HK1239486B - Systems and methods for using eye movements to determine traumatic brain injury - Google Patents

Description

BACKGROUND OF THE INVENTION

The present disclosure generally relates to systems and methods for acquiring data from a subject and, more particularly, to systems and methods for gathering and analyzing information about the subject's eye movements to determine or predict a state of the subject, including conditions such as traumatic brain injury (TBI) and other neurological injuries and diseases.

Brain injury can affect motor and cognitive function in the injured subject, and may increase the subject's vulnerability to a subsequent brain injury. When the brain injury is caused by trauma, such as an impact or piercing of the head, the subject is many times more likely to suffer a more severe injury the next time a similar trauma occurs. Concussion and other TBls are currently at the forefront of sports medicine discussions, particularly for contact sports, because the risk to players is significant and the presence of a TBI cannot always be quickly diagnosed. For example, American football players are constantly at risk of a concussion, but often return to the game after a TBI because their visible symptoms were not cause for concern and a quick objective test is not available.

Another problem with diagnosing TBI is that most symptoms can be transient. Thus, with the passage of time it becomes more difficult to detect an injury, and medical examinations and accident investigations can be compromised. Early, quick, and objective detection of the physiological effects of TBI is needed.

The eye movements of people with neurological disease differ significantly from those of healthy people. The eyes in both populations do not stay perfectly still during visual fixation. Fixational eye movements and saccadic intrusions continuously change the position of the gaze. Microsaccades are rapid, small-magnitude involuntary saccades that occur several times each second during fixation; microsaccades counteract visual fading and generate strong neural transients in the early visual system. Microsaccades may also drive perceptual flips in binocular rivalry. Microsaccade rates and directions are moreover modulated by attention, and thus generate rich spatio-temporal dynamics. Further, fixational eye movements as a whole enhance fine spatial acuity. Abnormalities and intrusions in these eye movements can belie neurological impairments.

It would be beneficial to be able to detect TBI and differentially diagnose it from another neurological injury or disease in a non-invasive manner. The following disclosure provides one such differential diagnostic method.

US 8 808 179 B1 describes a method and an apparatus for detecting minor traumatic brain injury. The method and the corresponding apparatus present visual stimulus with a predetermined direction and speed of movement. The movement of an eye of the subject is then monitored focusing on a fast eye velocity component in determining a corresponding parameter. This parameter is then compared to a reference value.

US 2010/094161 A1 describes a method of diagnosis of traumatic brain injury. The method comprises a head mounted google that presents at least one virtual reality based visual stimulus to the subject and a objective physiological response of the subject to the stimulus is obtained. Based on this response, the presence of a traumatic brain injury is diagnosed.

US 2013/336547 A1 describes a method of assessing a person's identity and a corresponding system. It teaches to measure the eye-movement of a person and based on the eye-movement to estimate one or more anatomical characteristics of an oculomotor plan of the person and one or more brain's control strategies in guiding visual attention via exhibition of complex eye-movement patterns. Based on these anatomical characteristics and on properties related to the complex eye movement patterns, the person's identity is assessed.

WO 2015/167992 A1 (Article 54(3) EPC document) describes a system and a method for determining the physiological state of the user, in particular for determining a level of intoxication of the user. The described method and system measure eye movement data and extract eye movement dynamics from these data. These eye movement dynamics are then compare baseline data in order to determine a neurologic impairment caused by an intoxicated state of the user.

WO 2015/051272 A1 (Article 54(3) EPC document) describes a device, method and system for detecting mild traumatic brain injury with a visualisation unit and tracking recording the users eye movement. In response to performing a series of tasks that eye movement of the user is recorded and compared to standard eye movement data from a person not suffering from mild traumatic brain injury. Whether the user has suffered from a mild traumatic brain injury is determined from analysing the difference between recorded eye movement data and eye movement data from persons not suffering from mild traumatic brain injury.

SUMMARY OF THE INVENTION

The present invention overcomes drawbacks of previous technologies by providing systems and methods that afford a number of advantages and capabilities not contemplated by, recognized in, or possible in traditional system or known methodologies related to tracking or determining a subject's state, including the detection of traumatic brain injury (TBI) and other neurological injuries and diseases.

In one embodiment of the present invention, systems and methods are provided for monitoring, recording, and/or analyzing eye movements in situ to determine whether oculomotor dynamics are being affected by the onset or presence of TBI. Eye saccades and the velocity of intersaccadic eye drift are detectably affected by the onset or presence of these conditions. A system and method alerts a user to the presence of these states or conditions in a testing environment. In particular, a system in accordance with the present invention may include devices and device assemblies that record baseline data of a subject and generate a data model representing the eye movement data of the subject, and further the system may include device and device assemblies that record eye movement data in situ and compare it to the data model to determine if the user is affected by TBI. In one aspect, a sensor arrangement may include a camera and recording assembly for detecting and recording the eye movements.

