Mapping Brain Injury with Symmetrical-channels' EEG Signal Analysis – A Pilot Study

Abstract

A technique for detecting brain injury at the bedside has great clinical value, but conventional imaging techniques (such as computed tomography [CT] and magnetic resonance imaging) are impractical. In this study, a novel method–the symmetrical channel electroencephalogram (EEG) signal analysis–was developed for this purpose. The study population consisted of 45 traumatic brain injury patients and 10 healthy controls. EEG signals in resting and stimulus states were acquired, and approximate entropy (ApEn) and slow-wave coefficient were extracted to calculate the ratio values of ApEn and SWC for injured and uninjured areas. Statistical analyses showed that the ratio values for both ApEn and SWC between injured and uninjured brain areas differed significantly (P < 0.05) for both resting and name call stimulus states. A set of criteria (range of ratio values) to determine whether a brain area is injured or uninjured was proposed and its reliability was verified by statistical analyses and CT images.

Traumatic Brain Injury and Neuronal Functionality Changes in Sensory Cortex

“…Event related potentials and EEG spectral power have been used to identify deficits in response inhibition (Roche et al, 2004 ) with TBI causing changes in N2 and P3 waveform components in response to visual stimuli and changes in EEG alpha power (Roche et al, 2004 ). A recent exciting development in the use of EEG for bedside monitoring of TBI is the finding (Li et al, 2014 ) that novel EEG analysis technique of symmetrical channel EEG analysis (SESA) combined with sophisticated signal processing and statistical analyses of the approximate entropy (a measure of the regularity and unpredictability of signal fluctuations over time-series data such as the EEG) and the Slow Wave Coefficient (the ratio of the summed power in the delta and theta bands to the summed power in the alpha and beta bands, i.e., the ratio of the power in the low frequency bands to the power in the high frequency bands) well indexes unilateral TBI, the most common form of TBI. Other newer techniques are currently being developed and offer potential to distinguish severe TBI from mild and also TBI from other brain disorders (Tsirka et al, 2011 ; Prichep et al, 2012 ; McBride et al, 2013 ; Teel et al, 2014 ).…”

Is there a role for computerized electroencephalographic analysis in the diagnosis of cerebral sinus venous thrombosis?

“…Approximate entropy (ApEn) and slow wave coefficient (SWC) are methods in the field of EEG analysis with the capability to localize the area with abnormal function. ApEn is a nonlinear method to describe the level of complexity and regularity in a time series, thereby measuring the irregulariy of signals (10). SWC provides an indicator of cerebral dysfunction by estimating the predominance of low frequency bands in a tracing of EEG and is defined as: (eq.…”

“…The spectra of these frequency bands are extracted from EEG by the implementation of Fourier transform technique. It is shown that brain injury causes increase in slow wave and decrease in fast wave bands in the power spectrum of EEG [10]. Full explanation and the application of these methods can be found in the referring article [10].…”

“…It is shown that brain injury causes increase in slow wave and decrease in fast wave bands in the power spectrum of EEG [10]. Full explanation and the application of these methods can be found in the referring article [10].…”

Electroencephalographic Biomarkers of Experimentally Induced Stress

“…Электроэнцефалография широко используется в клинической практике для изучения нарушений сознания (Thul, 2016), последствий черепно-мозговых травм (Yi, 2014), инсультов (Zappasod, 2014), в качестве надежного инструмента выявления маркеров эффективности фармакотерапии и мишеней для терапии при депрессиях (Fingelkurts, 2015; Spronk, 2011), тревожных расстройствах (Harrewijn, 2016), шизофрении (Takahashi, 2010), эпилепсии (Staljanssens, 2017), обсессивнокомпульсивном расстройстве (Fontenelle, 2006), аутизме (Jeste, 2015), ПТСР (Shim, 2017), нейродегенеративных заболеваниях (Poil, 2013).…”