Quantification of injury-related EEG signal changes using distance measures

Morphological descriptors for automatic detection of epileptiform events

The purpose of this study was to analyze morphological characteristics of electroencephalogram (EEG) signals in order to define a representation of epileptiform events that can distinguish them from other events occurring in the signal. There are several studies on parameterization of EEG signals, particularly for automatic detection of paroxysms related to epilepsy. Considering that during the automatic detection process the morphological characteristics pertaining to these events may get mixed up if only conventional descriptors are used, it was necessary to create a new set of parameters that reveal more differences between them. The parameters are fed to artificial neural networks and the individual and collective contribution of each parameter was evaluated by statistical process. The proposed method achieved a success rate of 80-90%, sensitivity and specificity between 85% and 96%.

An adaptive error modeling scheme for the lossless compression of EEG signals

Lossless compression of EEG signal is of great importance for the neurological diagnosis as the specialists consider the exact reconstruction of the signal as a primary requirement. This paper discusses a lossless compression scheme for EEG signals that involves a predictor and an adaptive error modeling technique. The prediction residues are arranged based on the error count through an histogram computation. Two optimal regions are identified in the histogram plot through a heuristic search such that the bit requirement for encoding the two regions is minimum. Further improvement in the compression is achieved by removing the statistical redundancy that is present in the residue signal by using a context-based bias cancellation scheme. Three neural network predictors, namely, single-layer perceptron, multilayer perceptron, and Elman network and two linear predictors, namely, autoregressive model and finite impulse response filter are considered. Experiments are conducted using EEG signals recorded under different physiological conditions and the performances of the proposed methods are evaluated in terms of the compression ratio. It is shown that the proposed adaptive error modeling schemes yield better compression results compared to other known compression methods.

Discrimination of cerebral ischemic states using bispectrum analysis of EEG and artificial neural network

No doubt a noninvasive technique for detection of cerebral ischemic extent, before the formation of the focus, is extremely valuable. This paper presents a new approach to early evaluate the degree of ischemic injury by combining bispectrum estimation of electroencephalograms (EEGs) with artificial neural network (ANN). The graded ischemic injuries in 24 Sprague-Dawley (SD) rats were induced for different periods of 8, 18, 30 min by infusing physiological saline along the left blood stream, based on the model for rat ischemic cerebral injury described in this paper. Four channels of EEG were collected in each rat at scheduled time of ischemia. The maximum bicoherence index and the weighted center of bispectrum (WCOB) were extracted from the EEGs and were used as input feature vector of a four-layer (12-7-2-1) ANN for prediction. Training and testing the ANN used the 'leave one out' strategy. The levels of ischemic injury were verified and classified by observing the ischemic area by conventional hematoxylin and eosin (HE) staining and the heat shock protein (HSP70) test. The proposed method was able to correctly detect ischemic extent in average accuracy of 91.67% of the cases. The results show that this scheme can be expected to diagnose ischemic cerebral injury in its earlier phases.

The effect of hypobaric hypoxia on multichannel EEG signal complexity

OBJECTIVE

The objective of this study was the development and evaluation of nonlinear electroencephalography parameters which assess hypoxia-induced EEG alterations, and describe the temporal characteristics of different hypoxic levels' residual effect upon the brain electrical activity.

METHODS

Multichannel EEG, pO2, pCO2, ECG, and respiration measurements were recorded from 10 subjects exposed to three experimental conditions (100% oxygen, hypoxia, recovery) at three-levels of reduced barometric pressure. The mean spectral power of EEG under each session and altitude were estimated for the standard bands. Approximate Entropy (ApEn) of EEG segments was calculated, and the ApEn's time-courses were smoothed by a moving average filter. On the smoothed diagrams, parameters were defined.

RESULTS

A significant increase in total power and power of theta and alpha bands was observed during hypoxia. Visual interpretation of ApEn time-courses revealed a characteristic pattern (decreasing during hypoxia and recovering after oxygen re-administration). The introduced qEEG parameters S1 and K1 distinguished successfully the three hypoxic conditions.

CONCLUSIONS

The introduced parameters based on ApEn time-courses are assessing reliably and effectively the different hypoxic levels. ApEn decrease may be explained by neurons' functional isolation due to hypoxia since decreased complexity corresponds to greater autonomy of components, although this interpretation should be further supported by electrocorticographic animal studies.

