Patent Application
20220395226
The invention concerns noninvasive detection of anomalies of the brain, and in particular the invention is concerned with detection of concussion in a patient. The equipment and the method of the invention investigate skull motion produced by pulsatile cerebral blood flow, as measured by cranial accelerometry using one or more accelerometers attached to a patient's head.
Traumatic Brain Injuries (TBI) are classified according to the severity of acute symptoms and mechanism of injury. Mild TBI: person is awake; eyes open—symptoms can include confusion, disorientation, memory loss, headache, and brief loss of consciousness. Moderate TBI: person is lethargic; eyes open to stimulation, symptoms can include loss of consciousness lasting 20 minutes to 6 hours, some brain swelling or bleeding causing sleepiness, but still arousable. Sever TBI: person is unconscious; eyes do not open, even with stimulation—symptoms include loss of consciousness lasting more than 6 hours. A concussion is typically viewed as a type of mild TBI that can cause a brief loss of consciousness and usually does not cause permanent physiological brain injury (in a single event).
The definition of concussion has undergone a reevaluation in recent years with the diagnosis of a concussion being made based on: only loss of consciousness; a list of symptoms; and neurophysiological measures of concussion. A concussion is “a complex pathophysiologic process affecting the brain, induced by traumatic biomechanical forces.” It is caused either by a direct blow to the head or by a blow to the body with forces transmitted to the head. A concussion results in neurological dysfunction that is usually transient and resolves spontaneously. The majority of concussions do not result in loss of consciousness; however, they result in clinical and cognitive symptoms that commonly resolve in a sequential course. Older data on the number of concussions sustained in a contact sport may be under-reported by an order of magnitude or more relative to more current assessments in light of the changes in definition and diagnosis.
Approximately 300,000 sports concussions are reported annually but most authorities believe this is vastly under reported due to lack of awareness, misunderstanding of the definition of concussion and more recent realization that even, what were considered minor head impacts previously, are in fact concussions. Football in particular has received much media attention because of the frequency of concussions and other forms of traumatic brain injury (TBI).
Sports impacts are only one source of concussion. 18% of concussions are pediatric, 0-4 years of age. Another important segment is persistent TBI that accounts for up to 15% of all TBI. Persistent TBI does not resolve within 90 days and in some cases never resolves. Separating persistent TBI from PTSD is important to direct therapy for the symptoms.
Standard neuroimaging may be useful to evaluate for skull fractures, extra-axial bleeding, and contusions; however, these are unlikely to be present after concussions, given the likely microstructural nature of this injury. Importantly, no abnormalities are seen acutely on standard neuroimaging (computed tomography and magnetic resonance imaging (MRI)). Functional magnetic resonance imaging (fMRI), which measures changes in blood flow during brain activation, shows potential in the evaluation of concussion. However, fMRI is expensive, time-consuming, and not applicable when metal such as orthodontic braces are present and is not easily transportable or widely available. Multimodal MRI studies, notably including diffusion tensor imaging to detect axonal pathology and susceptibility weighted imaging to detect micro-hemorrhage, have been proposed as relevant imaging modalities. Dysfunction in cerebrovascular blood flow and auto-regulation, as demonstrated by fMRI and multimodal MRI, may one day be considered an objective definitive diagnostic measure of concussion. Accordingly conventional neuroimaging techniques are either ineffective, impractical, or too expensive for widespread concussion assessment.
Given the lack of a validated definition, or of proven physiological diagnostic testing or imaging criteria, concussions in clinical practice are not diagnosed or followed to resolution by objective findings or imaging, but rather by the constellation of neurocognitive signs and symptoms that commonly accompany mild TBI. The list of these signs and symptoms is long, and many of them are included in the symptom score of Sport Concussion Assessment Tool 2 (SCAT2), and now SCAT3. Other tests used to identify and follow concussions include Standardized Assessment of Concussions, Balance Error Scoring System, and Immediate Post-Concussion Assessment and Cognitive Testing. However, many of these neurocognitive tests are somewhat subjective and/or rely on self-reporting of symptoms by the patient.
