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This paper presents a novel architecture and its implementation for a versatile, miniaturised mote which can communicate concurrently using a variety of combinations of ISM bands, has increased processing capability, and interoperability with mainstream GSM technology. All these features are integrated in a small form factor platform. The platform can have many configurations which could satisfy a...
The need for reliable detection of head movement artefacts in an ambulatory EEG system has been demonstrated in previous work. In this paper we propose the use of gyroscopes in detecting artefacts in EEG. A collection of features are extracted from the gyroscope signals and ranked using Mutual Information Evaluation Function. Linear Discriminant Analysis is subsequently used as a means of separating...
This paper details the process of oral food challenges (`allergy tests') and steps followed to investigate whether automatic classification of the tests is possible. It has been observed by trained staff that the mood and physiological signals of a subject being tested for allergies can change during the test if they are sensitive to the allergen they are being tested against. Data from thirteen subjects...
The need for reliable detection of artefacts in raw and processed EEG is widely acknowledged. In this paper, we present the results of an investigation into appropriate features for artefact detection in the REACT ambulatory EEG system. The study focuses on EEG artefacts arising from head movement. The use of one generalised movement artefact class to detect movement artefacts is proposed. Temporal,...
Compression of biosignals is an important means of conserving power in wireless body area networks and ambulatory monitoring systems. In contrast to lossless compression techniques, lossy compression algorithms can achieve higher compression ratios and hence, higher power savings, at the expense of some degradation of the reconstructed signal. In this paper, a variant of the lossy JPEG2000 algorithm...
Body Sensor Networks (BSNs) have tremendous potential in facilitating the real-time monitoring of the health of an individual in their own environment. However to truly exploit this potential, the powerful signal processing and analysis techniques available in the hospital environment must also be deployed in BSNs. In this paper, techniques in algorithm development, communications, hardware architecture...
REACT (Real-Time EEG Analysis for event deteCTion) is a Support Vector Machine based technology which, in recent years, has been successfully applied to the problem of automated seizure detection in both adults and neonates. This paper describes the implementation of REACT on a commercial DSP microprocessor; the Analog Devices Blackfin®. The primary aim of this work is to develop a prototype system...
This paper examines whether an appropriate algorithm, developed for use with neonatal data, could also be used, without alteration, for the detection of seizures in adults with epilepsy. The performance of a feature extraction and SVM classifier system is evaluated on databases of 17 neonatal patients and 15 adult patients. Mean ROC curve areas of 0.96 and 0.94 for neonatal and adult databases respectively...
Ambulatory EEG requires signal processing algorithms which are efficient in terms of how much power they use in their computation, while at the same time providing accurate decision-making capabilities. Two methods of achieving this are to downsample the EEG and to perform bitwidth reduction on the data. The effect of performing both of these techniques to varying extents is investigated in this paper...
Gaussian process (GP) probabilistic models have attractive advantages over parametric and neural network modeling approaches. They have a small number of tuneable parameters, can be trained on relatively small training sets, and provide a measure of prediction certainty. In this paper, these properties are exploited to develop two methods of highlighting the presence of neonatal seizures from electroencephalograph...
A method is proposed for automatically choosing independent components (ICs) of interest from neonatal EEG data, with the aim of using them in further analysis to detect seizures. This procedure greatly reduces the amount of information which needs to be processed in the seizure detection system, and reduces the effect of noise and artefacts on its performance. The fast ICA algorithm is used to generate...
In this study neonatal EEG has been analysed with information theory, complexity, SVD-based and nonlinear dynamic systems theory, or chaos theory, approaches. The analysis has been carried out to determine, given the amount of extra time needed to generate the chaos theory results, if they are considerably better than their information theory, complexity and SVD-based counterparts. The results show...
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