Emergency Department Triage of Traumatic Head Injury Using a Brain Electrical Activity Biomarker: A Multisite Prospective Observational Validation Trial

Recent developments in pre-hospital and in-hospital triage for endovascular stroke treatment

Triage describes the assignment of resources based on where they can be best used, are most needed, or are most likely to achieve success. Triage is of particular importance in time-critical conditions such as acute ischemic stroke. In this setting, one of the goals of triage is to minimize the delay to endovascular thrombectomy (EVT), without delaying intravenous thrombolysis or other time-critical treatments including patients who cannot benefit from EVT. EVT triage is highly context-specific, and depends on availability of financial resources, staff resources, local infrastructure, and geography. Furthermore, the EVT triage landscape is constantly changing, as EVT indications evolve and new neuroimaging methods, EVT technologies, and adjunctive medical treatments are developed and refined. This review provides an overview of recent developments in EVT triage at both the pre-hospital and in-hospital stages. We discuss pre-hospital large vessel occlusion detection tools, transport paradigms, in-hospital workflows, acute stroke neuroimaging protocols, and angiography suite workflows. The most important factor in EVT triage, however, is teamwork. Irrespective of any new technology, EVT triage will only reach optimal performance if all team members, including paramedics, nurses, technologists, emergency physicians, neurologists, radiologists, neurosurgeons, and anesthesiologists, are involved and engaged. Thus, building sustainable relationships through continuous efforts and hands-on training forms an integral part in ensuring rapid and efficient EVT triage.

Quantitative Electroencephalography Objectivity and Reliability in the Diagnosis and Management of Traumatic Brain Injury: A Systematic Review

Background. Persons with a history of traumatic brain injury (TBI) may exhibit short- and long-term cognitive deficits as well as psychiatric symptoms. These symptoms often reflect functional anomalies in the brain that are not detected by standard neuroimaging. In this context, quantitative electroencephalography (qEEG) is more suitable to evaluate non-normative activity in a wide range of clinical settings. Method. We searched the literature using the "Medline" and "Web of Science" online databases. The search was concluded on February 23, 2023, and revised on July 12, 2023. It returned 134 results from Medline and 4 from Web of Science. We then applied the PRISMA method, which led to the selection of 31 articles, the most recent one published in March 2023. Results. The qEEG method can detect functional anomalies in the brain occurring immediately after and even years after injury, revealing in most cases abnormal power variability and increases in slow (delta and theta) versus decreases in fast (alpha, beta, and gamma) frequency activity. Moreover, other findings show that reduced beta coherence between frontoparietal regions is associated with slower processing speed in patients with recent mild TBI (mTBI). More recently, machine learning (ML) research has developed highly reliable models and algorithms for the detection of TBI, some of which are already integrated into commercial qEEG equipment. Conclusion. Accumulating evidence indicates that the qEEG method may improve the diagnosis and management of TBI, in many cases revealing long-term functional anomalies in the brain or even neuroanatomical insults that are not revealed by standard neuroimaging. While FDA clearance has been obtained only for some of the commercially available equipment, the qEEG method allows for systematic, cost-effective, non-invasive, and reliable investigations at emergency departments. Importantly, the automated implementation of intelligent algorithms based on multimodally acquired, clinically relevant measures may play a key role in increasing diagnosis reliability.

Modern Prehospital Screening Technology for Emergent Neurovascular Disorders

Stroke is a serious neurological disease and a significant contributor to disability worldwide. Traditional in-hospital imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI) remain the standard modalities for diagnosing stroke. The development of prehospital stroke detection devices may facilitate earlier diagnosis, initiation of stroke care, and ultimately better patient outcomes. In this review, the authors summarize the features of eight stroke detection devices using noninvasive brain scanning technology. The review summarizes the features of stroke detection devices including portable CT, MRI, transcranial Doppler ultrasound , microwave tomographic imaging, electroencephalography, near-infrared spectroscopy, volumetric impedance phaseshift spectroscopy, and cranial accelerometry. The technologies utilized, the indications for application, the environments indicated for application, the physical features of the eight stroke detection devices, and current commercial products are discussed. As technology advances, multiple portable stroke detection instruments exhibit the promising potential to expedite the diagnosis of stroke and enhance the time taken for treatment, ultimately aiding in prehospital stroke triage.

