, tracking eye movements, fixation, saccades, microsaccades, tremors, and square wave eye movements (or components thereof). While some training data can be automatically labeled, some manual overrides may be applied to correct any errors and fine-tune the labeling. The performance of the transformer model improves as more data is collected and labeled from patients. The transformer model can perform far better than conventional heuristic models because it can be trained with a large amount of training data from points where the context of a sequence of movements before and after can be obtained. Furthermore, the model may be able to accommodate secondary effects, such as the current pupil position, which may be important because certain parts of the eye range of movement can affect performance, along with sufficient data.

[0141] The mean and standard deviation of the states can be initialized using the percentiles of the input data. Saccades can be further classified using information from annotations on target position data; for example, corrective saccades can be identified if they precede the start of target display. The smallest saccades, e.g., those with amplitudes below a given threshold, can be classified as microsaccades and / or microsaccade intrusions, and can occur in either horizontal or vertical meridians. Saccades are voluntary eye actions of looking at an object, based on human reaction time, while microsaccades are involuntary fixation movements, and microsaccades can typically be of similar speed but have an amplitude an order of magnitude smaller, e.g., 0.2° to 1.5°, and are not affected by reaction time. These measures together can be used in several forms of analysis.

[0142] In certain forms, the frequency of microsaccades can be measured when an object is presented to a patient during a fixation task. If multiple fixed objects are presented to the patient for fixation, the microsaccade frequency can be calculated as the average of the microsaccade frequencies for multiple individual objects.

[0143] In some forms, a positive diagnosis of TBI may be made if the frequency of microsaccades exceeds a certain threshold. In some forms, a diagnosis of mild TBI (i.e., mTBI or concussion) may be made if the frequency of microsaccades exceeds a first, lower threshold but is lower than a second, higher threshold. In such forms, a diagnosis of moderate or severe TBI may be made if the frequency of microsaccades exceeds the second, higher threshold. In certain forms, the threshold may be the absolute value of the microsaccade frequency measured by experimental observations in previous patients. In other forms, the threshold may be calculated from a control value of microsaccade frequency measured experimentally as "normal." For example, the threshold may be a certain percentage (%) above the control value. This percentage (%) may also be measured by experimental observations in previous patients.

[0144] 7.3.1.6. Gaze error and target error In an exemplary form, eye data representing eye movement and object data during a fixation task are analyzed to measure a measure of the mean error between the position of the fixed object and the patient's gaze position during the fixation task. For example, the measure of mean error could be the root mean square error (RMSE) between the patient's gaze and the object position, which may also be called the root mean square deviation (RMSD). In other forms, any other statistical measure of the error between the position of the fixed object and the patient's gaze position during the fixation task may be used. If multiple fixed objects are presented to the patient for gazing, the measure of mean error may be calculated as the average of the errors for multiple individual objects.

[0145] The error between the position of a fixed object and the patient's gaze position can be expressed by any appropriate parameter, such as physical distance, a scale equivalent to distance (e.g., pixels on a display screen), or an angle representing the difference in angle between the direction of eye gaze and the direction of the object from the eye.

[0146] In an eye-tracking system 300 that measures the gaze direction of the eye 101, if calibration errors exist, the measure of the mean error may be asymmetrical. To address this possibility, in some forms, a best-fit line can be identified at the participant's central gaze position. The central gaze position is presumably directed towards an object, and the gaze data is adjusted accordingly. This approach can compensate for cases where the user and the device may move relative to each other after calibration.

[0147] In some forms, a positive diagnosis of TBI may be made if a measure of the mean error between the position of a fixed object and the patient's gaze position exceeds a certain threshold. In some forms, a diagnosis of mild TBI (i.e., mTBI or concussion) may be made if the measure of the mean error exceeds a first, lower threshold but is lower than a second, higher threshold. In such forms, a diagnosis of moderate or severe TBI may be made if the measure of the mean error exceeds the second, higher threshold. In certain forms, the threshold may be the absolute value of the mean error measured by experimental observations in previous patients. It will be understood that such an absolute value may depend on the specific settings of the device used, e.g., the size of the display screen 310 and the distance of the display screen from the eye 101. In other forms, the threshold may be calculated from a control value of the mean error measured experimentally as a "normal" value. For example, the threshold may be a certain percentage (%) above the control value. This percentage (%) may also be measured by experimental observations in previous patients.

