A novel quantitative EEG injury measure of global cerebral ischemia

Clinical Neurophysiology 111 (2000) 1779±1787

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A novel quantitative EEG injury measure of global cerebral ischemia q R.G. Geocadin a, R. Ghodadra b, T. Kimura c, H. Lei b, D.L. Sherman b, D.F. Hanley a, N.V. Thakor b,* a Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA c Department of Anesthesiology-Critical Care Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA b

Accepted 12 June 2000

Abstract Objective: To develop a novel quantitative EEG (qEEG) based analysis method, cepstral distance (CD) and compare it to spectral distance (SD) in detecting EEG changes related to global ischemia in rats. Methods: Adult Wistar rats were subjected to asphyxic-cardiac arrest for sham, 1, 3, 5 and 7 min (n ˆ 5 per group). The EEG signal was processed and ®tted into an autoregressive (AR) model. A pre-injury baseline EEG was compared to selected data segments during asphyxia and recovery. The dissimilarities in the EEG segments were measured using CD and SD. A segment measured was considered abnormal when it exceeded 30% of baseline and its duration was used as the index of injury. A comprehensive Neurode®cit Score (NDS) at 24 h was used to assess outcome and was correlated with CD and SD measures. Results: A higher correlation was found with CD and asphyxia time (r ˆ 0:81, P , 0:001) compared to SD and asphyxia time (r ˆ 0:69, P , 0:001). Correlation with cardiac arrest time (MAP , 10 mmHg) showed that CD was superior (r ˆ 0:71, P , 0:001) to SD (r ˆ 0:52, P ˆ 0:002). CD obtained during global ischemia and 90 min into recovery correlated signi®cantly with NDS at 24 h after injury (Spearman coef®cient ˆ 20.83, P , 0:005), and was more robust than the traditional SD (Spearman coef®cient ˆ 20.63, P , 0:005). Conclusion: The novel qEEG-based injury index from CD was superior to SD in quantifying early cerebral dysfunction after cardiac arrest and in providing neurological prognosis at 24 h after global ischemia in adult rats. Studying early qEEG changes after asphyxic-cardiac arrest may provide new insights into the injury and recovery process, and present opportunities for therapy. q 2000 Elsevier Science Ireland Ltd. All rights reserved. Keywords: Quantitative EEG; Global ischemia; Asphyxia; Prognosis; Neurological outcome

1. Introduction About 70 000 persons per year are successfully resuscitated after cardiac arrest in both hospital and community settings in the United States. Around 60% of those persons subsequently die because of extensive brain injury and only 3±10% resume their former life-style (Krause et al., 1986; O'Neil et al., 1996). The neurological recovery after successful resuscitation from cardiac arrest largely in¯uq This work is supported by NIH Grants RO1-NS 24282, ROI-NS 35528 and R43 NS 38016, and the David S. Dana Research Prize (RGG, D.L.S.). Portions of this work were previously presented in the 123rd Meeting of the American Neurological Association in Montreal, Canada (October 1998) and in the 28th Annual Meeting of the Society for Neuroscience at Los Angeles, CA, USA (November 1998). * Corresponding author. Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Room 701 Traylor Building, 720 Rutland Avenue, Baltimore, MD 21205, USA. Tel.: 11-410-955-0077; fax: 11-410-614-8796. E-mail address: [email protected] (N.V. Thakor).

ences the morbidity and mortality of these patients. (Earnest et al., 1980; Longstreth et al., 1983). Despite the magnitude of the problem, only clinical neurological assessment is used to monitor brain injury and no real time objective methods to detect and monitor brain injury exist at present time. With no proven effective therapy existing for postanoxic encephalopathy, the role of clinicians is limited to supportive care, prevention of complications and providing prognosis for neurological recovery based on the patient's clinical status and supported by laboratory and diagnostic studies. EEG is a sensitive but non-speci®c measure of brain function (Clancy, 1993) and its use in cerebrovascular diseases is limited (Nuwer, 1997). EEG has been used for prognostication after resuscitation from cardiac arrest with some success (Bassetti et al., 1996; Rothstein et al., 1991; Scollio-Lavizzari and Bassetti, 1987). In most of the applications, the EEG recording results in long traces with marked inter-observer variability (Williams et al., 1985).