EEG classification of traumatic brain injury and stroke from a nonspecific population using neural networks

Traumatic Brain Injury (TBI) and stroke are devastating neurological conditions that affect hundreds of people daily. Unfortunately, detecting TBI and stroke without specific imaging techniques or access to a hospital often proves difficult. Our prior research used machine learning on electroencephalograms (EEGs) to select important features and to classify between normal, TBI, and stroke on an independent dataset from a public repository with an accuracy of 0.71. In this study, we expanded to explore whether featureless and deep learning models can provide better performance in distinguishing between TBI, stroke and normal EEGs by including more comprehensive data extraction tools to drastically increase the size of the training dataset. We compared the performance of models built upon selected features with Linear Discriminative Analysis and ReliefF with several featureless deep learning models. We achieved 0.85 area under the curve (AUC) of the receiver operating characteristic curve (ROC) using feature-based models, and 0.84 AUC with featureless models. In addition, we demonstrated that Gradient-weighted Class Activation Mapping (Grad-CAM) can provide insight into patient-specific EEG classification by highlighting problematic EEG segments during clinical review. Overall, our study suggests that machine learning and deep learning of EEG or its precomputed features can be a useful tool for TBI and stroke detection and classification. Although not surpassing the performance of feature-based models, featureless models reached similar levels without prior computation of a large feature set allowing for faster and cost-efficient deployment, analysis, and classification.

Clinical Policy: Critical Issues in the Management of Adult Patients Presenting to the Emergency Department With Mild Traumatic Brain Injury: Approved by ACEP Board of Directors, February 1, 2023 Clinical Policy Endorsed by the Emergency Nurses Association (April 5, 2023)

This 2023 Clinical Policy from the American College of Emergency Physicians is an update of the 2008 “Clinical Policy: Neuroimaging and Decisionmaking in Adult Mild Traumatic Brain Injury in the Acute Setting.” A writing subcommittee conducted a systematic review of the literature to derive evidence-based recommendations to answer the following questions: 1) In the adult emergency department patient presenting with minor head injury, are there clinical decision tools to identify patients who do not require a head computed tomography? 2) In the adult emergency department patient presenting with minor head injury, a normal baseline neurologic examination, and taking an anticoagulant or antiplatelet medication, is discharge safe after a single head computed tomography? and 3) In the adult emergency department patient diagnosed with mild traumatic brain injury or concussion, are there clinical decision tools or factors to identify patients requiring follow-up care for postconcussive syndrome or to identify patients with delayed sequelae after emergency department discharge? Evidence was graded and recommendations were made based on the strength of the available data. Widespread and consistent implementation of evidence-based clinical recommendations is warranted to improve patient care.

Prehospital Electroencephalography to Detect Traumatic Brain Injury during Helicopter Transport: A Pilot Observational Cohort Study

OBJECTIVE

Early recognition of traumatic brain injury (TBI) is important to facilitate time-sensitive care. Electroencephalography (EEG) can identify TBI, but feasibility of EEG has not been evaluated in prehospital settings. We tested the feasibility of obtaining single-channel EEG during air medical transport after trauma. We measured association between quantitative EEG features, early blood biomarkers, and abnormalities on head computerized tomography (CT).

METHODS

We performed a pilot prospective, observational study enrolling consecutive patients transported by critical care air ambulance from the scene of trauma to a Level I trauma center. During transport, prehospital clinicians placed a sensor on the patient's forehead to record EEG. We reviewed EEG waveforms and selected 90 seconds of recording for quantitative analysis. EEG data processing included fast Fourier transform to summarize component frequency power in the delta (0-4 Hz), theta (4-8 Hz), and alpha (8-13 Hz) ranges. We collected blood samples on day 1 and day 3 post-injury and measured plasma levels of two brain injury biomarkers (ubiquitin C-terminal hydrolase L1 [UCH-L1] and glial fibrillary acidic protein [GFAP]). We compared predictors between individuals with and without CT-positive TBI findings.

RESULTS

Forty subjects were enrolled, with EEG recordings successfully obtained in 34 (85%). Reasons for failure included uncharged battery (n = 5) and user error (n = 1). Data were lost in three cases. Of 31 subjects with data, interpretable EEG signal was recorded in 26 (84%). Mean age was 48 (SD 16) years, 79% were male, and 50% suffered motor vehicle crashes. Eight subjects (24%) had CT-positive TBI. Subjects with and without CT-positive TBI had similar median delta power, alpha power, and theta power. UCH-L1 and GFAP plasma levels did not differ across groups. Delta power inversely correlated with UCH-L1 day 1 plasma concentration (r = -0.60, p = 0.03).