In a contemplated embodiment of the present invention, a system includes the features of claim 1.

In another embodiment of the present invention, a method of analyzing eye movement data includes the features of claim 5.

The foregoing and other advantages of the invention will appear from the following description. In the description, reference is made to the accompanying drawings which form a part hereof, and in which there is shown by way of illustration a preferred embodiment of the invention. Such embodiment does not necessarily represent the full scope of the invention, however, and reference is made therefore to the claims and herein for interpreting the scope of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will hereafter be described with reference to the accompanying drawings, wherein like reference numerals denote like elements.

• FIG. 1 is a diagram of a detection system in accordance with the present invention.
• FIG. 2 is a flowchart illustrating a method for detecting traumatic brain injury in accordance with the present invention.

DETAILED DESCRIPTION

The systems and methods for detecting onset, presence, and progression of particular states, including traumatic brain injury (TBI), through observation of eye movements described herein. TBI is shown by the inventors to affect oculomotor dynamics, including saccadic metrics and intersaccadic drift metrics, with increasing severity as the injury progresses. In particular, intersaccadic drift velocity increases as TBI develops and progresses, and select oculomotor dynamics can be tracked against a baseline to alert a subject before the effects of TBI impair the subject's ability to perform certain actions, such as operating a motor vehicle.

The systems and methods described herein are offered for illustrative purposes only, and are not intended to limit the scope of the present invention in any way. Indeed, various modifications of the invention in addition to those shown and described herein will become apparent to those skilled in the art from the foregoing description and the following examples and fall within the scope of the appended claims. For example, specific disclosure related to the detection of TBI is provided, although it will be appreciated that the systems and methods may be applied for detection of any neurological injury or disease and for any subject without undue experimentation.

Using the approach of the present invention, a detection system is configured to record eye movement data from a user, compare the eye movement data to a data model comprising threshold eye movement data samples, and from the comparison make a determination whether or not the user's brain function is suffering from or is subject to impairment by TBI. The detection system is configured to alert the user or another party to take corrective action if onset or presence of a dangerous impaired condition is detected.

Referring to FIG. 1, an embodiment of the detection system 10 includes a sensing arrangement 12 configured to detect and record eye movement dynamics of the user. The sensing arrangement 12 includes one or more sensors suitable for collecting the eye movement data. Such sensors may include a camera or other imaging or motion tracking device capable of recording at a suitably high speed and level of detail so that the user's eye movement dynamics, including saccades and intersaccadic drift, are captured. A monocular arrangement of one or more sensors for one of the user's eyes may be used, or one or more sensors may be included for each eye to obtain binocular data. In some embodiments, the sensors may be miniaturized or otherwise compact, portable, and non-invasive. The sensors may further be vehicle-independent, and may be wireless, to facilitate integration of the sensors into any deployment of the detection system 10. For example, the sensing arrangement 12 may include sensors that are integrated into eyewear, such as on the frame or within the lenses of a pair of glasses. This allows for eye movement data collected even as the user turns his head, and allows the sensors to be positioned close to the eyes. In another example, the sensors may be integrated into a heads-up display for a vehicle. In another example, the sensors may be integrated into a handheld scanning device.

The sensing arrangement 12 further includes integrated or discrete devices for processing, storing, and transmitting collected data. Such devices may include a processor, volatile and/or permanent memory, a wired or wireless transmitter, and associated power circuits and power supply for operating the devices. Software modules may define and execute instructions for operating the sensors, configuring databases, registers, or other data stores, and controlling transmission of the data. The collected data may be shared via transmission to a control unit 14 that may be integrated with or disposed physically remotely from the sensing arrangement 12. The eye movement data, or a subset thereof, may be transmitted in real-time as it is captured by the sensors, or it may be stored for later transmission.

The control unit 14 may use the processing hardware (i.e., processor, memory, and the like) of the sensing arrangement 12, or may include its own processing hardware for analyzing the eye movement data and generating an alert to the user if needed. The control unit 14 may include a plurality of modules that cooperate to process the eye movement data in a particular fashion, such as according to the methods described below. Each module may include software (or firmware) that, when executed, configures the control unit 14 to perform a desired function. A data analysis module 16 may extract information from the eye movement data for comparison to the data model. The data analysis module 16 may include one or more data filters, such as a Butterworth or other suitable bandpass filter, that retain only desired signal elements of the eye movement data. The data analysis module 16 may include program instructions for calculating, from the eye movement data, one or more eye movement dynamics, such as saccades and/or intersaccadic drift velocities, of the user's eyes. The calculation may be performed substantially in real-time, such that a calculated intersaccadic drift velocity may be considered the current drift velocity of the user's eyes.