1388-2457/00/$ - see front matter q 2000 Elsevier Science Ireland Ltd. All rights reserved. PII: S 1388-245 7(00)00379-5

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qEEG has been used to reduce these dif®culties. This technique has been con®ned to feature analysis, conventional power spectrum analysis, parametric description of EEG through linear auto regressive (AR) modeling, or frequency analysis based on the clinically accepted frequency bins associated with d , u , a , and b waves (Bell et al., 1990; Issakson et al., 1981; Levy et al., 1980; Williams et al., 1985). Presently, the application of qEEG has very limited clinical utility, thus it is used mainly as an investigational tool (Nuwer, 1997). Power spectrum has been widely used to characterize EEG (Giaquinto et al., 1994; Lin et al., 1995) via the fast Fourier transform (FFT) and other power spectrum density estimation techniques (Blinowska et al., 1988; Madkour et al., 1993). Linear AR modeling (Akay et al., 1996; Madhvan et al., 1991) has also been used and was able to reduce experimental data while preserving important features such as time-varying changes, dominant frequency components, as well as their amplitudes and powers. In this article a new method is introduced to detect and analyze real time EEG changes. We utilize AR modeling to investigate the transient properties of EEG, which could be vital for the early detection of brain injuries. The brain's response to graded injury will be studied using quantitative characterizations of EEG signals based on distance measures, which are methods of differentiating spectra on the basis of a single continuous criterion. Our ®rst goal is to show that the methods of distance measurement analysis identify signi®cant variation in EEG, which re¯ects alteration in cerebral function during injury. The ability of the spectrum-based EEG distance measures spectral distance (SD) and cepstral distance (CD), are compared as they detect cerebral dysfunction after cardiac arrest. Our second goal is to determine if the distance measures are useful in providing prognosis of neurological recovery after global asphyxic injury. Using these measures, we will quantitatively study the recovery of EEG during the hyperacute period after global ischemia and de®ne its relationship to neurobehavioral recovery at 24 h.

2. Methods and materials 2.1. The animal model 2.1.1. Experimental asphyxic-cardiac arrest and resuscitation The Animal Care and Use Committee of the Johns Hopkins Medical Institutions approved the experimental protocol used in this study. Twenty-®ve adult male Wistar rats (300 ^ 25 g) were randomly assigned to a surgical sham group (without asphyxia) and graded asphyxia of 1, 3, 5 and 7 min (n ˆ 5 per group). Asphyxic cardiac arrest and resuscitation protocol was performed as modi®ed from Katz and colleagues (Katz et al., 1995). Anesthesia was induced with 4% halothane and 50% oxygen (O2) 1 50% nitrogen (N2) at 4 l/min and followed by trachea intubation with a 14-G

plastic catheter by direct laryngoscopy. The rat was ventilated at 40 breaths per minute by a rodent ventilator (Harvard Apparatus Model 683) and provided with humidi®ed 50% FIO2 and 0.5±1.5% halothane, at tidal volumes of 1 ml/100 g, positive expiratory end pressure (PEEP) of 3 cm H2O. Body temperature of 37.0 ^ 0.58C was maintained. Femoral vessels were cannulated (Intermedic Non-Radiopaque PE-90 catheters, PE 90, Becton Dickinson) to monitor mean arterial pressure (MAP), sampling of arterial blood gases (ABG), as well as administer ¯uid and drug. After preparation, the rat was immobilized with vecuronium (2 mg/kg, i.v.) and baseline EEG and physiologic measurements were made. Halothane at 0.5±1.5% was administered during the baseline recording. Gas washout phase followed the baseline recording with additional vecuronium (1 mg/kg, i.v.). Halothane was washed out with 100% oxygen for 3 min followed by room air for 2 min with unchanged ventilator settings. This was followed by global asphyxia, which was induced for graded periods of 1, 3, 5 or 7 min by clamping the tracheal tube and stopping and disconnecting the ventilator. Cardiac arrest was observed with asystole and non-pulsatile MAP ,10 mmHg. After the predetermined asphyxia, CPR was initiated with effective ventilation and oxygenation (100% O2), epinephrine (0.01 mg/kg, i.v.), sternal chest compression (200 compressions/min) and NaHCO3 (1 mmol/kg i.v.) to normalize arterial pH. Return of spontaneous circulation (ROSC) is achieved when MAP of .60 mmHg is maintained. Halothane (0.5±1%) is given to maintain anesthesia throughout the ®rst 90 min after ROSC for EEG recording. The rat was subsequently weaned off the ventilator and extubated. At all points in the experiment, the rats were monitored and treated accordingly for signs of pain and distress. 2.1.2. Neurological evaluation An independent observer using a Neuro De®cit Score (NDS) serially evaluated the neurologic functional outcome after graded injury. The overall template of the NDS is patterned after the standard neurologic examination in humans. We also incorporated some elements from other functional outcome scales developed for global cerebral ischemia models in rats (Katz et al., 1995), in dogs (Vaagenes et al., 1984) and neonatal piglet NDS (Goel et al., 1996; Sherman et al., 1999). The NDS and its components can be found in Table 1. The NDS was determined at 2, 6, 12 and 24 h after ROSC. The NDS ranges from 80 (best) to 0 (brain dead) and it includes a subscore of general behavioral de®cit: consciousness as normal, stuporous or unresponsive and arousal with eye opening and respiration as normal, abnormal (hypo or hyperventilation) or absent. Brainstem functions subscores are assessed with: (1) olfaction as response to smell of food; (2) vision, as head movement to light; presence of (3) pupillary light re¯ex; (4) corneal re¯ex; (5) startle re¯ex; response to (6) whisker stimulation and (7) swallowing

