Probabilistic assessment of the impact of bottom sediment on doses to humans from a groundwater-mediated radionuclide release in a farm-lake scenario.

Radionuclide transport with groundwater flow and subsequent doses to people are an aspect to be studied when assessing the long-term safety of geological nuclear waste repositories. A scenario where the radionuclide release migrates through a three-layer sediment structure of a lake in a farming environment is presented in this paper. The sediment column consists of deep (till), intermediate (glacio-aquatic sediment) and top layers (clay). The radionuclide release is assumed to enter the deep sediment layer from a bedrock fracture system at a rate of 1 Bq yr-1. The main objectives of the paper are to investigate the most contributing parameters, especially linked to the sediment layers, to the overall dose estimates for humans. The sensitivity analysis was conducted in two phases where the Morris method was used for screening and the Extended Fourier Amplitude Sensitivity Testing and Sobol's methods were used for estimating total-order indices. The studied radionuclides, 36Cl, 135Cs, 129I, 94Nb, 237Np, 90Sr, 99Tc and 238U, exhibit differences in how the sediment layers affect the concentration in the lake water used for drinking, irrigation and watering cattle and subsequently the dose conversion factors for humans through ingestion, inhalation and external radiation.

Relationship between approximate entropy and visual inspection of irregularity in the EEG signal, a comparison with spectral entropy.

BACKGROUND: Several measures have been developed to quantify the change in EEG from wakefulness to deep anaesthesia. Measures of signal complexity or entropy have been popular and even applied in commercial monitors. These measures quantify different features of the signal, however, and may therefore behave in an incomparable way when calculated for standardized EEG patterns. METHODS: Two measures widely studied for anaesthesia EEG analysis were considered: spectral entropy and approximate entropy. First, we generated surrogate signals which had the same spectral entropy as a prototype signal, the sawtooth wave. Secondly, EEG samples where rhythmic pattern caused a peak in the power spectrum in the α-frequency band were modified by enhancing or suppressing the corresponding rhythm. RESULTS: We found that the value of spectral entropy does not, in general, correlate with the visual impression of signal regularity. Also, the two entropy measures interpret a standardized artificially modified EEG signal in opposite directions: spectral peak of increasing amplitude in the α-frequency band causes spectral entropy to increase but decreases approximate entropy when low frequencies are present in the signal. CONCLUSIONS: Spectral entropy and approximate entropy of EEG are two totally different measures. They change similarly in deepening anaesthesia due to an increase in slow activity. In some cases, however, they may change in opposite directions when the EEG signal properties change during anaesthesia. Failure to understand the behaviour of these measures can lead to misinterpretation of the monitor readings or study results if no reference to the raw EEG signal is taken.

Higuchi fractal dimension and spectral entropy as measures of depth of sedation in intensive care unit.

The ability of two easy-to-calculate nonlinear parameters, the Higuchi fractal dimension (HDf) and spectral entropy, to follow the depth of sedation in the intensive care unit is assessed. For comparison, the relative beta ratio is calculated. The results are evaluated using clinical assessment of the Ramsay score. The results show that the HD/sub f/ discriminates well between Ramsay scores 2-4 while beta ratio is superior for deeper levels of sedation. The value of the HD/sub f/ correlates highly with the cutoff frequency of the low-pass prefilter while spectral entropy is sensitive to the length of the analysis window.

Exploring the IBIS data library contents: tools for data visualisation, (pre-) processing and screening. Improved Monitoring for Brain Dysfunction in Intensive Care and Surgery.

During the IBIS project a high-quality data library of continuous and intermittent physiological signals and variables from patients during intensive care and surgery has been collected. To facilitate exploration of the full content of this data library a data browser was developed, which offers a flexible graphical display of the collection of multivariate data. To supplement the functionality of the display of the 'raw' data, a set of screening and pre-processing tools has been developed. A separate trend analysis tool offers a convenient overview of an entire recording focusing on the slow changes in the general state of the patient and the interaction between different physiological subsystems seen from a long-term perspective. A frequency analysis tool for processing the electroencephalography (EEG) signals has been integrated in the data browser to facilitate a quick screening of the cerebral function. The data library is the foundation of the development and validation of biosignal interpretation methods. This process can potentially be more productive using the described tool for algorithm prototyping based on a graphical network specifying the interaction between data processing primitives.

Non-linear algorithms for processing biological signals.

This paper illustrates different approaches to the analysis of biological signals based on non-linear methods. The performance of such approaches, despite the greater methodological and computational complexity is, in many instances, more successful compared to linear approaches, in enhancing important parameters for both physiological studies and clinical protocols. The methods introduced employ median filters for pattern recognition, adaptive segmentation, data compression, prediction and data modelling as well as multivariate estimators in data clustering through median learning vector quantizers. Another approach described uses Wiener-Volterra kernel technique to obtain a satisfactory estimation and causality test among EEG recordings. Finally, methods for the assessment of non-linear dynamic behaviour are discussed and applied to the analysis of heart rate variability signal. In this way invariant parameters are studied which describe non-linear phenomena in the modelling of the physiological systems under investigation.

Adaptive segmentation of burst-suppression pattern in isoflurane and enflurane anesthesia.

In this paper a developed novel algorithm for adaptive segmentation of Burst-suppression EEG is presented. The algorithm can detect bursts, suppression and artifacts, dividing the signal into corresponding segments. A compact representation of burst-suppression EEG, useful in monitoring long-term recordings, is presented. In the second part of the paper the burst-suppression patterns of isoflurane and enflurane anesthesia are compared. It is found that bursts as well as suppression segments are shorter in enflurane anesthesia while the coefficient of variability of the segment lengths is similar for the two anesthetics.

DC-level detection of burst-suppression EEG.

The EEG signal is usually recorded with low time constant analog prefilters to avoid low frequency artefacts. During this kind of recording the frequency components below the cutoff frequency of the analog prefilter (usually below about 1 to 3 Hz) are lost. By visual examination of some experimental recordings taken with a higher time constant, it was noticed that during burst-suppression EEG the DC-level of the signal rises sharply when the burst begins and falls when the burst ends. Thus, a burst actually consists of a mixed frequency discharge on a pulse-like DC-shift. We developed a filter algorithm to estimate the change in the DC-level during bursts as accurately as possible.

Trends in nonlinear signal processing.

In this paper, we review several nonlinear filtering methods having desirable complementary properties to those of linear filters. Most of these methods are based on the median filter. Basic properties of these filters as well as some of their applications are reviewed.

Nonlinear interpretation of respiratory sinus arrhythmia in anesthesia.

A non-parametric method is presented for modelling nonlinear dynamic mechanisms of respiratory sinus arrhythmia (RSA) in anesthesia caused by positive pressure ventilation. RR interval sequences are shown with Tsay's linearity test to contain both short-term and long-term nonlinear components, which cannot completely be modelled with optimal linear methods. The nonlinear approach is based on Wiener's theory for broad-band random input signal. The input-output model is formed for tracheal pressure and RR interval sequence. Second-order and third-order nonlinearities in RSA fluctuation are found and demonstrated.