QEEG Training Module



Fig. 1
One-hour QEEG panel and corresponding raw EEG. (a) Example of a QEEG panel consisting of the following QEEG tools: rhythmicity spectrogram (displayed for the left and right hemispheres), CDSA (displayed for the left and right hemispheres), asymmetry index (displayed as both absolute and relative values), and aEEG (displayed for the left and right hemispheres). Black arrows denote electrographic seizures. (b) The corresponding raw EEG (16 s) for one of the seizures is shown displaying a left hemispheric seizure



Often, the QEEG trends are broken down into separate graphical displays of the left and right hemispheres; however, this can be modified to have greater spatial resolution of various areas of the brain. Many QEEG trends display a color-coded graphical representation of various EEG parameters (with the colors varying between software programs). Some QEEG trends report numerical information in a bar-graph format over time, such as the alpha-delta ratio or burst-suppression ratio. This highlights another advantage of QEEG; unlike raw EEG, QEEG is able to provide a method of converting the subjective data of raw EEG signals into objective QEEG data.

QEEG software is sold separately from EEG software. Persyst (Persyst Development Corporation, Prescott, AZ) is a commonly used QEEG software and is compatible with clinical EEG software. The QEEG trends in this chapter were created from Persyst. This software can often be seen running at the bedside in neuro ICUs on a split screen shared with the continuously running conventional EEG.



Quantitative EEG Trends


There are numerous QEEG trends available. Typically, neurophysiologists at an individual institution will select the trends that are displayed on the real-time QEEG display that is running at the bedside. Therefore, the QEEG display may vary in appearance from institution to institution. The basics of some of the most commonly used trends for seizure detection will be discussed, since this chapter is directed at non-neurophysiologists, but more detail and information about other QEEG trends can be found elsewhere in this text.


Frequency-Based Trends


Before individual frequency-based QEEG trends are explained, it is first important to understand the concept of Fourier domain analysis, as this is the basis for many of the frequency-based QEEG trends. Fourier domain analysis refers to the contribution of different frequencies to the EEG signal. The EEG signal is represented as a weighted sum of sine waves of different frequencies. For each frequency, there is an amplitude. A Fourier spectrum displays a plot of amplitude vs. frequency. From this, a specific parameter called power can be calculated (Fig. 2). The power is the area under the Fourier spectrum curve within a given frequency range (i.e., delta power). In other words, the power is the amplitude (or voltage) of the EEG within a specific frequency range. This may be expressed as an absolute power (delta power, theta power, alpha power, and beta power) or as a relative power compared with the total power of all the frequency ranges (i.e., alpha-delta ratio). The Fourier spectrum changes over time as the EEG signals change due to medication effect, state changes, seizures, ischemia, etc. Frequency-based QEEG trends are able to show these changes in a graphical display over time.

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Fig. 2
A diagrammatic representation of the creation of a Fourier spectrum plot. (a) The raw EEG signal is represented as a weighted sum of sine waves of different frequencies. For each frequency band (delta, theta, alpha, and beta), the amplitude is calculated. The amplitude is then plotted against frequency. (b) A sample Fourier spectrum curve is shown. The power of each frequency band (color-coded) is calculated as the area under the curve at specific frequency intervals


Color Density Spectral Array


An example color density spectral array (CDSA) trend is shown in Fig. 3. Often, the CDSA trend is displayed separately for the left and right hemispheres. Time is shown on the x-axis and the EEG frequency is shown on the y-axis. The various colors represent the power (described above) of various frequency bands. Cooler colors (blue and green) indicate lower power and warmer colors (red and yellow) indicate higher power. Seizures appear on the CDSA trend as increased power, and can be visualized as an episode of an increased amount of warmer colors (Fig. 3). Seizures tend to appear as an arch-like shape on the CDSA trend.

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Fig. 3
Recurrent left hemispheric seizures displayed on the CDSA trend (displayed for the left and right hemispheres). Black arrows denote electrographic seizures. An increase in power is seen during seizure activity resulting in warmer colors (pink and red) replacing areas previously occupied by areas of lower power (represented by cooler colors, blue, teal, and green). With each seizure, there is an initial abrupt increase in power in the alpha and theta frequency ranges. This quickly decreases, and the seizure evolves into having more power in the lower-frequency ranges before cessation


