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1
Steffert, Tony. "Real-time electroencephalogram sonification for neurofeedback." Thesis, Open University, 2018. http://oro.open.ac.uk/57965/.
Повний текст джерелаАнотація:
Electroencephalography (EEG) is the measurement via the scalp of the electrical activity of the brain. The established therapeutic intervention of neurofeedback involves presenting people with their own EEG in real-time to enable them to modify their EEG for purposes of improving performance or health. The aim of this research is to develop and validate real-time sonifications of EEG for use in neurofeedback and methods for assessing such sonifications. Neurofeedback generally uses a visual display. Where auditory feedback is used, it is mostly limited to pre-recorded sounds triggered by the EEG activity crossing a threshold. However, EEG generates time-series data with meaningful detail at fine temporal resolution and with complex temporal dynamics. Human hearing has a much higher temporal resolution than human vision, and auditory displays do not require people to focus on a screen with their eyes open for extended periods of time - e.g. if they are engaged in some other task. Sonification of EEG could allow more rapid, contingent, salient and temporally detailed feedback. This could improve the efficiency of neurofeedback training and reduce the number and duration of sessions for successful neurofeedback. The same two deliberately simple sonification techniques were used in all three experiments of this research: Amplitude Modulation (AM) sonification, which maps the fluctuations in the power of the EEG to the volume of a pure tone; and Frequency Modulation (FM) sonification, which uses the changes in the EEG power to modify the frequency. Measures included, a listening task, NASA task load index; a measure of how much work it was to do the task, Pre & post measures of mood, and EEG. The first experiment used pre-recorded single channel EEG and participants were asked to listen to the sound of the sonified EEG and try and track the activity that they could hear by moving a slider on a computer screen using a computer mouse. This provided a quantitative assessment of how well people could perceive the sonified fluctuations in EEG level. The tracking accuracy scores were higher for the FM sonification but self-assessments of task load rated the AM sonification as easier to track. The second experiment used the same two sonifications, in a real neurofeedback task using participants own live EEG. Unbeknownst to the participants the neurofeedback task was designed to improve mood. A Pre-Post questionnaire showed that participants changed their self-rated mood in the intended direction with the EEG training, but there was no statistically significant change in EEG. Again the FM sonification showed a better performance but AM was rated as less effortful. The performance of sonifications in the tracking task in experiment 1 was found to predict their relative efficacy at blind self-rated mood modification in experiment 2. The third experiment used both the tracking as in experiment 1 and neurofeedback tasks as in experiment 2, but with modified versions of the AM and FM sonifications to allow two-channel EEG sonifications. This experiment introduced a physical slider as opposed to a mouse for the tracking task. Tracking accuracy increased, but this time no significant difference was found between the two sonification techniques on the tracking task. In the training task, once more the blind self-rated mood did improve in the intended direction with the EEG training, but as again there was no significant change in EEG, this cannot necessarily be attributed to the neurofeedback. There was only a slight difference between the two sonification techniques in the effort measure. In this way, a prototype method has been devised and validated for the quantitative assessment of real-time EEG sonifications. Conventional evaluations of neurofeedback techniques are expensive and time consuming. By contrast, this method potentially provides a rapid, objective and efficient method for evaluating the suitability of candidate sonifications for EEG neurofeedback.
2
Nicolau, Nicoletta. "Automatic artefact removal from electroencephalograms." Thesis, University of Reading, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.430848.
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3
Ng, Cheng Man. "Electroencephalogram analysis based on empirical mode decomposition." Thesis, University of Macau, 2011. http://umaclib3.umac.mo/record=b2493507.
Повний текст джерела
4
Antoniu, Angela. "Localization of the sources of the electroencephalogram." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0001/MQ59772.pdf.
Повний текст джерела
5
Fatoorechi, Mohsen. "Electroencephalogram signal acquisition in unshielded noisy environment." Thesis, University of Sussex, 2015. http://sro.sussex.ac.uk/id/eprint/55034/.
Повний текст джерелаАнотація:
Researchers have used electroencephalography (EEG) as a window into the activities of the brain. High temporal resolution coupled with relatively low cost compares favourably to other neuroimaging techniques such as magnetoencephalography (MEG). For many years silver metal electrodes have been used for non-invasive monitoring electrical activities of the brain. Although these electrodes provide a reliable method for recording EEG they suffer from noise, such as offset potentials and drifts, and usability issues, e.g. skin prepa- ration and short circuiting of adjacent electrodes due to gel running. Low frequency noise performance is the key indicator in determining the signal to noise ratio of an EEG sensor. In order to tackle these issues a prototype Electric Potential Sensor (EPS) device based on an auto-zero operational amplifier has been developed and evaluated. The absence of 1/f noise in these devices makes them ideal for use with signal frequencies ~10Hz or less. The EPS is a novel active electrode electric potential sensor with ultrahigh input impedance. The active electrodes are designed to be physically and electrically robust and chemically and biochemically inert. They are electrically insulated (anodized) and scalable. These sensors are designed to be immersed in alcohol for sterilization purposes. A comprehensive study was undertaken to compare the results of EEG signals recorded by the EPS with different commercial systems. These studies comprised measurements of both free running EEG and Event Related Potentials. Strictly comparable signals were observed with cross correlations of higher than 0.9 between the EPS and other systems.
6
Tcheslavski, Gleb V. "Coherence and Phase Synchrony Analysis of Electroencephalogram." Diss., Virginia Tech, 2005. http://hdl.handle.net/10919/30186.
