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- Löfhede, Johan, 1978, et al.
(author)
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Automatic classification of background EEG activity in healthy and sick neonates
- 2010
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In: Journal of Neural Engineering. - : IOP Publishing. - 1741-2560 .- 1741-2552. ; 7:1
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Journal article (peer-reviewed)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.
(author)
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Comparing a Supervised and an Unsupervised Classification Method for Burst Detection in Neonatal EEG
- 2008
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In: 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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Conference paper (peer-reviewed)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.
(author)
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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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In: 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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Conference paper (peer-reviewed)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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| 6. |
- Löfhede, Johan, 1978, et al.
(author)
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Classifying neonatal EEG
- 2007
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In: Proceedings of Medicinteknikdagarna 2007. Annual conference of Svensk Förening för Medicinsk Teknik och Fysik. Oct, 2007. Örebro..
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Conference paper (other academic/artistic)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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| 7. |
- Thordstein, Magnus, et al.
(author)
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Effects of inflammation on cerebral electric activity in fetal sheep
- 2008
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In: 2nd Congress of the European Academy of Paediatrics, Nice 23-28 okt 2008.
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Conference paper (other academic/artistic)abstract
- OBJECTIVE Intrauterine infections can by themselves induce fetal brain damage but also potentiate the effects of other harmful influences such as asphyxia and seizures. Using an EEG technique that permits the recording of extremely low frequencies, often called DC EEG, changes in the level, i.e. DC shifts can be detected. The DC level has been suggested to depend mainly on the potential over the blood brain barrier (BBB), in turn decided primarily by the arterial level of pCO2. Fetuses affected by infection/inflammation that produce detrimental effects on the brain, may have elevated levels of pCO2 and disturbance of the BBB. We aimed at investigating the possibility that the DC EEG could be used to detect the effects of inflammation on the fetal brain. METHODS Fetal sheep were instrumented at 97 days of gestation with catheters, four active EEG electrodes placed on the dura mater as well as extracranial reference and ground electrodes. After three days of recovery, the bacterial endotoxin lipopolysaccharide (LPS) was given to the fetus (200 ng i.v.). RESULTS Exposure to LPS induced a positive DC shift in parallel to the assumed affection of cerebral function and to the pCO2 elevation. This change was not always obvious in standard EEG. CONCLUSIONS These recordings of fetal DC EEG appear to be the first to be done. They indicate that the effects of inflammation on cerebral function can be monitored by DC EEG. Such monitoring might be feasible also during late stages of labour and in neonates.
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| 8. |
- Thordstein, Magnus, et al.
(author)
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Effects of inflammation on cerebral electric activity in fetal sheep
- 2007
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In: Sjätte Graz-symposiet om utvecklings-neurologi, Graz 3-5 maj 2007.
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Conference paper (other academic/artistic)abstract
- OBJECTIVEIntrauterine infections can by themselves induce fetal brain damage but also potentiate the effects of other harmful influences such as asphyxia and seizures. Using an EEG technique that permits the recording of extremely low frequencies, often called DC EEG, changes in the level, i.e. DC shifts can be detected. The DC level has been suggested to depend mainly on the potential over the blood brain barrier (BBB), in turn decided primarily by the arterial level of pCO2.Fetuses affected by infection/inflammation that produce detrimental effects on the brain, may have elevated levels of pCO2 and disturbance of the BBB. We aimed at investigating the possibility that the DC EEG could be used to detect the effects of inflammation on the fetal brain.METHODSFetal sheep were instrumented at 97 days of gestation with catheters, four active EEG electrodes placed on the dura mater as well as extracranial reference and ground electrodes. After three days of recovery, the bacterial endotoxin lipopolysaccharide (LPS) was given to the fetus (200 ng i.v.).RESULTSExposure to LPS induced a positive DC shift in parallel to the assumed affection of cerebral function and to the pCO2 elevation. This change was not always obvious in standard EEG.CONCLUSIONSThese recordings of fetal DC EEG appear to be the first to be done. They indicate that the effects of inflammation on cerebral function can be monitored by DC EEG. Such monitoring might be feasible also during late stages of labour and in neonates.
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