CONCLUSIONS

Prehospital EEG acquisition is feasible during air transport after trauma.

Long-Term Changes in Brain Connectivity Reflected in Quantitative Electrophysiology of Symptomatic Former National Football League Players

Exposure to repetitive head impacts (RHI) has been associated with long-term disturbances in cognition, mood, and neurobehavioral dysregulation, and reflected in neuroimaging. Distinct patterns of changes in quantitative features of the brain electrical activity (quantitative electroencephalogram [qEEG]) have been demonstrated to be sensitive to brain changes seen in neurodegenerative disorders and in traumatic brain injuries (TBI). While these qEEG biomarkers are highly sensitive at time of injury, the long-term effects of exposure to RHI on brain electrical activity are relatively unexplored. Ten minutes of eyes closed resting EEG data were collected from a frontal and frontotemporal electrode montage (BrainScope Food and Drug Administration-cleared EEG acquisition device), as well as assessments of neuropsychiatric function and age of first exposure (AFE) to American football. A machine learning methodology was used to derive a qEEG-based algorithm to discriminate former National Football League (NFL) players (n = 87, 55.40 ± 7.98 years old) from same-age men without history of RHI (n = 68, 54.94 ± 7.63 years old), and a second algorithm to discriminate former players with AFE <12 years (n = 33) from AFE ≥12 years (n = 54). The algorithm separating NFL retirees from controls had a specificity = 80%, a sensitivity = 60%, and an area under curve (AUC) = 0.75. Within the NFL population, the algorithm separating AFE <12 from AFE ≥12 resulted in a sensitivity = 76%, a specificity = 52%, and an AUC = 0.72. The presence of a profile of EEG abnormalities in the NFL retirees and in those with younger AFE includes features associated with neurodegeneration and the disruption of neuronal transmission between regions. These results support the long-term consequences of RHI and the potential of EEG as a biomarker of persistent changes in brain function.

40 Hz Blue LED Relieves the Gamma Oscillations Changes Caused by Traumatic Brain Injury in Rat

Background

Photobiomodulation (PBM) using low-level light-emitting diodes (LEDs) can be rapidly applied to various neurological disorders safely and non-invasively.

Materials and Methods

Forty-eight rats were involved in this study. The traumatic brain injury (TBI) model of rat was set up by a controlled cortical impact (CCI) injury. An 8-channel cortex electrode EEG was fixed to two hemispheres, and gamma oscillations were extracted according to each electrode. A 40 hz blue LED stimulation was set at four points of the frontal and parietal regions for 60 s each, six times per day for 1 week. Modified Neurologic Severity Scores (mNSS) were used to evaluate the level of neurological function.

Results

In the right-side TBI model, the gamma oscillation decreased in electrodes Fp2, T4, C4, and O2; but significantly increased after 1 week of 40 hz Blue LED intervention. In the left-side TBI model, the gamma oscillation decreased in electrodes Fp1, T3, C3, and O1; and similarly increased after 1 week of 40 hz Blue LED intervention. Both left and right side TBI rats performed significantly better in mNSS after 40 hz Blue LED intervention.

Conclusion

TBI causes the decrease of gamma oscillations on the injured side of the brain of rats. The 40 hz Blue LED therapy could relieve the gamma oscillation changes caused by TBI and improve the prognosis of TBI.

The Power of Public-Private Partnership in Medical Technology Innovation: Lessons From the Development of Fda-Cleared Medical Devices for Assessment of Concussion

Given the convergence of the long and challenging development path for medical devices with the need for diagnostic capabilities for mild traumatic brain injury (mTBI/concussion), the effective role of public–private partnership (PPP) can be demonstrated to yield Food and Drug Administration (FDA) clearances and innovative product introductions. An overview of the mTBI problem and landscape was performed. A detailed situation analysis of an example of a PPP yielding an innovative product was further demonstrated. The example of PPP has led to multiple FDA clearances and product introductions in the TBI diagnostic product category where there was an urgent military and public need. Important lessons included defining the primary public and military health objective for new product introduction, the importance of the government–academia–industry PPP triad with a “collaboration towards solutions” Quality-by-Design (QbD) mindset to assure clinical validity with regulatory compliance, the development of device comparators and integration of measurements into a robust, evidence-based statistical and FDA pathway, and the utility of top-down, flexible, practical action while operating within governmental guidelines and patient safety.