Повний текст джерелаАнотація:
Phase Synchrony (PS) and coherence analyses of stochastic time series - tools to discover brain tissue pathways traveled by electrical signals - are considered for the specific purpose of processing of the electroencephalogram (EEG).
We propose the Phase Synchrony Processor (PSP), as a tool for implementing phase synchrony analysis, and examine its properties on the basis of known signals. Long observation times and wide filter bandwidths can decrease bias in PS estimates. The value of PS is affected by the difference in frequency of the sequences being analyzed and can be related to that frequency difference by the periodic sinc function.
PS analysis of the EEG shows that the average PS is higher - for a number of electrode pairs - for non-ADHD than for ADHD participants. The difference is more pronounced in the δ rhythm (0-3 Hz) and in the γ rhythm (30-50 Hz) PS. The Euclidean classifier with electrode masking yields 66 % correct classification on average for ADHD and non-ADHD subjects using the δ and γ1 rhythms.
We observed that the average γ1 rhythm PS is higher for the eyes closed condition than for the eyes open condition. The latter may potentially be used for vigilance monitoring. The Euclidean discriminator with electrode masking shows an average percentage of correct classification of 78 % between the eyes open and eyes closed subject conditions.
We develop a model for a pair of EEG electrodes and a model-based MS coherence estimator aimed at processing short (i.e. 20 samples) EEG frames. We verify that EEG sequences can be modeled as AR(3) processes degraded by additive white noise with an average SNR of approximately 11-12 dB.
Application of the MS coherence estimator to the EEG suggests that MS coherence is generally higher for non-ADHD individuals than for ADHD participants when evaluated for the θ rhythm of EEG. Also, MS coherence is consistently higher for ADHD subjects than for the majority of non-ADHD individuals when computed for the low end of the δ rhythm (i.e. below 1 Hz).
ADHD produces more measurable effects in the frontal lobe EEG and for participants performing attention intensive tasks.Ph. D.
7
Chang, Nathalie. "Dipole localization using simulated intracerebral electroencephalograms." Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=82475.
Повний текст джерелаАнотація:
Interpreting intracerebral recordings in the search of an epileptic focus can be difficult because the amplitude of the potentials is misleading. Small generators located near the electrode site generate large potentials, which could swamp the signal of a nearby epileptic focus. In order to address this problem, two inverse problem algorithms, beamforming and RAP-MUSIC, were used with simulated intracerebral potentials to calculate equivalent dipole positions. Three dipoles were positioned in a semi-infinite plane medium near three intracerebral electrodes. Their potentials were simulated and contaminated with both white and correlated noise. Localization simulations for each type of noise showed that the two methods detected the sources accurately with RAP-MUSIC reporting lower orientation errors. A spatial resolution analysis for both methods was also performed to assess the separation ability of both methods. Beamforming adequately distinguished the sources separated by 1.2 cm, whereas RAP-MUSIC separated sources as close as 0.4--0.6 cm.
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Corradini, Paula L. "CLINICAL APPLICATIONS OF THE QUANTITATIVE ELECTROENCEPHALOGRAPH." Thesis, Laurentian University of Sudbury, 2014. https://zone.biblio.laurentian.ca/dspace/handle/10219/2154.
Повний текст джерелаАнотація:
Clinical psychology is a discipline that assesses and treats individuals experiencing a variety of psychological disorders; including brain injuries. Employing neuroimaging tools can reveal biological correlates that have not been previously studied in detail. The quantitative electroencephalograph (QEEG) is a dynamic neuroimaging tool that allows for the measurement of brain activity. QEEG source localization analysis has provided additional construct validity for neuropsychological tests by revealing increased activation in the associated brain regions. In addition, differences in resting brain activity have been found depending on the severity of neuropsychological impairment. Finally, enhancement of memory in normal individuals is shown by applying a weak physiologically-patterned electromagnetic field over the left hemisphere. Therefore, by integrating the QEEG with elements of clinical psychology it is possible to provide construct validity to neuropsychological tests, show differences in brain activation depending on the severity of neuropsychological impairment, and study emerging therapeutic techniques that could enhance memory.
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Lopez, de Diego Silvia Isabel. "Automated Interpretation of Abnormal Adult Electroencephalograms." Master's thesis, Temple University Libraries, 2017. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/463281.
Повний текст джерелаАнотація:
Electrical and Computer EngineeringM.S.E.E.
Interpretation of electroencephalograms (EEGs) is a process that is still dependent on the subjective analysis of the examiner. The interrater agreement, even for relevant clinical events such as seizures, can be low. For instance, the differences between interictal, ictal, and post-ictal EEGs can be quite subtle. Before making such low-level interpretations of the signals, neurologists often classify EEG signals as either normal or abnormal. Even though the characteristics of a normal EEG are well defined, there are some factors, such as benign variants, that complicate this decision. However, neurologists can make this classification accurately by only examining the initial portion of the signal. Therefore, in this thesis, we explore the hypothesis that high performance machine classification of an EEG signal as abnormal can approach human performance using only the first few minutes of an EEG recording. The goal of this thesis is to establish a baseline for automated classification of abnormal adult EEGs using state of the art machine learning algorithms and a big data resource – The TUH EEG Corpus. A demographically balanced subset of the corpus was used to evaluate performance of the systems. The data was partitioned into a training set (1,387 normal and 1,398 abnormal files), and an evaluation set (150 normal and 130 abnormal files). A system based on hidden Markov Models (HMMs) achieved an error rate of 26.1%. The addition of a Stacked Denoising Autoencoder (SdA) post-processing step (HMM-SdA) further decreased the error rate to 24.6%. The overall best result (21.2% error rate) was achieved by a deep learning system that combined a Convolutional Neural Network and a Multilayer Perceptron (CNN-MLP). Even though the performance of our algorithm still lags human performance, which approaches a 1% error rate for this task, we have established an experimental paradigm that can be used to explore this application and have demonstrated a promising baseline using state of the art deep learning technology.
