Central Nervous System Monitoring

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Chapter 11 Central Nervous System Monitoring

Nearly half of the 1 million patients undergoing cardiac surgery each year worldwide will likely experience persistent cognitive decline.¹ The direct annual cost to U.S. insurers for brain injury from just one type of cardiac surgery, myocardial revascularization, is estimated at $4 billion. Furthermore, the same processes that injure the central nervous system (CNS) also appear to cause dysfunction of other vital organs. Thus, there are enormous clinical and economic incentives to improve CNS protection during cardiac surgery.

Historically, there has been little enthusiasm for neurophysiologic monitoring during cardiac surgery because of the presumed key role of macroembolization. It is widely assumed that most brain injuries during adult cardiac surgery result from cerebral embolization of atheromatous or calcified material dislodged from sclerotic blood vessels during their manipulation. Until the introduction of myocardial revascularization without cardiopulmonary bypass (CPB) or aortic clamp application, these injuries often have been viewed as unavoidable and untreatable.

Technical developments have begun to alter this perception. First, CNS injuries still occur despite reductions in aortic manipulation with the new approaches to coronary artery bypass and aortic surgery.² Second, neurophysiologic studies have implicated hypoperfusion and dysoxygenation as major causative factors in CNS injury (Box 11-1). Because these functional disturbances are often detectable and correctable there is an impetus to examine the role of neurophysiologic monitoring in CNS protection.

BOX 11-1 Factors Contributing to Brain Injury during Cardiac Surgery

• Atheromatous emboli from aorta manipulation

• Lipid microemboli from recirculation of unwashed cardiotomy suction

• Gaseous microemboli from air leakage and cavitation

• Cerebral hypoperfusion from dysautoregulation

• Cerebral hyperperfusion

• Cerebral hyperthermia

ELECTROENCEPHALOGRAPHY

Electroencephalographic (EEG) monitoring for ischemia detection has been performed since the first CPB procedures, but this long experience is not broad. Limited use appears to have several causes.

First, small, practical, and affordable EEG monitors have only recently become available.

Second, the traditional diagnostic approach to EEG analysis depended on complex pattern recognition of 16-channel analog waveforms to identify focal ischemic changes. Because this analytic format necessitated extensive training and constant vigilance, cardiac surgery EEG monitoring directly by anesthesia providers has often been viewed as impractical. However, it has been shown that a four-channel recording, which included bilateral activity from both the anterior and posterior circulation, was effective in identifying focal ischemia. In addition, computerized processing of EEG signals provides simplified trend displays that have helped to overcome many of the earlier complexities.

Third, EEG analysis during cardiac surgery was often confounded by anesthetics, hypothermia, and roller-pump artifacts. Fortunately, the troublesome roller pumps have been replaced with centrifugal pumps or eliminated entirely; moderate to deep hypothermia is now usually applied only in aortic arch reconstruction; and fast-track anesthesia protocols avoid marked EEG suppression.

Physiologic Basis of Electroencephalography

EEG-directed interventions designed to correct cerebral hypoperfusion during cardiac surgery require an appreciation of the underlying neurophysiologic substrate. Scalp-recorded EEG signals reflect the temporal and spatial summation of long-lasting (10 to 100 ms) postsynaptic potentials, which arise from columnar cortical pyramidal neurons (Fig. 11-1).

Figure 11-1 The production of EEG waves. Scalp electrodes record potential differences that are caused by postsynaptic potentials in the cell membrane of cortical neurons. The closed loops of the lighter dashed lines represent the summation of extracellular currents produced by the postsynaptic potentials. Open segments of the heavier dashed lines connect all points having the same voltage level. The two scalp electrodes record changes in the voltage difference over time (top trace at upper right). The lower trace from a microelectrode inserted in a single cortical neuron has little direct relationship to the summated EEG wave.

(Modified from Fisch BJ: EEG Primer, 3rd ed. New York, Elsevier, 1999.)

EEG rhythms represent regularly recurring waveforms of similar shape and duration. These signal oscillations depend on the synchronous excitation of a neuronal population. The descriptive nature of conventional EEG characterizes the oscillations (measured in cycles per second [cps] or Hertz [Hz]) as sinusoids that were classified according to their amplitude and frequency. The terminology used to describe the frequency bands of the most common oscillatory patterns is illustrated in Figure 11-2 and listed in Box 11-2.

Figure 11-2 The specific EEG characteristics of the different stages of the human sleep-wake cycle are shown. Note the appearance of the four most common frequency bands from the lowest frequency delta, through theta and alpha to high-frequency beta. An even higher gamma frequency band (25 to 55 cps) is also described.

