A Nonstationary Model of Newborn EEG

The detection of seizure in the newborn is a critical aspect of neurological research. Current automatic detection techniques are difficult to assess due to the problems associated with acquiring and labelling newborn electroencephalogram (EEG) data. A realistic model for newborn EEG would allow confident development, assessment and comparison of these detection techniques. This paper presents a model for newborn EEG that accounts for its self-similar and non-stationary nature. The model consists of background and seizure sub-models. The newborn EEG background model is based on the short-time power spectrum with a time-varying power law. The relationship between the fractal dimension and the power law of a power spectrum is utilized for accurate estimation of the short-time power law exponent. The newborn EEG seizure model is based on a well-known time-frequency signal model. This model addresses all significant time-frequency characteristics of newborn EEG seizure which include; multiple components or harmonics, piecewise linear instantaneous frequency laws and harmonic amplitude modulation. Estimates of the parameters of both models are shown to be random and are modelled using the data from a total of 500 background epochs and 204 seizure epochs. The newborn EEG background and seizure models are validated against real newborn EEG data using the correlation coefficient. The results show that the output of the proposed models has a higher correlation with real newborn EEG than currently accepted models (a 10% and 38% improvement for background and seizure models, respectively)

Multi-frequency study of DEM L299 in the Large Magellanic Cloud

We have studied the HII region DEM L299 in the Large Magellanic Cloud to understand its physical characteristics and morphology in different wavelengths. We performed a spectral analysis of archived XMM-Newton EPIC data and studied the morphology of DEM L299 in X-ray, optical, and radio wavelengths. We used H alpha, [SII], and [OIII] data from the Magellanic Cloud Emission Line Survey and radio 21 cm line data from the Australia Telescope Compact Array (ATCA) and the Parkes telescope, and radio continuum data from ATCA and the Molonglo Synthesis Telescope. Our morphological studies imply that, in addition to the supernova remnant SNR B0543-68.9 reported in previous studies, a superbubble also overlaps the SNR in projection. The position of the SNR is clearly defined through the [SII]/H alpha flux ratio image. Moreover, the optical images show a shell-like structure that is located farther to the north and is filled with diffuse X-ray emission, which again indicates the superbubble. Radio 21 cm line data show a shell around both objects. Radio continuum data show diffuse emission at the position of DEM L299, which appears clearly distinguished from the HII region N 164 that lies south-west of it. We determined the spectral index of SNR B0543-68.9 to be alpha=-0.34, which indicates the dominance of thermal emission and therefore a rather mature SNR. We determined the basic properties of the diffuse X-ray emission for the SNR, the superbubble, and a possible blowout region of the bubble, as suggested by the optical and X-ray data. We obtained an age of 8.9 (3.5-18.1) kyr for the SNR and a temperature of 0.64 (0.44-1.37) keV for the hot gas inside the SNR, and a temperature of the hot gas inside the superbubble of 0.74 (0.44-1.1) keV. We conclude that DEM L299 consists of a superposition of SNR B0543-68.9 and a superbubble, which we identified based on optical data.Comment: Accepted for publication in Astronomy and Astrophysics. 17 pages, 16 figure

