| 1. |
- Flisberg, Anders, 1958, et al.
(författare)
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EEG and spectral edge frequency : analysis in posthypoxic newborn piglets
- 2010
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Ingår i: Neuro - endocrinology letters. - : Brain Research Promotion. - 0172-780X. ; 31:2, s. 181-186
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE: To evaluate the frequency content of the electroencephalogram (EEG) during recovery after a severe hypoxic insult in newborn piglets. METHODS: EEG was continuously monitored in nine newborn piglets exposed to a severe hypoxic period. Power spectra in five frequency bands were calculated using Fourier transformation. Spectral edge frequency 90 (SEF90) was defined as the frequency below which 90% of the power in the EEG was located. The piglets were divided into two groups; Group 1 represented piglets with some EEG recovery and Group 2 represented piglets without any EEG recovery. RESULTS: The recovery of the EEG in Group 1 had the same time course in all frequency bands. SEF90 indicates recovery earlier than the value of total power. But SEF90 also signals activity in the EEGs that were almost completely suppressed. When SEF90 was calculated during periods of periodic EEG activity during the very early phase of recovery, the values fell within the same range as during the control period. CONCLUSION: Spectral analysis of continuous EEG in newborn piglets exposed to very severe hypoxia showed that no specific frequency band of the EEG preceded the other ones during recovery. The results of the SEF90 measure, demonstrates the need for critical analysis of the raw EEG before any reliable estimation of cerebral function can be made.
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| 2. |
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| 3. |
- Buendia, Rubén, et al.
(författare)
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Robustness study of the different immittance spectra and frequency ranges in bioimpedance spectroscopy analysis for assessment of total body composition.
- 2014
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Ingår i: Physiological measurement. - : IOP Publishing. - 1361-6579 .- 0967-3334. ; 35:7, s. 1373-95
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Tidskriftsartikel (refereegranskat)abstract
- The estimation of body fluids is a useful and common practice for assessment of disease status and therapy outcomes. Electrical bioimpedance spectroscopy (EBIS) methods are noninvasive, inexpensive and efficient alternatives for determination of body fluids. One of the main source of errors in EBIS measurements in the estimation of body fluids is capacitive coupling. In this paper an analysis of capacitive coupling in EBIS measurements was performed and the robustness of the different immittance spectra against it tested. On simulations the conductance (G) spectrum presented the smallest overall error, among all immittance spectra, in the estimation of the impedance parameters used to estimate body fluids. Afterwards the frequency range of 10-500kHz showed to be the most robust band of the G spectrum. The accuracy of body fluid estimations from the resulting parameters that utilized G spectrum and parameters provided by the measuring device were tested on EBIS clinical measurements from growth hormone replacement therapy patients against estimations performed with dilution methods. Regarding extracellular fluid, the correlation between each EBIS method and dilution was 0.93 with limits of agreement of 1.06 ± 2.95 l for the device, 1.10 ± 2.94 l for G [10-500kHz] and 1.04 ± 2.94 l for G [5-1000kHz]. Regarding intracellular fluid, the correlation between dilution and the device was 0.91, same as for G [10-500kHz] and 0.92 for G [5-1000kHz]. Limits of agreement were 0.12 ± 4.46 l for the device, 0.09 ± 4.45 for G [10-500kHz] and 0.04 ± 4.58 for G [5-1000kHz]. Such close results between the EBIS methods validate the proposed approach of using G spectrum for initial Cole characterization and posterior clinical estimation of body fluids status.
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| 5. |
- Juthberg, R., et al.
(författare)
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Neuromuscular electrical stimulation in garments optimized for compliance
- 2023
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Ingår i: European Journal of Applied Physiology. - : Springer. - 1439-6319 .- 1439-6327.
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Tidskriftsartikel (refereegranskat)abstract
- PurposePhysical inactivity is associated with muscle atrophy and venous thromboembolism, which may be prevented by neuromuscular electrical stimulation (NMES). This study aimed to investigate the effect on discomfort, current amplitude and energy consumption when varying the frequency and phase duration of low-intensity NMES (LI-NMES) via a sock with knitting-integrated transverse textile electrodes (TTE).MethodsOn eleven healthy participants (four females), calf-NMES via a TTE sock was applied with increasing intensity (mA) until ankle-plantar flexion at which point outcomes were compared when testing frequencies 1, 3, 10 and 36 Hz and phase durations 75, 150, 200, 300 and 400 µs. Discomfort was assessed with a numerical rating scale (NRS, 0–10) and energy consumption was calculated and expressed in milli-Joule (mJ). Significance set to p ≤ 0.05.Results1 Hz yielded a median (inter-quartile range) NRS of 2.4 (1.0–3.4), significantly lower than both 3 Hz with NRS 2.8 (1.8–4.2), and 10 Hz with NRS 3.4 (1.4–5.4) (both p ≤ .014). Each increase in tested frequency resulted in significantly higher energy consumption, e.g. 0.6 mJ (0.5–0.8) for 1 Hz vs 14.9 mJ (12.3–21.2) for 36 Hz (p = .003). Longer phase durations had no significant effect on discomfort despite generally requiring significantly lower current amplitudes. Phase durations 150, 200 and 400 µs required significantly lower energy consumption compared to 75 µs (all p ≤ .037).ConclusionLI-NMES applied via a TTE sock produces a relevant plantar flexion of the ankle with the best comfort and lowest energy consumption using 1 Hz and phase durations 150, 200 or 400 µs.
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| 6. |
- 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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| 7. |
- Löfhede, Johan, 1978
(författare)
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Classification of Burst and Suppression in the Neonatal EEG
- 2007
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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.
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| 8. |
- Löfhede, Johan, et al.
(författare)
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Classification of burst and suppression in the neonatal electroencephalogram
- 2008
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Ingår i: Journal of Neural Engineering. - : Institute of Physics Publishing Ltd.. - 1741-2560 .- 1741-2552. ; 5:4, s. 402-410
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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