Shanbao Tong

Time-dependent entropy estimation of EEG rhythm changes following brain ischemia.

AbstractOur approach is motivated by the need to generate a rigorous measure of the degree of disorder (or complexity) of the EEG signal in brain injury. Entropy is a method to quantify the order/disorder of a time series. It is the first time that a time-dependent entropy (TDE) is used in the quantification of brain injury level. The TDE was sensitive enough to monitor the significant changes in the subjects brain rhythms during recovery from global ischemic brain injury. Among the different entropy measures, we used Tsallis entropy. This entropy is parametrized and is able to match with the particular properties of EEG, like long-range rhythms, spikes, and bursts. The method was tested in a signal composed of segments of synthetic signals (Gaussian and uniform distributions) and segments of real signals. The real signal segments were extracted from normal EEG, EEG recordings from early recovery, and normal EEG corrupted by simulated spikes and bursts. Adult Wistar rats were subjected to asphyxia-cardiac arrest for 3 and 5 min. The TDE detected the pattern of ischemia-induced EEG alterations and was able to discriminate the different injury levels. Two parameters seem to be good descriptors of the recovery process; the mean entropy and the variance of the estimate followed opposite trends, with the mean entropy decreasing and its variance increasing with injury.

Nonextensive entropy measure of EEG following brain injury from cardiac arrest

The nonextensive entropy measure is developed to study the electroencephalogram (EEG) during the recovery of the brains electrical function from asphyxic cardiac arrest (ACA) injury. The statistical characteristics of the Tsallis-like time-dependent entropy (TDE) for different signal distributions are investigated. Both the mean and the variance of TDE show good specificity to the ACA brain injury and its recovery. ACA brain injury results in a decrease in entropy while a good electrophysiological recovery shows a rapid return to a higher entropy level. There is a reduction in the mean and increase in the variance of TDE after brain injury followed by a gradual recovery upon resuscitation. The nonextensive TDE is expected to provide a novel quantitative EEG strategy for monitoring the brain states.

Removal of ECG interference from the EEG recordings in small animals using independent component analysis

In experiments involving small animals, the electroencephalogram (EEG) recorded during severe injury and accompanying resuscitation exhibit the strong presence of electrocardiogram (ECG). For improved quantitative EEG (qEEG) analysis, it is therefore imperative to remove ECG interference from EEG. In this paper, we validate the use of independent component analysis (ICA) to effectively suppress the interference of ECG from EEG recordings during normal activity, asphyxia and recovery following asphyxia. Two channels of EEG from five rats were recorded continuously for 2 h. Simultaneous recording of one channel ECG was also made. Epochs of 4 s and 1 min were selected from baseline, asphyxia and recovery (every 10 min) and their independent components and power spectra were calculated. The improvement in normalized power spectrum of EEG obtained for all animals was 7.71+/-3.63 db at the 3rd minute of recovery and dropped to 1.15+/-0.60 db at 63rd minute. The application of ICA has been particularly useful when the power of EEG is low, such as that observed during early brain hypoxic-asphyxic injury. The method is also useful in situations where accurate indications of EEG signal power and frequency content are needed.

Parameterized entropy analysis of EEG following hypoxic-ischemic brain injury

Abstract In the present study Tsallis and Renyi entropy methods were used to study the electric activity of brain following hypoxic–ischemic (HI) injury. We investigated the performances of these parameterized information measures in describing the electroencephalogram (EEG) signal of controlled experimental animal HI injury. The results show that (a):xa0compared with Shannon and Renyi entropy, the parameterized Tsallis entropy acts like a spatial filter and the information rate can either tune to long range rhythms or to short abrupt changes, such as bursts or spikes during the beginning of recovery, by the entropic index q ; (b):xa0Renyi entropy is a compact and predictive indicator for monitoring the physiological changes during the recovery of brain injury. There is a reduction in the Renyi entropy after brain injury followed by a gradual recovery upon resuscitation.

The nonlinear dynamical analysis of the EEG in schizophrenia with temporal and spatial embedding dimension

We applied nonlinear dynamics theory to EEG analysis of schizophrenic patients and estimated the correlation dimension with both temporal embedding and spatial embedding. A higher D2 was found when using a time-delay embedding method. Especially at F7 and Fp1, a significant increase showed. We concluded that more complex activity occurred in certain lobes of schizophrenic patients. Using the spatial embedding method, a relative lower global correlation dimension was obtained. This shows that there might be a diffuse slow wave activity through a schizophrenes global cerebrum. Finally, we discuss the study from three angles of clinical semiology, spectrum analysis and neuropsychology and draw some conclusions about the relationship between the nonlinear analysis of schizophrenia EEG and clinical research. It seems that the theory of a nonlinear dynamics system is a powerful tool for EEG research and may prove useful in complementing visual analysis of EEG accompanied with other study means for brain electrical activity.We applied nonlinear dynamics theory to EEG analysis of schizophrenic patients and estimated the correlation dimension with both temporal embedding and spatial embedding. A higher D2 was found when using a time-delay embedding method. Especially at F7 and Fp1, a significant increase showed. We concluded that more complex activity occurred in certain lobes of schizophrenic patients. Using the spatial embedding method, a relative lower global correlation dimension was obtained. This shows that there might be a diffuse slow wave activity through a schizophrenes global cerebrum. Finally, we discuss the study from three angles of clinical semiology, spectrum analysis and neuropsychology and draw some conclusions about the relationship between the nonlinear analysis of schizophrenia EEG and clinical research. It seems that the theory of a nonlinear dynamics system is a powerful tool for EEG research and may prove useful in complementing visual analysis of EEG accompanied with other study means for brain electrical activity.