[0148] 7.3.1.7.Square wave eye movement Rectangular wave eye movements were selected as a strong candidate for an eye movement scale to correlate with mTBI due to their involuntary nature and correlation with various neurological conditions. These movements are thought to result from a malfunction of cortical cells that suppress saccades during fixation events.

[0149] In an exemplary form, eye data representing eye movements during a fixation task are analyzed to measure a measure of one or more features of square wave eye junctions (SWJs) during the fixation task. For ease of understanding, references to SWJ analysis in this chapter should be understood to include the various forms of SWJ described herein, including typical SWJs, malformed SWJs, biphasic SWJs, and polyphasic SWJs.

[0150] In certain forms, measuring one or more measures may involve the classification of two or more phases. In examples, phases may be classified as two or more of the following: primary discontinuous bias, secondary discontinuous bias, discontinuous recovery, gradual recovery, discontinuous spike, and coastal phase.

[0151] In certain forms, measuring one or more scales may involve quantifying one or more features of each of two or more phases.

[0152] In certain forms, measuring the frequency and / or amplitude of SWJs may depend on identifying saccades using the methods described above, and for each pair of saccades, identifying one or more of the following: 1) a magnitude index, i.e., an index comparing the magnitudes of the two saccades in the pair; 2) the angle between both saccade vectors; and 3) an interval between saccades (ISI), i.e., the time between saccades in the pair.

[0153] In the eye movement data, the angle between saccades ranges from 0° to 180°, which focuses on the horizontal meridian and takes saccade amplitude into account.

[0154] In certain forms, the model may be fitted to a dataset of saccade pairs or sequences (identified as SWJs), for example, by clustering their features using a diagnostic covariance matrix so that correlations between features are not captured. The model may be a machine learning approach fitted from suitable patient training data, and eye movements can be classified using a Bayesian-Gaussian mixture model (GMM), ex-Gaussian model, or gamma distribution model between saccade intervals. In an example where a GMM is used, the GMM component with the mean closest to 180° may be considered an SWJ cluster. Certain features of the cluster, such as the mean and variance, may be measured and used later to identify SWJs. Other components may not be used in some forms.

[0155] Subsequently, during detection, a two-component Hidden Markov Model (HMM) can be used to assign pairs of saccades to either a SWJ state or an "other" state. The HMM may use a Gaussian distribution for the observations (pairs of saccades). The SWJ state can be initialized with the mean and variance of the SWJ component from the GMM. The "other" state can be initialized with the mean and variance of the entire training dataset.

[0156] A transition matrix can be set up to force an "other" state to follow an SWJ state. The "other" state can transition to the SWJ state or to itself with a probability of 0.5. In this way, saccades in pairs that are part of an SWJ cannot be identified as part of another saccade that occurs immediately afterward.

[0157] In certain forms, detecting a SWJ may then involve running the Viterbi algorithm using this HMM to obtain the sequence of the most likely state.

[0158] In certain forms, a bidirectional attention-based transformer model can be used to classify all eye movements. This provides a single, integrated model for classifying eye movements over time. To train the transformer, sample eye data can be coded into a series of motion vectors, which are labeled into different classes such as tracking eye movements, fixation, saccades, microsaccades, tremors, and square wave eye movements. This labeling can be automated using all the processes described. A subsequent process may exist that allows a professional to reclassify any labels as needed. A transformer trained with this labeled data can utilize a series of forward and backward movements over time. Furthermore, the model can accommodate different types of eyes and secondary effects such as the current pupil position, which can be important because, with sufficient data, certain parts of the range of eye movements can affect performance. This model can be more accurate than a professional human, while being more computerically efficient and robust than statistical models used to automate the labeling of training data. This model can be retrained again when new data is collected, further increasing the accuracy of the classification.

