Methods and systems for detecting concussions and other neurological disorders or impairments using saccades

Abstract

A method for assessing cranial nerve function, the method includes presenting a stimulus to a patient, capturing position data from at least one eye of the subject using an eye tracker having one or more sensors, filtering the data to remove noise, detecting one or more saccades from the position data, computing at least one saccade summary metric, computing at least one saccade directional metric, and predicting a probability of a concussion based on the at least one saccade summary metric or the at least one saccade directional metric.

Description

METHODS AND SYSTEMS FOR DETECTING CONCUSSIONS AND OTHER NEUROLOGICAL DISORDERS OR IMPAIRMENTS USING SACCADESCROSS-REFERENCE PARAGRAPH

[0001] This application claims priority to U.S. Serial No. 63 / 486,513, filed February 23, 2023, the content of which is hereby incorporated by reference in its entirety as if fully set forth herein.FIELD OF DISCLOSURE

[0002] The present disclosure relates to methods and systems for detecting neurological disorders or impairments. For example, the disclosure relates to detecting concussions based on saccades using an eye tracking system.BACKGROUND

[0003] Many central nervous system injuries and abnormalities can be challenging to diagnose and localize within the nervous system. Methods and systems that use eye tracking measurement to help diagnose and / or localize a number of different central nervous system injuries and abnormalities, such as but not limited to increased intracranial pressure (ICP), concussion, traumatic brain injury (TBI), reduced or impaired cranial nerve function, substance abuse, a neurological disorder or impairment, and the like have been developed.

[0004] It would be highly advantageous to continue to improve these methods and systems, such as those used for detecting concussions. For example, it would also be ideal to improve upon the methods and systems used to assess, quantify or analyze the presence or severity of concussions. At least some of these objectives will be discussed in the present application.BRIEF SUMMARY

[0005] In some embodiments, a method for assessing cranial nerve function, the method includes presenting a stimulus to a patient, capturing position data from at least one eye of the subject using an eye tracker having one or more sensors, filtering the data to remove noise, detecting one or more saccades from the position data, computing at least onesaccade summary metric, computing at least one saccade directional metric, and predicting a probability of a concussion based on the at least one saccade summary metric or the at least one saccade directional metric.

[0006] In some embodiments, a method for detecting a concussion includes presenting a stimulus to a patient, capturing position data from at least one eye of the subject using an eye tracker having a camera, filtering the data to remove noise, detecting one or more saccades from the position data, and visualizing the one or more saccades on a radial graph. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] FIG. 1 is a schematic diagram illustrating a system for diagnosing, identifying and / or quantifying a neurological impairment in a subject, according to one embodiment.

[0008] FIG. 2 is a schematic diagram illustrating a method for diagnosing, identifying and / or quantifying a neurological impairment in a subject, according to one embodiment.

[0009] FIG. 3 is a schematic diagram illustrating a method for predicting a neurological impairment in a subject, according to one embodiment.

[0010] FIGS. 4A-D are schematic diagrams showing radial graphs for visualizing and assessing saccades.DETAILED DESCRIPTION

[0011] Referring to FIG. 1, a schematic of an eye tracking and diagnostic device 100 is illustrated, according to one embodiment. The eye tracking device 100 may take the form of a mobile phone, handheld device, tablet, laptop, desktop, kiosk or similar, and the eye tracking system may be used to track a patient’s eye movement and diagnose one or more eye movement abnormalities. It will be understood that the eye tracking device can include more, fewer, or different components and can have a variety of different configurations, such as those without a display or touchscreen, including wearable devices and the like. Additionally, some of the components may be positioned on one or more circuit boards or similar’ carriers.

[0012] Generally, eye tracking device 100 may include one or more processors 110, a memory 120 (e.g., microSD card), a power source 130, a telemetry unit 140, camera(s) 150 and a display 160. Any of these components may be optional. Processor 110 may include a single microcontroller, or divided amongst two or more microcontrollers. In thisexample, a processor 110 is included to generate a stimulus and obtain data from the data capture device, for example, a camera 150. It will be understood that other parameters and sensors may be used to capture additional data from the user. Any processor may be used and can be as simple as an electronic device that, for example, is capable of receiving and interpreting instructions from an external programming unit and performing calculations based on the various algorithms described herein.

