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ABSTRACT: Hidden Markov Models (HMM) and Support Vector Machines (SVM) using unsupervised and supervised training, respectively, were compared with respect to their ability to correctly classify burst and suppression in neonatal EEG. Each classifier was fed five feature signals extracted from EEG signals from six full term infants who had suffered from perinatal asphyxia. Visual inspection of the EEG by an experienced electroencephalographer was used as the gold standard when training the SVM, and for evaluating the performance of both methods. The results are presented as receiver operating characteristic (ROC) curves and quantified by the area under the curve (AUC). Our study show that the SVM and the HMM exhibit similar performance, despite their fundamental differences.
Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE; 09/2008
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ABSTRACT: Hidden Markov Models (HMM) and Support Vector Machines (SVM) using unsupervised and supervised training, respectively, were compared with respect to their ability to correctly classify burst and suppression in neonatal EEG. Each classifier was fed five feature signals extracted from EEG signals from six full term infants who had suffered from perinatal asphyxia. Visual inspection of the EEG by an experienced electroencephalographer was used as the gold standard when training the SVM, and for evaluating the performance of both methods. The results are presented as receiver operating characteristic (ROC) curves and quantified by the area under the curve (AUC). Our study show that the SVM and the HMM exhibit similar performance, despite their fundamental differences.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 02/2008; 2008:3836-9.
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ABSTRACT: Fisher's linear discriminant, a feed-forward neural network (NN) and a support vector machine (SVM) are compared with respect to their ability to distinguish bursts from suppression in burst-suppression electroencephalogram (EEG) signals using five features inherent in the EEG as input. The study is based on EEG signals from six full term infants who have suffered from perinatal asphyxia, and the methods have been trained with reference data classified by an experienced electroencephalographer. The results are summarized as area under the curve (AUC) values derived from receiver operating characteristic (ROC) curves for the three methods, and show that the SVM is slightly better than the others, at the cost of a higher computational complexity.
Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE; 09/2007
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ABSTRACT: A support vector machine (SVM) was trained to distinguish bursts from suppression in burst-suppression EEG, using five features inherent in the electro-encephalogram (EEG) as input. The study was based on data from six full term infants who had suffered from perinatal asphyxia, and the machine was trained with reference classifications made by an experienced electroencephalographer. The results show that the method may be useful, but that differences between patients in the data set makes optimization of the system difficult
Neural Engineering, 2007. CNE '07. 3rd International IEEE/EMBS Conference on; 06/2007
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ABSTRACT: Eight features inherent in the electroencephalogram (EEG) have been extracted and evaluated with respect to their ability to distinguish bursts from suppression in burst-suppression EEG. The study is based on EEG from six full term infants who had suffered from lack of oxygen during birth. The features were used as input in a neural network, which was trained on reference data segmented by an experienced electroencephalographer. The performance was then evaluated on validation data for each feature separately and in combinations. The results show that there are significant variations in the type of activity found in burst-suppression EEG from different subjects, and that while one or a few features seem to be sufficient for most patients in this group, some cases require specific combinations of features for good detection to be possible
Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE; 10/2006
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ABSTRACT: In the search for how neonatal EEG is affected by asphyxia it is of importance to find reliable estimates of EEG power spectra. Several spectral estimation methods do exist, but since the true spectra are unknown it is hard to tell how well the estimators perform. Therefore a model to generate simulated EEG with known spectrum is proposed and the model is used to evaluate performance of several parametric and Fourier based spectral estimators.
Engineering in Medicine and Biology Society, 2004. IEMBS '04. 26th Annual International Conference of the IEEE; 10/2004
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ABSTRACT: A project involving recording and analysing EEG together with cardiovascular signals and temperature has been initiated. The aim of this project is to establish difficulties and possibilities involved with implementing a system for remote sessions and analysing EEG in correlation with other physiological signals. One objective is to find indicators of cerebral function during postasphyxia neonatal intensive care and pediatric cardiopulmonary bypass surgery with hypothermia. Remote sessions for joint interpretation have been carried out between pediatricians and clinical neurophysiologists, and EEG has been analyzed using frequency analyzing tools. One result is the discovery of reversible spectral changes coinciding with blood pressure falls, which may indicate loss of autoregulation function. This finding is one outcome from initial use of a system, developed during the project to facilitate communication about, and analysis of the recorded signals. Thus, already from a limited number of remote sessions and the use of basic signal processing techniques, important results have been achieved and better insight has been gained of how cerebral function is affected by cardiopulmonary bypass surgery. In this paper, we present our experiences from introducing a system for remote consultations, and evaluate the use for such a system in the current applications.
IEEE Transactions on Information Technology in Biomedicine 01/2004; · 1.68 Impact Factor