[0159] The methods for identifying SWJs are described, and as a result, in certain forms, during a fixation task, the frequency of SWJs when an object is presented to the patient, and / or a measurement consisting of defined components of an SWJ, can be used as an inference for injury. When a series of fixed objects are presented to the patient for gazing, the SWJ frequency and / or amplitude can be calculated as the average of the SWJ frequency / amplitude for multiple individual objects, while the frequency of SWJ errors is cumulative.

[0160] In some forms, a positive diagnosis of TBI may be made if the frequency and / or amplitude of SWJ measurements exceed a certain threshold. In some forms, a diagnosis of mild TBI (i.e., mTBI or concussion) may be made if the frequency and / or measurement of SWJs exceeds a first, lower threshold but is lower than a second, higher threshold. In such forms, a diagnosis of moderate or severe TBI may be made if the frequency and / or amplitude of SWJs exceeds the second, higher threshold. In certain forms, the threshold may be the absolute value of microsaccade and failure frequency, as well as a measure measured by experimental observations in previous patients. In other forms, the threshold may be calculated from a control value of SWJ frequency measured experimentally as "normal." For example, the threshold may be a certain percentage (%) above the control value. This percentage (%) may also be measured by experimental observations in previous patients.

[0161] In certain embodiments, measuring an indicator of the presence of TBI in a patient may be based, at least in part, on the accumulated total number of SWJ occurrences. In some embodiments, a biphasic or polyphasic SWJ may be counted as a single SWJ occurrence. In other embodiments, each phase of a biphasic or polyphasic SWJ may be counted as a single SWJ occurrence.

[0162] In certain forms, SWJs with more phases may result in greater weighting. In certain forms, hierarchical weighting may be applied, at least partially, based on the complexity of the SWJ. In certain forms, weighting may be based, at least partially, on the number of phases of the SWJ. For example, greater weighting may result in an SWJ with more phases.

[0163] For example, hierarchical weighting can be applied, among which, 1. A typical SWJ is given as weighted W1, 2. A deformed SWJ is given as weighted W2, 3. A polyphasic SWJ[2] (i.e., measured as having two phases) is given as a weighted W3, 4. A polyphasic SWJ[3] is given as W4, 5. A polyphasic SWJ[4] is given as a weighted W5, and 6. Polyphasic SWJ[n] is W n+1 Given as, Here, W1 <W2<W3…<W n+1 That is the case.

[0164] In certain forms, the weighting can be adjusted based on one or more measures of one or more features of the relevant phases. For example, the weighting can be biased based on features such as the amplitude of the peak bias.

[0165] For example, an algorithm that implements this weighting is: Indicators of TBI presence = (typical SWJ) 合計 *W1)+(Deformed SWJ) 合計 *W2)+(polyphasic SWJ[2] 合計 *W3)+…+(Polymorphic SWJ[n] 合計 *W n+1 ) may include.

[0166] This algorithm (wherein W1=1, W2=2, W3=3, W n+ In an embodiment implementing (where =n+1), it may be measured that during fixation, there were two healthy SWJs, four malformed SWJs, two polyphasic[2]SWJs, and one polyphasic[4]SWJ. Thus, the score for the index of TBI presence is calculated as (2*1)+(4*2)+(2*3)+(1*5)=2+8+6+5=21.

[0167] As a further example where each phase is counted as an occurrence, an algorithm that implements this weighting is: Indicators of TBI presence = (typical SWJ) 合計 *W1)+(Deformed SWJ) 合計 *W2)+(polyphasic SWJ[2] phase 合計 *W3)+…+(Polyphasic SWJ[n] phase 合計 *W n+1 ) may include.

[0168] In this embodiment, 合計 This is the number of phases measured within the SWJ.

