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  • 1. Flisberg, A
    et al.
    Kjellmer, I
    Löfhede, J
    University of Borås, School of Engineering.
    Lindecrantz, K
    University of Borås, School of Engineering.
    Thordstein, M
    Prognostic capacity of automated quantification of suppression time in the EEG of post-asphyctic full-term neonates2011In: Acta Paediatrica, ISSN 0803-5253, E-ISSN 1651-2227, Vol. 100, no 10, p. 1338-1343Article in journal (Refereed)
    Abstract [en]

    Aim: To evaluate the prognostic capacity of a new method for automatic quantification of the length of suppression time in the electroencephalogram (EEG) of a group of asphyxiated newborn infants. Methods: Twenty-one full-term newborn infants who had been resuscitated for severe birth asphyxia were studied. Eight channel continuous EEG was recorded for prolonged time periods during the first days of life. Artefact detection or rejection was not applied to the signals. The signals were fed through a pretrained classifier and then segmented into burst and suppression periods. Total suppression length per hour was calculated. All surviving patients were followed with structured neurodevelopmental assessments to at least 18 months of age. Results: The patients who developed neurodevelopmental disability or died had significant suppression periods in their EEG during the first days of life while the patients who had a normal follow-up had no or negligible amount of suppression. Conclusions: This new method for automatic quantification of suppression periods in the raw, neonatal EEG discriminates infants with good from those with poor outcome.

  • 2. Flisberg, Anders
    et al.
    Kjellmer, I
    Löfhede, Johan
    University of Borås, School of Engineering.
    Löfgren, N
    Rosa-Zurera, M
    Lindecrantz, Kaj
    University of Borås, School of Engineering.
    Thordstein, M
    Does indomethacin for closure of patent ductus arteriosus affect cerebral function?2010In: Acta Paediatrica, ISSN 0803-5253, E-ISSN 1651-2227, Vol. 99, no 10, p. 1493-1497Article in journal (Refereed)
    Abstract [en]

    Objective: To study whether indomethacin used in conventional dose for closure of patent ductus arteriosus affects cerebral function measured by Electroencephalograms (EEG) evaluated by quantitative measures. Study design: Seven premature neonates with haemodynamically significant persistent ductus arteriosus were recruited. EEG were recorded before, during and after an intravenous infusion of 0.2 mg/kg indomethacin over 10 min. The EEG was analysed by two methods with different degrees of complexity for the amount of low-activity periods (LAP, “suppressions”) as an indicator of affection of cerebral function. Results: Neither of the two methods identified any change in the amount of LAPs in the EEG as compared to before the indomethacin infusion. Conclusion: Indomethacin in conventional dose for closure of patent ductus arteriosus does not affect cerebral function as evaluated by quantitative EEG.

  • 3. Löfgren, Nils
    et al.
    Thordstein, Magnus
    Löfhede, Johan
    University of Borås, School of Engineering.
    Lindecrantz, Kaj
    University of Borås, School of Engineering.
    Kjellmer, Ingemar
    Dean, Justin
    Mallard, Carina
    Slowly Altering Electrical Potentials over the Head During Hypoxia and LPS Exposure2007Conference paper (Refereed)
  • 4.
    Löfhede, Johan
    University of Borås, School of Engineering.
    Classification of Burst and Suppression in the Neonatal EEG2007Licentiate thesis, monograph (Other academic)
    Abstract [en]

    The brain requires a continuous supply of oxygen and even a short period of reduced oxygen supply risks severe and lifelong consequences for the affected individual. The delivery is a vulnerable period for a baby who may experience for example hypoxia (lack of oxygen) that can damage the brain. Babies who experience problems are placed in an intensive care unit where their vital signs are monitored, but there is no reliable way to monitor the brain directly. Monitoring the brain would provide valuable information about the processes going on in it and could influence the treatment and help to improve the quality of neonatal care. The scope of this project is to develop methods that eventually can be put together to form a monitoring system for the brain that can function as decision-support for the physician in charge of treating the patient. The specific technical problem that is the topic of this thesis is detection of burst and suppression in the electroencephalogram (EEG) signal. The thesis starts with a brief description of the brain, with a focus on where the EEG originates, what types of activity can be found in this signal and what they mean. The data that have been available for the project are described, followed by the signal processing methods that have been used for preprocessing, and the feature functions that can be used for extracting certain types of characteristics from the data are defined. The next section describes classification methodology and how it can be used for making decisions based on combinations of several features extracted from a signal. The classification methods Fisher’s Linear Discriminant, Neural Networks and Support Vector Machines are described and are finally compared with respect to their ability to discriminate between burst and suppression. An experiment with different combinations of features in the classification has also been carried out. The results show similar results for the three methods but it can be seen that the SVM is the best method with respect to handling multiple features.