Review of wearable technologies and machine learning methodologies for systematic detection of mild traumatic brain injuries

Mild traumatic brain injuries (mTBIs) are the most common type of brain injury. Timely diagnosis of mTBI is crucial in making 'go/no-go' decision in order to prevent repeated injury, avoid strenuous activities which may prolong recovery, and assure capabilities of high-level performance of the subject. If undiagnosed, mTBI may lead to various short- and long-term abnormalities, which include, but are not limited to impaired cognitive function, fatigue, depression, irritability, and headaches. Existing screening and diagnostic tools to detect acute andearly-stagemTBIs have insufficient sensitivity and specificity. This results in uncertainty in clinical decision-making regarding diagnosis and returning to activity or requiring further medical treatment. Therefore, it is important to identify relevant physiological biomarkers that can be integrated into a mutually complementary set and provide a combination of data modalities for improved on-site diagnostic sensitivity of mTBI. In recent years, the processing power, signal fidelity, and the number of recording channels and modalities of wearable healthcare devices have improved tremendously and generated an enormous amount of data. During the same period, there have been incredible advances in machine learning tools and data processing methodologies. These achievements are enabling clinicians and engineers to develop and implement multiparametric high-precision diagnostic tools for mTBI. In this review, we first assess clinical challenges in the diagnosis of acute mTBI, and then consider recording modalities and hardware implementation of various sensing technologies used to assess physiological biomarkers that may be related to mTBI. Finally, we discuss the state of the art in machine learning-based detection of mTBI and consider how a more diverse list of quantitative physiological biomarker features may improve current data-driven approaches in providing mTBI patients timely diagnosis and treatment.

An Observation of Application of Structural Brain Injury Devices for Traumatic Brain Injury Evaluation in Austere Military Prehospital Settings

INTRODUCTION

Military units lack the ability to quickly, objectively, and accurately assess individuals that have suffered a closed head injury for structural brain injury and functional brain impairments in forward settings, where neurological assessment equipment and expertise may be lacking. With acute traumatic brain injury patients, detached medical providers are often faced with a decision to wait and observe or medically evacuate, both of which have cascading consequences. Structural brain injury assessment devices, when employed in forward environments, have the potential to reduce the risk of undiagnosed and/or mismanaged traumatic brain injuries given their high negative predictive value and suggested increased specificity compared to common subjective clinical decision rules. These handheld devices are portable and have an ease of use, from combat medic to physician, allowing for use in austere environments, safely keeping soldiers with their teams when able and suggesting further evaluation via computed tomography (CT) scan when warranted.

METHODS

Data collected on 13 encounters at 5 locations were retrospectively analyzed using descriptive statistics.

RESULTS

A total number of 13 examinations were performed using the BrainScope One device during the 9-month deployment. The Structural Injury Classification was negative for 11 of the patients. Two of the 11 patients underwent head CT scans, which confirmed the absence of intracranial hemorrhage. Of the two positive Structural Injury Classification exams, one was CT negative and no CT was performed for the other based on clinical judgment.

CONCLUSION

The data from this study suggest that structural brain injury devices may provide value by ruling out serious brain injury pathology while limiting excessive medical evacuations from austere settings, where neurological assessment equipment and expertise may be lacking, reducing unnecessary head CT scans.

Concussion assessment potentially aided by use of an objective multimodal concussion index

Objective

Prompt, accurate, objective assessment of concussion is crucial as delays can lead to increased short and long-term consequences. The purpose of this study was to derive an objective multimodal concussion index (CI) using EEG at its core, to identify concussion, and to assess change over time throughout recovery.

Methods

Male and female concussed ( N = 232) and control ( N = 206) subjects 13–25 years were enrolled at 12 US colleges and high schools. Evaluations occurred within 72 h of injury, 5 days post-injury, at return-to-play (RTP), 45 days after RTP (RTP + 45); and included EEG, neurocognitive performance, and standard concussion assessments. Concussed subjects had a witnessed head impact, were removed from play for ≥ 5 days using site guidelines, and were divided into those with RTP < 14 or ≥14 days. Part 1 describes the derivation and efficacy of the machine learning derived classifier as a marker of concussion. Part 2 describes significance of differences in CI between groups at each time point and within each group across time points.

