Automated electroencephalogram identifies abnormalities in the ED

Naunheim, Rosanne; Treaster, Matthew; English, Joy; Casner, Teya

doi:10.1016/j.ajem.2010.03.010

Cited by 7 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nine studies were identified that investigate the utility of discriminant function analysis of qEEG data for the detection of mTBI (Table 5 ). Four studies investigate the efficacy of the same discriminant function currently under development by BrainScope, Inc. (McCrea et al, 2010 ; Naunheim et al, 2010a , b ; Naunheim and Casner, 2010 ; Barr et al, 2012 ; O’Neil et al, 2012 ; Prichep et al, 2012a , b ). McCrea et al ( 2010 ) introduces the BrainScope method and device in which seven qEEG features comprise the discriminant function.…”

Section: Measures Represented In the Down Selected Qeeg Literature Inmentioning

confidence: 99%

Traumatic Brain Injury Detection Using Electrophysiological Methods

Rapp

Keyser

Albano

et al. 2015

Front. Hum. Neurosci.

118

View full text Add to dashboard Cite

Measuring neuronal activity with electrophysiological methods may be useful in detecting neurological dysfunctions, such as mild traumatic brain injury (mTBI). This approach may be particularly valuable for rapid detection in at-risk populations including military service members and athletes. Electrophysiological methods, such as quantitative electroencephalography (qEEG) and recording event-related potentials (ERPs) may be promising; however, the field is nascent and significant controversy exists on the efficacy and accuracy of the approaches as diagnostic tools. For example, the specific measures derived from an electroencephalogram (EEG) that are most suitable as markers of dysfunction have not been clearly established. A study was conducted to summarize and evaluate the statistical rigor of evidence on the overall utility of qEEG as an mTBI detection tool. The analysis evaluated qEEG measures/parameters that may be most suitable as fieldable diagnostic tools, identified other types of EEG measures and analysis methods of promise, recommended specific measures and analysis methods for further development as mTBI detection tools, identified research gaps in the field, and recommended future research and development thrust areas. The qEEG study group formed the following conclusions: (1) Individual qEEG measures provide limited diagnostic utility for mTBI. However, many measures can be important features of qEEG discriminant functions, which do show significant promise as mTBI detection tools. (2) ERPs offer utility in mTBI detection. In fact, evidence indicates that ERPs can identify abnormalities in cases where EEGs alone are non-disclosing. (3) The standard mathematical procedures used in the characterization of mTBI EEGs should be expanded to incorporate newer methods of analysis including non-linear dynamical analysis, complexity measures, analysis of causal interactions, graph theory, and information dynamics. (4) Reports of high specificity in qEEG evaluations of TBI must be interpreted with care. High specificities have been reported in carefully constructed clinical studies in which healthy controls were compared against a carefully selected TBI population. The published literature indicates, however, that similar abnormalities in qEEG measures are observed in other neuropsychiatric disorders. While it may be possible to distinguish a clinical patient from a healthy control participant with this technology, these measures are unlikely to discriminate between, for example, major depressive disorder, bipolar disorder, or TBI. The specificities observed in these clinical studies may well be lost in real world clinical practice. (5) The absence of specificity does not preclude clinical utility. The possibility of use as a longitudinal measure of treatment response remains. However, efficacy as a longitudinal clinical measure does require acceptable test–retest reliability. To date, very few test–retest reliability studies have been published with qEEG data obtained from TBI patients or from healthy contro...

show abstract

Section: Measures Represented In the Down Selected Qeeg Literature Inmentioning

confidence: 99%

Traumatic Brain Injury Detection Using Electrophysiological Methods

Rapp

Keyser

Albano

et al. 2015

Front. Hum. Neurosci.

118

View full text Add to dashboard Cite

show abstract

“…This paper will describe the development of a hand-held tool using a classification algorithm based on quantitative features of brain electrical activity for rapid, objective, and reliable assessment of the likelihood of the presence of structural brain injury for the triage of patients who present with mild TBI to the ED. Throughout the period of development independent publications have presented evidence of the potential clinical utility of the classification algorithm in development [15][16][17][18]. This paper will describe the development of a quantitative biomarker or index, based on brain electrical activity that is sensitive to the presence of structural brain injury.…”

Section: Introductionmentioning

confidence: 98%

Classification algorithms for the identification of structural injury in TBI using brain electrical activity

Prichep

Dastidar

Jacquin

et al. 2014

Computers in Biology and Medicine

View full text Add to dashboard Cite

“…This frequency was chosen because 50/60 Hz mains interference is a common source of common-mode noise in EEG recordings [14]. A channel's A D was obtained by measuring the IA's output when a sinusoid generated by a XC90-III medical calibrator (Oxford Instruments, Abingdon, England) was inputted differentially.…”

Section: Methodsmentioning

confidence: 99%

Novel wireless electroencephalography system with a minimal preparation time for use in emergencies and prehospital care

et al. 2014

View full text Add to dashboard Cite

BackgroundAlthough clinical applications such as emergency medicine and prehospital care could benefit from a fast-mounting electroencephalography (EEG) recording system, the lack of specifically designed equipment restricts the use of EEG in these environments.MethodsThis paper describes the design and testing of a six-channel emergency EEG (emEEG) system with a rapid preparation time intended for use in emergency medicine and prehospital care. The novel system comprises a quick-application cap, a device for recording and transmitting the EEG wirelessly to a computer, and custom software for displaying and streaming the data in real-time to a hospital. Bench testing was conducted, as well as healthy volunteer and patient measurements in three different environments: a hospital EEG laboratory, an intensive care unit, and an ambulance. The EEG data was evaluated by two experienced clinical neurophysiologists and compared with recordings from a commercial system.ResultsThe bench tests demonstrated that the emEEG system's performance is comparable to that of a commercial system while the healthy volunteer and patient measurements confirmed that the system can be applied quickly and that it records quality EEG data in a variety of environments. Furthermore, the recorded data was judged to be of diagnostic quality by two experienced clinical neurophysiologists.ConclusionsIn the future, the emEEG system may be used to record high-quality EEG data in emergency medicine and during ambulance transportation. Its use could lead to a faster diagnostic, a more accurate treatment, and a shorter recovery time for patients with neurological brain disorders.

show abstract

Automated electroencephalogram identifies abnormalities in the ED

Cited by 7 publications

References 14 publications

Traumatic Brain Injury Detection Using Electrophysiological Methods

Traumatic Brain Injury Detection Using Electrophysiological Methods

Classification algorithms for the identification of structural injury in TBI using brain electrical activity

Novel wireless electroencephalography system with a minimal preparation time for use in emergencies and prehospital care

Contact Info

Product

Resources

About