| 1. |
- Flisberg, Anders, 1958, et al.
(författare)
-
Does indomethacin for closure of patent ductus arteriosus affect cerebral function?
- 2010
-
Ingår i: Acta Paediatrica. - : Wiley. - 0803-5253 .- 1651-2227. ; 99:10, s. 1493-1497
-
Tidskriftsartikel (refereegranskat)abstract
- 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.
|
|
| 2. |
- Löfhede, Johan, 1978, et al.
(författare)
-
Automatic classification of background EEG activity in healthy and sick neonates
- 2010
-
Ingår i: Journal of Neural Engineering. - : IOP Publishing. - 1741-2560 .- 1741-2552. ; 7:1
-
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%.
|
|
| 3. |
- Löfhede, Johan, 1978
(författare)
-
Classification of Burst and Suppression in the Neonatal EEG
- 2007
-
Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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 pre-processing, 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.
|
|
| 4. |
|
|
| 5. |
- Löfhede, Johan, 1978, et al.
(författare)
-
Classifying neonatal EEG
- 2007
-
Ingår i: Proceedings of Medicinteknikdagarna 2007. Annual conference of Svensk Förening för Medicinsk Teknik och Fysik. Oct, 2007. Örebro..
-
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.
|
|
| 6. |
- Löfhede, Johan, 1978, et al.
(författare)
-
Comparison of Three Methods for Classifying Burst and Suppression in the EEG of Post Asphyctic Newborns
- 2007
-
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
-
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.
|
|
| 7. |
- Löfhede, Johan, 1978, et al.
(författare)
-
Detection of bursts in the EEG of post asphyctic newborns
- 2006
-
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
-
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.
|
|
| 8. |
|
|
| 9. |
- Löfhede, Johan, 1978, et al.
(författare)
-
Soft Textile Electrodes for EEG Monitoring
- 2010
-
Ingår i: [12]10th IEEE International Conference on Information Technology and Applications.
-
Konferensbidrag (refereegranskat)
|
|
| 10. |
- Löfhede, Johan, 1978
(författare)
-
The EEG of the Neonatal Brain – Classification of Background Activity
- 2009
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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 easily and non-invasively can be recorded 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. This thesis addresses the problem of automatic classification of neonatal EEG and proposes methods that would be possible to use in bed-side 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.
|
|
