Classification of Traumatic Brain Injury Severity Using Informed Data Reduction in a Series of Binary Classifier Algorithms

Prichep, Leslie S.; Jacquin, A.; Filipenko, Julie; Dastidar, Samanwoy Ghosh; Zabele, S.; Vodenčarević, Asmir; Rothman, Nat

doi:10.1109/tnsre.2012.2206609

Cited by 48 publications

(50 citation statements)

References 54 publications

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“…Based on this threshold, this algorithm has been demonstrated to have a sensitivity of 95% and NPV of 98%. 1 This Index was calculated off-line for each subject in the study, blinded to any information about the clinical status of the patient. It is important to point out that patient age was taken into account before calculation of the TBI-Index because all EEG features were age-regressed before inclusion in discriminant analyses.…”

Section: Quantitative Analysis Of Brain Electrical Activitymentioning

confidence: 99%

“…Limited access to CT, situations necessitating rapid triage, concurrent severe multisystem injury, patient tracking to determine the need for repeated CTs, or even on-scene scans (such as sporting events or military settings) are all areas where a need exists to rapidly identify intracranial bleeds. Prichep and colleagues 1 described the development of the TBIIndex, which was based on brain electrical activity recorded from a limited forehead montage using a handheld device. This index demonstrated high sensitivity and specificity in the identification of mTBI.…”

Section: Introductionmentioning

confidence: 99%

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Use of Brain Electrical Activity for the Identification of Hematomas in Mild Traumatic Brain Injury

Hanley

Chabot

Mould

et al. 2013

Journal of Neurotrauma

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This study investigates the potential clinical utility in the emergency department (ED) of an index of brain electrical activity to identify intracranial hematomas. The relationship between this index and depth, size, and type of hematoma was explored. Ten minutes of brain electrical activity was recorded from a limited montage in 38 adult patients with traumatic hematomas (CT scan positive) and 38 mild head injured controls (CT scan negative) in the ED. The volume of blood and distance from recording electrodes were measured by blinded independent experts. Brain electrical activity data were submitted to a classification algorithm independently developed traumatic brain injury (TBI) index to identify the probability of a CT + traumatic event. There was no significant relationship between the TBI-Index and type of hematoma, or distance of the bleed from recording sites. A significant correlation was found between TBI-Index and blood volume. The sensitivity to hematomas was 100%, positive predictive value was 74.5%, and positive likelihood ratio was 2.92. The TBI-Index, derived from brain electrical activity, demonstrates high accuracy for identification of traumatic hematomas. Further, this was not influenced by distance of the bleed from the recording electrodes, blood volume, or type of hematoma. Distance and volume limitations noted with other methods, (such as that based on near-infrared spectroscopy) were not found, thus suggesting the TBI-Index to be a potentially important adjunct to acute assessment of head injury. Because of the life-threatening risk of undetected hematomas (false negatives), specificity was permitted to be lower, 66%, in exchange for extremely high sensitivity.

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Section: Quantitative Analysis Of Brain Electrical Activitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Use of Brain Electrical Activity for the Identification of Hematomas in Mild Traumatic Brain Injury

Hanley

Chabot

Mould

et al. 2013

Journal of Neurotrauma

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“…Details of the derivation of the classification algorithms are described elsewhere. 11,12 The EEGs from the stroke patients in this study were not part of the database used to develop the classification algorithm.…”