  • 5.
    Löfhede, Johan
    University of Borås, School of Engineering.
    The EEG of the neonatal brain: classification of background activity2009Doctoral thesis, monograph (Other academic)
    Abstract [en]

    The brain requires a continuous supply of oxygen and nutrients, and even a short period of reduced oxygen supply can cause severe and lifelong consequences for the affected individual. The unborn baby is fairly robust, but there are of course limits also for these individuals. The most sensitive and most important organ is the brain. When the brain is deprived of oxygen, a process can start that ultimately may lead to the death of brain cells and irreparable brain damage. This process has two phases; one more or less immediate and one delayed. There is a window of time of up to 24 hours where action can be taken to prevent the delayed secondary damage. One recently clinically available technique is to reduce the metabolism and thereby stop the secondary damage in the brain by cooling the baby. It is important to be able to quickly diagnose hypoxic injuries and to follow the development of the processes in the brain. For this, the electroencephalogram (EEG) is an important tool. The EEG is a voltage signal that originates within the brain and that can be recorded easily and non-invasively at bedside. The signals are, however, highly complex and require special competence to interpret, a competence that typically is not available at the intensive care unit, and particularly not continuously day and night. This thesis addresses the problem of automatic classification of neonatal EEG and proposes methods that would be possible to use in bedside monitoring equipment for neonatal intensive care units. The thesis is a compilation of six papers. The first four deal with the segmentation of pathological signals (burst suppression) from post-asphyctic full term newborn babies. These studies investigate the use of various classification techniques, using both supervised and unsupervised learning. In paper V the scope is widened to include both classification of pathological activity versus activity found in healthy babies as well as application of the segmentation methods on the parts of the EEG signal that are found to be of the pathological type. The use of genetic algorithms for feature selection is also investigated. In paper VI the segmentation methods are applied on signals from pre-term babies to investigate the impact of a certain medication on the brain. The results of this thesis demonstrate ways to improve the monitoring of the brain during intensive care of newborn babies. Hopefully it will someday be implemented in monitoring equipment and help to prevent permanent brain damage in post asphyctic babies.

  • 6.
    Löfhede, Johan
    et al.
    University of Borås, School of Engineering.
    Degerman, Johan
    Löfgren, Nils
    Thordstein, Magnus
    Flisberg, Anders
    Kjellmer, Ingemar
    Lindecrantz, Kaj
    University of Borås, School of Engineering.
    Comparing a Supervised and an Unsupervised Classification Method for Burst Detection in Neonatal EEG2008In: Proceedings of Engineering in Medicine and Biology Society, EMBS 2008. 30th Annual International Conference of the IEEE, 20-24 August, 2008, IEEE , 2008, p. 3836-3839Conference paper (Refereed)
    Abstract [en]

    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.

  • 7.
    Löfhede, Johan
    et al.
    University of Borås, School of Engineering.
    Löfgren, N.
    Thordstein, M.
    Flisberg, A.
    Kjellmer, I.
    Lindecrantz, Kaj
    University of Borås, School of Engineering.
    Classifying Neonatal EEG2007Conference paper (Refereed)
  • 8.
    Löfhede, Johan
    et al.
    University of Borås, School of Engineering.
    Löfgren, N.
    Thordstein, M.
    Flisberg, A.
    Kjellmer, I.
    Lindecrantz, Kaj
    University of Borås, School of Engineering.
    Comparison of Three Methods for Classifying Burst and Suppression in the EEG of Post Asphyctic Newborns2007In: Proceedings of the 29th Annual International Conference of the IEEE EMBS, Cité Internationale, Lyon, France, August 23-26, 2007, IEEE , 2007Conference paper (Refereed)
  • 9.
    Löfhede, Johan
    et al.
    University of Borås, School of Engineering.
    Löfgren, N
    Thordstein, M
    Flisberg, A
    Kjellmer, I
    Lindecrantz, Kaj
    University of Borås, School of Engineering.
    Detection of bursts in the EEG of post asphyctic newborns2006Conference paper (Refereed)
  • 10.
    Löfhede, Johan
    et al.
    University of Borås, School of Engineering.
    Löfgren, N.
    Thordstein, M.
    Flisberg, I.
    Kjellmer, I.
    Lindecrantz, Kaj
    University of Borås, School of Engineering.
    Classifying Burst and Suppression in the EEG of Post Asphyctic Newborns Using a Support Vector Machine2007In: Proceedings of the 3rd International IEEE EMBS Conference on Neural Engineering, Kohala Coast, Hawaii, USA, May 2-5, 2007, IEEE , 2007Conference paper (Refereed)
  • 11.
    Löfhede, Johan
    et al.
    University of Borås, School of Engineering.
    Löfgren, Nils
    Thordstein, Magnus
    Flisberg, Anders
    Kjellmer, Ingemar
    Lindecrantz, Kaj
    University of Borås, School of Engineering.
    Classification of burst and suppression in the neonatal electroencephalogram2008In: Journal of Neural Engineering, ISSN 1741-2560, E-ISSN 1741-2552, Vol. 5, no 4, p. 402-410Article in journal (Refereed)
    Abstract [en]