Temple University--Theses
10
Janwattanapong, Panuwat. "Connectivity Analysis of Electroencephalograms in Epilepsy." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3906.
Повний текст джерелаАнотація:
This dissertation introduces a novel approach at gauging patterns of informa- tion flow using brain connectivity analysis and partial directed coherence (PDC) in epilepsy. The main objective of this dissertation is to assess the key characteristics that delineate neural activities obtained from patients with epilepsy, considering both focal and generalized seizures. The use of PDC analysis is noteworthy as it es- timates the intensity and direction of propagation from neural activities generated in the cerebral cortex, and it ascertains the coefficients as weighted measures in formulating the multivariate autoregressive model (MVAR). The PDC is used here as a feature extraction method for recorded scalp electroencephalograms (EEG) as means to examine the interictal epileptiform discharges (IEDs) and reflect the phys- iological changes of brain activity during interictal periods. Two experiments were set up to investigate the epileptic data by using the PDC concept.
For the investigation of IEDs data (interictal spike (IS), spike and slow wave com- plex (SSC), and repetitive spikes and slow wave complex (RSS)), the PDC analysis estimates the intensity and direction of propagation from neural activities gener- ated in the cerebral cortex, and analyzes the coefficients obtained from employing MVAR. Features extracted by using PDC were transformed into adjacency matrices
using surrogate data analysis and were classified by using the multilayer Perceptron (MLP) neural network. The classification results yielded a high accuracy and pre- cision number.
The second experiment introduces the investigation of intensity (or strength) of information flow. The inflow activity deemed significant and flowing from other regions into a specific region together with the outflow activity emanating from one region and spreading into other regions were calculated based on the PDC results and were quantified by the defined regions of interest. Three groups were considered for this study, the control population, patients with focal epilepsy, and patients with generalized epilepsy. A significant difference in inflow and outflow validated by the nonparametric Kruskal-Wallis test was observed for these groups.
By taking advantage of directionality of brain connectivity and by extracting the intensity of information flow, specific patterns in different brain regions of interest between each data group can be revealed. This is rather important as researchers could then associate such patterns in context to the 3D source localization where seizures are thought to emanate in focal epilepsy. This research endeavor, given its generalized construct, can extend for the study of other neurological and neurode- generative disorders such as Parkinson, depression, Alzheimers disease, and mental illness.
11
Wang, Yuehe. "Model based dynamic analysis of human sleep electroencephalogram." Thesis, University of Leicester, 1997. http://hdl.handle.net/2381/30210.
Повний текст джерелаАнотація:
For sleep classification, automatic electroencephalogram (EEG) interpretation techniques are of interest because they are labour saving, in contrast to manual (visual) methods. More importantly, some automatic methods, which offer a less subjective approach, can provide additional information which it is not possible to obtain by manual analysis. An extensive literature review has been undertaken to investigate the background of automatic EEG analysis techniques. Frequency domain and time domain methods are considered and their limitations are summarised. The weakness in the R & K rules for visual classification and from which most of the automatic systems borrow heavily are discussed. A new technique - model based dynamic analysis - was developed in an attempt to classify the sleep EEG automatically. The technique comprises of two phases, these are the modelling of EEG signals and the analysis of the model's coefficients using dynamic systems theory. Three techniques of modelling EEG signals are compared: the implementation of the non-linear prediction technique of Schaffer and Tidd (1990) based on chaos theory; Kalman filters and a recursive version of a radial basis function for modelling and forecasting the EEG signals during sleep. The Kalman filter approach produced good results and this approach was used in an attempt to classify the EEG automatically. For classifying the model's (Kalman filter's) coefficients, a new technique was developed by a state-space approach. A 'state variable' was defined based on the state changes of the EEG and was shown to be correlated with the depth of sleep. Furthermore it is shown that this technique may be useful for automatic sleep staging. Possible applications include automatic staging of sleep, detection of micro-arousals, anaesthesia monitoring and monitoring the alertness of workers in sensitive or potentially dangerous environments.
12
Henderson, Geoffrey T. "Early detection of dementia using the human electroencephalogram." Thesis, University of Plymouth, 2004. http://hdl.handle.net/10026.1/2356.
Повний текст джерелаАнотація:
Improved life expectancy has led to a significant increase in the number of people in the high-risk age groups that will develop Alzheimer's disease and other dementia. Efforts are being made to develop treatments that slow the progress of these diseases. However, unless a sufferer is diagnosed in the early stages the treatments cannot give the maximum benefit. Therefore, there is an urgent need for a practical, decision support tool that will enable the earliest possible detection of dementia within the large at-risk population. Current techniques such as Magnetic Resonance Imaging (MRI) that are used to diagnose and assess neurological disorders require specialist equipment and expert clinicians to interpret results. Such techniques are inappropriate as a method of detecting individual subjects with early dementia within the large at-risk population, because everyone within the at-risk group would need to be tested regularly and this would carry a very high cost. Therefore, it is desirable to develop a low cost method of assessment. This thesis describes research into the use of automated EEG analysis to provide the required testing for dementia. The research begins with a review of previous automated EEG analysis, particularly fractal dimension measures. Initial investigation into the nature of the fractal dimension of the EEG are conducted, including problems encountered when applying fractal measures in affine space. More appropriate fractal methods were evaluated and the most promising of these methods was blind tested using an independent clinical data set. This method was estimated to achieve 67% sensitivity to probable early Alzheimer's disease and 17% sensitivity to vascular dementia (as confirmed by a clinical neurophysiologist from the EEG) with a specificity of 99.9%.