Effect of acute hypoxic preconditioning on qEEG and functional recovery after cardiac arrest in rats.

Acute hypoxic preconditioning (AHPC) can confer neuroprotection from global cerebral ischemia such as cardiac arrest. We hypothesize that acute neuroprotection by AHPC will be detected early by quantitative EEG (qEEG) entropy analysis after asphyxial cardiac arrest (aCA). Cerebral ischemia lowers EEG signal randomness leading to low entropy. A qEEG entropy index defined as the duration when the entropy measure is 15% below uninjured baseline entropy is used as a measure of injury. We compared 3 groups of adult Wistar rats: (1) untreated controls that were subjected to 5 min of aCA and were resuscitated (n = 5); (2) AHPC-treated group with 10% FI O2 for 30 min, then 25 min of room air, 5 min of aCA followed by resuscitation (n = 5); and (3) a surgical sham group (no aCA) (n = 3). Functional outcome was assessed by neurodeficit score (NDS) which consisted of level of consciousness, cranial nerve, motor-sensory function, and simple behavioral tests (best = 100 and brain dead = 0). We found that increasing entropy index of injury at 0-5 h from return of spontaneous circulation (ROSC) is associated with worsening NDS at 24 h (linear regression: r = 0.81, P < 0.001). The NDS of the group sham (84.7 +/- 2.8) (mean +/- SEM) and AHPC group (84.6 +/- 2.9, P > 0.05) was better than control injury group (52.2 +/- 8.4, P < 0.05) (ANOVA with Tukey test). We therefore conclude that AHPC confers acute neuroprotection at 24 h, which was detected by qEEG entropy during the first 5 h after injury.

Spatiotemporal characteristics of low-frequency functional activation measured by laser speckle imaging

Changes in neuronal activity have been shown to be accompanied by alteration in regional cerebral blood flow . In the present study, laser speckle imaging (LSI) was employed to measure stimulus-evoked neuronal activities in rat barrel cortex. The spatiotemporal characteristics of hemodynamic response to mechanical stimuli from 1 to 3 Hz were examined. Time to peak amplitude reduced from 4.5 to 3.5 s with increasing frequencies. Spatially, the response was confined to a small circular region at the beginning and then spread out asymmetrically to the surrounding regions. The maximal area of activation ranged from 2.2 to 3.5 mm/sup 2/, while the time to reach maximal area occurred between 5.5 and 6 s. Moreover, there was a high correlation between LSI and laser-Doppler flowmetry in terms of peak response magnitude and the time to reach peak. These two values were linearly dependent on stimulus frequency whereas area of activation and time to maximal area appeared to be independent of this parameter. LSIs high sensitivity, low cost of the equipment, and size and complexity make this a suitable technique for fundamental neurophysiological investigations.

Quantitative EEG assessment

The authors present quantitative results to support the idea that hypothermia-related changes in the brains electrical activity can be objectively tracked in real time by quantitative EEG (qEEG). This presents the potential for qEEG as a real-time monitoring technique to evaluate hypothermia therapy for brain injury. The potential ability of qEEG to provide an objective estimation of injury and recovery is also presented. This may be used to stratify the degree of injury sustained by the brain. The use of IQ in the preliminary work presented here showed that the qEEG measure indicates that EEG data under hypothermia contained more information than those under normothermia. In the longer term, the authors hope that the qEEG tool may be useful in the intensive care setting as a simple and easy-to-interpret measure that will enhance bedside care

Quantitative EEG Assessment of Brain Injury and Hypothermic Neuroprotection after Cardiac Arrest

In this paper we provide a quantitative electroencephalogram (EEG) analysis to study the effect of hypothermia on the neurological recovery of brain after cardiac arrest. We hypothesize that the brain injury results in a reduction in information of the brain rhythm. To measure the information content of the EEG a new measure called information quantity (IQ), which is the Shannon entropy of decorrelated EEG signals, is developed. For decorrelating EEG signals, we use the discrete wavelet transform (DWT) which is known to have good decorrelating properties and to show a good match to the standard clinical bands in EEG. In simulation for measuring the amount of information, the IQ shows better tracking capability for dynamic amplitude change and frequency component change than conventional entropy-based measures. Experiments are carried out in rodents to monitor the neurological recovery after cardiac arrest. In addition, EEG signal recovery under normothermic (37degC) and hypothermic (33degC) resuscitation following 5, 7 and 9 minutes of cardiac arrest is recorded and analyzed. Experimental results show that the IQ is higher for hypothermic than normothermic rats. The results quantitatively support the hypothesis that hypothermia accelerates the recovery of brain injury after cardiac arrest

Time-frequency complexity of EEG following hypoxic-ischemic brain injury

The complexity of EEG signal has been extensively studied in different domains such as time, frequency and chaotic index. In this study we define a novel measure, time frequency complexity (TFC), based on the matching pursuit (MP) algorithm. It describes the structural complexity of EEG signals from the joint time-frequency distribution of the signals. The MP algorithm, introduced by Mallat and Zhang, describes a general procedure to compute adaptive signal representations by decomposing a signal into a linear expansion with redundant basis functions, called atoms. We define the TFC of EEG with the Shannon entropy in the time-frequency plane computed by the MP algorithm. TFC is shown to be sensitive to the structural change (such as spiky/bursting activity) in the EEG signal following brain injury and its recovery. We studied the EEG of 5 min of hypoxic-ischemic (HI) brain injury. The preliminary results show that TFC could be useful for indicating different stages of brain injury and the recovery.