A comparison module 18 may receive the processed eye movement data from the data analysis module 16 and may compare it to the data model as described in detail below. The control unit 14 may include or have access to a model data store 20 that stores the data model. The model data store 20 may be a database, data record, register, or other suitable arrangement for storing data. In some embodiments, the data model may simply be a threshold drift velocity, and may thus be stored as a single data record in memory accessible by the comparison module 18. In other embodiments, the data model may be a lookup table, linked list, array, or other suitable data type depending on the data samples for eye movement dynamics needed to be stored in the data model.

In some embodiments, the control unit 14 may include a data model generator 22. The data model generator 22 is a module that receives eye movement data collected by the sensing arrangement 12 during a modeling step as described below. The data model generator 22 may extract, or cause the data analysis module 16 to extract, information from the collected eye movement data that will constitute the threshold eye movement data samples in the data model. The data model generator 22 may then create the data model from the threshold eye movement data samples, and may store the data model in the model data store 20. In other embodiments, the data model may be generated and stored in the model data store 20 by a separate modeling unit (not shown) of the system 10. The modeling unit may include its own sensing arrangement, processing hardware, and program modules. One suitable modeling unit may be the EyeLink 1000 by SR Research Ltd. of Mississauga, Ontario, Canada.

The control unit 14 communicates with an alerting arrangement 24 configured to produce an alert to the user according to the results of the data comparison in the comparison module 18. The alerting arrangement may be any suitable indicator and associated hardware and software for driving the indicator.

Suitable indicators include, without limitation: a visual display such as one or more light-emitting diodes, a liquid crystal display, a projector, and the like; a bell, buzzer, or other audible signaling means; and a piezoelectric or other vibrating device.

The detection system 10 may be used to execute any suitable method of detecting dangerous conditions that are indicated by eye movement data. Referring to FIG. 2, the detection system 10 may execute a method of detecting onset or presence of TBI in the user. At step 100, the system may record baseline measurements of the eye movement dynamics for the data model. The baseline measurements are taken of a subject which may or may not be the user. It may be advantageous that the data model use baseline measurements of the user himself in order to individualize the operation of the system, but the baseline measurements may be taken from a non-user subject, or taken from a plurality of subjects and averaged if desired. The conditions in which the baseline measurements are recorded may depend on the desired specificity of the data model. In some embodiments, the baseline measurements may be taken in normal conditions. In other embodiments, the baseline measurements may be taken in known injured conditions.

At step 105, the system may calculate one or more threshold drift velocities from the recorded baseline measurements. The threshold drift velocities may depend on the format of the collected baseline measurements. For example, where only normal-condition or only injured-condition baseline measurements were taken, a single threshold drift velocity (i.e., threshold-normal or threshold-TBI drift velocity) may be calculated. At step 110, the system may generate the data model for the baseline-tested subject(s). The data model may represent the progression of the intersaccadic drift velocity of the subject from normal conditions to injured conditions, and further beyond a TBI threshold into increasingly severe injury. The data model may be generated and stored in any suitable format that allows the system to subsequently compare eye movement data collected in situ from the user against the data model to determine the user's current impairment.

The steps 100, 105, 110 for obtaining the data model may be performed at any suitable time before testing the user in situ for signs of TBI. In one embodiment, the steps 100-110 may be performed far in advance and remotely from the test environment. In another embodiment, the steps 100-110 may be performed in the test environment, immediately preceding testing the user. For example, the user may activate the system 10, such as by donning and activating eyewear housing the sensing arrangement 12, which initiates step 100 of recording the baseline measurements in the present conditions. This may be in normal conditions, such as when the user is about to drive his vehicle in the morning, and only the normal eye movement data would be collected as baseline measurements. In still other embodiments, the data model may be created by the system 10 or another system using a different method than described above.

At step 115, optionally the system may calibrate itself to the user if the data model or comparison method require it. For example, the data model may be a standardized model generated from baseline measurements of (a) non-user subject(s), or the comparison method may determine the presence of TBI from a percentage deviation from the user's threshold-normal drift velocity value(s). See below. In such an embodiment, the system calibrates (step 115) by recording a calibration set, such as ten seconds or less but preferably five seconds or less, of eye movement data of the user when the system is activated in the test environment under normal conditions. The system may compare the calibration data to the data model. In one embodiment, this involves determining a deviation of the user's threshold-normal drift velocity from the threshold-normal drift velocity of the model. The system can then adapt the data model to the user.