R.G. Geocadin et al. / Clinical Neurophysiology 111 (2000) 1779±1787 Table 1 Neurode®cit Score (NDS) for rats a (A) General behavioral de®cit Consciousness Arousal Respiration (B) Brain-stem function Olfaction Vision Pupillary re¯ex Corneal re¯ex Startle re¯ex Whisker stimulation Swallowing (C) Motor assessment Strength (left and right side tested and scored separately) (D) Sensory assessment Pain (left and right side tested and scored separately) (E) Motor behavior Gait coordination Balance on beam (F) Behavior Righting re¯ex Negative geotaxis Visual placing Turning alley (G) Seizures

Normal [10], Stuporous [5], Comatose [0] Eyes open spontaneously [3], Eyes open to pain [1], No eye opening [0] Normal [6], Abnormal [0], Absent [0]: Total score ˆ 19 Present [3], Absent [0] Present [3], Absent [0] Present [3], Absent [0] Present [3], Absent [0] Present [3], Absent [0] Present [3], Absent [0] Present [3], Absent [0]: Total score ˆ 21 Normal [3], Stiff/weak [1], No movement/paralyzed [0]: Total score ˆ 6 Brisk withdrawal with pain [3], Weak or abnormal response [1], No withdrawal [0]: Total score ˆ 6 Normal [3], Abnormal [1], Absent [0] Normal [3], Abnormal [1], Absent [0], Total score ˆ 6 Normal [3], Abnormal [1], Absent [0] Normal [3], Abnormal [1], Absent [0] Normal [3], Abnormal [1], Absent [0] Normal [3], Abnormal [1], Absent [0], Total score ˆ 12 No Seizure [10], Focal Seizure [5], General Seizure [0], Total score ˆ 10

a NDS were obtained within 2 h after extubation, 6, 12 and 24 h after return of spontaneous circulation (ROSC). The range of the NDS is Normal ˆ 80 and Brain dead ˆ 0.

liquids or solids are also tested. Subscore in motor assessment included strength testing as normal, abnormal (either stiff or weak) and absence of movement. Sensory assessment subscore included response to limb pinch as brisk withdrawal, weak or abnormal response (extension or ¯exion posture) and no response. Motor behavior subscore was tested with gait coordination as normal, abnormal or none. Balance on beam is normal if the rat can cross a 2 cm wide by 1-m long beam suspended 0.5 meter above the ¯oor. Abnormal is scored if the rat attempts and does not continue or stays momentarily and falls. Absent is scored when the rat falls off immediately upon placing on the beam. Other behavior re¯exes subscores evaluated the following: (1)

