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

Hanley, Daniel F.; Prichep, Leslie S.; Huff, J. Stephen; Naunheim, Rosanne; Garrett, John S.; Jones, Elizabeth B.; Wright, David W.; O'Neill, John; Badjatia, Neeraj; Gandhi, Dheeraj; Curley, Kenneth C.; Chiacchierini, Richard P.; O’Neil, Brian J.; Hack, Dallas

doi:10.1111/acem.13175

Cited by 36 publications

(36 citation statements)

References 28 publications

(35 reference statements)

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“…This classifier was demonstrated to obtain extremely high accuracy for predicting the likelihood of being CT+, with high negative predictive value (NPV) and sensitivity to any traumatic bleeding and to hematomas. Specificity was significantly higher than standard CT decision rules (for details see Hanley and colleagues 21 and Prichep and associates 27 ).…”

Section: Ahead 300 Structural Injury Classificationmentioning

confidence: 95%

“…The adjudication followed a rigorous and quantitative procedure involving sequential evaluation by imaging specialists and physician specialist readers with imagebased initial independent determination of CT+ or CT-, requiring unanimity for final determinations. 21 Evaluation of clinical signs and symptoms included the Standardized Assessment of Concussion scale (SAC) 22,23 and the Concussion Symptom Inventory (CSI), 24 acquired by trained ED personnel.…”

Section: Clinical Assessmentsmentioning

confidence: 99%

“…The likelihood that a patient was CT positive (CT+) was predicted by the application of the EEG-based structural injury classification algorithm (Ahead 300 device FDA 510(k) clearance, K161068) described in detail elsewhere. 21 This algorithm was independently developed using a least absolute shrinkage and selection operator (LASSO) methodology, 26 which uses a regularized logistic regression model. The classifier consists of a weighted combination of selected linear and non-linear QEEG features, enhanced with selected clinical features.…”

Section: Ahead 300 Structural Injury Classificationmentioning

confidence: 99%

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

Hanley

Prichep

Badjatia³

et al. 2018

Journal of Neurotrauma

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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.

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Section: Ahead 300 Structural Injury Classificationmentioning

confidence: 95%

Section: Clinical Assessmentsmentioning

confidence: 99%

Section: Ahead 300 Structural Injury Classificationmentioning

confidence: 99%

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

Hanley

Prichep

Badjatia³

et al. 2018

Journal of Neurotrauma

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“…The historical evolution of QEEG will be explored with emphasis on significant steps its development. The following will be highlighted: 1) Early steps in quantification that pave the way, including normative equations (John et al, 1980), source localization (Pascual-Marqui, Esslen, Kochi, & Lehman, 2002), and Default Mode Network (Buckner, AndrewsHanna, & Schacter, 2008); 2) QEEG treatment predictive biomarkers, including cognitive decline (Jelic et al, 2000;Prichep et al, 2006) and OCD (Dohrmann, Stengler, Jahn, & Olbrich, 2017); 3) QEEG as a surrogate for advanced neuroimaging, including TBI (Hanley et al, 2017) and chronic pain (Prichep et al, 2017). Impact of the "perfect storm" represented by advances over the last decade in technology, signal processing, and machine learning classification methodologies will be discussed in this context.…”

Section: Martin Teichermentioning

confidence: 99%

Proceedings of the 2017 ISNR Conference: Keynotes, Invited, and Student Award Presentations

2017

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“…Their proposed method classified CT scan positive patients from CT scan negative patients. Hanley et al [ 34 ] proposed a brain structural injury classifier (i.e., classifying CT positive and CT negative patients) based on a binary discriminant classification algorithm, which was derived using a Least Absolute Shrinkage and Selection Operator methodology. Power, phase, coherence were extracted from the resting-state EEG as input features to the classifier.…”

Section: Introductionmentioning

confidence: 99%

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

Lai

Ibrahim

Hamid

et al. 2020

Sensors

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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.

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

Cited by 36 publications

References 28 publications

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

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

Proceedings of the 2017 ISNR Conference: Keynotes, Invited, and Student Award Presentations

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

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