[0013] A memory 120 may include data in the form of a dataset for performing various steps of the algorithm. For example, data from camera 150 regarding a characteristic of the eyes may be passed to the processor 110 and compared against a dataset stored in memory 120 to determine if further diagnosis is necessary. Additionally, data relating to characteristics of the eyes may be sent from a programming unit to processor 110 and the processor may determine the appropriate course of action or alert a user and / or clinician. Communication between an optionally external programming unit and processor 110 may be accomplished via communication of an antenna with telemetry unit 140 (e.g., WiFi, BLUETOOTH® module, near field communication, serial bus, etc.). Additionally, device 100 may be in communication with one or more wearable devices or external devices to enable the user to continuously monitor or track the data. Telemetry unit 140 may be capable of transmitting data from memory 120 to a server or a network. As discussed, the system may include a single microcontroller. Additionally, systems having one or more microcontrollers may include a one or more power sources, or one or more displays 160 (e.g., a touchscreen LCD that can also function as a data input device).

[0014] Any power source 130 can be used including, for example, a battery such as a primary battery or a rechargeable battery. Examples of other power sources include external power sources (e.g., power outlet, USB charger), super capacitors, nuclear or atomic batteries, mechanical resonators, infrared collectors, thermally-powered energy sources, flexural powered energy sources, bioenergy power sources, fuel cells, bioelectric cells, osmotic pressure pumps, and the like. If the power source 130 is a rechargeable battery, the battery may be recharged using an antenna of a telemetry unit 140, if desired. Power may be provided to the battery for recharging by inductively coupling the battery through the antenna to a recharging unit external to the user.

[0015] In some embodiments, camera(s) 150 are monochrome machine vision cameras and arc used to capture the eye movements of the patient. Visible light and / or color cameras may also be used. Camera(s) 150 may capture between 15 and 500 frames of gaze data per second for each eye, with an average precision of 0.005 to 2 degrees. In one example, the illuminators may be infrared, and may use dark pupil eye tracking, in which the infrared sources are offset from camera 150. This technique typically provides better results across ethnicities and varied lighting conditions. The gaze tracking ranges up to 100 degrees horizontally and 70 degrees vertically. The distance between the subject’s eyes and the camera is between 0.5 cm and 70 cm. In some examples, camera 150 includes one to four cameras. For example, the cameras may include one or two infrared cameras for each eye, and one or two visible light cameras for each eye. Other eye-tracking methods (e.g., non- IR-based systems) may also be used, for example, a smartphone visible-light camera, wearable eye-tracking glasses that may or may not use IR or visible light cameras.

[0016] Eye Tracking Computer

[0017] In one embodiment, camera 150 may be driven by an ARM-based embedded computer. The specifications for this eye tracking computer are shown in Table 2.0018] System Application Computer

[0019] In one embodiment, the system application runs on a mini-ITX board running Windows 10 Pro. The specifications are shown in Table 3.0020] Stimulus Display

[0021] A camera computer, which may be provided by the same manufacturer as the manufacturer of camera(s) 150, may run the real-time software for camera 150. The system may detect eye motion events, such as saccades, blinks, and fixations, and computes the gaze coordinates for each eye at 15 to 500 Hz, storing the raw data until it is needed by the application. The application computer may be a small form-factor PC that runs a system application for the system. The system application provides the user interface, controls the logic flow, displays a stimulus (e.g., a video), processes the raw data from the camera computer, and stores results in persistent storage.

[0022] The user interacts with the system application through one or more displays having physical buttons or keys, or touchscreen intcrfacc(s) 160. Displays 160 may provide stimulus media to the patient, and may include one or more built-in speakers, headphone adapters or audio output to provide optional audio for the stimulus media.

[0023] Display 160, according to one embodiment, is used to present a video that may last any suitable length of time, such as 220 seconds in one embodiment. In one embodiment, the only purpose of a stimulus screen is to display the visual stimulus and the terms “display” and “stimulus screen” are synonymous. The video may be one of several predetermined videos, visual patterns, or light-emitting devices. The videos may include music videos, clips from children’s movies, sports clips, talent performances, “reality TV” clips, etc. The choice of videos may be designed to appeal to a broad group of subjects. Users of the device may choose which video to display or may ask the patient which one they would like to watch. Alternatively, the device may choose an appropriate video based on input data. The visual patterns may include geometric or natural shapes or designs, moving or not moving. The light-emitting devices may include LED’s or fluorescent illumination devices. In one embodiment, stimulus screen is an Fsuoech 2.4" IPS TFT LCD Display, with the specifications shown below in Table 4.In another embodiment, one or more LED lights arc used to evoke extreme eye movements from the subject. These lights may turn on and off in pre-determined or random patterns and are peripheral to the stimulus display.