13
Thakkar, Kairavee K. "A Geometric Analysis of Time Varying Electroencephalogram Vectors." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613745734396658.
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Liu, Hui. "Online automatic epileptic seizure detection from electroencephalogram (EEG)." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0012941.
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Duta, Mihaela D. "The study of vigilance using neural networks analysis of EEG." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.301454.
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16
Vigon, Laurence Celine. "Independent component analysis techniques and their performance evaluation for electroencephalography." Thesis, Sheffield Hallam University, 2002. http://shura.shu.ac.uk/20479/.
Повний текст джерелаАнотація:
The ongoing electrical activity of the brain is known as the electroencephalogram (EEG). Evoked potentials (EPs) are voltage deviations in the EEG elicited in association with stimuli. EPs provide clinical information by allowing an insight into neurological processes. The amplitude of EPs is typically several times less than the background EEG. The background EEG has the effect of obscuring the EPs and therefore appropriate signal processing is required for their recovery. The EEG waveforms recorded from electrodes placed on the scalp contains the ongoing background EEG, EPs from various brain sources as well as signal components with sources external to the brain. An example of externally generated signal which is picked up by the electrodes on the scalp is the electrooculogram (EOG). This signal is generated by the eyes when eye movements or blinks are performed. Saccade-related EEG waveforms were recorded from 7 normal subjects. A signal source separation technique, namely the independent component analysis (ICA) algorithm of Bell and Sejnowski (hereafter refereed to as BS_ICA), was employed to analyse the recorded waveforms. The effectiveness of the BS_ICA algorithm as well as that of the ICA algorithm of Cardoso, was investigated for removing ocular artefact (OA) from the EEG. It was quantitavely demonstrated that both ICA algorithms were more effective than the conventional correlation-based techniques for removing the OA from the EEG.A novel iterative synchronised averaging method for EPs was devised. The method optimally synchronised the waveforms from successive trials with respect to the event of interest prior to averaging and thus preserved the features of the signals components that were time-locked to the event. The recorded EEG waveforms were analysed using BS_ICA and saccade-related components (frontal and occipital pre-saccadic potentials, and the lambda wave) were extracted and their scalp topographies were obtained. This initial study highlighted some limitations of the conventional ICA approach of Bell and Sejnowski for analysing saccade-related EEG waveforms. Novel techniques were devised in order to improve the performance of the ICA algorithm of Bell and Sejnowski for extracting the lambda wave EP component. One approach involved designing a template-model that represented the temporal characteristics of a lambda wave. Its incorporation into the BS_ICA algorithm improved the signal source separation ability of the algorithm for extracting the lambda wave from the EEG waveforms. The second approach increased the effective length of the recorded EEG traces prior to their processing by the BS_ICA algorithm. This involved abutting EEG traces from an appropriate number of successive trials (a trial was a set of waveforms recorded from 64 electrode locations in a experiment involving a saccade performance). It was quantitatively demonstrated that the process of abutting EEG waveforms was a valuable pre-processing operation for the ICA algorithm of Bell and Sejnowski when extracting the lambda wave. A Fuzzy logic method was implemented to identify BS_ICA-extracted single-trial saccade-related lambda waves. The method provided an effective means to automate the identification of the lambda waves extracted by BS_ICA. The approach correctly identified the single-trial lambda waves with an Accuracy of 97.4%.
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Rmeily, Patrick. "Reliable and efficient transmission of compressive-sensed electroencephalogram signals." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/50026.
Повний текст джерелаАнотація:
As technologies around us are emerging at a rapid rate, wireless body sensor networks (WBSN)s are increasingly being deployed to provide comfort and
safety to patients. WBSNs can monitor the patient's health and transmit the collected data to a remote location where it can be assessed.
Such data is collected and transmitted using low battery devices such as specialized sensors or even smart phones. To elongate the battery life, the energy spent on acquiring, processing and transmitting the data should be minimized. The thesis addresses the case of electroencephalogram (EEG)signals. It studies the energy spent in the sensor node, mainly in the processing stage, i.e. after acquiring the data and before transmitting it to a certain receiver-end in a wireless fashion. To minimize this energy, the number of bits to be processed and transmitted must be minimized. Compressive sampling (CS) is ideal for such a purpose as it requires minimal number of computations to compress a signal.
For transmitting the signals acquired by CS, we studied their quantization followed by 2 different schemes. Scheme 1 applies lossless Huffman coding for further compression that allows perfect reconstruction. This is followed by a Reed-Solomon (RS) code to protect the data from errors during transmission. Scheme 2 does not apply any further compression. It only quantizes the data and applies the RS code to it.
Both schemes were then enhanced by adding an interleaver and deinterleaver that improved the results. The data was then sent in packets over a transmission channel which was simulated using a 2-state Markov model.
Under ideal channel conditions, Scheme 1 with Huffman compression decreased the total number of bits sent by 5.45 %. The best scheme however was scheme 2 followed by an interleaver. It achieved the best signal reconstruction results under normal or noisy channel conditions.Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate
18
Lee, Pamela Wen-Hsin. "Mutual information derived functional connectivity of the electroencephalogram (EEG)." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/219.