Results

Sensitivity = 84.9%, specificity = 76.0%, and AUC = 0.89 were obtained on a test Hold-Out group representing 20% of the total dataset. EEG features reflecting connectivity between brain regions contributed most to the CI. CI was stable over time in controls. Significant differences in CI between controls and concussed subjects were found at time of injury, with no significant differences at RTP and RTP + 45. Within the concussed, differences in rate of recovery were seen.

Conclusions

The CI was shown to have high accuracy as a marker of likelihood of concussion. Stability of CI in controls supports reliable interpretation of CI change in concussed subjects. Objective identification of the presence of concussion and assessment of readiness to return to normal activity can be aided by use of the CI, a rapidly obtained, point of care assessment tool.

Quantitative EEG Analysis in Clinical Practice: Concussion Injury

Concussion is a common brain injury. The American Academy of Neurology provides a definition of concussion: "Concussion is a traumatically, or biomechanically, induced alteration of brain function. Emphasis is placed on a pathophysiological process, or functional disruption, as opposed to anatomic, structural, or tissue injury.". The incidence of mild traumatic brain injury (mTBI) is estimated at 200 per 100 000. The Centers for Disease Control and Prevention (CDC) estimates 3.8 million sport and recreational mTBIs occurring in the United States each year. A more recent CDC assessment estimates 2.5 million concussion injuries in high school sports alone. The controlled environment and opportunity for direct surveillance and observation has made the sports arena the scientific "wet lab" for the study of mTBI natural history, short- and long-term consequences and opportunities to intervene. Quantitative EEG methods have been utilized in the assessment and management of mTBI and lends to provide a cost-effective procedure that has the sensitivities needed to identify pathology where routine visual inspection of the EEG has failed.

Validation of a Machine Learning Brain Electrical Activity-Based Index to Aid in Diagnosing Concussion Among Athletes

IMPORTANCE

An objective, reliable indicator of the presence and severity of concussive brain injury and of the readiness for the return to activity has the potential to reduce concussion-related disability.

OBJECTIVE

To validate the classification accuracy of a previously derived, machine learning, multimodal, brain electrical activity-based Concussion Index in an independent cohort of athletes with concussion.

DESIGN, SETTING, AND PARTICIPANTS

This prospective diagnostic cohort study was conducted at 10 clinical sites (ie, US universities and high schools) between February 4, 2017, and March 20, 2019. A cohort comprising a consecutive sample of 207 athletes aged 13 to 25 years with concussion and 373 matched athlete controls without concussion were assessed with electroencephalography, cognitive testing, and symptom inventories within 72 hours of injury, at return to play, and 45 days after return to play. Variables from the multimodal assessment were used to generate a Concussion Index at each time point. Athletes with concussion had experienced a witnessed head impact, were removed from play for 5 days or more, and had an initial Glasgow Coma Scale score of 13 to 15. Participants were excluded for known neurologic disease or history within the last year of traumatic brain injury. Athlete controls were matched to athletes with concussion for age, sex, and type of sport played.

MAIN OUTCOMES AND MEASURES

Classification accuracy of the Concussion Index at time of injury using a prespecified cutoff of 70 or less (total range, 0-100, where ≤70 indicates it is likely the individual has a concussion and >70 indicates it is likely the individual does not have a concussion).

RESULTS

Of 580 eligible participants with analyzable data, 207 had concussion (124 male participants [59.9%]; mean [SD] age, 19.4 [2.5] years), and 373 were athlete controls (187 male participants [50.1%]; mean [SD] age, 19.6 [2.2] years). The Concussion Index had a sensitivity of 86.0% (95% CI, 80.5%-90.4%), specificity of 70.8% (95% CI, 65.9%-75.4%), negative predictive value of 90.1% (95% CI, 86.1%-93.3%), positive predictive value of 62.0% (95% CI, 56.1%-67.7%), and area under receiver operator characteristic curve of 0.89. At day 0, the mean (SD) Concussion Index among athletes with concussion was significantly lower than among athletes without concussion (75.0 [14.0] vs 32.7 [27.2]; P < .001). Among athletes with concussion, there was a significant increase in the Concussion Index between day 0 and return to play, with a mean (SD) paired difference between these time points of -41.2 (27.0) (P < .001).

CONCLUSIONS AND RELEVANCE

These results suggest that the multimodal brain activity-based Concussion Index has high classification accuracy for identification of the likelihood of concussion at time of injury and may be associated with the return to control values at the time of recovery. The Concussion Index has the potential to aid in the clinical diagnosis of concussion and in the assessment of athletes' readiness to return to play.

