Piece-wise EEG analysis: an objective evaluation

Temporal order of nonlinear dynamics in human brain

In previous spectral analysis investigations, we demonstrated that the spontaneous activity of the alpha EEG is not stationary but rather shows cyclic alterations with a circa 1-min periodicity. Following the conclusion that a power increase in the alpha band implies a neuronal synchronization, and vice versa, an associated decrease of the EEG complexity was postulated. Accordingly, a rhythmic variation, i.e., a temporal order of the nonlinear dynamics with similar period length, was expected. Bipolar 4-min EEG recordings were obtained from 20 awake subjects (mean age: 23.5+/-2.5 years) with eyes closed for the EEG leads C3, C4, Oz, and Fz according to the 10-20 system. For the automatic evaluation of spontaneous alterations of complexity, a sliding computation of the so-called correlation dimension, using an analysis window length of 20 s continuously shifted by 1 s, was performed. The time series of complexity exhibited an oscillatory behavior with a mean period length of 58.7 s; the Friedman test statistic revealed no significant topological differences. For the rejection of the null hypothesis that the observed periodicity is a random one, two-group t-tests and ANOVA with repeated measures were performed, comparing the corresponding amplitudes and period lengths with those derived from 20 pseudo-random signals (taken from a multivariate Gaussian normal distribution). The mean relative change of EEG complexity was highly significantly increased (P<0.0001) compared to that of random data. Likewise, the difference of mean period lengths was also significant (P<0.01). The results indicate that the coupling strength of the neural network of the brain changes periodically, with a cyclic alteration from a central to a parallel processing mode of information, reflecting state transitions from synchronized, low-complex EEG activity to desynchronized high-complex activity, and vice versa. Various neuronal control mechanisms that may be acting as pacemakers responsible for the temporal order of such transients are discussed. A disturbance of the temporal order may be of pathophysiological significance.

Computer based sleep recording and analysis

Sleep analysis is based on polysomnography. Modern polysomnographic systems are computer based. Visual and automatic analysis of sleep and respiration is supported by most computer based systems. Four functions can be distinguished in computer based polysomnography: recording, documentation during the recording, automatic and visual analysis and report generation. This review compiles the minimal requirements for digital sleep recording, documentation, analysis and reporting. The basic principles of automatic sleep analysis are reported. The requirements and the basic principles for the analysis of non-electroencephalography (EEG) signals, such as respiration, snoring, oxygen saturation, electrocardiography (ECG) and options are reported. New developments in sleep EEG processing are discussed to enlighten how computer based sleep analysis can add quantative parameters to the rules for visual sleep staging established by Rechtschaffen and Kales 30 years ago. This helps to extend our understanding of sleep.

Context related artefact detection in prolonged EEG recordings

The need for reliable detection of artefacts in raw and processed EEG is widely acknowledged. Although different EEG analysis systems have been described, only few general applicable artefact recognition techniques have emerged. This paper tackles the problem of artefact detection in seven 24 h EEG recordings in the intensive care unit. ICU recordings have received less attention than, e.g. epilepsy monitoring, although recordings in this environment present an interesting application area. The EEG data used here was recorded during the difficult circumstances of an explorative ICU study. The data set includes a diverse set of EEG patterns, as well as EEG artefacts. The study investigates objective artefact detection methods based on statistical differences between signal parameters, using time-varying autoregressive modelling (AR) and Slope detection. In addition to matching the performance of artefact detection against two human observers, the study focuses on the optimal settings for context incorporation by testing the algorithms for different time windows and epoch lengths. Results indicate that a relatively short period (20-40 s) provides sufficient context information for the methods used. The combined AR and Slope detection parameters yielded good performance, detecting approximately 90% of the artefacts as indicated by the consensus score of the human observers.

Maximum a posteriori estimation of change points in the EEG

A new approach for EEG segmentation is introduced. This is based on a methodology for optimal segmentation of non-stationary signals derived from the maximum a posteriori estimation principle. It is a model-based, not sequential approach that allows for segmentation at different resolution levels. The features of the methodology are illustrated by its application to EEG recordings containing several types of spectral changes due to normal and pathological variations of spontaneous brain rhythmic activities, as well as physiological artifacts.

Quantitative analysis of electroencephalograms: is there chaos in the future?

The history of quantitative, computerized electroencephalogram (EEG) analysis is reviewed. It is shown that, until very recently, the basic approach to EEG analysis involved the assumption that the EEG is stochastic. Consequently, statistical pattern recognition techniques, segmentation procedures, syntactic methods, knowledge-based approaches, and even artificial neural network methods have been developed with different levels of success. A fundamentally different approach to computerized EEG analysis, however, is making its way into the laboratories. The basic idea, inspired by recent advances in the area of non-linear dynamics, and especially the theory of chaos, is to view an EEG as the output of a deterministic system of relatively simple complexity, but containing non-linearities. This suggests that studying the geometrical dynamics of EEGs, and the development of neurophysiologically realistic models of EEG generation may produce more successful automated EEG analysis techniques than the classical, stochastic methods. Evidence supporting the non-linear dynamics paradigm is reviewed, and possible research paths are indicated.

Evaluation of a syntactic pattern recognition approach to quantitative electroencephalographic analysis

This paper reports an experiment designed to evaluate the usefulness of a syntactic pattern recognition approach to quantitative EEG analysis. This approach provides a systematic way of organizing and interpreting spatially and temporally distributed activity in an EEG record. Many automated scoring systems evaluate only background activity, ignoring the significant transient events that may occur in an EEG. In contrast, syntactic methods allow evaluation of background activity in combination with transient events. In this paper, 454 EEGs recorded from a population of renal patients are evaluated using (1) the syntactic approach, and (2) a method based on discriminant analysis. The main finding is that the syntactic approach outperforms the discriminant analysis method when each technique is compared to visual scoring.