Повний текст джерелаАнотація:
Monitoring the functional connectivity between brain networks is becoming increasingly important in elucidating brain functionality in normal and disease states. Current methods of detecting networks in the recorded EEG such as correlation and coherence are limited by the fact that they assume stationarity of the relationship between channels, and rely on linear dependencies. Here we utilize mutual information (MI) as the metric for determining nonlinear statistical dependencies between electroencephalographic (EEG) channels. Previous work investigating MI between EEG channels in subjects with widespread diseases of the cerebral cortex had subjects simply rest quietly with their eyes closed. In motor disorders such as Parkinson’s disease (PD), abnormalities are only expected during performance of motor tasks, but this makes the assumption of stationarity of relationships between EEG channels untenable. We therefore propose a novel EEG segmentation method based on the temporal dynamics of the cross-spectrogram of the computed Independent Components (ICs). After suitable thresholding of the MI values between channels in the temporally segmented EEG, graphical theoretical analysis approaches are applied to the derived networks. The method was applied to EEG data recorded from six normal subjects and seven PD subjects on and off medication performing a motor task involving either their right hand only or both hands simultaneously. One-way analysis of variance (ANOVA) tests demonstrated statistically significant difference between subject groups. This proposed segmentation/MI network method appears to be a promising approach for EEG analysis.
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McGroggan, N. "Neutral network detection of epileptic seizures in the electroencephalogram." Thesis, University of Oxford, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.249426.
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Mathew, Blesy Anu. "ENTROPY OF ELECTROENCEPHALOGRAM (EEG) SIGNALS CHANGES WITH SLEEP STATE." UKnowledge, 2006. http://uknowledge.uky.edu/gradschool_theses/203.
Повний текст джерелаАнотація:
We hypothesized that temporal features of EEG are altered in sleep apnea subjects comparedto normal subjects. The initial aim was to develop a measure to discriminate sleep stages innormals. The longer-term goal was to apply these methods to identify differences in EEGactivity in sleep apnea subjects from normals. We analyzed the C3A2 EEG and anelectrooculogram (EOG) recorded from 9 normal adults awake and in rapid eye movement(REM) and non-REM sleep. The EEG signals were filtered to remove EOG contamination. Twomeasures of the irregularity of EEG signals, Sample Entropy (SpEn) and Tsallis Entropy, wereevaluated for their ability to discriminate sleep stages. SpEn changes with sleep state, beinglargest in Wake. Stage 3/4 had the smallest SpEn (0.57??0.11) normalized to Wake values,followed by Stage 2 (0.72??0.09), REM (0.75??0.1) and Stage 1 (0.89??0.05). This pattern wasconsistent in all the polysomnogram records analyzed. Similar pattern was observed in leadO1A2 as well. We conclude that SpEn may be useful as part of a montage for assessing sleepstate. We analyzed data from sleep apnea subjects having obstructive and central apnea eventsand have made some preliminary observations; the SpEn values were more similar across sleepstages and also high correlation with oxygen saturation was observed.
21
Winski, R. "Adaptive techniques for signal enhancement in the human electroencephalogram." Thesis, Keele University, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.372829.
Повний текст джерела
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Smith, Phillip James. "Complexity of the Electroencephalogram of the Sprague-Dawley Rat." Case Western Reserve University School of Graduate Studies / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1277913687.
Повний текст джерела
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Riddington, Edward Peter. "Automated interpretation of the background EEG using fuzzy logic." Thesis, University of Plymouth, 1998. http://hdl.handle.net/10026.1/1109.
Повний текст джерелаАнотація:
A new framework is described for managing uncertainty and for dealing with artefact corruption to introduce objectivity in the interpretation of the electroencephalogram (EEG). Conventionally, EEG interpretation is time consuming and subjective, and is known to show significant inter- and intra-personnel variation. A need thus exists to automate the interpretation of the EEG to provide a more consistent and efficient assessment. However, automated analysis of EEGs by computers is complicated by two major factors. The difficulty of adequately capturing in machine form, the skills and subjective expertise of the experienced electroencephalbgrapher, and the lack of a reliable means of dealing with the range of EEG artefacts (signal contamination). In this thesis, a new framework is described which introduces objectivity in two important outcomes of clinical evaluation of the EEG, namely, the clinical factual report and the clinical 'conclusion', by capturing the subjective expertise of the electroencephalographer and dealing with the problem of artefact corruption. The framework is separated into two stages .to assist piecewise optimisation and to cater for different requirements. The first stage, 'quantitative analysis', relies on novel digital signal processing algorithms and cluster analysis techniques to reduce data and identify and describe background activities in the EEG. To deal with artefact corruption, an artefact removal strategy, based on new reUable techniques for artefact identification is used to ensure that artefact-free activities only are used in the analysis. The outcome is a quantitative analysis, which efficiently describes the background activity in the record, and can support future clinical investigations in neurophysiology. In clinical practice, many of the EEG features are described by the clinicians in natural language terms, such as very high, extremely irregular, somewhat abnormal etc. The second stage of the framework, 'qualitative analysis', captures the subjectivity and linguistic uncertainty expressed.by the clinical experts, using novel, intelligent models, based on fuzzy logic, to provide an analysis closely comparable to the clinical interpretation made in practice. The outcome of this stage is an EEG report with qualitative descriptions to complement the quantitative analysis. The system was evaluated using EEG records from 1 patient with Alzheimer's disease and 2 age-matched normal controls for the factual report, and 3 patients with Alzheimer's disease and 7 age-matched nonnal controls for the 'conclusion'. Good agreement was found between factual reports produced by the system and factual reports produced by qualified clinicians. Further, the 'conclusion' produced by the system achieved 100% discrimination between the two subject groups. After a thorough evaluation, the system should significantly aid the process of EEG interpretation and diagnosis.