[0169] One embodiment (wherein W1=1, W2=2, W3=3, W n+ In (where =n+1), it can be measured that during fixation, there were two healthy SWJs, four malformed SWJs, two polyphasic[2]SWJs, and one polyphasic[4]SWJ. Therefore, the score for the index of TBI is calculated as (2*1)+(4*2)+([2*2]*3)+([1*4]*5)=2+8+12+20=42.

[0170] The higher the total score (i.e., the rating), the more symptomatic the patient is of TBI. As mentioned above, the rating can indicate relative severity based on comparison with data measured experimentally.

[0171] It should be understood that alternative methods exist for constructing metrics, and the examples provided herein are not intended to limit the scope to all forms of this technology.

[0172] 7.3.1.8. Exhaustion following a moving object In an exemplary form, eye data representing eye movements and object data during a tracking eye movement task are analyzed to measure a degree of weakness in tracking eye movements following a moving object.

[0173] In certain forms, detecting changes in gaze dynamics during the acceleration phase of a tracking eye movement task is treated as a change point detection ("weakness point") problem. This method may involve analyzing data collected during the tracking eye movement task and using rank statistics and dynamic programming (repeatedly storing calculated values ​​for later analysis) to identify changes in the patient's gaze accuracy during tracking eye movements and investigate change points, e.g., optimal intrinsic change points. Exemplary, suitable algorithms are mentioned below. Weakness points can be measured by one or more of these algorithms (or any other suitable algorithms) that use rank statistics to find individual, intrinsic weakness points. In some forms, the algorithm essentially graphs the entire distribution of errors as a function of time and then finds in the graph where it splits into two states. In some forms, the measure of weakness may be the time before weakness occurs in the tracking eye movement task. Weakness can occur when the accuracy of the eyes tracking a moving object falls below a threshold. More specifically, the method may involve identifying changes in the distribution of errors between the detected position of eye gaze and the position of the moving object on the display screen 310. In some forms, the error may be calculated from eye movement and object movement data as the Euclidean distance between the position of the object on the display screen 310 and the position of the patient's gaze at each point in time. In other forms, the error may be calculated as some other measure of the distance between the object position and the gaze position, or as the angular error between these positions.

[0174] In some forms, the velocity of eye movements during a tracking eye movement task can also be calculated. Velocity can be calculated as a distance measurement per unit time or as an angle measurement per unit time. Calculating eye movement velocity can enable the calculation of a measure of the user's eye movement fluidity, i.e., a measure of how well the velocity of eye movements is maintained and how uniformly the direction changes. This can be useful for calculating further measures, in addition to the error between object position and gaze position, as it is possible to smoothly track a patient's gaze or track short eye movements while still achieving low Euclidean errors (i.e., precision and lag analysis). However, smoother movement (i.e., higher fluidity) at a uniform velocity indicates better performance. In some forms, one or more filters can be applied to the eye data to reduce the noise level in the eye data for the tracking eye movement task, thus allowing for investigation of the smoothness of movement. In some forms, linear quadratic estimation, such as a Kalman filter, can be applied.

[0175] In certain forms, algorithms can be applied to detect deterioration in the eye's ability to track moving objects. These algorithms may also be called change-point detection algorithms. In certain forms, exemplary algorithms may rely on rank statistics, as described in Lung-Yut-Fong, A., Levy-Leduc, C., & Cappe, O, 2015, Homogeneity and change-point detection tests for multivariate data using rank statistics, Journal de la Societe Francaise de Statistique, 156(4), 133-162. Exceptional algorithms may also employ dynamic programming, i.e., repeatedly storing calculated values ​​for later analysis and investigating optimal intrinsic change points. In certain forms, change-point detection algorithms can be implemented in the Ruptures Python package (Truong, C., Oudre, L., & Vayatis, N., 2020, Selective review of offline change point detection methods, Signal Processing, 167, 107299).

