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<p>Drowsiness is difficult to see for the untrained eye and would not classify as artifact, however it is data that must be thrown out, unless the quality of someone’s sleep or how quickly they fall asleep are being monitored. In this video, within one minute of closing his eyes, this patient falls asleep, generating beta spindles, high amplitude alpha, K-complexes down the vertex, and low amplitude beta. These findings are part of falling asleep! We see this type of EEG activity in NREM 2 and NREM3 stages of sleep.</p><p style="text-align: center; ">&nbsp;&nbsp;<video width="600" height="400" controls="true" class=""><source src="@@PLUGINFILE@@/drowsiness.mp4">@@PLUGINFILE@@/drowsiness.mp4</video>&nbsp;&nbsp;<br></p>
The EEG architecture of sleep will be covered further in the chapter on neurophysiology. If you happen to edit a drowsy recording, this is the kind of map you will get! This is not good data, because is it not representative of the patient in normal eyes closed conditions. He is asleep and we see high amplitude central theta (because of the vertex sharp waves) as well as high amplitude frontal beta (because of the beta spindles-the stabilizing mechanism of sleep).<br>
<div style="text-align: center; "><img src="@@PLUGINFILE@@/linkears-7.png?time=1564143083126" alt="" width="600" height="803" role="presentation" class="img-responsive atto_image_button_text-bottom"></div>
<p style="text-align: center; "><i>Image 1q: The drowsy EEG was showing vertex slow waves and frontal spindling beta (aka sleep spindles), which show up in a fast fourier transform as high amplitude central theta and high amplitude frontal beta. Image generated from Neuroguide version 3.0.6.</i></p>
<p style="text-align: left;"><br></p><p style="text-align: left;">Sometimes you will catch a patient falling into stage 1 of sleep (aka NREM1). This is the beginning of drowsiness. The video below is what we see when a patient enters stage NREM1, alpha followed by alpha dropout (this coheres to the vigilance stages of A1, A2, A3, and B1). The image below is of the vigilance model and details what we can expect to see as a patient is falling asleep.&nbsp; &nbsp; &nbsp; &nbsp;</p><p style="text-align: left;">&nbsp;&nbsp;<video controls="true"><source src="@@PLUGINFILE@@/vigilance%20.mp4">@@PLUGINFILE@@/vigilance%20.mp4</video>&nbsp;</p><p style="text-align: left;"><div><div><h2>EEG-vigilance algorithm “VIGALL”</h2></div></div><div><div><p>In parallel to the transition from active wakefulness to deep sleep the human brain takes on different global functional states. These functional states are reflected in the spectral composition and topography of the EEG and have been termed vigilance stages. These states correspond to different levels of alertness at the behavioral level. Several stages can be separated during the transition from tense to relaxed wakefulness and further on to drowsiness until sleep onset.</p><p>In 1937, Loomis et al. (1937), later modified by Roth (1961), Bente (1964) and others (e.g. (Klimesch, 1999; Ulrich &amp; Frick, 1986); proposed classifications for vigilance stages occurring during transition from active wakefulness to sleep onset. They are based on the following EEG phenomena during eyes closed, which have been demonstrated in several studies:</p><p><strong>A1)</strong>&nbsp;Posterior alpha mostly seen after eye-closing with a frequency of 8-12 Hz and an occipital focus. This oscillation has been referred to as “idling rhythm” (Niedermeyer, 1997) because it marks a state of relaxed wakefulness, corresponding to vigilance stage A1 according to Bente (1964) and Loomis (Loomis et al., 1937).</p><p><strong>A2-A3)</strong>&nbsp;Alpha power anteriorisation occurs increasingly after several minutes of relaxed wakefulness. Alpha peak frequency shows a slight decrease. This phenomenon is reported to occur during transition to drowsiness (Broughton &amp; Hasan, 1995; Connemann et al., 2005; De Gennaro, Ferrara, Curcio &amp; Cristiani, 2001; De Gennaro et al., 2004; De Gennaro et al., 2005; Pivik &amp; Harman, 1995) and corresponds to vigilance stage A2 and A3 (Bente, 1964; Loomis et al., 1937).</p><p><strong>B1)</strong>&nbsp;Low voltage EEG is increasingly observed during lower vigilance stages. The alpha rhythm disappears (alpha drop-out) and beta power increases (De Gennaro et al., 2001; Tanaka, Hayashi &amp; Hori, 1996; Tanaka, Hayashi &amp; Hori, 1997). This EEG pattern corresponds to vigilance stage B1 (Roth, 1961). The EEG in this state is similar to the EEG during intense mental activity and eyes open condition.</p><p><strong>B2-3)</strong>&nbsp;Increased delta and theta activity is observed in parallel with increasing subjective drowsiness (Strijkstra, Beersma, Drayer, Halbesma &amp; Daan, 2003; Tanaka et al., 1996; Tanaka et al., 1997), corresponding to vigilance stages B2 and B3 (Roth, 1961).</p><p><strong>C)</strong>&nbsp;The occurrence of K-complexes and sleep spindles mark the beginning of definite sleep &nbsp;(Cash et al., 2009; De Gennaro &amp; Ferrara, 2003; Tanaka et al., 1997).</p><p>Based on these EEG features a computer-based algorithm has been created for separating different EEG-vigilance stages (also see figure 4) for consecutive EEG segments. The first version of the algorithm “VIGALL” (Vigilance Algorithm Leipzig) was based upon the Fast Fourier-derived power of the four main EEG frequency bands alpha, beta, delta and theta during two-second segments of continuous EEG data at different sites. An improved second version of the algorithm now takes into account the intracortical source power (derived by Low Resolution Tomography-LORETA) of different regions of interest (ROIs).</p><img src="@@PLUGINFILE@@/vigilance%202.jpg" alt="Vigilance model" width="667" height="490" class="img-responsive atto_image_button_left"><br>

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