Beyond Technologies of Electroencephalography-Based Brain-Computer Interfaces: A Systematic Review From Commercial and Ethical Aspects

The deployment of electroencephalographic techniques for commercial applications has undergone a rapid growth in recent decades. As they continue to expand in the consumer markets as suitable techniques for monitoring the brain activity, their transformative potential necessitates equally significant ethical inquiries. One of the main questions, which arises then when evaluating these kinds of applications, is whether they should be aligned or not with the main ethical concerns reported by scholars and experts. Thus, the present work attempts to unify these disciplines of knowledge by performing a comprehensive scan of the major electroencephalographic market applications as well as their most relevant ethical concerns arising from the existing literature. In this literature review, different databases were consulted, which presented conceptual and empirical discussions and findings about commercial and ethical aspects of electroencephalography. Subsequently, the content was extracted from the articles and the main conclusions were presented. Finally, an external assessment of the outcomes was conducted in consultation with an expert panel in some of the topic areas such as biomedical engineering, biomechatronics, and neuroscience. The ultimate purpose of this review is to provide a genuine insight into the cutting-edge practical attempts at electroencephalography. By the same token, it seeks to highlight the overlap between the market needs and the ethical standards that should govern the deployment of electroencephalographic consumer-grade solutions, providing a practical approach that overcomes the engineering myopia of certain ethical discussions.

Preliminary investigation of a multimodal enhanced brain function index among high school and collegiate concussed male and female athletes

Objective: The primary purpose of this study was to examine the longitudinal effects of sports-related concussion (SRC) on a multi-faceted assessment battery which included neuropsychological testing, symptom reporting, and enhanced brain function index (eBFI) among athletes with and without SRC. A secondary purpose was to explore longitudinal sex differences among these measures in athletes with and without SRC. Methods: A case-control, repeated-measures design was used for this study. A total of 186 athletes (concussed group:n= 87 controls:n= 99) participated in the study. A repeated-measures design was used in which each athlete was tested at four time points following an SRC: within 72 h of injury (Day 0; 2.0 ± 0.9 days following injury), 5 days following injury (Day 5; 5.0 ± 0.0), at return to play (RTP; 18.3 ± 13.8 days following injury), and within 45 days following RTP (RTP45; 66.2 ± 19.0 days following injury). All analyses were conducted separately using a 2 (Group: concussed, control) × 2 (Sex: male, female) × 4 (Time:Day 0, Day 5, RTP, RTP45) univariate multi-level model including the random intercept for each participant. A higher eBFI score indicates a better performance. Alpha level was set aprior at .05. This study was registered on clinicaltrials.gov (Objective Brain Function Assessment of mTBI/Concussion in College/high school Athletes NCT02477943, NCT02661633, CAS 13-25 NCT03963804). Results: Concussed athletes exhibited impaired eBFI within 72 h of SRC and at Day 5 compared to controls (p<.001). Analysis of eBFI scores between male and female athletes revealed a main effect of sex (p=.05), with female athletes exhibiting lower eBFI (33.9 ± 30.7) relative to male athletes (40.4 ± 33.0), however, it did not indicate interactions between sex, group, and time (p's ≥ 0.786). Conclusion: The eBFI appears to be a useful tool in determining concussed athletes during the acute stages of an SRC. However, this index may lack the sensitivity to detect sex-related differences between groups at various time points during recovery.

Classification of Non-Severe Traumatic Brain Injury from Resting-State EEG Signal Using LSTM Network with ECOC-SVM

Traumatic brain injury (TBI) is one of the common injuries when the human head receives an impact due to an accident or fall and is one of the most frequently submitted insurance claims. However, it is often always misused when individuals attempt an insurance fraud claim by providing false medical conditions. Therefore, there is a need for an instant brain condition classification system. This study presents a novel classification architecture that can classify non-severe TBI patients and healthy subjects employing resting-state electroencephalogram (EEG) as the input, solving the immobility issue of the computed tomography (CT) scan and magnetic resonance imaging (MRI). The proposed architecture makes use of long short term memory (LSTM) and error-correcting output coding support vector machine (ECOC-SVM) to perform multiclass classification. The pre-processed EEG time series are supplied to the network by each time step, where important information from the previous time step will be remembered by the LSTM cell. Activations from the LSTM cell is used to train an ECOC-SVM. The temporal advantages of the EEG were amplified and able to achieve a classification accuracy of 100%. The proposed method was compared to existing works in the literature, and it is shown that the proposed method is superior in terms of classification accuracy, sensitivity, specificity, and precision.