[0176] Figures 8A to 8D show radially transformed eye tracking data in a tracking eye movement task according to one embodiment of this technology. These figures show eye tracking data in a tracking eye movement task in which an object 303 moves in a circular orbit on a display screen 310. The charts plot the radius of the object and gaze against the angular position of the object / gaze, where the angular position is radially transformed, so the object is always at an angle of 0°. In these charts, the black dot 810 represents the position of the object 303 on the display screen 310. The blue dot 820 represents the position of the eye gaze on the display screen 310. Due to the radial transformation, the coordinates of the blue dot 820 in the chart represent the error from the black dot 810, which is the object. Figure 8A shows eye tracking data during the fixation phase of the task, before the object 303 begins its movement. Figure 8B shows eye tracking data during the low-speed phase of tracking eye movements when the object 303 moves in a circular orbit at a constant speed. Figure 8C shows eye tracking data during the acceleration phase of tracking eye movements when the object 303 is moving in a circular orbit while its speed is accelerating. The data shown in Figure 8C is from before the patient was measured to have reached a point of exhaustion. Figure 8D shows eye tracking data during the acceleration phase of tracking eye movements when the object 303 is moving in a circular orbit while its speed is accelerating, and also from after the patient was measured to have reached a point of exhaustion.

[0177] Figures 9A to 9D illustrate eye tracking data in a tracking eye movement task according to another embodiment of the present technology. These figures show eye tracking data in a tracking eye movement task in which an object 303 moves in a circular orbit on a display screen 310. The charts plot the horizontal (x) and vertical (y) coordinates of the object and gaze on the display screen 310. In these charts, black dots or lines 910 represent the position of the object 303 on the display screen 310. Blue dots 920 represent the position of the eye gaze on the display screen 310. Red dots / lines 930 represent the regression of the blue dots 920, showing the time-averaged position of the patient's gaze, which can be used, for example, in cases of suboptimal calibration of the eye tracker. Figure 9A shows eye tracking data during the fixation phase of the task, before the object 303 begins its movement. Figure 9B shows eye tracking data during the low-speed phase of tracking eye movements when the object 303 moves in a circular orbit at a constant speed. Figure 9C shows eye tracking data during the acceleration phase of tracking eye movements when the object 303 is moving in a circular orbit while its speed is accelerating. The data shown in Figure 9C is from before the patient was measured to have reached a point of exhaustion. Figure 9D shows eye tracking data during the acceleration phase of tracking eye movements when the object 303 is moving in a circular orbit while its speed is accelerating, and also from after the patient was measured to have reached a point of exhaustion.

[0178] In some forms, the presence of TBI, or other neurological conditions (limited to neuromuscular junction disorders and diseases that particularly affect the extraorbital muscles, such as orbital myositis or thyroid eye disease, and neurodegenerative diseases), can be measured using a quantified value relating to a point of weakness. For example, in some forms, the measure of weakness may be the time before weakness occurs in a tracking eye movement task. A positive diagnosis of TBI may be made if this time is below a certain threshold. In some forms, a diagnosis of mild TBI (i.e., mTBI or concussion) may be made if the time before weakness is below a first, higher threshold but above a second, lower threshold. In such forms, a diagnosis of moderate or severe TBI may also be made if the time before weakness is below both the first and second, lower thresholds. In certain forms, the threshold may be the absolute value of the time before weakness, measured by experimental observations in previous patients. In other forms, the threshold may be calculated from a control value of the time before weakness, measured experimentally as the "normal" value. For example, the threshold could be a specific percentage (%) below a control value. This percentage (%) can also be measured by previous experimental observations of patients.

[0179] 7.3.1.9. Combination of scales Numerous scales, each of which can be used individually to measure the presence of TBI, have been described above. In certain forms, step 605 of analyzing the data may include measuring any combination of two or more of the aforementioned scales. Two or more of these scales may be combined to measure an indicator of the presence of TBI.