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righting re¯ex (animal placed on its back and is able to correct to upright position); (2) turning alley (the animal is made to walk and turn back at the end of a 15 cm by 0.5 m alley); (3) visual placing (the animal is lifted and is able to visually orient itself to objects and depth); and (4) negative geotaxis (animal is placed on its back on a plane angled at 458 and the animal corrects itself and moves up the incline). And the last subscore deals with the occurrence of seizures (convulsive or non-convulsive). 2.2. Data acquisition and analysis 2.2.1. Signal acquisition Two channels of EEG using subdermal needle electrodes (Grass Instruments, Quincy, MA) in right frontal-parietal, left-frontal-parietal areas, one channel of ECG and one channel of arterial blood pressure were recorded continuously before the insult, during the insult, and for the ®rst 90 min of recovery. Data were saved on analog tapes using ac preampli®ers (Model 8-18D, Grass Instruments, Quincy, MA) coupled to a 7-channel FM Tape Data Recorder (Model MR-30, TEAC Corp., Japan). The ampli®er gain setting on each ac preampli®er was set at 10 000 for the EEG channels and 5000 for the ECG channel. The cutoff frequencies for EEG recordings were set at 0.3 and 70 Hz for the high-pass and low-pass ®lters, respectively. For ECG recordings, the cutoff frequencies were set at 0.3±300 Hz. Ambient noise was eliminated using a 60 Hz Notch ®lter. The EEG and ECG signals were digitized simultaneously using the data acquisition package CODAS (DATAQ Instruments Inc., Akron, OH). A sampling frequency of 250 Hz/channel and 12 bit A/D conversion were used for the EEG signals. 2.2.2. Signal preprocessing The entire data set was divided into overlapping segments. The segment lengths from 2.1 to 4.1 s were tested. The optimum segment length was the length at which the variance in the requirement of the model order was minimum. The standard deviation in optimum order was minimum for a segment length of 3.3 s. Each segment was then analyzed using the AR modeling technique. Each of these segments was preprocessed for noise reduction before the ®nal analysis: ®rst, the dc value, i.e. the mean for each segment, was subtracted. Second, a polynomial of second order was ®tted and subtracted from the mean subtracted data to minimize baseline wander. In the ®nal step, the mean and polynomial subtracted data were bandpass ®ltered (0.2 and 50 Hz) using a sixth-order Butterworth ®lter. 2.2.3. Analysis A popular mathematical model to precisely de®ne a time series like the EEG, is the autoregressive process (Akay et al., 1996; Bodenstein and Praetorius, 1977). The AR model of time series is

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x‰k Š

R.G. Geocadin et al. / Clinical Neurophysiology 111 (2000) 1779±1787 P X iˆ1

ai x‰k 2 iŠ 1 e‰kŠ

…1†

where P is the model order of the AR process and e[k] the residual error in the time series x[k]. To determine the AR model for a segment of EEG, the order P must be determined ®rst and than the parameter set {ai, i ˆ 1,¼,P} can be estimated. The model order was obtained by minimizing an Akaike information theoretic function AIC (Akaike, 1974) given by AIC…P† ˆ Nln…rP † 1 2P where P is the model order, N is the number of data points and r p is the error variance for model order P. This function is evaluated for a number of P values, and the optimum P is the one that minimizes AIC(P). The mean optimum order for 3.3 s segments was 6.53. Thus in our study the optimum segment length was chosen to be 3.3 and optimum model order to be six. For the sake of completeness, the same approach was used for obtaining the optimum length and model order for two other time points: 30 and 90 min of recovery. The optimum segment length obtained for the 30 min was 3.0 s, and optimum model order was 6.0. For the 90 min recovery point, the optimum length was 3.4 s, and the mean optimum order for 6.1. The lower mean for recovery re¯ects the presence of slow activity after an asphyxic insult. Using the model order (P ˆ 6) determined by the above procedure, Burg's algorithm (Burg, 1978; Marple, 1987) was then used to obtain the AR parameters throughout the baseline and experiment periods. Using the AR parameters we can calculate SD and CD according to Appendix A (A1 and A2). The reference spectra were based on one minute of initial baseline data. Following this period for each subsequent 3.3 s epoch we generate its respective candidate test spectrum. A ¯owchart for ®nding the Cepstral coef®cients is shown is shown in Fig. 1.

Fig. 1. A ¯owchart for ®nding the cepstral coef®cients is shown. We estimate the sample spectrum from the Fourier transform using a single 3.3 s sample of EEG from either the reference (Appendix A (Eq. A1a)) or test sequences (Eq. A1b). The logarithm of the sample spectrum is found (Eq. A4). The inverse FFT is found to generate the cepstral coef®cients, cn as shown in (Eq. A5a±c). Using Eq. (A6) the cepstral distance is found directly.