    Fisher's linear discriminant (FLD), a feed-forward artificial neural network (ANN) and a support vector machine (SVM) were compared with respect to their ability to distinguish bursts from suppressions in electroencephalograms (EEG) displaying a burst-suppression pattern. Five features extracted from the EEG were used as inputs. The study was based on EEG signals from six full-term infants who had suffered from perinatal asphyxia, and the methods have been trained with reference data classified by an experienced electroencephalographer. The results are summarized as the area under the curve (AUC), derived from receiver operating characteristic (ROC) curves for the three methods. Based on this, the SVM performs slightly better than the others. Testing the three methods with combinations of increasing numbers of the five features shows that the SVM handles the increasing amount of information better than the other methods.

  • 12.
    Löfhede, Johan
    et al.
    University of Borås, School of Engineering.
    Löfgren, Nils
    Thordstein, Magnus
    Flisberg, Anders
    Kjellmer, Ingmar
    Lindecrantz, Kaj
    University of Borås, School of Engineering.
    Classification of Burst Suppression and Tracé Alternant in Neonatal EEG2008In: Proceedings of Medicinteknikdagarna 2008. Annual conference of Svensk Förening för Medicinsk Teknik och Fysik, Göteborg, Oct., 2008, 2008Conference paper (Refereed)
  • 13.
    Löfhede, Johan
    et al.
    University of Borås, School of Engineering.
    Seoane Martínez, Fernando
    University of Borås, School of Engineering.
    Thorstein, M.
    Soft Textile Electrodes for EEG Monitoring2010Conference paper (Refereed)
  • 14.
    Löfhede, Johan
    et al.
    University of Borås, School of Engineering.
    Thordstein, Magnus
    Löfgren, Nils
    Flisberg, Anders
    Rosa-Zurera, Manuel
    Kjellmer, Ingemar
    Lindecrantz, Kaj
    University of Borås, School of Engineering.
    Automatic classification of background EEG activity in healthy and sick neonates2010In: Journal of Neural Engineering, ISSN 1741-2560, E-ISSN 1741-2552, Vol. 7, no 1Article in journal (Refereed)
    Abstract [en]

    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%.

  • 15.
    Sandsjö, Leif
    et al.
    University of Borås, School of Engineering.
    Löfhede, Johan
    University of Borås, School of Engineering.
    Eriksson, Siw
    University of Borås, Swedish School of Textiles.
    Guo, Li
    University of Borås, Swedish School of Textiles.
    Thordstein, Magnus
    EEG Measurements using Textile Electrodes2012Conference paper (Refereed)
  • 16. Thordstein, Magnus
    et al.
    Flisberg, Anders
    Inganäs, L.
    Mourtzis, A.
    Karlsson, L.
    Rex, K.
    Löfhede, Johan
    University of Borås, School of Engineering.
    Löfgren, Nils
    Förbättrad användning av aEEG för övervakning av centralnervös funktion hos nyfödda barn. En prospektiv, populationsbaserad studie.2008In: Proceedings of Medicinteknikdagarna 2008, 2008Conference paper (Refereed)
  • 17. Thordstein, Magnus
    et al.
    Kjellmer, Ingemar
    Löfgren, Nils
    Löfhede, Johan
    University of Borås, School of Engineering.
    Lindecrantz, Kaj
    University of Borås, School of Engineering.
    Dean, Justin
    Mallard, Carina
    Effects of inflammation on cerebral electric activity in fetal sheep2008Conference paper (Other academic)
    Abstract [en]

    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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