[0180] For example, in certain forms, two or more scales may be combined as a weighted average. This may allow for the application of more weights to some of the scales. For example, in one embodiment, the weighted average may be calculated as the sum of any two or more of the above scales, each weighted by one factor, with the sum of the factors being 1. In one embodiment, the three scales, in order from the largest weight to the smallest weight, may be a scale of one or more features of SWJ during a fixation task, a scale of the frequency of microsaccades during a fixation task, and a scale of tracking eye movement weakness following a moving object.

[0181] 7.3.1.10. Reference scales for individual patients As described above, one or more scales may be measured from eye-tracking data, and these scales may be used to measure the presence of TBI in a patient. In certain forms, measuring an index may involve comparing one or more of the scales, and / or any combination of two or more of the scales, to one or more predetermined thresholds. In certain forms, a scale may provide an index of the presence of TBI in the patient in question without any prior testing. For example, one or more predetermined thresholds may be measured from scales measured from other patients. A scale may be an absolute value that can indicate the presence (or severity) of TBI in a patient, or a scale may be a value that can be compared to a “normal” value measured from previous experimental observations of patients in order to make a measurement.

[0182] In other forms, one or more predetermined thresholds may be measured from scales measured from the patient at one or more earlier points in time. For example, in some forms, an individual patient may have these scales calculated one or more times, and these scales are considered reference values ​​for establishing a standard for the measures applied to that patient. These provide comparison points for future analysis of the patient. When an investigation is conducted at a future point in time, the same scales may be measured (using the methods described above) and compared to the reference scales for the same patient. If one or more of the scales (or a combination of scales, e.g., a weighted average) measure different changes beyond a certain threshold difference (which may be an absolute or percentage difference) from an equivalent reference value, this may indicate the presence of TBI in the patient. Again, different thresholds may be used to measure whether the diagnosis is mild TBI or more severe TBI.

[0183] It should be noted that patients suffering from chronic conditions may exhibit increased criteria (e.g., malformations and, in some cases, polyphasic SWJ) over a considerable period of time after injury (e.g., more than 6 months). For example, a healthy patient may have a score ranging from 0 to 5 using the following algorithm: Indicators of TBI presence = (typical SWJ) 合計 *W1)+(Deformed SWJ) 合計 *W2)+(polyphasic SWJ[2] 合計 *W3)+…+(Polymorphic SWJ[n] 合計 *W n+1 ) In the formula, W1=1, W2=2, W3=3, W n+ =n+1

[0184] In contrast, scores of around 15-20 are not uncommon in patients who are still experiencing moderate symptoms.

[0185] In one exemplary form, repeating the patient's test (i.e., collecting eye data representing eye movements during the fixation task at subsequent points in time) allows for the measurement of changes in an indicator of the presence of TBI over time. This change can be used as an indicator of neuronal recovery.

[0186] Current assessments in patients may be particularly useful for individuals at high risk of traumatic brain injury, such as those participating in contact sports.

[0187] 7.3.1.11. Metric Output Referring again to Figure 6, the exemplary method in accordance with the main body may include step 606, which outputs an indicator of the presence of TBI in the patient from the data analysis system 400. The indicator may be output as a quantitative and / or qualitative assessment of the risk of the patient having TBI. For example, in some forms, the output indicator may be a simple binary output of a positive or negative diagnosis of TBI or mTBI. In other forms, the output indicator may be an indicator of the measured risk of the presence of TBI or mTBI. Such risk factors may be measured based on the amount by which one or more measures are above / below the corresponding threshold, as measured from previous experimental observations. Risk factors may be expressed quantitatively (e.g., "there is an 80% chance that TBI is present") or qualitatively (e.g., "the patient is very likely to have TBI"). In other forms, the output indicator may provide a qualitative description of the indicated severity, e.g., "no TBI detected," "mild TBI detected," "moderate TBI detected," "severe TBI detected," etc. Measurements of these bands may be based on how the scale compares multiple thresholds measured from previous experimental observations.