2.2.4. SD and CD index of injury After preprocessing the EEG data, EEG during the asphyxia and recovery periods were divided as 3.3 s of EEG segments and were compared to the preinjury-baseline EEG segments. The qEEG analysis was carried out to compare and quantify these differences. The dissimilarity in the EEG segments was measured using the two EEG `distance measures', SD and CD. When the SD and CD of EEG segments exceeded an arbitrary threshold of 30% of their respective baseline values, the segment was considered abnormal. The duration that CD and SD is abnormal is used as the injury index and correlated with the Neuro De®cit Score (NDS) at 24 h. 2.2.5. Statistics Linear regression analysis was used to establish the relationship of SD and CD (dependent factor) to the duration of asphyxia and cardiac arrest (independent factor). Spearman rank order coef®cient was used to determine the correlation of the SD and CD measured and the NDS at 24 h after ROSC. Factor analysis was performed on the constituent NDS subscores for all subjects. ANOVA was used to compare physiologic parameters. Values are expressed as mean ^ standard deviation. A P value ,0.05 is considered signi®cant. 3. Results 3.1. The animal model: asphyxic cardiac arrest Twenty adult Wistar rats were subjected to graded asphyxia while 5 had sham procedure. No cardiac arrest was observed in the sham, 1 and 3 min asphyxia groups. Cardiac arrest was observed in rats subjected to 5 and 7 min of asphyxia, with a mean duration of 106 ^ 59.0 and 181 ^ 39.4 s (P , 0:05), respectively. Successful CPR was followed by the return of spontaneous circulation (ROSC) and the maintenance of MAP .60 mmHg. CPR was not necessary for the sham, 1 and 3 min asphyxia groups. The mean durations of CPR needed for successful resuscitation in the 5 and 7 min asphyxia groups were not signi®cantly different at 54.7 ^ 12 and 48.6 ^ 6 s, respectively. Serial arterial blood gas (ABG) parameters namely: pH, pO2, pCO2 and HCO3 were tested at 14 min pre-asphyxia, at 2, 15, 30, 45 and 90 min post asphyxia in all animals. No arterial blood gases were obtained during the actual asphyxia period due to the short duration of asphyxia and, extremely low and non-pulsatile arterial blood pressure. Pre-asphyxia ABG parameters were not signi®cantly different for all groups. The ABG collected at 2 min after the asphyxia showed an expected increase in pCO2 and diminished pH and HCO3 of the groups subjected to 5 and 7 min asphyxia. The hypercarbia was corrected with resumption of mechanical ventilation and bicarbonate infusion. The ABG parameters returned to pre-asphyxia range and were not

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signi®cantly different in all groups at 45 min after ROSC. In the groups with no documented cardiac arrest, the post asphyxia ABGs of rats were not different between groups (sham, 1 and 3 min groups). 3.2. Neurode®cit Score (NDS) The evaluation of graded asphyxic injury in the animals subjected to sham and progressively increasing asphyxia of 1, 3, 5 and 7 min showed a strati®cation of the injury as re¯ected in the evolution of the NDS over the period of 24 h (Fig. 2). Statistically distinct differences in the NDS were demonstrated between three clusters of animals: sham, 1 and 3 min duration, then 5-min duration and the 7-min duration of injury. Pair-wise multiple comparison (Student±Newman±Keuls) at 12 and 24 h after ROSC showed 3 levels of NDS strati®cation namely, sham, 1 and 3 min asphyxia groups, followed by the 5-min asphyxia group, and the 7-min of asphyxia group (P , 0:05). Evaluation of the overall NDS, including all subscores (Table 1) in the entire cohort as weighted by the ®rst principal component, showed that the global ischemia affected all of the NDS subscores, except for the speci®c NDS subscores (eye-opening, vision, corneal re¯exes, and occurrence seizures). The rest NDS subscore components showed almost uniform weighting by factor analysis and accounted for over 50% of the variance in the model by the ®rst principal component. 3.3. Evolution of EEG Baseline was the controlled period of EEG recording before inducing graded asphyxia. A 1-min episode from the baseline was taken and divided into segments of 3.3 s. Fig. 3A shows 5 s of a typical baseline EEG. During the washout period (Fig. 3), the distance measures do not regis-

Fig. 3. Sample traces of raw EEG segments at different stages of the experiment in a rat subjected to 7-min of asphyxial injury. (A) Raw 5-s EEG taken from a 1-min EEG episode at baseline, (B) raw 5-s EEG taken from a 1-min EEG episode during gas washout phase, (C) raw 5-s EEG taken from a 1min EEG episode 2 min after start of asphyxia, (D) raw 5-s EEG taken from a 1-min EEG episode 30 after ROSC. Note the presence of burst suppression in the EEG signal, (C and D) taken from a 1-min EEG episode 60 min after ROSC. Note the EEG attains a more continuous but slow activity.