(Modified from Yli-Hankala A [ed]: Handbook of Four-Channel EEG in Anesthesia and Critical Care. Helsinki, GE Medical, Datex-Ohmeda Division, 2004.)

BOX 11-2 EEG Frequency Bands

Delta	0.5 to 2 Hz
Theta	3 to 7 Hz
Alpha	8 to 12 Hz
Beta	13 to 24 Hz
Gamma	25 to 55 Hz

Practical Considerations of Electroencephalographic Recording and Signal Processing

Standardized electrode placement is based on the International 10-20 System (Fig. 11-3). It permits uniform spacing of electrodes, independent of head circumference, in scalp regions known to correlate with specific areas of cerebral cortex. Four anatomic landmarks are used: the nasion, inion, and preauricular points.

Figure 11-3 The position of the electrodes in the International 10-20 system according to scalp measurements. The sagittal hemicircumference (labeled AA′) is measured from the root of one zygoma (just anterior to the ear) to the other, across the vertex. The third measurement is the ipsilateral hemicircumference (XX′) measured from a point 10% of the coronal hemicircumference above the zygoma. Through these intersecting lines all of the scalp electrodes may be located, except frontal (F3, F4) and parietal (P3, P4). The frontal and parietal electrodes are placed along the frontal or parietal coronal line midway between the middle electrode and the electrode marked in the circumferential ring.

The frequency range involved in production of the EEG waveform is termed its bandwidth. The upper and lower bandwidth boundaries are controlled by filters that reject frequencies above and below the EEG bandwidth. Both the appearance of the unprocessed EEG waveform and the value of univariate numeric EEG descriptors such as the mean frequency may be heavily influenced by signal bandwidth. The same cerebral biopotential recorded by different EEG devices may result in dissimilar waveforms and numeric values.

Display of Electroencephalographic Information

Time-Domain Analysis

Traditional display of the EEG is a graph of biopotential voltage (y-axis) as a function of time and, consequently, is described as a time-domain process. The objective of a diagnostic EEG is to identify the most likely cause of a detected abnormality at one moment in time. Typically, a diagnostic EEG is obtained under controlled conditions, using precisely defined protocols. Recorded EEG appearance is visually compared with reference patterns. Interpretation is based on recognition of unique waveform patterns that are pathognomonic for specific clinical conditions. In contrast, the goal of EEG monitoring is to identify clinically important change from an individualized baseline. Unlike diagnostic EEG interpretation, monitoring requires immediate assessment of continuously fluctuating signals in an electronically hostile, complex, and poorly controlled recording environment. Therefore, of necessity, interpretation relies less on pattern recognition and more on statistical characterization of change. Simple numerical descriptors thus may appropriately form an integral part of EEG monitoring.

Both EEG diagnostic and monitoring interpretations are based, in part, on the “Law of the EEG” (Box 11-3). It states that amplitude and dominant frequency are inversely related. Simultaneous decreases in both amplitude and frequency may indicate ischemia or anoxia (Fig. 11-4).

BOX 11-3 Law of the EEG

• In the absence of a pathologic process, EEG amplitude and frequency are inversely related.

• Simultaneous decrease may indicate ischemia, anoxia, or excessive hypnosis.

• Simultaneous increase may indicate seizure or artifact.

Figure 11-4 This two-channel EEG recording was made immediately after induction of anesthesia, prior to head repositioning for insertion of a central venous catheter. Anesthetic induction apparently uncovered a preexisting asymmetry that was not evident in the waking EEG. Although the patient had a history of an earlier mild cerebrovascular accident and transient ischemic attacks, he appeared neurologically normal at preoperative assessment.

(Modified from Yli-Hankala A [ed]: Handbook of Four-Channel EEG in Anesthesia and Critical Care. Helsinki, GE Medical, Datex-Ohmeda Division, 2004.)

Time domain analysis of traditional electroencephalography uses linear amplitude voltage and time scales. The amplitude range of EEG signals is quite large (several hundred microvolts) and univariate statistical measures of its central tendency and dispersion may contain clinically useful information. Furthermore, amplitude variation may present clinically significant changes in reactivity that can be obscured by frequency-domain analysis. Advances in the technology of EEG amplitude integration have prompted a resurgent interest in this attractively simple approach, particularly in pediatrics.