Rhythmicity Spectrogram


The rhythmicity spectrogram, rhythmic run detection and display, is a proprietary tool developed by Persyst, Inc. An example rhythmicity spectrogram is shown in Fig. 4. This trend is often displayed separately for the left and right hemispheres, but may be modified to display individual channels (Fig. 5) or groups of channels. It is very similar to the CDSA trend, because time is on the x-axis and frequency is on the y-axis (but on a logarithmic scale to accentuate lower frequencies). Although the power is displayed by color-coding (darker blue color indicating more power), it differs from CDSA by only displaying the power in components that have a high degree of rhythmicity, instead of displaying all the power. Seizures will appear as areas that are darker in color (i.e., more power). Since seizures often consist of a gradual increase (evolution) in frequency, amplitude, and/or rhythmicity, the progression of the seizure can often be appreciated on the rhythmicity spectrogram more so than other trends (Fig. 4). Seizures on rhythmicity spectrogram will show a gradual incline when the frequency is increasing or decline when the frequency is decreasing.

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Fig. 4
Example of two right hemispheric seizures on the rhythmicity spectrogram trend (displayed for the left and right hemispheres). Black arrows denote electrographic seizures. The seizure begins with an increased power (darker blue coloration) in alpha activity. As the seizure progresses (shown by the red arrow), there is gradual evolution of increased power into lower-frequency ranges (theta and delta)


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Fig. 5
A QEEG panel consisting of rhythmicity spectrograms derived from individual electrode pairs. Seizures are marked by vertical black arrows

The stereotyped nature of seizures can be appreciated on the rhythmicity spectrogram. The appearance of seizures on the rhythmicity spectrogram can differ greatly between patients, especially in the ICU setting. However, an individual patient tends to have a stereotyped appearance of recurrent seizures on QEEG trends, facilitating easier recognition over time (Fig. 6).

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Fig. 6
Example of seizure appearance variability on the rhythmicity spectrogram trend from four different critically ill patients. Despite the varying appearance between patients, the seizures tend to be stereotyped for each patient. (a) Brief right hemispheric seizures with increased power at various frequency bands and some spread to the left hemisphere. (b) Left hemispheric seizures with some spread to the right hemisphere. These are much longer in duration when compared to seizures in panel a. There is increased power at all frequency bands. (c) Left hemispheric seizures without spread to the right hemisphere. The increase in power is limited primarily to the theta and delta frequency ranges. (d) Right hemispheric seizures without spread to the left hemisphere. The increase in power is seen primarily in the delta frequency band


Asymmetry Index


In this trend there are two graphs that are separate or overlapping: the absolute asymmetry index and the relative asymmetry index (Fig. 7). Both trends compare the difference in power between homologous electrodes (i.e., the difference in power between F3 vs. F4, O1 vs. O2, etc.). The absolute asymmetry index (yellow trace) calculates the absolute difference, displaying a positive score always. There is an upward deflection with increasing asymmetry and a downward deflection with decreasing asymmetry. The relative asymmetry index (green trace) is able to show lateralization for the asymmetry. An upward deflection represents more power in the right hemisphere and a downward deflection represents more power in the left hemisphere. A seizure would be seen as an upward deflection in the yellow trace and a corresponding upward or downward deflection in the green trace if the seizure was in the right hemisphere or left hemisphere, respectively.

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Fig. 7
Example of a right hemispheric seizure on the asymmetry index trend. There is a subtle, upward deflection of the absolute asymmetry index (yellow trace) indicating a period of increased asymmetry. There is a corresponding upward deflection of the relative asymmetry index (green trace) indicating increased power in the right hemisphere. After seizure cessation (marked by the *), there is a downward deflection of the relative asymmetry index (green trace) that corresponds to postictal right-sided suppression (i.e., more power in the left hemisphere)

The asymmetry index is most helpful for detecting unilateral seizures. If a bilateral or generalized seizure resulted in similar power in each hemisphere, the difference in power between homologous electrodes would be small or none. Therefore, generalized or bilateral seizures will likely not result in deflections of the asymmetry indices. Furthermore, if there was a large amount of diffuse muscle artifact occurring during a seizure, it is unlikely that the asymmetry index will show the seizure well given that the total power would likely be similar in homologous electrodes.

The CDSA and rhythmicity spectrogram trends perform better in this scenario, since the power is calculated separately for the various frequency bands as opposed to displaying the total power. In other words, the muscle artifact (typically in the beta frequency range) on CDSA and rhythmicity spectrogram trends would be represented in the higher-frequency ranges while not affecting the lower-frequency ranges (where seizures tend to occur). This allows the power increase of lower-frequency seizures to be visually separated from the power increase of higher-frequency muscle artifact on CDSA and rhythmicity spectrogram trends. The asymmetry index would not be able to discriminate between the two types of increased power.

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Jul 12, 2017 | Posted by in NEUROLOGY | Comments Off on QEEG Training Module

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