ter any difference between the anesthetic baseline and the gas washout period. This indicates that the AR model order determined was not signi®cantly affected by halothane. Graded asphyxia was induced after the gas washout period. Fig. 3B shows the EEG response at 4 min into asphyxia in a representative animal. Isoelectric EEG was not observed in sham rats and those subjected to 1 min of asphyxia. The average times from start of arrest until isoelectric EEG was not signi®cantly different at 76 ^ 2.5 s for 3 min arrest, 67.2 ^ 8.0 s for 5 min arrest, and 78.4 ^ 19.3 for 7 min arrest. EEG recovery from isoelectricity (Fig. 3B) came as burst suppression pattern, with sporadic increase in signal power followed by electrical silence at the 15±30 min time point. Fig. 3C shows a raw EEG with a burst suppression pattern at 15 min after ROSC. Fig. 3D shows the raw EEG at 60 min after resuscitation for a typical animal. The EEG progresses to continuous slow activity during later recovery periods. 3.4. QEEG distance measures and injury indices

Fig. 2. Evolution of NDS in the rat asphyxic cardiac arrest model. There is an expected decline from baseline at time of asphyxia and graded recovery by NDS. At 12 and 24 h post ROSC shows NDS strati®cation into 3 clusters (0, 1 and 3 versus 5 versus 7 min asphyxia groups) by pairwise multiple comparison (P , 0:05, Student±Newman±Keuls).

Graphical representations of CD and SD in rats subjected to graded asphyxia are shown in Figs. 4 and 5. We took the maximum points during the baseline and examined the worst-case scenario of an increase in CD or SD right before injury. Using this approach, we still got a sizable difference in pre- and post-injury CD and SD. The CD and SD measures show no EEG difference during anesthetic baseline with halothane and in the absence of halothane during the gas washout phase, indicating that CD and SD, devel-

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Fig. 4. Cepstral distance (CD) between reference EEG (baseline) and current EEG signals at various stages of the experiment. The durations of asphyxia are indicated as: (a) 1 min, (b) 5 min, and (c) 7 min. The different phases of the experiment are as follows: (B) baseline phase, (G) gas washout phase, (A) asphyxic injury, and (R) indicates start of ROSC. Note that the distance does not register any difference in EEG between the baseline and gas-washout phase. The broken line represents the baseline and the dotted line represents the 30% of baseline distance measured. The duration above the 30% threshold is considered as the index of injury.

line EEG increases sharply (Figs. 4 and 5). Such an increase in the measure distance indicates alteration in the models describing the EEG processes. From the responses to these different conditions we can infer that the change in the properties and functionality of the EEG is due to brain dysfunction. We evaluated the impact of asphyxia and cardiac arrest on the EEG based indices of injury. Linear regression analysis performed to assess the relationship of the duration of asphyxia with CD derived index of injury correlated well (r ˆ 0:81, P , 0:001). This correlation was better than and asphyxia time with SD derived index of injury (r ˆ 0:69, P , 0:001). The duration of cardiac arrest and CD resulted in r ˆ 0:71, P , 0:001, while that with SD had r ˆ 0:51, P , 0:001. The correlation of injury indices with NDS at 24 h is shown in Fig. 6. CD had a better correlation by Spearman rank coef®cient at 20.83 (P , 0:005) than the traditional distance measure, SD with a Spearman rank coef®cient of 20.69 (P , 0:005). These results suggest that the CD during the initial 90 min after cardiac arrest provided a more robust quantitative measure of the electrical response to brain injury compared to SD. 4. Discussion Our study shows a robust correlation between the injury index derived from the CD and the NDS at 24 h after ROSC.

oped to detect injury and recovery was not readily affected by the anesthetic. Upon the initiation of asphyxia, the distance measures between the on-going EEG and the base-

Fig. 5. Spectral distance (SD) between the reference EEG (baseline) and the current EEG signals at various stages of the experiment are provided. Similar symbols and descriptions are provided as in Fig. 4.

Fig. 6. The qEEG distance (SD and CD) measures versus NDS at 24 h after resuscitation. (a) The spectral distance versus NDS at 24 h after resuscitation for all animals showed a Spearman rank coef®cient of -0.63 (P , 0:005). (b) The cepstral distance versus NDS at 24 h after resuscitation yielded a Spearman rank coef®cient of 20.83 (P , 0:005).