Non-invasive sensor technology for prehospital stroke diagnosis: Current status and future directions

BACKGROUND

The diagnosis of stroke in the prehospital environment is the subject of intense interest and research. There are a number of non-invasive external brain monitoring devices in development that utilize various technologies to function as sensors for stroke and other neurological conditions. Future increased use of one or more of these devices could result in substantial changes in the current processes for stroke diagnosis and treatment, including transportation of stroke patients by emergency medical services.

AIMS

The present review will summarize information about 10 stroke sensor devices currently in development, utilizing various forms of technology, and all of which are external, non-invasive brain monitoring devices.

SUMMARY OF REVIEW

Ten devices are discussed including the technology utilized, the indications for use (stroke and, when relevant, other neurological conditions), the environment(s) indicated for use (with a focus on the prehospital setting), a description of the physical structure of each instrument, and, when available, findings that have been published in peer-reviewed journals or otherwise reported. The review is organized based on the technology utilized by each device, and seven distinct forms were identified: accelerometers, electroencephalography (EEG), microwaves, near-infrared, radiofrequency, transcranial doppler ultrasound, and volumetric impedance phase shift spectroscopy.

CONCLUSIONS

Non-invasive external brain monitoring devices are in various stages of development and have promise as stroke sensors in the prehospital setting. Some of the potential applications include to differentiate stroke from non-stroke, ischemic from hemorrhage stroke, and large vessel occlusion (LVO) from non-LVO ischemic stroke. Successful stroke diagnosis prior to hospital arrival could transform the current diagnostic and treatment paradigm for this disease.

ERPs predict symptomatic distress and recovery in sub-acute mild traumatic brain injury

Mild traumatic brain injury (mTBI) can affect high-level executive functioning long after somatic symptoms resolve. We tested if simple EEG responses within an oddball paradigm could capture variance relevant to this clinical problem. The P3a and P3b components reflect bottom-up and top-down processes driving engagement with exogenous stimuli. Since these features are related to primitive decision abilities, abnormal amplitudes following mTBI may account for problems in the ability to exert executive control. Sub-acute (<2 weeks) mTBI participants (N = 38) and healthy controls (N = 24) were assessed at an initial session as well as a two-month follow-up (sessions 1 and 2). We contrasted the initial assessment to a comparison group of participants with chronic symptomatology following brain injury (N = 23). There were no group differences in P3a or P3b amplitudes. Yet in the sub-acute mTBI group, higher symptomatology on the Frontal Systems Behavior scale (FrSBe), a questionnaire validated as measuring symptomatic distress related to frontal lobe injury, correlated with lower P3a in session 1. This relationship was replicated in session 2. These findings were distinct from chronic TBI participants, who instead expressed a relationship between increased FrSBe symptoms and a lower P3b component. In the sub-acute group, P3b amplitudes in the first session correlated with the degree of symptom change between sessions 1 and 2, above and beyond demographic predictors. Controls did not show any relationship between FrSBe symptoms and P3a or P3b. These findings identify symptom-specific alterations in neural systems that vary along the time course of post-concussive symptomatology.

Objective Classification of mTBI Using Machine Learning on a Combination of Frontopolar Electroencephalography Measurements and Self-reported Symptoms

BACKGROUND

The reliable diagnosis of a mild traumatic brain injury (mTBI) is a pervasive problem in sports and in the military. The frequency and severity of each occurrence, while difficult to quantify, may impact long term cognitive function and quality of life. Despite the new revelations concerning brain disfunction from head injuries, individuals still feel pressure to remain on the field despite a debilitating injury. In this study, we evaluated the accuracy of a system that could be employed on the sidelines or in the locker room to provide an immediate objective mTBI assessment.

METHODS

Participants consisted of 38 individuals with a recent mTBI and 47 controls with no history of mTBI within the last 5 years. Participants were administered a simple symptom questionnaire, behavioral tests, and resting state EEG was measured using three frontopolar electrodes. An advanced machine learning algorithm called boosting was utilized to classify subjects into either injured or controls using power spectral densities on 1-min of resting EEG and the symptom questionnaire.

RESULTS

Results based on leave-one-out cross-validation revealed that the addition of EEG measurements boosted the accuracy to approximately 91 ± 2% compared to 82 ± 4% from the symptom questionnaire alone.