[0024] Touchscreen Interface

[0025] The system may include a designated touchscreen interface 162 (which may also be referred to as an “operator console” or simply “touchscreen”) to be used by the technician to interact with the system application. Touchscreen interface 162 may includeonly a touch screen display, meaning that there is no keyboard or other input device. Of course, alternative embodiments may include a keyboard and / or other input dcvicc(s). In one embodiment, touchscreen interface 162 may be a separate Fsuoech 2.4" IPS TFT LCD Display, with the specifications set forth below in Table 5. Alternatively, touchscreen 162 and display 160 may be the same component.

[0026] Saccade Detection Method

[0027] FIG. 2 illustrates a general overview of a saccade detection method. In some embodiments, pupil position data are collected from a subject using an eye-tracking device, such as that described above. The subject may look into the device and observe a stimulus, such as a video or pattern, to capture the person’s attention and / or evoke eye movements (step 210). The stimulus may consist of pseudorandom changes in illumination and / or images that the subject follows with their eyes. If images are used, an image may move in any direction (e.g., left, right, up or down) in pseudorandom patterns or remain fixed on the screen in one position. In some examples, the movements of the image may be abrupt. In some other examples, the movements of the image may be smooth. Alternatively, the movements may include any combination of smooth and abrupt movements or fixed positioning. A naive subject will not be able to predict the changes in illumination or image movement. In some examples, the image may evoke interest from the subject. For example, the image may be a football, flower, car, or other common object, and may change during the stimulus. The stimulus may be presented for 20 seconds, 30 seconds, 45 seconds, 1 minute, 1 minute and thirty 30 seconds, or 2 minutes in total. In another example, LED lights that are outside of the stimulus display area may turn on and off to evoke rapid eye movements from the subject.

[0028] During the stimulus presentation, one or more camera(s) (e.g., infrared) captures the images of the subject’s eyes and calculates each pupil position and other metrics (e.g., pupil size, etc.) and stores them in the device memory (step 220). In some examples, a designated series may be assigned to each of the subject’s eyes (e.g., a first series for position data of the left eye and a second series for position data of the right eye).

[0029] After the stimulus is complete, the device may filter the captured pupil position to remove noise (step 230). The data may be normalized and / or adjusted for age, sex, medical history, race or other characteristics (step 240). For example, some types of eye movement may vary by age, and adjustments can be made to account for these age variations. The data may be analyzed to identify one or more saccades within the position data, and saccade data may be derived from the position data (step 250). In some examples, one or more saccades may be detected using a heuristic algorithm that looks for a sudden rise in velocity, acceleration or jerk followed by a sustained level velocity, acceleration or jerk followed by a sudden drop in velocity, acceleration or jerk.

[0030] In some examples, predetermined thresholds may set for detecting saccades and the predetermined thresholds can be tuned to be more or less sensitive for detecting the saccades. For example, it may be desirable in some stimulus scenarios to only detect saccades within a certain range of velocities or total amplitudes. In some examples, an upper bound for saccades may be in the range of 500-700 degrees / sec in velocity. In some examples, the lower bound of a saccade velocity is not well defined.

[0031] Saccades Concussion Prediction Algorithm

[0032] Various metrics may be derived from the data, including metrics directly relating to the saccades. In some examples, one or more summary metrics may be used to describe the saccade data, such as the saccade rate (calculated as the number of saccades per minute), the average velocity of a saccade, the peak velocity of a saccade, the average travel length of a saccade, and the travel direction of the saccade (step 260). These summary metrics may be calculated based on the entire data set (i.e., on both eyes). Alternatively, the metrics may be derived for each individual eye, or with respect to the proximity of saccades to stimulus events. In some examples, computing moments for linear and for circular distributions as appropriate allows measurement of the distribution of saccades in various directions and the relative strength or balance among the directions (step 270). Thisapproach may allow characterization of samples and the description of individuals with reference to different kinds of samples. In some examples, the values of a plurality of moments may be used to derive summary metrics. For example, the mean of a distribution may be defined as the zeroth moment, the variance as the first moment, etc. Mean, standard deviation, skew and / or kurtosis may also be used to describe a distribution and understand a subject’s result in the context of the distribution. In some examples, the relative strength / balance could be defined as the ratio of velocity or magnitude of opposing or adjacent saccades. Either the saccade summary metrics, the saccade direction metrics, the individual saccade events or any combination of the three may be sent to a prediction algorithm as inputs (step 280).