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The application of AR modeling and distance estimate to graded global brain injury shows that CD can be used to quantify the degree of change between segments (before and after injury) of EEG. This suggests that CD can provide crucial information about the real time electro-cortical changes during the actual brain injury and recovery. These ®ndings also suggest that neurological recovery after cardiac arrest can be easily detected and quantitatively monitored in this rodent model. The potential application of these methods to humans seem apparent, however there is a need for careful study re-design, adaptation of methods, re-assessment of the effects of drugs (i.e. halothane) on EEG and application of the analysis to human subjects. This novel qEEG analysis based on AR modeling is developed to detect changes in the EEG signal. AR method was chosen because it has the advantages of computational ef®ciency, freedom from data windowing effects, and effectiveness in dealing with short data lengths, robustness (high signal to noise ratio), and a better peak resolution in the frequency spectrum as compared with conventional FFTbased methods of power spectrum estimation (Gath et al., 1992). A potential limitation of this method, however, is that it loses the information about the full frequency spectrum (since only the information about the spectral peaks is kept). The parameter estimation method used in this study is to some extent dependent on the initial phase of the different frequency component and can cause slight frequency distortion. Since we used a ®xed model order (P ˆ 6), AR restricts the frequency spectrum to three peaks, and this could lead to a distortion in signals where the higher or lower model order is optimal. A possible alternative is to use adaptive AR modeling. Adaptive AR detects an optimal order for different segments and, hence, gives a different number of peaks in the spectrum from individual segments. This modi®cation can complicate the interpretation of result, without giving any more useful information, and, for this reason, this modi®cation was avoided. An extension of the linear AR models is to include non-linear function as a part of the regression vector. Such a non-linear AR model would result in perhaps a more accurate quanti®cation of the nature and extent of injury but such a model would be also more dif®cult to interpret. The animal model, adapted from Katz and colleagues (Katz et al., 1995), provides a calibrated global ischemic injury as re¯ected in both measures: EEG and NDS (Fig. 2). The model showed that three main groupings of the asphyxia groups (0, 1, 3 versus 5 versus 7 min) based on NDS. Apparently more focused investigations can be performed using only 3 groups: sham, 5 and 7 min. The use of animal models such as this is important to the validation of quantitative approaches that could prove helpful in evaluating human hypoxic ischemic injury where arrest time is frequently undetermined. The use of two EEG channels for data collection in this study may also limit the detection of regional injuries.

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Further studies using more channels for EEG may enhance injury detection and outcome prediction. Another limitation apparent with our distance measures is the need for a baseline EEG for comparison. This limitation may not affect laboratory experimental conditions because EEG can be obtained before and after the injury, and CD can be used to monitor not only the evolution of injury and recovery of the brain. However a potential application in some human clinical situations, such as an unwitnessed cardiac arrest in the community, an EEG baseline may not be available and may limit the use of CD measure. Novel methods need to be developed to provide analysis that does not require a baseline. But in cases of elective surgical procedures requiring cardiac bypass, a baseline EEG may be obtainable prior to global ischemia by cardiac bypass procedure and our method may be readily applicable. As more post-surgical neurological complications of cardiac by-pass surgery are recognized, better neurological injury detection may minimize these complications. It is also possible to obtain baseline EEG on patient at high risk for cardiac arrest, such as those admitted to cardiac care units for angina pectoris or myocardial infarction. In the event of cardiac arrest, the baseline EEG can be used to monitor neurologic injury. The unavailability of a well accepted neurological outcome measure after global cerebral ischemia in rats has led us to modify some of the existing neurological outcome scales and develop the NDS. The NDS mirrors the standard human neurological examination scheme, as well as some existing animal scoring schemes. The NDS was devised to closely mimic clinical evaluations in experimental condition. It is clear that in both human (Levy et al., 1985) and animal (Katz et al., 1995) studies, neurological assessment correlate with the type of survival, and to the degree of histopathological injury (Katz et al., 1995; Radovsky et al., 1997). Except for the subscores of eye opening, vision, corneal re¯ex and occurrence of seizures, all subscores of the NDS showed almost equal weighting in terms of its impact on the signi®cance of the neurological outcome measured at 24 h. This supports the NDS as a global measure of neurological outcome. Twenty-four hour period observation is limited and longer periods (.24 h) will help further validate the scoring system in terms of overall outcome as well as subscore outcomes. We postulate that the four subscores (eye opening, vision, corneal re¯ex and occurrence of seizures) will better re¯ect the degree of ischemic injury with longer (.7 min) asphyxia and during prolonged observation periods (.1 day) after recovery from cardiac arrest. We also believe that occurrence of seizures beyond the 24-h period could be an important prognostic factor that has the potential to in¯uence long-term outcome. The graded injury was assessed by two measures in this study: a real-time qEEG indicator and a neurobehavioral scale. These two measures provided an overall evaluation of the animal neurological outcome at 24 h. However, a histological correlation study needs to be undertaken to further enhance the understanding of these events.