Section: Discussionmentioning

confidence: 99%

Identification of Acute Stroke Using Quantified Brain Electrical Activity

Michelson

Hanley

Chabot

et al. 2015

Academic Emergency Medicine

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Objectives: Acute stroke is a leading cause of brain injury and death and requires rapid and accurate diagnosis. Noncontrast head computed tomography (CT) is the first line for diagnosis in the emergency department (ED). Complicating rapid triage are presenting conditions that clinically mimic stroke. There is an extensive literature reporting clinical utility of brain electrical activity in early diagnosis and management of acute stroke. However, existing technologies do not lend themselves to easily acquired rapid evaluation. This investigation used an independently derived classifier algorithm for the identification of traumatic structural brain injury based on brain electrical activity recorded from a reduced frontal montage to explore the potential clinical utility of such an approach in acute stroke assessment.Methods: Adult patients (age 18 to 95 years) presenting with stroke-like and/or altered mental status symptoms were recruited from urban academic EDs as part of a large research study evaluating the clinical utility of quantitative brain electrical activity in acutely brain-injured patients. All patients from the parent study who had confirmed strokes, and a control group of stroke mimics (those with final ED diagnoses of migraine or syncope), were selected for this study. All stroke patients underwent head CT scans. Some patients with negative CTs had further imaging with magnetic resonance imaging (MRI). Ten minutes of electroencephalographic data were acquired on a hand-held device in development, from five frontal electrodes. Data analyses were done offline. A Structural Brain Injury Index (SBII) was derived using an independently developed binary discriminant classification algorithm whose input was specified features of brain electrical activity. The SBII was previously found to have high accuracy in the identification of traumatic brain-injured patients who were found to have brain injury on CT (CT+). This algorithm was applied to patients in this study and used to classify patients as CT+ or not CT+. Performance was assessed using sensitivity, specificity, and negative and positive predictive values (NPV, PPV).Results: Forty-eight stroke patients (31 ischemic and 17 hemorrhagic) and 135 stroke mimic controls were included. Within the ischemic population, approximately half were CT-but later confirmed for stroke with MRI (CT-/MRI+). Sensitivity to stroke was 91.7%, specificity 50.4% (to stroke mimic), NPV 94.4%, and PPV 39.6%. Eighty percent of the CT-/MRI+ ischemic strokes were correctly identified at the time of the CT-scan.Conclusions: Despite a small population and the use of a classifier without the benefit of training on a stroke population, these data suggest that a rapidly acquired, easy-to-use system to assess brain electrical activity at the time of evaluation of acute stroke could be a valuable adjunct to current clinical practice.ACADEMIC EMERGENCY MEDICINE 2015;22:67-72

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“…Details of these algorithms are given elsewhere. 3 When an artifact was identified in any channel, data from all channels at that time point were removed. Artifact-free data (60-120 sec) were concatenated after removal of artifact, with the minimum for any artifact-free segment being 2.5 sec.…”

Section: Electrophysiological Data Acquisition and Analysismentioning

confidence: 99%

“…Prichep and colleagues 3,4 described the development of TBI-Index using a binary classification algorithm based on selected quantitative features of brain electrical activity recorded from five electrodes placed on the forehead. In a prospective validation study (funded in part by the Department of Defense, contract #W911QY-12-C-0004, Assessment of Head Injury in the Emergency Department) using such an algorithm (Genetic algorithm, GA), high sensitivity (85.3%, 95% CI 78.8, 90.4) and negative predictive value (NPV) (96.5-92.2% at 10-20% prevalence) were obtained for identification of CT positive (CT + ) TBI in a large population (n = 552) of mTBI patients.…”

Section: Introductionmentioning

confidence: 99%

Identification of Hematomas in Mild Traumatic Brain Injury Using an Index of Quantitative Brain Electrical Activity

Prichep

Naunheim

Mould

et al. 2015

Journal of Neurotrauma

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Rapid identification of traumatic intracranial hematomas following closed head injury represents a significant health care need because of the potentially life-threatening risk they present. This study demonstrates the clinical utility of an index of brain electrical activity used to identify intracranial hematomas in traumatic brain injury (TBI) presenting to the emergency department (ED). Brain electrical activity was recorded from a limited montage located on the forehead of 394 closed head injured patients who were referred for CT scans as part of their standard ED assessment. A total of 116 of these patients were found to be CT positive (CT + ), of which 46 patients with traumatic intracranial hematomas (CT + ) were identified for study. A total of 278 patients were found to be CT negative (CT -) and were used as controls. CT scans were subjected to quanitative measurements of volume of blood and distance of bleed from recording electrodes by blinded independent experts, implementing a validated method for hematoma measurement. Using an algorithm based on brain electrical activity developed on a large independent cohort of TBI patients and controls (TBI-Index), patients were classified as either positive or negative for structural brain injury. Sensitivity to hematomas was found to be 95.7% (95% CI = 85.2, 99.5), specificity was 43.9% (95% CI = 38.0, 49.9). There was no significant relationship between the TBI-Index and distance of the bleed from recording sites (F = 0.044, p = 0.833), or volume of blood measured F = 0.179, p = 0.674). Results of this study are a validation and extension of previously published retrospective findings in an independent population, and provide evidence that a TBI-Index for structural brain injury is a highly sensitive measure for the detection of potentially life-threatening traumatic intracranial hematomas, and could contribute to the rapid, quantitative evaluation and treatment of such patients.

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Classification of Traumatic Brain Injury Severity Using Informed Data Reduction in a Series of Binary Classifier Algorithms

Cited by 48 publications

References 54 publications

Use of Brain Electrical Activity for the Identification of Hematomas in Mild Traumatic Brain Injury

Use of Brain Electrical Activity for the Identification of Hematomas in Mild Traumatic Brain Injury

Identification of Acute Stroke Using Quantified Brain Electrical Activity

Identification of Hematomas in Mild Traumatic Brain Injury Using an Index of Quantitative Brain Electrical Activity

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