Newborn EEG seizure detection using adaptive time-frequency signal processing

Dysfunction in the central nervous system of the neonate is often first identified through seizures. The diffculty in detecting clinical seizures, which involves the observation of physical manifestations characteristic to newborn seizure, has placed greater emphasis on the detection of newborn electroencephalographic (EEG) seizure. The high incidence of newborn seizure has resulted in considerable mortality and morbidity rates in the neonate. Accurate and rapid diagnosis of neonatal seizure is essential for proper treatment and therapy. This has impelled researchers to investigate possible methods for the automatic detection of newborn EEG seizure. This thesis is focused on the development of algorithms for the automatic detection of newborn EEG seizure using adaptive time-frequency signal processing. The assessment of newborn EEG seizure detection algorithms requires large datasets of nonseizure and seizure EEG which are not always readily available and often hard to acquire. This has led to the proposition of realistic models of newborn EEG which can be used to create large datasets for the evaluation and comparison of newborn EEG seizure detection algorithms. In this thesis, we develop two simulation methods which produce synthetic newborn EEG background and seizure. The simulation methods use nonlinear and time-frequency signal processing techniques to allow for the demonstrated nonlinear and nonstationary characteristics of the newborn EEG. Atomic decomposition techniques incorporating redundant time-frequency dictionaries are exciting new signal processing methods which deliver adaptive signal representations or approximations. In this thesis we have investigated two prominent atomic decomposition techniques, matching pursuit and basis pursuit, for their possible use in an automatic seizure detection algorithm. In our investigation, it was shown that matching pursuit generally provided the sparsest (i.e. most compact) approximation for various real and synthetic signals over a wide range of signal approximation levels. For this reason, we chose MP as our preferred atomic decomposition technique for this thesis. A new measure, referred to as structural complexity, which quantifes the level or degree of correlation between signal structures and the decomposition dictionary was proposed. Using the change in structural complexity, a generic method of detecting changes in signal structure was proposed. This detection methodology was then applied to the newborn EEG for the detection of state transition (i.e. nonseizure to seizure state) in the EEG signal. To optimize the seizure detection process, we developed a time-frequency dictionary that is coherent with the newborn EEG seizure state based on the time-frequency analysis of the newborn EEG seizure. It was shown that using the new coherent time-frequency dictionary and the change in structural complexity, we can detect the transition from nonseizure to seizure states in synthetic and real newborn EEG. Repetitive spiking in the EEG is a classic feature of newborn EEG seizure. Therefore, the automatic detection of spikes can be fundamental in the detection of newborn EEG seizure. The capacity of two adaptive time-frequency signal processing techniques to detect spikes was investigated. It was shown that a relationship between the EEG epoch length and the number of repetitive spikes governs the ability of both matching pursuit and adaptive spectrogram in detecting repetitive spikes. However, it was demonstrated that the law was less restrictive forth eadaptive spectrogram and it was shown to outperform matching pursuit in detecting repetitive spikes. The method of adapting the window length associated with the adaptive spectrogram used in this thesis was the maximum correlation criterion. It was observed that for the time instants where signal spikes occurred, the optimal window lengths selected by the maximum correlation criterion were small. Therefore, spike detection directly from the adaptive window optimization method was demonstrated and also shown to outperform matching pursuit. An automatic newborn EEG seizure detection algorithm was proposed based on the detection of repetitive spikes using the adaptive window optimization method. The algorithm shows excellent performance with real EEG data. A comparison of the proposed algorithm with four well documented newborn EEG seizure detection algorithms is provided. The results of the comparison show that the proposed algorithm has significantly better performance than the existing algorithms (i.e. Our proposed algorithm achieved a good detection rate (GDR) of 94% and false detection rate (FDR) of 2.3% compared with the leading algorithm which only produced a GDR of 62% and FDR of 16%). In summary, the novel contribution of this thesis to the fields of time-frequency signal processing and biomedical engineering is the successful development and application of sophisticated algorithms based on adaptive time-frequency signal processing techniques to the solution of automatic newborn EEG seizure detection

Resolution analysis of the T class time-frequency distributions

The T-class of time-frequency distributions (TFDs) is a newly proposed subclass of the general quadratic TFD class. The TFDs of this subclass are characterized by their time-lag kernels which are functions of time only. In this paper, we report the results of our investigation related to the time and frequency resolution of the T-class TFDs. It is shown, analytically, for the case of a linear chirp, that the frequency resolution is a function of both the smoothing parameter of the TFD kernel and the rate of change in instantaneous frequency. The results are then illustrated with a number of synthetic examples

Automatic Newborn EEG Seizure Spike and Event Detection Using Adaptive Window Optimization

Paroxysmal events such as spikes in the newborn EEG are key indicators of central nervous system (CNS) functioning. Newborn EEG seizure events, which are characterised by repetitive spiking events, correspond to CNS dysfunction. Detection and identification of seizure is crucial so that steps can be taken to alleviate the factors causing seizure and to reduce the risk of brain damage. This paper provides a new EEG spike detection method based on an adaptive window optimization algorithm which has been used for an adaptive spectrogram. This technique is assessed using synthetic and real signals containing spikes. The spike detection method is then incorporated into an automatic newborn EEG seizure detection algorithm, which is evaluated using EEG recordings from 8 neonates

Atomic Decomposition for Detecting Signal Structure Changes: Application to EEG

Atomic decomposition has been a popular tool tor extracting information about localised signal structures. This is a direct consequence of incorporating redundant time-frequency and time-scale dictionaries for signal decomposition. In this paper, we propose a measure of signal complexity related to a given decomposition dictionary and based on the number of atoms needed to represent the signal. This measure is directly extracted from the atomic decomposition and one of the potential applications is the detection of,changes in signal structure. For example, automatic newborn EEG seizure detection can be achieved by,detecting the change in signal structure as the EEG changes from the background state to the seizure state. This complexity measure is evaluated using two atomic decomposition methods; namely Basis Pursuit, (BP) and Matching Pursuit (MP)

A matching pursuit-based signal complexity measure for the analysis of newborn EEG

This paper presents a new relative measure of signal complexity, referred to here as relative structural complexity, which is based on the matching pursuit (MP) decomposition. By relative, we refer to the fact that this new measure is highly dependent on the decomposition dictionary used by MP. The structural part of the definition points to the fact that this new measure is related to the structure, or composition, of the signal under analysis. After a formal definition, the proposed relative structural complexity measure is used in the analysis of newborn EEG. To do this, firstly, a time-frequency (TF) decomposition dictionary is specifically designed to compactly represent the newborn EEG seizure state using MP. We then show, through the analysis of synthetic and real newborn EEG data, that the relative structural complexity measure can indicate changes in EEG structure as it transitions between the two EEG states; namely seizure and background (non-seizure)