Frequency Domain Analysis

An alternative method, frequency domain analysis, is exemplified by the prismatic decomposition of white light into its component frequencies (i.e., color spectrum). As the basis of spectral analysis, the Fourier theorem states that a periodic function can be represented, in part, by a sinusoid at the fundamental frequency and an infinite series of integer multiples (i.e., harmonics). The Fourier function at a specific frequency equals the amplitude and phase angle of the associated sinusoid. Graphs of amplitude and phase angle as functions of frequency are called Fourier spectra (i.e., spectral analysis). The EEG amplitude spectral scale (Fig. 11-5) squares voltage values to eliminate troublesome negative values. Squaring changes the unit of amplitude measure from microvolts to either picowatts (pW) or nanowatts (nW). However, a power amplitude scale tends to overemphasize large-amplitude changes. Clinically important changes in lower amplitude components that are readily discernible in the linearly scaled unprocessed EEG waveform may become invisible in power spectral displays.

Figure 11-5 EEG frequency spectral pattern obtained from a patient anesthetized with 1 MAC isoflurane. It illustrates some of the many univariate numeric EEG descriptors.

Simplification of the large amount of spectral information generally has been achieved through the use of univariate numeric descriptors. Most commonly, the power contained in a specified traditional EEG frequency band (delta, theta, alpha, or beta) is calculated in absolute, relative, or normalized terms.

The most widely used univariate frequency descriptors are (1) peak power frequency (the single frequency of the spectrum that contains the highest amplitude) (Box 11-4), (2) median power frequency (frequency below which 50% of the spectral power occurs), (3) mean spectral frequency (sum of power contained at each frequency of the spectrum times its frequency divided by the total power), (4) spectral edge frequency (SEF; frequency below which a predetermined fraction, usually 95%, of the spectral power occurs), and (5) suppression ratio (SR; percent of flat-line EEG contained within sampled epochs).

BOX 11-4 Common Univariate EEG Descriptors Detecting Ischemia

• Total power

• Peak power frequency

• Mean frequency

• Median frequency

• 95% Spectral edge frequency (SEF)

• Suppression ratio (SR)

Pronk evaluated computer-processed univariate descriptors of EEG changes occurring before, during, and after CPB.³ Mean spectral frequency alone was sufficient to adequately describe all EEG changes except those occurring at very low amplitudes. Addition of a single-amplitude factor improved agreement with visual interpretation to 90%. Further factor addition did not improve agreement.

Multivariate (i.e., composed of several variables) descriptors have been developed to improve simple numeric characterization of clinically important EEG changes. With this approach, algorithms are used to generate a single number that represents the pattern of amplitude-frequency-phase relationships occurring in a single epoch. Several commercially available monitors provide unitless numbers that have been transformed to an arbitrary 0-to-100 scale. Each monitor provides a different probability estimate of patient response to verbal instruction. Current examples of these descriptors include the bispectral index (BIS), the patient state index (PSI), and spectral entropy.^4,⁵ BIS and PSI are empirically derived proprietary indices developed from proprietary patient databases. In contrast, spectral entropy is neither empirical nor proprietary but rather represents the novel application of long-established physical sciences entropy equations to the analysis of cranial biopotentials. Each product is designed to require the use of proprietary self-adhesive forehead sensors. Collectively, these products are now in widespread use as objective measures of hypnotic effect (Box 11-5).

BOX 11-5 Multivariate EEG Descriptors Primarily Measuring Hypnotic Effect

• Bispectral index (BIS)

• Patient state index (PSI)

• Spectral entropy

Most hypnotics decrease EEG complexity (i.e., variability) in a dose-related fashion. This long-established observation provides the rationale for the use of nonproprietary spectral entropy analysis as an objective measure of hypnotic effect.⁶ The absence of an empiric rule-based approach avoids the need for arbitrary weighting coefficients and minimizes the potentially distorting influences of very low- and very high-amplitude EEG signals.

The use of pseudo-three-dimensional plots to display successive power spectra as a function of time was popularized by Bickford, who coined the term compressed spectral array (CSA). This technique now represents one of the most common displays of computer-processed EEG in the frequency domain. Popularity stems from enormous data compression. For example, the essential information contained in a 4-hour EEG recording consuming more than 1000 pages of unprocessed waveforms can be displayed in CSA format on a single page.

With CSA (Fig. 11-6), successive power spectra of brief (2- to 60-second) EEG epochs are displayed as smoothed histograms of amplitude as a function of frequency. Spectral compression is achieved by partially overlaying successive spectra, with time represented on the z-axis. Hidden-line suppression improves clarity by avoiding overlap of successive traces. Although the display is aesthetically attractive, it has limitations. The extent of data loss due to spectral overlapping depends on the nonstandard axial rotation that varies among EEG monitors.