Because less-than-optimal recovery or repeated concussion potentially leads to worse clinical outcome, a technology to objectively detect concussion is needed.
A new physiological phenomenon developed pursuant to the invention uses highly sensitive accelerometers to detect movement of the human skull during a normal cardiac cycle. In TBI, cerebral autoregulation can be impaired and persons can develop brain edema. The changes cerebral autoregulation and/or edema can affect other physical properties of brain tissue and adjacent structures. Because pulsatility of cerebral blood flow is transmitted through tissue to the skull surface, the present invention seeks to measure this phenomenon using highly sensitive cranial accelerometers in resting subjects suspected of having suffered a concussion. By measuring physical properties indicative of cerebral autoregulation dysfunction or subtle amounts of brain edema concussion can be assessed. Concussion does not involve any identifiable anomaly in the blood vessels or in tissue between a blood vessel and a sensor of the invention. It does, however, involve a signature, or a family of signatures, to which pressure pulse signals emanating from the vessels can be matched, or substantially or approximately matched to an approximate degree of certainty.
According to the invention, when a patient has suffered a blow to the head that may have caused concussion, at least one of the sensors of the equipment described below (and preferably more, or all) are secured on the head. Pressure pulse readings are taken from all sensors, which are accelerometers. If concussion is present (at least concussion above a very mild level), readings on at least some of the sensors, and sometimes all sensors, will indicate concussion when compared with a library of concussion signatures (with an approximately known degree of certainty). Such a library has been developed, based on testing of numerous subjects known to have concussion in ranges from mild to moderate, and on testing of non-concussion subjects for control. Preferably, but not necessarily, the data are analyzed in the frequency domain. The apparatus and method of the invention enable detection of concussion. Also notable is that the method and system of the invention can detect a patient's level of concussion in most instances, from mild to severe and determine if the patient has recovered from the concussion. With the method and system of the invention, brain concussion is detected in a human patient by detecting and measuring natural motion of the patient's head due to blood flow in the brain and resultant movement of tissue in the brain and analyzing that accelerometer data.
In general terms, accelerometer data is collected using the device disclosed below from a large number of concussed subjects and non-concussed subjects. The data is analyzed and compared for example in the frequency domain. The data can be analyzed to determine algorithms which differentiate between the concussion and non-concussion subjects. In an embodiment, an expert system or artificial intelligence system can be trained using the training set comprising the accelerometer data collected using the device from the large number of concussed subjects and non-concussed subjects. After such training, the expert system or artificial intelligence system can be used to differentiate between concussion and non-concussion subjects.
Accelerometer data is collected from subjects using a headset which includes one or more accelerometer and supporting electronics for recording signals from such accelerometer(s). At least one accelerometer (and preferably at least two) is attached to the patient's head such that the accelerometer measures the acceleration of the head where attached. In an embodiment, a single sensor consisting of 3-axis accelerometer can be used. Acceleration of the skull is detected and measured using the accelerometer, and these measurements are sent to a computer. The data includes frequency and intensity of vibrations of the skull at the location of the accelerometer. The data from the accelerometer are analyzed and investigated. If the data shows changes in frequency content of skull motion in selected frequency ranges as compared with data corresponding to non-concussion, this is taken to indicate probable concussion.
The acceleration of the skull can be measured with apparatus other than actual accelerometers. Laser-based interferometers, including fiber-based interferometers can be used to measure movement at selected parts on the skull, and from these measurements acceleration intensity and frequency can be determined. A waveform is obtained by averaging data over a number of heartbeats, which can be, for example, about 45 heartbeats, or a range of about 40 to 60 heartbeats.