CONCLUSION

This study demonstrated the potential benefit of including EEG measurements to diagnose suspected brain injury patients. This is a step toward accurate and objective classification measurements that can be implemented on the field as a future injury assessment tool.

Emergency Department Time Course for Mild Traumatic Brain Injury Workup

Introduction

Mild traumatic brain injury (mTBI) is a common cause for visits to the emergency department (ED). The actual time required for an ED workup of a patient with mTBI in the United States is not well known. National emergency medicine organizations have recommended reducing unnecessary testing, including head computed tomography (CT) for these patients.10.

Methods

To examine this issue, we developed a care map that included each step of evaluation of mTBI (Glasgow Coma Scale Score 13-15) - from initial presentation to the ED to discharge. Time spent at each step was estimated by a panel of United States emergency physicians and nurses. We subsequently validated time estimates using retrospectively collected, real-time data at two EDs. Length of stay (LOS) time differences between admission and discharged patients were calculated for patients being evaluated for mTBI.

Results

Evaluation for mTBI was estimated at 401 minutes (6.6 hours) in EDs. Time related to head CT comprised about one-half of the total LOS. Real-time data from two sites corroborated the estimate of median time difference between ED admission and discharge, at 6.3 hours for mTBI.

Conclusion

Limiting use of head CT as part of the workup of mTBI to more serious cases may reduce time spent in the ED and potentially improve overall ED throughput.

Intracranial Pathology (CT+) in Emergency Department Patients With High GCS and High Standard Assessment of Concussion (SAC) Scores

OBJECTIVE

To demonstrate that a subpopulation of patients with mild/moderate traumatic brain injury (TBI) had intracranial pathology despite having a Glasgow Coma Scale (GCS) score of 15 and a Standardized Assessment of Concussion (SAC) score of 25 or higher.

SETTING

A network of 11 US emergency departments (ED) enrolling patients in a multisite study of TBI.

PARTICIPANTS

Men and women between the ages of 18 and 85 years admitted to a participating ED having sustained a closed head injury within the prior 72 hours and a GCS score of 13 to 15 at the time of enrollment.

DESIGN

Prospective observational study.

MAIN MEASURES

GCS, SAC, computed tomography (CT) positive or negative for intracranial pathology, Marshall scoring of CT scans.

RESULTS

Of 191 patients with intracranial pathology (CT+) and having a SAC score recorded, 24% (46/191) had a SAC score in the normal range (≥25) as well as a GCS score of 15. All causes of CT+ brain injury were present in both SAC groups.

CONCLUSION

A normal GCS score and a SAC score do not exclude the possibility of significant intracranial injury.

A Brain Electrical Activity Electroencephalographic-Based Biomarker of Functional Impairment in Traumatic Brain Injury: A Multi-Site Validation Trial

The potential clinical utility of a novel quantitative electroencephalographic (EEG)-based Brain Function Index (BFI) as a measure of the presence and severity of functional brain injury was studied as part of an independent prospective validation trial. The BFI was derived using quantitative EEG (QEEG) features associated with functional brain impairment reflecting current consensus on the physiology of concussive injury. Seven hundred and twenty adult patients (18-85 years of age) evaluated within 72 h of sustaining a closed head injury were enrolled at 11 U.S. emergency departments (EDs). Glasgow Coma Scale (GCS) score was 15 in 97%. Standard clinical evaluations were conducted and 5 to 10 min of EEG acquired from frontal locations. Clinical utility of the BFI was assessed for raw scores and percentile values. A multinomial logistic regression analysis demonstrated that the odds ratios (computed against controls) of the mild and moderate functionally impaired groups were significantly different from the odds ratio of the computed tomography (CT) postive (CT+, structural injury visible on CT) group (p = 0.0009 and p = 0.0026, respectively). However, no significant differences were observed between the odds ratios of the mild and moderately functionally impaired groups. Analysis of variance (ANOVA) demonstrated significant differences in BFI among normal (16.8%), mild TBI (mTBI)/concussed with mild or moderate functional impairment, (61.3%), and CT+ (21.9%) patients (p < 0.0001). Regression slopes of the odds ratios for likelihood of group membership suggest a relationship between the BFI and severity of impairment. Findings support the BFI as a quantitative marker of brain function impairment, which scaled with severity of functional impairment in mTBI patients. When integrated into the clinical assessment, the BFI has the potential to aid in early diagnosis and thereby potential to impact the sequelae of TBI by providing an objective marker that is available at the point of care, hand-held, non-invasive, and rapid to obtain.