At step 120, the system may record in situ eye movement data from the user continuously or at predetermined intervals while the system is activated. At step 125, the system may calculate, in real-time or at predetermined intervals, the user's current drift velocity. At step 130, the system may compare the current drift velocity to the data model to determine whether TBI has occurred. Such progression may be calculated within any suitable paradigm. Examples include, without limitation: ratio or percentage by which the current drift velocity exceeds the user's or the data model's threshold-normal drift velocity; ratio or percentage by which the current drift velocity is below or above the threshold-TBI drift velocity; comparison of current drift velocity to points on a curve between threshold-normal and threshold-TBI values in the data model; and the like. If the user is neither injured nor within a predetermined proximity to the threshold-TBI value of the data model, the system returns to step 120 and continues recording current data. In one configuration, the system can be optionally interrupted from continuing recording in situ measurements by an administrator or instructions installed in the control unit. Such instructions can be stopping recording when a predetermined duration has reached, the measurements are noisy or weak, or the administrator has entered a stop signal. If the user's condition warrants (i.e., the current drift velocity is above or within a certain range of the threshold-TBI value), at step 135 the system may alert the user to take corrective action.

In addition or alternatively to the methods described herein, the system may record and analyze eye movement data using any of the methods and system components described in copending U.S. Pat. App. Ser. No. 14/220,265 , co-owned by the present applicant.

The described system and methods may be implemented in any environment and during any task that may subject the user to dangerous conditions that affect eye movements. The various configurations presented above are merely examples and are in no way meant to limit the scope of this disclosure. Variations of the configurations described herein will be apparent to persons of ordinary skill in the art, such variations being within the intended scope of the present application. In particular, features from one or more of the above-described configurations may be selected to create alternative configurations comprised of a sub-combination of features that may not be explicitly described above. In addition, features from one or more of the above-described configurations may be selected and combined to create alternative configurations comprised of a combination of features which may not be explicitly described above. Features suitable for such combinations and sub-combinations would be readily apparent to persons skilled in the art upon review of the present application as a whole. The subject matter described herein and in the recited claims intends to cover and embrace all suitable changes in technology.

Claims (13)

1. A system (10) for detecting a traumatic brain injury (TBI), comprising:

   a sensing arrangement (12) configured to collect eye movement data of a user;

   a control unit (14) in communication with the sensing arrangement, the control unit being configured to:

   compare the eye movement data to a plurality of baseline measurements of eye movement dynamics, wherein comparing the eye movement data to the baseline measurements comprises calculating a current intersaccadic drift velocity of the user and comparing the current intersaccadic drift velocity to a plurality of threshold drift velocities of the baseline measurements, including a normal threshold drift velocity and a TBI threshold drift velocity; and

   if the eye movement data diverges from one or more of the baseline measurements by a threshold amount, generate an alert for an alerting arrangement (24) configured to indicate a presence or a severity of the TBI for delivery to the user.
2. The system of claim 1, wherein the eye movement data comprises one or more saccade parameters.
3. The system of claim 2, wherein comparing the eye movement data to the baseline measurements comprises calculating a current intersaccadic drift velocity of the user from the one or more saccade parameters.
4. The system of claim 1, wherein the control unit (14) is also configured to record the one or more baseline measurements of eye movement dynamics.
5. A method of analyzing eye movement data, the method comprising:

   a) receiving recorded eye movement data of one or both of a user's eyes;

   b) comparing (130) the eye movement data to a plurality of baseline measurements of eye movement dynamics, wherein comparing the eye movement data to the baseline measurements comprises calculating a current intersaccadic drift velocity of the user and comparing the current intersaccadic drift velocity to a plurality of threshold drift velocities of the baseline measurements, including a normal threshold drift velocity and a TBI threshold drift velocity;and

   c) if the eye movement data diverges from one or more of the baseline measurements by a threshold amount, delivering an alert (135) to the user indicating the results of the data comparison.
6. The method of claim 5, wherein the eye movement data comprises one or both of saccade parameters and intersaccadic drift parameters.
7. The method of claim 5, wherein step a) includes receiving one or more baseline measurements of eye movement dynamics of one or both of the user's eyes.
8. The method of claim 5, wherein the baseline measurements are collected from a subject other than the user.
9. The method of claim 5, wherein the baseline measurements are collected before the recorded eye movement data received in step a).
10. The method of claim 5, wherein the eye movement data further comprises one or more saccade parameters.
11. The method of claim 10, wherein step b) includes calculating a current intersaccadic drift velocity of the user from the saccade parameters.
12. The method of claim 5, wherein step a) includes calibrating the recorded eye movement data to one or both of a data model comprising the baseline measurements, and a comparison method.
13. The method of claim 12, wherein calibrating the recorded eye movement data to the data model comprises:

    receiving a calibration set of eye movement data from the user \under normal conditions;

    comparing the calibration set of eye movement data to the data model;

    determining a deviation of the calibration set of eye movement data from the data model; and

    adapting the data model to include the deviation and generate an adapted data model for the user.