Importantly, the system and method of the invention do not require baseline data for the particular patient being tested. A graph of the data, either in the time domain or in the frequency domain, will reveal, in a patient having concussion, a particular pattern that will not be seen in a graph of data from a non-concussed patient. The pattern indicating concussion can be observed visually by a trained physician or technician, or the data can be analyzed using an algorithm operated by a computer.
Although the data comparison step to determine concussion can be accomplished by a trained physician reviewing a graph, it is preferably performed by an algorithm operated on a computer, which can be the same computer to which the raw data is fed.
Importantly, the data corresponding to non-concussion, to which the accelerometer-derived data is compared, preferably are data observed from a plurality of normal persons without concussion; baseline data for the particular patient being examined is not required. In contrast, with cognitive testing pre-season baseline data for football players, for example, is required.
Using the system and method of the invention, concussion can be determined with a high degree of sensitivity and also specificity, without any baseline data for a patient, and it can be determined at a very early stage, by observing results at repeated intervals (daily or more frequently) immediately following a potential concussive event. In addition, the period of recovery from concussion can be monitored accurately, and the point of and the time of actual recovery can be confirmed.
These and other objects, advantages and features of the invention will be apparent from the following description of a preferred embodiment, considered along with the accompanying drawings.
Preferred embodiments of the invention are explained below in terms of system components and tests that have been performed. Initial discussion is in regard to testing performed to identify a specific pattern indication of concussion using cranial accelerometry (phase 1), to test the identified pattern against blinded data to verify its efficacy in detecting concussion (phase 2).
Concussion can be detected using a single temporally-placed sensor.
The concussion headset system collects and stores data derived by sensing small motions of the surface of the head of the subject produced by pulsatile cerebral blood flow and its impact on the skull. This is accomplished by accelerometer 110 that is positioned within the adjustable headset 100 to contact the scalp temporally. Transduced accelerometer data from accelerometer 110 is digitized with the digitizer 120 and recordings are stored in data storage 122. The recordings can be downloaded from data storage 122 to the laptop 150 for analysis as described below. Acceleration of the skull is detected and measured using the accelerometer, and these measurements are sent to a computer. The data include frequency and intensity of vibrations of the skull at the location of the accelerometer. The data from the accelerometer are analyzed and investigated. If the data shows a change in frequency content of skull motion in selected frequency ranges as compared with data corresponding to non-concussion, this is taken to indicate probable concussion.
For example, in an embodiment, accelerometer 110 is a three axis accelerometer. A three axis accelerometer comprises three single axis accelerometers oriented orthogonally to each other. An example of a three axis accelerometer is a 3-Axis digital accelerometer module available from the TESSEL PROJECT. The 3-Axis digital accelerometer module utilizes an MMA8452Q, 3-axis, 12-bit/8-bit digital accelerometer device available from NXP SEMICONDUCTORS. This smart, low-power accelerometer device incorporates three capacitive, micro-machined transducers oriented on three axes. The device also incorporates an analog to digital converter (see digitizer 120 of
The system collects and stores data derived by sensing small motions produced by pulsatile cerebral blood flow and its impact on the skull. This is accomplished by six highly sensitive accelerometers 10 that are positioned within an adjustable headset 8 to contact the scalp bitemporally, bifrontally, occipitally, and at the skull vertex. Note that concussion can be detected using only a single temporally-placed sensor, although two opposed temporal sensors are preferred. The accelerometers are very sensitive, typically 500 mV/g or more. Such an accelerometer is a model from Dytran Instruments, Inc. of California. A heart rate sensor 13 is also provided using a PPG (photoplethysmography). An omnidirectional sound pressure level (SPL) sensor 11 is positioned on the top of the headset for the purpose of ambient noise eradication (see
Transduced accelerometer data are digitized with the digital signal processor 12 and sampled with the laptop computer 20 (see
All of the collected data is digitized and, in a preferred embodiment, a fast Fourier transform (FFT) is performed on the data. The digitized data from the cohorts of concussion subjects 300 and non-concussion subjects 310 (training set) is then analyzed at step 320 using one or more of machine learning, mathematic analysis, expert system, neural net system, and/or artificial intelligence system. The analysis is used to develop a differential diagnostic algorithm 330. The analysis reveals one or more putative diagnostic algorithm. The putative diagnostic algorithm is then verified against a blinded set of accelerometry data from a second cohort of concussion subjects and second cohort of non-concussion subjects (validation set). See, e.g.