SIGNIFICANCE

The introduced qEEG parameters seem to be appropriate for assessing the hypoxia-related neurophysiological state of patients in the hyperbaric chambers in the treatment of decompression sickness, carbon dioxide poisoning, and mountaineering.

Spectral distance for ARMA models applied to electroencephalogram for early detection of hypoxia

A novel measure of spectral distance is presented, which is inspired by the prediction residual parameter presented by Itakura in 1975, but derived from frequency domain data and extended to include autoregressive moving average (ARMA) models. This new algorithm is applied to electroencephalogram (EEG) data from newborn piglets exposed to hypoxia for the purpose of early detection of hypoxia. The performance is evaluated using parameters relevant for potential clinical use, and is found to outperform the Itakura distance, which has proved to be useful for this application. Additionally, we compare the performance with various algorithms previously used for the detection of hypoxia from EEG. Our results based on EEG from newborn piglets show that some detector statistics divert significantly from a reference period less than 2 min after the start of general hypoxia. Among these successful detectors, the proposed spectral distance is the only spectral-based parameter. It therefore appears that spectral changes due to hypoxia are best described by use of an ARMA- model-based spectral estimate, but the drawback of the presented method is high computational effort.

Quantitative multichannel EEG measure predicting the optimal weaning from ventilator in ICU patients with acute respiratory failure

OBJECTIVE

The objective of this study was to develop a novel quantitative multichannel EEG (qEEG) based analysis method, called Global Field Damping Time (GFDT), in order to detect potential EEG changes of patients admitted to the ICU with acute respiratory failure, and correlate them to the patients' recovery outcome predicting the optimal time-point to disconnect the patient from mechanical ventilation.

METHODS

Twenty-nine adult patients with acute respiratory failure out of 98 admitted to the Intensive Care Unit of Saint Paul General Hospital were enrolled, and among them only 15 completed the study. The patients were classified in 3 groups according to their outcome after 3 months follow-up. The patients were intubated with fraction of inspired oxygen (FiO2) of 100%. Neurological Deficit Scores (NDS) were measured 24 h after intubation to assess patients' neurological condition. Twenty-four hours after patient's intubation, FiO2 was decreased to 40% (weaning session), followed by a 5 min early recovery session, a 5 min recovery 1 session and a 5 min recovery 2 session. EEG recordings were performed during this experimental procedure. Multichannel EEG segments were processed and fitted into a multivariate autoregressive (mAR) model, and single channel EEG segments into a scalar autoregressive (sAR) model. The mAR and the sAR models of arbitrary order p were decomposed into mp and p oscillators and relaxators, respectively. Damping time of each oscillator and each relaxator, and the Global Field Damping Time (GFDT) as a weighted damping time were estimated for both mAR and sAR models.

RESULTS

A statistically significant increase of mAR model's GFDT during the weaning session was observed in the subjects of all groups. Comparing the 3 patients' groups, statistically significant differences for mAR model's GFDT were observed for the weaning and early recovery session. Linear regression analysis between NDS and mean mAR model's GFDT showed statistical significance during weaning session, early recovery session, and recovery 1 session. There was no statistical significance for SaO2 in the regression analysis with NDS. The sAR model's GFDT presented worst results in comparison with the mAR modelling GFDT in the identification of hypoxic conditions during weaning session and in the discrimination of patients with acute respiratory failure according to their neurological outcome.

CONCLUSIONS

Global Field Damping Time as correlated to the patients' neurological outcome appears to be a simple, compact, and substantial novel indicator of cerebral hypoxia and a potential predictor of the optimal time-point to disconnect the patient from the ventilator.

SIGNIFICANCE

Quantitative EEG seems to be an important tool for ICU clinicians assisting them to decide for the patients' optimal time-point to disconnect the patient from the ventilator.

Watermarking medical signals for telemedicine

Modern telecommunication infrastructure supports the possibility of delivering quality health care without the physical presence of medical experts. The integrity of biomedical signals being transmitted through communication channels must be established before their utilization. This paper investigates three digital watermarking techniques for signal integrity verification in an electroencephalogram (EEG) monitoring application for brain injury detection. The techniques studied are patchwork, least significant bit, and quantization watermarking methods. The three techniques are evaluated and compared in the following areas: sensitivity to noise contamination, robustness to EEG signal characteristic changes due to brain injury, and consistency under various communication channel models. The patchwork method performs best for noise contamination rejection among the three methods. The noise contamination detection rates of all three methods remain relatively stable across a wide range of EEG characteristics.