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Sepeng, Goitsemang Gomolemo. "The diagnostic outcomes of electroencephalogram performed on adult psychiatric patients at Dr George Mukhari Hospital, Garankuwa” over a period of January 2006 to December 2008." Thesis, University of Limpopo (Medunsa Campus), 2010. http://hdl.handle.net/10386/398.
Повний текст джерелаАнотація:
Thesis M Med (Psych)--University of Limpopo, 2010.INTRODUCTION: The yield of EEG amongst psychiatric patients has been reported to be low and the value of EEG in the practice of psychiatry is questionable.EEG is used as part of a diagnostic work up for patients with psychiatric disorders .Often the reason given for its use is to exclude epilepsy as a cause of psychiatric symptoms. Epilepsy is primarily a clinical diagnosis, but the EEG may provide strong support by the findings of inter – ictal Epileptogenic discharge METHOD: All the adult EEGs requested at Dr George Mukhari psychiatric hospital, over a 36 month period,were reviewed to describe the outcome of the requested EEG reports. The study is a simple retrospective analysis of 111 consecutive EEG requested to the department of Neurology at DGMH from psychiatric unit at DGMH. Subjects were both inpatients and outpatients. All the EEG was reported by a qualified Neurologist. Data were extracted from the EEG request form and the patients’ clinical files, which reported on the clinical reason for the EEG test, nature of psychiatric diagnosis of patients, the psychiatric treatment received prior to the EEG test and the nature of the EEG results RESULTS: There were 111 EEG reports analysed, and 69 EEG reports for males and 42 EEG reports for females. The reason for EEG request was dominated mainly by exclusion of epilepsy. Majority of the patients were diagnosed with a psychotic disorder , followed second by a mood disorder , all of which was attributed to GMC (epilepsy).About 62.73% of patients were on a combination of treatment of antipsychotic drug and anticonvulsants, whilst 34.55% were on antipsychotic monotherapy prior to the EEG test. Further analysis of the requested EEG form was carried out in whom the test was to determine whether or not the patients were suffering from epilepsy. EEG abnormalities were identified amongst 24% of the patients. About 11,7% of patients presented with non specific EEG results. Out of a total number of 111 patients whom an EEG test was requested and epilepsy was highly suspected from clinical presentation, only 14 patients (12.6%),presented with epileptiform discharge on their EEG results. However majority of the patients (76%) demonstrated normal EEG pattern, which doesn’t exclude a diagnosis of epilepsy. CONCLUSION: The yield of EEG in psychiatry is low. To diagnose epilepsy as a cause of psychiatric presentation,clinicians should continue to rely on the clinical history of attacks and not the EEG. In the practice of psychiatry it is not recommended to routinely order an EEG to exclude a diagnosis of epilepsy, more so to confirm a psychiatric diagnosis. The presence of a psychiatric symptoms in patients who presents with epilepsy, is rarely associated with meaningful EEG changes
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Hegde, Anant. "Spatio-temporal dependency analysis of epileptic intracranial electroencephalograph." [Gainesville, Fla.] : University of Florida, 2006. http://purl.fcla.edu/fcla/etd/UFE0013522.
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Pascual-Marqui, Roberto Domingo. "Functional imaging of the human brain based on the electroencephalogram /." Zürich, 2003. http://opac.nebis.ch/cgi-bin/showAbstract.pl?sys=000253398.
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Cabrerizo, Mercedes. "Subdural electroencephalogram analysis for extracting discriminating measures in epileptogenic data." FIU Digital Commons, 2006. http://digitalcommons.fiu.edu/etd/1960.
Повний текст джерелаАнотація:
This dissertation introduces an integrated algorithm for a new application dedicated at discriminating between electrodes leading to a seizure onset and those that do not, using interictal subdural EEG data. The significance of this study is in determining among all of these channels, all containing interictal spikes, why some electrodes eventually lead to seizure while others do not. A first finding in the development process of the algorithm is that these interictal spikes had to be asynchronous and should be located in different regions of the brain, before any consequential interpretations of EEG behavioral patterns are possible. A singular merit of the proposed approach is that even when the EEG data is randomly selected (independent of the onset of seizure), we are able to classify those channels that lead to seizure from those that do not. It is also revealed that the region of ictal activity does not necessarily evolve from the tissue located at the channels that present interictal activity, as commonly believed.
The study is also significant in terms of correlating clinical features of EEG with the patient's source of ictal activity, which is coming from a specific subset of channels that present interictal activity. The contributions of this dissertation emanate from (a) the choice made on the discriminating parameters used in the implementation, (b) the unique feature space that was used to optimize the delineation process of these two type of electrodes, (c) the development of back-propagation neural network that automated the decision making process, and (d) the establishment of mathematical functions that elicited the reasons for this delineation process.
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Chander, Rahul. "Algorithms to detect High Frequency Oscillations in human intracerebral electroencephalogram." Thesis, McGill University, 2008. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=18767.
