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  • 1. Atefi, Seyed Reza
    et al.
    Seoane, Fernando
    Högskolan i Borås, Institutionen för Vårdvetenskap.
    The Emergence of Electrical Bioimpedance Monitoring for Prompt Detection of Stroke Damage2014Konferensbidrag (Refereegranskat)
  • 2.
    Löfhede, Johan
    Högskolan i Borås, Institutionen Ingenjörshögskolan.
    The EEG of the neonatal brain: classification of background activity2009Doktorsavhandling, monografi (Övrigt vetenskapligt)
    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.

  • 3.
    Löfhede, Johan
    et al.
    Högskolan i Borås, Institutionen Ingenjörshögskolan.
    Degerman, Johan
    Löfgren, Nils
    Thordstein, Magnus
    Flisberg, Anders
    Kjellmer, Ingemar
    Lindecrantz, Kaj
    Högskolan i Borås, Institutionen Ingenjörshögskolan.
    Comparing a Supervised and an Unsupervised Classification Method for Burst Detection in Neonatal EEG2008Ingår i: Proceedings of Engineering in Medicine and Biology Society, EMBS 2008. 30th Annual International Conference of the IEEE, 20-24 August, 2008, IEEE , 2008, s. 3836-3839Konferensbidrag (Refereegranskat)
    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.

  • 4.
    Löfhede, Johan
    et al.
    Högskolan i Borås, Institutionen Ingenjörshögskolan.
    Löfgren, Nils
    Thordstein, Magnus
    Flisberg, Anders
    Kjellmer, Ingemar
    Lindecrantz, Kaj
    Högskolan i Borås, Institutionen Ingenjörshögskolan.
    Classification of burst and suppression in the neonatal electroencephalogram2008Ingår i: Journal of Neural Engineering, ISSN 1741-2560, E-ISSN 1741-2552, Vol. 5, nr 4, s. 402-410Artikel i tidskrift (Refereegranskat)
    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.

  • 5.
    Löfhede, Johan
    et al.
    Högskolan i Borås, Institutionen Ingenjörshögskolan.
    Löfgren, Nils
    Thordstein, Magnus
    Flisberg, Anders
    Kjellmer, Ingmar
    Lindecrantz, Kaj
    Högskolan i Borås, Institutionen Ingenjörshögskolan.
    Classification of Burst Suppression and Tracé Alternant in Neonatal EEG2008Ingår i: Proceedings of Medicinteknikdagarna 2008. Annual conference of Svensk Förening för Medicinsk Teknik och Fysik, Göteborg, Oct., 2008, 2008Konferensbidrag (Refereegranskat)
  • 6.
    Löfhede, Johan
    et al.
    Högskolan i Borås, Institutionen Ingenjörshögskolan.
    Thordstein, Magnus
    Löfgren, Nils
    Flisberg, Anders
    Rosa-Zurera, Manuel
    Kjellmer, Ingemar
    Lindecrantz, Kaj
    Högskolan i Borås, Institutionen Ingenjörshögskolan.
    Automatic classification of background EEG activity in healthy and sick neonates2010Ingår i: Journal of Neural Engineering, ISSN 1741-2560, E-ISSN 1741-2552, Vol. 7, nr 1Artikel i tidskrift (Refereegranskat)
    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%.

  • 7.
    Seoane, Fernando
    et al.
    Högskolan i Borås, Institutionen Ingenjörshögskolan.
    Lindecrantz, Kaj
    Högskolan i Borås, Institutionen Ingenjörshögskolan.
    Olsson, Torsten
    Kjellmer, Ingemar
    Flisberg, Anders
    Bågenholm, Ralph
    Brain electrical impedance at various frequencies: the effect of hypoxia2004Ingår i: Engineering in Medicine and Biology Society, 2004. EMBC 2004. Conference Proceedings. 26th Annual International Conference of the, 2322 - 2325 Vol.3, 2004Konferensbidrag (Refereegranskat)
    Abstract [en]