[0033] A predictive algorithm 300 and the flow of data is shown in FIG. 3. The derived saccade metrics may be submitted to a prediction engine that was trained by data from normal and concussed subjects, collected in a controlled study. The prediction engine may generate a composite index score between 0 and 100, with 0 meaning the subject is likely not concussed and 100 meaning that the subject is highly likely concussed.

[0034] In another example, another prediction engine may be trained by data from concussed subjects based on symptoms and clinical assessment of vestibular and ocular function. The prediction engine may generate a composite index score between 0 and 100, with 0 meaning that the subject is likely to have normal vestibular or ocular function and 100 meaning that the subject is highly likely to have abnormal vestibular or ocular function.

[0035] It will be understood that similar analysis may also be conducted to ensure that an individual is fit for a given task (e.g., that the individual is not sleep-deprived, intoxicated, tired, etc.). The outlined methods of FIGS. 2 and 3 are exemplary, and variations may be made without departing from the scope of the disclosure. For example, alternative embodiments may include fewer steps, greater numbers of steps and / or different ordering of steps.

[0036] Saccades Visualization

[0037] In some embodiments, the identified saccades may also be visualized as a graph (e.g., a radial graph), where each saccade event is plotted as a vector starting at the center of a graph. Each saccade may be represented by a marker, the marker having a respective magnitude and a direction. The magnitude of the marker may be represented by the lengthof a marker. Specifically, the length of the marker may represent the distance of the saccade, and the thickness of the marker may represent the velocity of the saccade. Markers that exceed the axis of the graph may terminate with an arrow. The direction of the marker may match a direction of an eye movement during a saccade. Saccades may be graphed individually or as summary vectors to simplify the description of samples or individuals, and this visualization may help physicians understand a patient’s cranial nerve function. Abnormal saccade movement patterns, such as in a brain injured patient, may indicate that one or more cranial nerves is not functioning correctly.

[0038] FIG. 4A illustrates a radial graph 400A showing normal saccades. The radial graphs 400A is represented as a circle having four quadrants 410,420,430,440 that are defined by upward and downward eye movements, as well as left and right eye movements (e.g., quadrants corresponding to upward-and-left, upward-and-right, downward-and-left, and downward-and-right eye movements, or quadrants corresponding to upward, leftward, downward, rightward where the divisions are at 45-degree angles from the origin of the graph). The graph 400A may also have an outer circumference 405 which represents the graph limit. In this example, the graph limit may be set at a 10-degree amplitude, although it will be understood that the graph limit may be set to another maximum amplitude of an eye movement (e.g., 5 degrees, 10 degrees, 15 degrees, 20 degrees, 25 degrees, 30 degrees, 35 degrees, etc.). In this example, a number of markers 450 are displayed on the graph, each originating at the center of the radial graph 400A. The direction and amplitude of each saccade is plotted from the center outward to visualize the relative strength of saccade amplitudes. The radial graph 400A shows saccades of both of a user’s eye. In some examples, first colored markers (e.g., blue as shown) represent the left eye saccades, and second colored markers (e.g., red as shown) represent the right eye saccades. By way of example, marker 450a is a blue colored marker representing a left eye saccade disposed in quadrant 430 showing that the saccade was down and to the left. This particular saccade was relatively faster and covered more distance than others as shown by the thickness and the length of marker 450a. In this example, a saccade is defined as a fast eye movement, fixating on two different points, which persists between 1 ms and 500 ms in duration. It will be understood that the threshold of a saccade may be selected as desired.

[0039] FIG. 4B illustrates a second radial graph 400B showing a user that has experienced a number of high velocity saccades. As shown, each of the markers is relatively thicker than those shown in FIG. 4A. In fact, almost all of the markers in FIG. 4B are thicker than relative markers in FIG. 4A. In some examples, the thickness or weight of the marker is proportional to the velocity of a saccades (e.g., a marker that is twice as thick as another represents double the velocity). Additionally, notice that two of the markers including marker 450b extend to the graph limit, or terminate past the graph limit as shown by the arrowhead on marker 450b.