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We believe that two points are evident in this study. First, that the injury index based on CD was better than the SD index in detecting cerebral dysfunction after cardiac arrest, and in providing neurological outcome prognostication at 24 h after global cerebral ischemia in rodents. And second, the importance of studying the very early period after ischemic injury when neurons are still viable and active changes are occurring in response to the injury. Studying the very early electrophysiological changes in the brain after global ischemia may provide new insights into the mechanisms of injury and recovery processes. CD can be an effective tool to study this critical period of neuronal injury in global ischemia and the potential applications can lead to early diagnostic and therapeutic strategies in global ischemia initially in the rodent model and ultimately in human clinical trials.

P X

er …k† ˆ r…k† 2

iˆ1

ai r…k 2 i†

Appropriate to distance measures this error variance or gain factor is ignored so that the normalized spectrum can be obtained. The normalized spectra are de®ned as 1 Srr …v† ˆ P X …2jiv† 2 ai e 1 2

…A2a†

iˆ1

and 1 Stt …v† ˆ 2 P X …2jiv† bi e 1 2

…A2b†

iˆ1

Therefore, the spectral distance measure can be de®ned by the of the difference of Srr and Stt

Acknowledgements The authors thank Dr Peter Safar, Dr Robert Hickey and the staff of the Safar Center for Resuscitation Research at the University of Pittsburgh for their assistance in the development of the animal model. The authors also thank Johnny Chao and Dr Jitendran Muthuswamy for their assistance in the experiments.

dx …r; t† ˆ

LX 21 lˆ0

…Srr …vl † 2 Stt …vl ††2

…A3†

where

vl ˆ

pl ¼ L

digital frequency, l ˆ 0,1,2,¼.,L-1.

Appendix A A.1. Spectral distance measure With the knowledge of the AR model parameters (Eq. 1 in main text), ai and bi ; i ˆ 1; ¼; P for the reference signal r(k) and the test signal t(k),respectively, their corresponding EEG spectra can be estimated as

s r2 s r2 ˆ Srr …v† ˆ 2 Ar …ejv † 2 P X …2jiv† ai e 1 2

…A1a†

iˆ1

and

s2 s t2 Stt …v† ˆ tjv ˆ P X At …e † …2jiv† bi e 1 2

…A1b†

A.2. Cepstral distance measure As detailed in Fig. 1, the cepstral coef®cients can be found from a 3 step process: (1) ®nd AR coef®cients and the denominator of the spectrum (Fig. 1; box 1), H…ejv † ˆ j v 1= A…e † (Fig. 1; box 2); (2) take logarithm of spectrum (Fig. 1; box 2) C…ejv † ˆ log…H…ejv ††

(3) Find inverse FFT. The result of this calculation are the cepstral coef®cients c…k† (Fig. 1; box 4). A preferable alternative is to ®nd the cepstral coef®cients can be obtained directly from the AR model coef®cients by the following recursion (Rabiner and Schafer, 1978) cr …1† ˆ 2a1

iˆ1

where Srr …v† and Stt …v† are the reference and test spectra, exponential or Pis the model order and ejv is the complex p ejv ˆ exp…jv† ˆ cos…v† 1 jsin…v†and j ˆ 21 or the unit imaginary number. The conversion between radians and frequency is v ˆ 2pf . Furthermore the numerator of each of these spectra represent the variance of the error in the model approximation, e…k†. In the case of the reference signal we have the reference signal error, er …k†and its variance, Var…er …k†† ˆ s r2 . The reference signal error is de®ned as

…A4†

cr …n† ˆ 2an 2

cr …n† ˆ 2

…A5a† nX 21 kˆ1

k c …k†an2k; …1 , n # p† n r

p X n2k cr …k†ak; …p 1 1 , n† n kˆ1

…A5b†

…A5c†

Similarly, ct …i†are obtained for t(k). Based on cepstral the coef®cients cr …i†, ct …i† of the reference signal r(k) and the test signal t(k),respectively, the cepstral distance measure is de®ned by the mean absolute value of the difference of cr …i†

R.G. Geocadin et al. / Clinical Neurophysiology 111 (2000) 1779±1787

and ct …i† for each i, 1¼n d1 …cr ; ct † ˆ

n X iˆ1

jct …i† 2 cr …i†j

…A6†

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