Повний текст джерелаАнотація:
Researchers have recently discovered high frequency oscillations (HFOs) of short duration in the 100-450 Hz band using the intracerebral electroencephalogram of epileptic patients (surgical candidates). New tools are being developed to study this phenomenon. The frequent occurrence of HFOs makes a visual identification tedious and time-consuming. Automated screening is much more efficient, repeatable, and objective. We introduced an original baseline selection method and enhanced two published HFO detection algorithms based on filters and wavelets. We then compared their performance to that of a human reviewer. Ten minutes of electroencephalogram from five patients was acquired by filtering in 0.1-500 Hz band and sampling at 2000 Hz. A human reviewer visually identified HFOs that were considered ground-truths to measure the performance of the two algorithms. The sensitivity and false discovery rate of the filter method were 75.9% and 10.6% respectively, while those for the Wavelet method were 70.8% and 13.1% respectively. Our methods provide satisfactory performance for HFO detection.Les chercheurs ont découvert récemment des oscillations haute fréquence de courte durée, dans la bande 100-450 Hz, en utilisant des électrodes intracérébrales sur des patients épileptiques (candidats à la chirurgie). Des nouveaux outils ont été développés pour étudier ces phénomènes. Le nombre élevé de ces oscillations rapides fait de leur identification visuelle une tache fastidieuse. La détection automatique est plus efficace, reproductible et objective. Nous avons mis en place une méthode de sélection originale de la ligne de base et amélioré deux algorithmes de détection basés sur l'utilisation de filtres et d'ondelettes. Nous avons par la suite fait la comparaison entre la performance des algorithmes et celle d'un expert. Dix minutes d'électroencéphalogramme de cinq patients ont été enregistrés avec un filtrage de 0.5 à 500 Hz et une fréquence d'échantillonnage de 2000 Hz. Une revue par un neurophysiologiste des oscillations détectées a permis de mesurer les performances des deux algorithmes. La sensibilité et le pourcentage de fausses détections de la méthode avec filtre sont respectivement de 75.9% et 10.6%, alors que pour la méthode avec ondelettes, la sensibilité et le pourcentage de fausses détections sont respectivement de 70.8% et 13.1%. Notre méthode donne des résultats satisfaisants pour la détection d'oscillations haute fréquence.
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Vennelaganti, Swetha. "AGING AND SLEEP STAGE EFFECTS ON ENTROPY OF ELECTROENCEPHALOGRAM SIGNALS." UKnowledge, 2008. http://uknowledge.uky.edu/gradschool_theses/553.
Повний текст джерелаАнотація:
The aging brain is characterized by alteration in synaptic contacts, which leads to decline of motor and cognitive functions. These changes are reflected in the age related shifts in power spectrum of electroencephalogram (EEG) signals in both wakefulness and sleep. Various non-linear measures have been used to obtain more insights from EEG analysis compared to the conventional spectral analysis. In our study we used Sample Entropy to quantify regularity of the EEG signal. Because elderly subjects arouse from sleep more often than younger subjects, we hypothesized that Entropy of EEG signals from elderly subjects would be higher than that from middle aged subjects, within a sleep stage. We also hypothesized that the entropy increases during and following an arousal and does not return to background levels immediately after an arousal. Our results show that Sample Entropy varies systematically with sleep state in healthy middle-aged and elderly female subjects, reflecting the changing regularity in the EEG. Sample Entropy is significantly higher in elderly in sleep Stage 2 and REM, suggesting that in these two sleep stages the cortical state is closer to wake than in middle-aged women. Sample Entropy is higher in post-arousal compared to the pre-arousal and stays high for a 30 sec period.
30
Song, Yuedong. "Electroencephalogram machine learning to assist diagnosis and treatment of epilepsy." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.709318.
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Borges, Ana Filipa Teixeira. "Spectral and coherence estimates on electroencephalogram recordings during arithmetical tasks." Master's thesis, Faculdade de Ciências e Tecnologia, 2009. http://hdl.handle.net/10362/10556.
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D'ROZARIO, Angela Louise. "Electroencephalogram (EEG) biomarkers of neurobehavioural dysfunction in obstructive sleep apnea." Thesis, The University of Sydney, 2014. http://hdl.handle.net/2123/9886.
Повний текст джерелаАнотація:
Obstructive sleep apnea (OSA) affects an estimated 2-4% of middle–aged adults yet we are still exploring how best to delineate the neurophysiological deficits that accompany this disorder. Untreated OSA leads to an increased risk of motor vehicle accidents. Traditional polysomnographic (PSG) metrics do not consistently correlate with daytime functioning. There is a clinical need for simple biomarkers to identify individuals susceptible to OSA-related cognitive deficits. There is a close relationship between EEG-based changes in brain activity and daytime function in healthy sleepers. No studies have explored quantitative EEG (qEEG) biomarkers during baseline sleep and resting wakefulness (baseline) as correlates of waking neurobehavioural performance during extended wakefulness in OSA. The aims of the thesis were 1) to identify qEEG biomarkers of neurobehavioural dysfunction and sleepiness in OSA and controls during 40-hours (h) of extended wakefulness, and 2) to develop and validate automated EEG artefact processing methods for subsequent qEEG analysis of waking and sleep EEG. EEG biomarkers were derived using conventional power spectral analysis and a novel qEEG analysis technique, detrended fluctuation analysis (DFA). This study showed that wake qEEG markers significantly correlated with impaired performance and increased sleepiness across 40-h of extended wakefulness in both groups. Baseline waking measures of the DFA scaling exponent, but not power spectra, were associated with impaired simulated driving after 24-h awake in OSA. Furthermore, OSA patients with greater EEG slowing during REM sleep showed a marked decline in performance after 24-h awake. These key findings highlight the potential utility of qEEG analysis to yield simple biomarkers of neurobehavioural impairment and sleepiness. Automated EEG artefact processing methods for resting awake and PSG recordings were developed and validated against a reference-standard of manual artefact recognition as part of this study. These proven artefact processing methods will be pivotal for exploring qEEG biomarkers in future studies.
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Young, Andrew Coady. "A Consensus Model for Electroencephalogram Data Via the S-Transform." Digital Commons @ East Tennessee State University, 2012. https://dc.etsu.edu/etd/1424.