[0188] 7.3.1.12. Exemplary Uses The table below shows illustrative data from real-world tests of rugby players (and one with a common injury) with compound SWJ and malformed SWJ during a 20-second fixation period. Many of these patients were tested before and after their injury. The score (i.e., an indicator of the presence of TBI) is calculated using the following algorithm: Indicators of TBI presence = (non 典型的なSWJ Typical SWJ*W1)+(non 奇形SWJ Malformed SWJ*W2)+(non 多相性SWJ[2] polymorphic SWJ[2]*W3)+…+(non-多相性SWJ[n] Polymorphic SWJ[n]*W n+1 ) In the formula, W1=1, W2=2, W3=3, W n+ =n+1

[0189] [Table 1]

[0190] During the first two weeks of injury, eye movement dysfunction may transiently worsen before improving due to the pathophysiology inherent in traumatic brain injury, and it should be noted that a neurometabolic cascade of “secondary” injuries may follow the physical injury to brain tissue (as described, for example, in Giza CC, Hovda DA. The new neurometabolic cascade of concussion. Neurosurgery. 2014 Oct;75 Suppl 4(0 4):S24-33.doi:10.1227 / NEU.0000000000000505.PMID:25232881;PMCID:PMC4479139).

[0191] Over time, in addition to the total score returning to baseline, it may also be observed that the underlying complexity of the SWJ event is reduced as recovery progresses.

[0192] For completeness, the examples provided above are given with respect to identified algorithms and weighting schemes, but it should be understood that this is not intended to limit the alternative approaches of the disclosure described herein.

[0193] 7.4. Other points to note Unless otherwise clearly indicated in the context, throughout the description and claims, the words “comprise,” “comprising,” and similar phrases shall be interpreted in a comprehensive sense, that is, “including, but not limited to,” and not in an exclusive or exhaustive sense.

[0194] All disclosures of applications, patents, and publications cited above and below are incorporated herein by reference, if any.

[0195] References to prior art in this specification do not constitute, nor should they be interpreted as, an acknowledgment or suggestion in any way that such prior art forms part of the common general knowledge of the art in any country of the world.

[0196] The above technology can also be broadly described as consisting of any or all of the parts, elements, or features described or indicated in the specification of the above application, either individually or collectively, in any combination of two or more of the above parts, elements, or features.

[0197] Where, in the preceding description, integers or their equivalents are referred to as components for which integers are known, those integers are incorporated herein as if they were listed separately.

[0198] It should be noted that various changes and modifications to the currently preferred embodiments described herein will be obvious to those skilled in the art. Such changes and modifications can be made without departing from the spirit and scope of the art and without impairing the associated advantages. Therefore, such changes and modifications are intended to be included within this art. [Explanation of symbols]

[0199] 101st 104th 106 Pupil 107 x-axis 10⁸ y-axis 200 eye tracking devices, eye analysis systems 210 Display Screens 300-Eyes Tracking System 303 Object Route 305 310 Display Screen 320 Camera 350 portable electronic devices, portable electronic equipment 400 Data Analysis Systems 402 Hardware Platform 404 Processor 406 memory 408 Instructions 410 data 412 Records 414 Display Devices 416 Network 418 Personal computers, tablet computers, smartphones 420 servers 422 memory 1000 positions 1000 1st dimension position 1002 Object 1012 Intermittent amplitude 1014 Peak bias 1020 Coastal aspect 1030 Intermittent recovery 1040 Gradual recovery 1050 Intermittent Spikes

Claims

1. A computer implementation method for investigating the presence of TBI in a patient, wherein the computer implementation method is During a fixation task in which the patient gazes at a fixed object, eye data representing the patient's gaze movements is received. Measuring one or more measures of the gaze eye movement of the eye from the eye data, wherein the one or more measures include a measure of one or more features of one or more rectangular wave oculi (SWJ) movements. To measure an indicator of the presence of TBI in the patient based on one or more of the above scales, Outputting the aforementioned indicators, Computer implementation methods, including those mentioned above.

2. The computer implementation method according to claim 1, wherein the one or more features include atypical features of the one or more SWJs.

3. The computer implementation method according to claim 1 or 2, wherein at least one of the one or more SWJs includes two or more phases.

