Monitoring Neurological Injury by qEEG - Quantitative EEG Analysis Methods and Clinical Applications - page 171

increased brain injury in animal models [33, 34] and clinical studies [35-37]. On the
other hand, induced hypothermia to 32ºC to 34 C is shown to be beneficial and

hence recommended for comatose survivors of CA [23, 38].Therapeutic hypother-
mia was recently shown to significantly mitigate brain injury in animal models
[39-41] and clinical trials [21, 42-44].
The effects of changes in brain temperature on EEG have been described as far
back as the 1930s. Among the reported studies, Hoagland found that hyperthermic
patients showed faster alpha rhythms (9 to 10 Hz) [45-47], whereas Deboer dem-
onstrated that temperature changes in animals and humans had an influence on
EEG frequencies and that the changes were similar in magnitude in the different spe-
cies [48, 49]. More recently, hypothermia has been shown to improve EEG activity
with reperfusion and reoxygenation [50-52]. Most of these results have been based
on clinical observations and neurologists' interpretations of EEG signals—both of
which can be quite subjective.

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7.2 Brain Injury Monitoring Using EEG

Classically, EEG signals have been analyzed using time, frequency, and joint
time-frequency domains. Time-domain analysis is useful in interpreting the features
in EEG rhythms such as spikes and waves indicative of nervous system disorders
such as epilepsy. Frequency-domain analysis is useful for interpreting systematic
changes in the underlying rhythms in EEG. This is most evident when spectral anal-
ysis reveals changes in the constituent dominant frequencies of EEG during different
sleep stages or after inhalation or administration of anesthetics. Brain injury, how-
ever, causes markedly different changes in the EEG signal. First of all, there is a sig-
nificant reduction in signal power, with the EEG reducing to isoelectric soon after
cardiac arrest (Figure 7.1). Second, the response tends to be nonstationary during
the recovery period. Third, a noteworthy feature of the experimental EEG record-
ings during the recovery phase after brain injury is that the signals contain both pre-
dictable or stationary and unpredictable or nonstationary patterns. The stationary
component of the EEG rhythm is the gradual recovery of the underlying baseline
rhythm, generally modeled by parametric models [16]. The nonstationary part of
the EEG activity includes seizure activity, burst-suppression patterns, nonreactive
or patterns, and generalized suppression. Quite possibly, the nonstationary part of
the EEG activity may hold information in the form of unfavorable EEG patterns
after CA.
Time-frequency, or wavelet, analysis provides a mathematically rigorous way
of looking at the nonstationary components of the EEG. However, in conditions
resulting from brain injury, neither time-domain nor frequency-domain approaches
are as effective due to nonstationary and unpredictable or transient signal patterns.
Injury causes unpredictable changes in the underlying statistical distribution of EEG
signal samples. Thus, EEG signal changes resulting from injury may be best evalu-
ated by using statistical measures that quantify EEGs as a random process.
Measures designed to assess the randomness of the signals should provide more
objective analysis of such complex signals. Signal randomness can be quantitatively
assessed with the entropy analysis. The periodic and predictable signals should