Determination of EEG activity propagation: pair-wise versus multichannel estimate

Performance of different estimators describing propagation of electroencephalogram (EEG) activity, namely: Granger causality, directed transfer function (DTF), direct DTF (dDTF), short-time DTF (SDTF), bivariate coherence, and partial directed coherence are compared by means of simulations and on the examples of experimental signals. In particular, the differences between pair-wise and multichannel estimates are studied. The results show unequivocally that in most cases, the pair-wise estimates are incorrect and a complete set of signals involved in a given process has to be used to obtain the correct pattern of EEG flows. Different performance of multivariate estimators of propagation depending on their normalization is discussed. Advantages of multivariate autoregressive model are pointed out.     

Topographic analysis of coherence and propagation of EEG activity during sleep and wakefulness

Overnight sleep EEG recorded from 21 derivations was studied in 8 healthy subjects. The vector autoregressive model was fitted to all 21 channels simultaneously. Ordinary, multiple and partial coherences and directed transfer functions were estimated for sleep stages and wakefulness. Ordinary coherences give rather trivial information that coherence decreases with distance. Partial coherences revealed specific structure that was well repeatable for the subjects studied. Differences in coherence patterns between sleep stages were found by means of statistical tests. An increase of coherence was found for sleep stages 2, 3 and 4. Directed transfer function made possible the identification of the main centers from which EEG activity is spreading during sleep and wakefulness. During sleep the influence of subcortical structures was manifested by propagation of activity from the fronto-central region. The range of this interaction was highest in sleep stages 3 and 4. An EEG analysis, based on the approach of treating time series as a realization of one process and on the simultaneous (not pair-wise) evaluation of signals offers new possibilities in the investigation of synchronization and functional relations in the brain.     

High resolution time-frequency analysis of otoacoustic emissions

High resolution time-frequency analysis of OAE signals evoked by stimuli of different strength was performed by means of the Matching Pursuit algorithm. The method relies on adaptive decomposition of a signal into waveforms of well-defined frequency and time localization. Energy of OAE as a function of time and frequency was evaluated for stimuli strength of 35-80 dB SPL. Dynamic characteristics of the signal were constructed. For strong stimuli decrease of the power of high frequency components was found. Matching Pursuit proved to be a method which offers high resolution parametrisation of OAE in time-frequency space and provides excellent possibilities of investigation of the signal generation mechanisms.     

The prediction of the excess functions of liquid mixtures using a modified hard-sphere equation of state

The two parameter hard-sphere equation of state used by Snider and Herrington to calculate excess functions for mixtures of condensed gases near their boiling points has been modified to take account of the fact that over a range of temperature the parameters are not constant. The parameter which represents the size of the molecules has been treated as a function of volume, while that associated with the intermolecular energy of attraction has been treated as a function of temperature. Excess functions calculated using the modified equation in general show improved agreement with the experimental values. Moreover the new equation has been used to predict the excess functions of binary mixtures of condensed gases over wide ranges of temperature and pressure, both for systems which have already been partially studied experimentally and for others (for example, fluorine + argon) for which no data yet exist.     

Diagnostica-a Bayesian decision-aid system-applied to hypertensiondiagnosis

Diagnostica is a Bayesian statistical tool designed to collect and store the patient's data, suggest a diagnosis, and explain the decision in terms of density distributions. The program is written in C language on MacIntosh support. It is describes using a case study of differential diagnosis between essential and secondary hypertensions. Seventeen experimental parameters were taken into consideration, all of them available during the first medical examination. The density distributions of all items were established from the ARTEMIS experimental database. Both a priori probabilities of different types of hypertension and loss coefficients are taken into account in the calculations. Diagnostica can be used in a `make diagnosis' mode or in an `edition' mode. In the first case it can serve a physician in everyday practice; in the second it becomes a tool for medical research     

On the statistical significance of event-related EEG desynchronization and synchronization in the time-frequency plane

We propose and discuss a complete framework for estimating significant changes in the average time-frequency density of energy of event-related signals. Addressed issues include estimation of time-frequency energy density (matching pursuit and spectrogram), choice of resampling statistics to test the hypothesis of change in one small region (resel), and correction for multiplicity (false discovery rate). We present estimation of the significance of event-related electroencephalograph desynchronization and synchronization (ERD/ERS) in the time-frequency plane.     

A simple system for detection of EEG artifacts in polysomnographic recordings

We present an efficient parametric system for automatic detection of electroencephalogram (EEG) artifacts in polysomnographic recordings. For each of the selected types of artifacts, a relevant parameter was calculated for a given epoch. If any of these parameters exceeded a threshold, the epoch was marked as an artifact. Performance of the system, evaluated on 18 overnight polysomnographic recordings, revealed concordance with decisions of human experts close to the interexpert agreement and the repeatability of expert's decisions, assessed via a double-blind test. Complete software (Matlab source code) for the presented system is freely available from the Internet at http://brain.fuw.edu.pl/artifacts.     

Bayesian statistics as applied to hypertension diagnosis

The Bayesian approach was applied to a specific case of medical diagnosis, i.e. essential hypertension and five types of secondary hypertension (fibrodysplasic renal artery stenosis, atheromatous renal artery stenosis, Conn's syndrome, renal cystic disease, and pheochromocytoma). Only blood pressures, general information and general biochemical data are taken into account. Nineteen items were finally selected through statistical investigation of the experimental data as being both discriminative and independent. The marginal density distributions of every item, and then joint density distribution functions were determined within six types of hypertension. The frequency of a given hypertension type within the hypertensive patients was used as prior probability of this state. The loss matrix was established by medical arguments. The expected loss corresponding to six possible decisions was calculated for all cases. Both the ratio of secondary hypertensions that could be inferred from this set of data (not including the results of complementary tests) and that of correct essential hypertension diagnosis provided to be satisfactory