4. The computer implementation method according to claim 3, wherein measuring one or more of the above-mentioned scales includes the classification of each of the two or more phases.

5. The computer implementation method according to claim 4, wherein each potential classification of the phase includes two or more of the following: primary intermittent bias, secondary intermittent bias, intermittent recovery, gradual recovery, intermittent spike, and coastal phase.

6. The computer implementation method according to any one of claims 3 to 5, wherein measuring one or more of the aforementioned measures includes quantifying one or more features of each of the two or more phases.

7. The computer implementation method according to any one of claims 1 to 6, wherein the indicator of the presence of TBI in the patient is at least partially based on a total accumulated number of SWJ cases.

8. The computer implementation method according to claim 7, wherein hierarchical weighting can be applied at least in part based on the complexity of each of the one or more SWJs.

9. The computer implementation method according to claim 8, wherein the weight is applied at least in part based on the number of phases of each of the one or more SWJs.

10. The computer implementation method according to claim 9, wherein the weight is adjusted based on one or more measures of one or more features of the related phases.

11. The aforementioned computer implementation method is When the eye data representing the movement of the fixed eye is captured, object data representing the position of the fixed object is received, To measure one or more scales of the eye's movement from the object data, A computer implementation method according to any one of claims 1 to 10, further comprising:

12. The computer implementation method according to any one of claims 1 to 11, wherein the one or more measures further include a measure of the mean error between the position of the fixed object and the gaze position of the patient during the fixation task.

13. The computer implementation method according to any one of claims 1 to 12, wherein the one or more measures further include a measure of the frequency of microsaccades in the fixation task.

14. The computer implementation method according to any one of claims 1 to 13, further comprising receiving eye data representing the eye movements of the patient's eyes during a tracking eye movement task when the patient is gazing at a moving object.

15. The computer implementation method according to claim 14, wherein the one or more measures further include a measure of weakness in tracking eye movements following the moving object.

16. The computer implementation method according to claim 15, wherein the measure of the weakness of tracking eye movements is the time before the weakness occurs in the tracking eye movement task.

17. The computer implementation method according to any one of claims 1 to 16, wherein the step of measuring the indicator includes combining two or more of the scales.

18. The computer implementation method according to any one of claims 1 to 17, wherein measuring the indicator includes comparing one or more scales, and / or combinations of two or more of the scales, with one or more predetermined thresholds.

19. The computer implementation method according to claim 18, further comprising measuring one or more predetermined thresholds from similar scales measured from other patients.

20. The computer implementation method according to claim 19, further comprising measuring one or more predetermined thresholds from similar scales measured from the patient at one or more early time points.

21. The computer implementation method according to any one of claims 1 to 20, further comprising controlling a display screen to display the fixed object to the patient.

22. The computer implementation method according to claim 21, further comprising controlling the display screen to display the fixed object in an eccentric position relative to the patient.

23. The computer implementation method according to any one of claims 1 to 22, further comprising measuring the direction of gaze by detecting the position of the iris of the eye.

24. The computer implementation method according to claim 23, wherein measuring the gaze direction by detecting the position of the iris of the eye includes applying an object detection algorithm to the eye data representing the movement of the eye.

25. The computer implementation method according to any one of claims 1 to 24, further comprising classifying the eye data representing the eye movements using a transformer model.

26. During the second fixation task, further eye data representing the fixation eye movement of the patient's eye is received, Based on the aforementioned further visual data, a second indicator of the presence of TBI in the patient is measured, A computer implementation method according to any one of claims 1 to 25, comprising measuring the progress of nerve recovery in the patient by comparing the second indicator of the presence of TBI with a previous indicator of the presence of TBI in the patient.

27. A system for investigating the presence of TBI in a patient, wherein the system is A system comprising a processor configured to carry out the computer implementation method described in any one of claims 1 to 26.

28. A computer-readable medium storing instructions for carrying out the computer implementation method according to any one of claims 1 to 26 for investigating the presence of TBI in a patient.

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