    Non-invasive multi-frequency measurements of transcephalic impedance, both reactance and resistance, can efficiently detect cell swelling of brain tissue and can be used for early detection of threatening brain damage. We have performed experiments on piglets to monitor transcephalic impedance during hypoxia. The obtained results have confirmed the hypothesis that changes in the size of cells modify the tissue impedance. During tissue inflammation after induced hypoxia, cerebral tissue exhibits changes in both reactance and resistance. Those changes are remarkably high, up to 71% over the baseline, and easy to measure especially at certain frequencies. A better understanding of the electrical behaviour of cerebral tissue during cell swelling would lead us to develop effective non-invasive clinical tools and methods for early diagnosis of cerebral edema and brain damage prevention.

  • 8.
    Seoane, Fernando
    et al.
    Högskolan i Borås, Institutionen Ingenjörshögskolan.
    Lindecrantz, Kaj
    Högskolan i Borås, Institutionen Ingenjörshögskolan.
    Olsson, Torsten
    Kjellmer, Ingemar
    Flisberg, Anders
    Bågenholm, Ralph
    Spectroscopy study of the dynamics of the transencephalic electrical impedance in the perinatal brain during hypoxia2005Ingår i: Physiological Measurement, ISSN 0967-3334, E-ISSN 1361-6579, Vol. 26, nr 5, s. 849-863Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Hypoxia/ischaemia is the most common cause of brain damage in neonates. Thousands of newborn children suffer from perinatal asphyxia every year. The cells go through a response mechanism during hypoxia/ischaemia, to maintain the cellular viability and, as a response to the hypoxic/ischaemic insult, the composition and the structure of the cellular environment are altered. The alterations in the ionic concentration of the intra- and extracellular and the consequent cytotoxic oedema, cell swelling, modify the electrical properties of the constituted tissue. The changes produced can be easily measured using electrical impedance instrumentation. In this paper, we report the results from an impedance spectroscopy study on the effects of the hypoxia on the perinatal brain. The transencephalic impedance, both resistance and reactance, was measured in newborn piglets using the four-electrode method in the frequency range from 20 kHz to 750 kHz and the experimental results were compared with numerical results from a simulation of a suspension of cells during cell swelling. The experimental results make clear the frequency dependence of the bioelectrical impedance, confirm that the variation of resistance is more sensitive at low than at high frequencies and show that the reactance changes substantially during hypoxia. The resemblance between the experimental and numerical results proves the validity of modelling tissue as a suspension of cells and confirms the importance of the cellular oedema process in the alterations of the electrical properties of biological tissue. The study of the effects of hypoxia/ischaemia in the bioelectrical properties of tissue may lead to the development of useful clinical tools based on the application of bioelectrical impedance technology.

  • 9. Thordstein, Magnus
    et al.
    Flisberg, Anders
    Inganäs, L.
    Mourtzis, A.
    Karlsson, L.
    Rex, K.
    Löfhede, Johan
    Högskolan i Borås, Institutionen Ingenjörshögskolan.
    Löfgren, Nils
    Förbättrad användning av aEEG för övervakning av centralnervös funktion hos nyfödda barn. En prospektiv, populationsbaserad studie.2008Ingår i: Proceedings of Medicinteknikdagarna 2008, 2008Konferensbidrag (Refereegranskat)
  • 10. Thordstein, Magnus
    et al.
    Kjellmer, Ingemar
    Löfgren, Nils
    Löfhede, Johan
    Högskolan i Borås, Institutionen Ingenjörshögskolan.
    Lindecrantz, Kaj
    Högskolan i Borås, Institutionen Ingenjörshögskolan.
    Dean, Justin
    Mallard, Carina
    Effects of inflammation on cerebral electric activity in fetal sheep2008Konferensbidrag (Övrigt vetenskapligt)
    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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