The diagnostic algorithm 350 can then be applied to a field diagnostic system. For example, the diagnostic algorithm may be provided to computer 20 of
In addition to concussion, cranial accelerometry data may be used to non-invasively diagnose a number of abnormal cranial conditions which cause micro or macro changes in the pulsatile blood flow with the brain or brain tissues. Such conditions include but are not limited to: stroke; ischemic stroke; vessel occlusion, hemorrhagic stroke; aneurysm; stenosis; peripheral bleeding; thrombotic, embolic, lacunar and hypo-perfusion strokes; intra-cranial hemorrhage; subarachnoid hemorrhage. Different diagnostic algorithms can be developed to allow a headset to diagnose these conditions. In order to develop such diagnostic algorithms different cohorts of patients with and without the condition must be assessed with the headset to provide the training data set and verification data set for development and verification of the diagnostic algorithm. Once developed the diagnostic algorithm and headset may be used for assessment of patients in the field suspected of having the particular condition.
A neural net is one example of a machine learning, expert system or artificial intelligence system. For example, the headsets disclosed herein can be used in a system and method for detecting a vascular condition non-invasively in the human body, by a collection of signal information from local, small regions of the vasculature. This is accomplished in a preferred embodiment by attaching, or contacting an array of accelerometers, or other sensors in a headset, to the head of subjects and recording vibration signals. The vibration signatures of blood vessel structures such as branches, aneurysms, stenosis or other structures using random, periodic, band limited or transient analysis provide a library for further processing. The library becomes part of a cerebral modeling arithmetic computer using basis functions, or other artificial neural network.
Neural networks typically are trained by inputting a set of known inputs and known outputs and allowing the weights of the neural connections to change to optimize the matching of the inputs and outputs. When a new input is presented to the neural network the output closest to the input is given the highest output even if the input is not a perfect match to any of the training set. The data to build the neural network (NN) and/or data store for correlation are obtained by acquiring data from patients with known pathologies as well as controls, and optionally with studies of constructed or cadaver vasculatures. The array of different accelerometer signatures produced by features within the vasculature is quite large due to the variable physiology of patients but there are generally only a few major underlying features. This type of data is ideal for using neural networks to identify the features causing the signature to be categorized. As the library of unique features and the associated signatures grows the neural network will improve in correctly identifying features in patients. The data are formatted according to the requirements of each processing method before being used for training or algorithm construction. The developed signature library and/or a diagnostic algorithm derived therefrom, is then used to diagnose conditions with the results presented to the physician in a clinically relevant manner.
Sport Concussion Assessment Tool 2 (SCAT2) was documented and accompanied all accelerometry recordings except those obtained postseason. In players with a concussion, multiple sequential measurements were obtained. Sport Concussion Assessment Tool 2 was used to assist clinical determination of concussion. Sport Concussion Assessment Tool 2 is a standardized tool introduced at the 2008 third Consensus Conference on Concussion in Sport held in Zurich. All scores from sections of the SCAT2 tool contribute to a possible total of 100 points. Scoring fewer points in any section lowers the aggregate score. For instance, baseline scores for high school athletes averaged 88.3+6.8. In our study, declines in SCAT2 scores were used to assist in the clinical determination of whether a head impact resulted in a concussion. SCAT2 or SCAT3 or similar neurocognitive testing can be used for other indications of concussion such as accidents.
Candidates for this observational study included all football players (grades 9-12) at a northern California high school during the 2011 football season.