[0040] FIG. 4C illustrates a third radial graph 400C showing a user that has experienced saccades a number of saccades with excessive distances. As shown, each of the markers is relatively longer than those shown in FIG. 4A, and many of the markers terminate with an arrowhead (e.g., marker 450c). In this example, a physician may quickly recognize that the patient is experiencing relatively longer saccade distances, which may indicate a neurological disorder or impairment. It will be understood that the system may measure the distance traveled (i.e., the distance along a curved path if the saccade was not a perfectly straight line) and / or the Euclidian distance between the start point and the end point. The saccade may be measured in degrees (or equivalently normalized units).

[0041] FIG. 4D illustrates a fourth radial graph 400D showing a user that has experienced unbalanced saccades. In this example, the user appears to be experiencing more saccades in the two upper quadrants than the two lower quadrants. Specifically, more markers are in the upper two quadrants 410, 420 than the two lower quadrants 430,440. This may indicate that one or more cranial nerves are not functioning properly because upward saccades are more numerous than downward saccades. Notice for example that the user has no left eye saccades in the downward-left quadrant 430. By plotting the saccades on the radial graph, a physician may more easily assess and diagnose the relative difference between saccades of both eyes, and the relative length, velocity or frequency of saccades in the different quadrants.

[0042] The foregoing is believed to be a complete and accurate description of various embodiments of a system and method for assessing substance abuse in a patient. The description is of embodiments only, however, and is not meant to limit the scope of the invention set forth in the claims.

[0043] Applicant believes this to be a full and accurate description of various embodiments of a method and system for diagnosing, identifying and / or quantifying concussion in a subject. The foregoing description is of embodiments only and is not intended to limit the scope of the claims that follow.

Claims

CLAIMS1. A method for assessing cranial nerve function, the method comprising: presenting a stimulus to a patient; capturing position data from at least one eye of the subject using an eye tracker having one or more sensors; filtering the data to remove noise; detecting one or more saccades from the position data; computing at least one saccade summary metric; computing at least one saccade directional metric; and predicting a probability of a concussion based on the at least one saccade summary metric or the at least one saccade directional metric.

2. The method of claim 1, wherein capturing position data from at least one eye comprises capturing position data from both eyes of the subject.

3. The method of claim 1, further comprising the step of normalizing and adjusting the position data for age, sex, medical history or other characteristic of the subject.

4. The method of claim 1, wherein computing at least one saccade summary metric comprises computing a saccade rate.

5. The method of claim 4, wherein the saccade rate comprises a number of saccades per minute.

6. The method of claim 1, wherein computing at least one saccade summary metric comprises computing an average velocity of a saccade.

7. The method of claim 1, wherein computing at least one saccade summary metric comprises computing a peak velocity of a saccade.

8. The method of claim 1, wherein computing at least one saccade summary metriccomprises computing an average travel length of a saccade.

9. The method of claim 1, wherein computing at least one saccade summary metric comprises computing a saccade rate, an average velocity, a peak velocity, and an average travel length.

10. The method of claim 1, wherein computing at least one saccade directional metric comprises computing a travel direction of a saccade.

11. The method of claim 1, wherein predicting a probability of a concussion comprises generating a composite index score between a minimum score and a maximum score.

12. A method for characterizing a concussion, the method comprising: presenting a stimulus to a patient; capturing position data from at least one eye of the subject using an eye tracker having one or more sensors; filtering the data to remove noise; detecting one or more saccades from the position data; and visualizing the one or more saccades on a radial graph.

13. The method of claim 12, wherein visualizing the one or more saccades on a radial graph comprises visualizing saccades from two eyes on a radial graph.

14. The method of claim 13, further comprising the step of discerning between saccades of a first eye and a second eye of the two eyes.

15. The method of claim 13, wherein visualizing the one or more saccades on a radial graph comprises generating a marker for each saccade.

16. The method of claim 15, wherein generating a marker for each saccade comprises forming a line originating from a center of a circle, the line having a length that isproportional to a travel length of an eye during a saccade.

17. The method of claim 15, wherein generating a marker for each saccade comprises forming a line originating from a center of a circle, the line having a thickness that is proportional to a travel velocity of an eye during a saccade.

18. The method of claim 15, wherein generating a marker for each saccade comprises forming a perimeter defined as a graph limit, the graph limit corresponding to a maximum amplitude of eye movement.

19. The method of claim 15, further comprising the step of dividing the radial graph into quadrants and comparing saccades of different quadrants.

20. The method of claim 19, wherein the quadrants comprise four quadrants corresponding to upward-and-left, upward-and-right, downward-and-left, and downward-and-right eye movements, or four quadrants corresponding to upward, downward, left and right.

Images