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- Löfhede, Johan, 1978, et al.
(författare)
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Automatic classification of background EEG activity in healthy and sick neonates
- 2010
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Ingår i: Journal of Neural Engineering. - : IOP Publishing. - 1741-2560 .- 1741-2552. ; 7:1
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Tidskriftsartikel (refereegranskat)abstract
- The overall aim of our research is to develop methods for a monitoring system to be used at neonatal intensive care units. When monitoring a baby, a range of different types of background activity needs to be considered. In this work, we have developed a scheme for automatic classification of background EEG activity in newborn babies. EEG from six full-term babies who were displaying a burst suppression pattern while suffering from the after-effects of asphyxia during birth was included along with EEG from 20 full-term healthy newborn babies. The signals from the healthy babies were divided into four behavioural states: active awake, quiet awake, active sleep and quiet sleep. By using a number of features extracted from the EEG together with Fisher’s linear discriminant classifier we have managed to achieve 100% correct classification when separating burst suppression EEG from all four healthy EEG types and 93% true positive classification when separating quiet sleep from the other types. The other three sleep stages could not be classified. When the pathological burst suppression pattern was detected, the analysis was taken one step further and the signal was segmented into burst and suppression, allowing clinically relevant parameters such as suppression length and burst suppression ratio to be calculated. The segmentation of the burst suppression EEG works well, with a probability of error around 4%.
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| 2. |
- Löfhede, Johan, et al.
(författare)
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Comparing a Supervised and an Unsupervised Classification Method for Burst Detection in Neonatal EEG
- 2008
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Ingår i: Proceedings of Engineering in Medicine and Biology Society, EMBS 2008. 30th Annual International Conference of the IEEE, 20-24 August, 2008. - : IEEE. - 1557-170X. - 9781424418145 ; , s. 3836-3839
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Konferensbidrag (refereegranskat)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.
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| 3. |
- Löfhede, Johan, 1978, et al.
(författare)
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Comparison of Three Methods for Classifying Burst and Suppression in the EEG of Post Asphyctic Newborns
- 2007
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Ingår i: Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE. - : IEEE. - 1557-170X. - 9781424407880 - 9781424407873 ; , s. 5136 - 5139
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Konferensbidrag (refereegranskat)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.
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| 4. |
- Löfhede, Johan, 1978, et al.
(författare)
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Detection of bursts in the EEG of post asphyctic newborns
- 2006
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Ingår i: 2006 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. - 1557-170X. - 9781424400324 ; , s. 2179-2182, s. 5-6
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Konferensbidrag (refereegranskat)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.
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| 5. |
- Löfhed, Johan, et al.
(författare)
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Soft textile electrodes for EEG monitoring
- 2010
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Konferensbidrag (refereegranskat)abstract
- There is a need for long term monitoring of the brain during intensive care. This is e.g. the case for newborn babies that have been exposed to hypoxia during delivery. Electroencephalography (EEG) is the technique of choice. To get a clear and detailed view of the brain activity a large number of EEG electrodes should be used. Applying traditional electrodes one by one is a time-consuming and technically demanding work and therefore electrode caps are sometimes used. The existing caps have however been found to be suboptimal for long term monitoring because they may induce too high a pressure on the scalp of the babies. We have tested three different types of textile electrodes with regard to their potential use for EEG monitoring. The results show that soft conducting textile materials can indeed be used for EEG monitoring.
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| 8. |
- Löfhede, Johan, 1978, et al.
(författare)
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Classifying neonatal EEG
- 2007
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Ingår i: Proceedings of Medicinteknikdagarna 2007. Annual conference of Svensk Förening för Medicinsk Teknik och Fysik. Oct, 2007. Örebro..
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- In spite of considerable medical progress during the last decades, the perinatal period is still one of the high-risk periods in any individual’s lifetime. In neonatal intensive care of today there is a serious lack of methods that allow continuous monitoring of cerebral function. While we consider it mandatory that good quality hospital care shall include facilities for continuous monitoring of respiratory and cardiac functions in severely ill patients we lack the same possibility when it comes to this most important organ of the body, the brain. The electroencephalogram (EEG) can provide information regarding the state of the brain, but is in its current form not suited for continuous monitoring. Not all neonatal EEG characteristics have been fully investigated or are fully understood, and the people with the necessary competence for interpreting them is not available at neonatal intensive care wards. Our approach is to design a decision support system suitable for continuous monitoring that uses classification algorithms to classify the EEG, for example as normal continuous, normal periodic and pathologic periodic. The EEG is a highly complex signal, and rather than estimating a single parameter, the focus has been on applying classification methods on ensembles of parameters that describe the characteristics of the EEG signal. These parameters have been chosen to enhance different aspects of the EEG signal, and by training classification algorithms with manually segmented data characteristic differences between these complex signals can be found. So far, various classification algorithms have been tested on the task of classifying segments of burst-suppression EEG (pathological periodic) into burst and suppression with rather satisfying results. As a next step we have planned to look into the classification of EEG signals as continuous and periodic, and classification of periodic signals as pathological or normal.
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