Visual inspection of data from concussed and non-concussed subjects revealed that in the non-concussed subjects, cranial motion had a series of peaks that typically declined (diminished in amplitude) with increased frequency.
In the subjects with concussion, several additional peaks, representing higher frequencies, appeared. These new peaks arose at frequencies including and between 5 and 12 times the heart rate frequency. Three high-frequency ranges were used in algorithm development to differentiate between concussed and non-concussed subjects.
Several algorithms are described to quantify the differences between concussed and non-concussed subjects. Other algorithms are also possible. The algorithm can be in several forms, but primarily it will detect, as noted above, an increase in energy of skull motion in frequency ranges above about the fourth harmonic of the patient's heartbeat. In a mathematical algorithm these data need to be “normalized”, and in one form of algorithm the averaged energy of, for example, the fifth and sixth harmonics of the heartbeat is compared against (i.e. divided by) a maximum value of one or more or an average of several of the lower harmonics, below the fourth harmonic. This ratio, which is often called herein R1, is compared against a selected threshold. Such a threshold, in one embodiment of the invention, is set at 1.0, but different thresholds can be selected, based on desired levels of sensitivity and specificity. The higher the threshold for R1 is set, the lower the sensitivity but the higher the specificity, and vice versa.
Preferably another ratio factor is also included in the described algorithm, which can be called R2. R2 represents “normalized” data from the eighth and ninth harmonic peaks. The average value of those peaks is also divided by a maximum or average of one or more of the lower harmonics, the same denominator used for R1. In one embodiment of the algorithm the R2 threshold is set at 0.66. Thus, concussion is indicated if R1≥1.0 and R2≥0.66. Concussion is contra-indicated in the case where both R1 and R2 are below the set thresholds.
One such algorithm defines up to three factors (R1, R2 and R3) based on the relative height of the peaks, with the higher frequencies compared with the lower. The lower three harmonic values (first, second and third) define the denominator for these three variables. R1 is defined as the average of harmonics 5 and 6 amplitude divided by the maximum of the lower three harmonic amplitudes. R2 is the average of harmonics 8 and 9 amplitude divided by the maximum of the lower three harmonic amplitudes. R3 is the average of harmonics 11 and 12 amplitude divided by the maximum of the lower three harmonic amplitudes. Higher values of R1 through R3 indicate higher energy within the higher frequencies of skull motion. R1 and R2 can be used to create a Boolean value (true or false). This Boolean value is true if R1>1.0 AND R2>0.66; a Boolean value of “true” defines concussion. R1 and R2 (or either of them) can also be used as a continuous variable that follows the clinical time course of concussion. Likewise R3 can used to discriminate between concussed and non-concussed subjects by its increase over non-concussed subjects' values and by comparing it to R1 and R2. In this case the values of R1, R2 and R3 should diminish with increasing harmonic number.
The sensitivity and specificity of the described algorithm in detecting concussion from a set of subjects playing American football are shown in Tables 1 and 2.
An important aspect of the invention is the ability to detect concussion early. Typically peak data exceeding thresholds, in the procedure described above, are not exhibited for a few days after a concussive event, e.g. about day 4. The factors R1 and R2 may not cross the thresholds until day 3 or day 4, or even longer in some cases. It is known through cognitive concussion testing that some delay occurs in concussion manifesting itself (although maximum indications in cognitive testing tend to occur earlier than those from the subject algorithm). However, the system of the invention can detect the rise of these factors toward concussion even in the first day or two after an event of trauma to the head. This is accomplished by observing the velocity of change in the patient's R1 and R2 values in the period leading up to the concussion symptoms exhibiting themselves, referred to herein as the period of “developing” concussion, typically about four days. With the system of the invention, the data can be observed as showing clear and definitive movement toward crossing the thresholds for R1 and R2, in a patient with concussion. Thus, days before the data analysis shows positive for concussion, the velocity of movement in that direction will indicate concussion. There previously existed no other reliable means for detecting concussion in this “developing” period.
After R1 and/or R2 reach peak values, i.e. in the case of R1 the maximization of the frequency-domain peaks at about the fifth and sixth harmonics of the patient's heartbeat, a period of usually several weeks of declining values ensues, called the “recovery” period.
To gather insight into the time course of the presence of the concussion pattern, we plotted factor R1 over time in subjects who were able to provide multiple recordings after concussion.
Likewise a visual examination of the time domain or frequency domain plots of the average brain pulse or a visual examination of both domains of the average brain pulse provides a rapid method of detecting concussion, as explained below with reference to
The method of the invention also encompasses additional techniques to optimize the identification of peaks on the plot of accelerometer data in the frequency domain, using algorithms. As discussed above, one preferred algorithm developed pursuant to the invention has been to look at the values at the fifth and sixth harmonics as a basis for R1, and also at the eighth and ninth harmonics as a basis for R2. When an algorithm is applied based on the harmonics, the peaks will seldom fall precisely at the harmonic frequencies being investigated. In spite of this, the algorithm functions very well, with high sensitivity and specificity. Improvement can be made, however, by more closely identifying the actual frequency locations of the peaks that tend to occur around the fifth and sixth harmonics and also around the eighth and ninth harmonics. This can be done visually on a computer monitor, and it could also be done using a pattern recognition program, similar to those used for facial pattern recognition. Techniques are also available to identify these peaks accurately using an algorithm that does not use the visual appearance of a time domain or frequency domain chart. As one example, the algorithm can include a maximizing or optimizing feature by which, after the data are analyzed at the fifth and sixth harmonics and the eighth and ninth harmonics (as well as at lower harmonics as discussed above), the frequency under examination can be shifted up or down for each of the fifth, sixth, eighth and ninth harmonics to find the peak value within a selected frequency band of the nominal harmonic. By this method the actual peaks of the four approximate harmonics that are used to calculate R1 and R2 can be pinpointed, for more accurate analysis.
Another related algorithm to detect concussion is a variation of CIWF, the Campbell diagram. Another important aspect of the invention is the use of a Campbell diagram as an indicator of concussion, or as a confirmation. The responses of a concussion patient's brain to vascular pulsing are frequency dependent, and the Campbell diagram was developed for frequency dependent functions, such as turbine design in jet engines. Vibration response in a turbine tends to be different at different revolution speeds. Since concussed brain responses are also frequency dependent, i.e. heartbeat rate dependent, a typical waterfall diagram of the gathered data on a patient will usually not produce sharp lines—a patient's heartbeat rate can vary with time. However, the data can be plotted to produce sharper lines, as eigenfrequency lines, if heart rate is represented on the vertical axis and frequency on the horizontal axis. The eigenfrequency of a concussion patient typically are essentially radial lines emanating and fanning out from the theoretical point 0,0. In particular, if the “hot” color bands (red, orange, indicating high intensity) follow such radial, fanning lines, this indicates the harmonics of the system are changing in frequency with the driving function. That is to say, as the speed decreases, then the harmonics as an ensemble decrease in a pattern of the fan going down to zero. If, on the other hand, the structure is not responding to the driving force, i.e. to the frequency, but is simply a structural response, then the bands will be vertical. Therefore, one can use these bands to detect whether this is a structural change. It appears that in the normal brain (without concussion), the bands tend to be vertical; changing the heart rate does not shift the peaks of the harmonics. With concussion, changing the heart rate does change the position of the harmonics such that they follow the eigenfrequency lines down to zero. This is another method of detecting concussion and potentially detecting it much earlier than the R1 or R2 or velocity can do. The Campbell diagram provides an efficient reference that can be used as a primary determination for concussion indication (or not), or which can be used as a check against the conclusion reached via another algorithm such as that discussed above. The harmonic peak locations can be compared to the eigenfrequency lines by a correlation function to determine how well the structure responds to the varying heart rate.
The Campbell diagram was developed to detect damaging harmonic resonances in rotating machinery such as jet turbine engines. In human subjects the heart rate varies with respiration, providing a natural variation to the brain pulse, so that the Campbell diagram is based on heart rate. Each of the individual heart rate brain pulse recordings is sorted. The CIWF plot is modified so that the vertical axis is no longer time but heart rate with the highest heart rate FFT line plots farther from the plot origin and the lowest heart rate FFT line plots closer to the plot origin (see
The precise pathophysiology of concussion remains undefined, but likely is related in some way to injury that includes blood flow abnormalities, fluid accumulation, and/or structural changes. A shift of harmonic energy toward higher frequencies (or an increase at the higher frequencies) is likely caused by an increase in brain resonance from the force induced by pulsatile blood flow entering the skull. This may be caused by a stiffer brain and could be accounted for by brain edema or perhaps changes in cellular structure. However, brain edema has not been demonstrated in persons with clinical concussion, so this explanation is by no means certain. It is possible that disruption of cerebral autoregulation might produce phase changes in brain resonance and result in higher harmonics of the cardiac-induced forcing function into the closed skull space.
It was not part of our study to determine which subjects continued to suffer from concussion at the end of the season as determined by SCAT2 test results. We did not obtain SCAT2 test results from subjects at the end of the season, so cannot conclude who did or did not continue to register a concussion by that test method. Throughout the study, we used clinical judgment to determine the presence or absence of concussion. It is a recognized limitation of concussion diagnosis that clinical judgment and psychometric testing are subject to variability. However, this clinical judgment could not have been biased by knowledge of accelerometry data because these data were kept blinded until they had been fully collected and analyzed.
The observation that in certain subjects the concussion pattern returned to the non-concussion pattern after reporting of symptoms resolution but then returned to the concussion pattern after exercising suggests that return to activity is perhaps premature if it is not based on an objective physiological determination.
In
Note that many of the subjects reported in the testing and chart of
The described methodology identifies and utilizes a novel, unique pattern of cranial accelerometry that correlates with human concussion. It is influenced by movements of the brain within the skull that occur during cardiac-induced blood flow pulsations.
The terms “intensity of frequency”, “frequency content”, and “amplitude of signal data” are used in the above description and in the claims. These terms refer to how much of each of a series of particular frequencies occurs in a waveform analyzed by the algorithm, through a band of frequencies (e.g. 1-28 Hz). In the frequency domain they refer to the amplitude, i.e. height of signal data in the plot of amplitude (which can be called energy or power) versus frequency.
The above described preferred embodiments are intended to illustrate the principles of the invention, but not to limit its scope. Other embodiments and variations to these preferred embodiments will be apparent to those skilled in the art and may be made without departing from the spirit and scope of the invention as defined in the following claims.
This application claims the benefit of priority to U.S. Provisional Application No. 62/811,410, filed Feb. 27, 2019 titled “HEADSET FOR DIAGNOSIS OF CONCUSSION”, which application is herein incorporated by reference in its entirety. This application is related to U.S. patent application Ser. No. 16/285,938, filed Feb. 26, 2019 which is a continuation application of U.S. patent application Ser. No. 14/788,683, filed Jun. 30, 2015 which claims the benefit of U.S. Provisional Application No. 62/019,280, filed Jun. 30, 2014 and is a continuation-in-part of U.S. patent application Ser. No. 14/565,337, filed Dec. 9, 2014, now U.S. Pat. No. 10/092,195, issued Oct. 9, 2018 which is a continuation-in-part of U.S. patent application Ser. No. 11/894,052, filed Aug. 17, 2007, now U.S. Pat. No. 8,905,932, issued Dec. 9, 2014 which claims the benefit of U.S. Provisional Application No. 60/838,624, filed Aug. 17, 2006, which applications are incorporated herein by reference in their entireties.
| Number | Date | Country | |
|---|---|---|---|
| 62811410 | Feb 2019 | US |