Повний текст джерелаАнотація:
A consensus model combines statistical methods with signal processing to create a better picture of the family of related signals. In this thesis, we will consider 32 signals produced by a single electroencephalogram (EEG) recording session. The consensus model will be produced by using the S-Transform of the individual signals and then normalized to unit energy. A bootstrapping process is used to produce a consensus spectrum. This leads to the consensus model via the inverse S-Transform of the consensus spectrum. The method will be applied to both a control and experimental EEG to show how the results can be used in clinical settings to analyze experimental outcomes.
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Li, Jiewei, and 李杰威. "Electroencephalograph feature extraction of somatosensory event related potential (ERP)." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/206587.
Повний текст джерелаАнотація:
Background:
The event related potential (ERP) is an important electrophysiological response to an internal or external stimulus on human body. In some studies, the ERP-based brain computer interface (BCI) systems were created in visual or auditory modality.
However, in these type of BCIs, either the eyes or ears of the users are occupied when they are making a choice. It is not convenient to communicate with others. Thus, a somatosensory ERP based BCI can be developed to overcome this issue. According to this, the analysis of somatosensory ERP features is necessary to evaluate if somatosensory ERP is eligible for BCIs as an input.
Objective:
1. To study ERP features and design of P300 experiment.
2. To compare three types of P300 features elicited by three modalities.
3. To produce ERP response by electrical stimuli delivered to different position, and analyze ERP features.
Methods:
Two experiments were conducted. In experiment 1, three modalities, including visual, auditory and electrical modality, were used to produce P300 response. Experiment 2 only presented electrical stimuli. In experiment 1 two electrical stimuli were presented with different intensities at one location, whereas four electrical stimuli were showed at different location with the same intensity. The amplitude and latency were compared among three modalities, and the ERP topography of experiment 2 was also analyzed.
Result and conclusion:
Fourteen subjects’ data were analyzed in our study. The amplitude and latency of electrical P300 were similar to auditory ERP. But the ERP of visual modality had the largest amplitude and shortest latency. This result shows that electrical P300 can work as well as auditory P300 in BCIs, but not as good as visual P300. In experiment 2, the latency of electrical ERP occurred around 280 ms, and the amplitude and the topography showed that the largest amplitude was located around Cz electrode. This type of ERP in experiment 2 was considered as P3a, which also can be used in BCI systems.published_or_final_version
Orthopaedics and Traumatology
Master
Master of Medical Sciences
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Jakaite, Livija. "Bayesian assessment of newborn brain maturity from sleep electroencephalograms." Thesis, University of Bedfordshire, 2012. http://hdl.handle.net/10547/293806.
Повний текст джерелаАнотація:
In this thesis, we develop and test a technology for computer-assisted assessments of newborn brain maturity from sleep electroencephalogram (EEG). Brain maturation of newborns is reflected in rapid development of EEG patterns over a number of weeks after conception. Observing the maturational patterns, experts can assess newborn’s EEG maturity with an accuracy ±2 weeks of newborn’s stated age. A mismatch between the EEG patterns and newborn’s physiological age alerts clinicians about possible neurological problems. Analysis of newborn EEG requires specialised skills to recognise the maturity-related waveforms and patterns and interpret them in the context of newborns age and behavioural state. It is highly desirable to make the results of maturity assessment most accurate and reliable. However, the expert analysis is limited in capability to estimate the uncertainty in assessments. To enable experts quantitatively evaluate risks of brain dysmaturity for each case, we employ the Bayesian model averaging methodology. This methodology, in theory, provides the most accurate assessments along with the estimates of uncertainty, enabling experts to take into account the full information about the risk of decision making. Such information is particularly important when assessing the EEG signals which are highly variable and corrupted by artefacts. The use of decision tree models within the Bayesian averaging enables interpreting the results as a set of rules and finding the EEG features which make the most important contribution to assessments. The developed technology was tested on approximately 1,000 EEG recordings of newborns aged 36 to 45 weeks post conception, and the accuracy of assessments was comparable to that achieved by EEG experts. In addition, it was shown that the Bayesian assessment can be used to quantitatively evaluate the risk of brain dysmaturity for each EEG recording.
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Ward, Christian Radcliffe. "Applications and Statistical Modeling of Electroencephalograms using Identity Vectors." Diss., Temple University Libraries, 2019. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/564773.
Повний текст джерелаАнотація:
Electrical and Computer EngineeringPh.D.
In recent years, electroencephalograms (EEGs) have been the subject of intense signal processing research. The ability of software to group, cluster, or identify trends in EEG data has applications that range from clinical support tools for neurologists to brain-computer interfaces. However, a persistent limitation in the development of EEG classification algorithms has been a lack of clinician labeled data which is necessary to train the supervised neural networks and deep learning systems. This work addresses this issue by presenting an unsupervised technique for classifying EEGs and elucidating common data modes that do not depend on labeled data. Specifically, this work introduces the application of Identity Vectors (I-Vectors) to EEG signals. I-Vectors were originally developed in the speech processing community to parse multiple facets of speaker data (speaker, language, accent, age, etc). The similarities between EEG and speech data suggest that I-Vectors are a strong candidate for developing data models that can differentiate between subjects, channels, and medical conditions. I-Vectors work by building a Universal Background Model (UBM) of signal features that is based on weighted Gaussian clusters. This UBM is then projected into a lower dimensional space through a Total Variability Matrix which seeks to maximize the differences between the UBM and a group of “enrollment” signals. Optionally, further dimensionality reduction can typically be achieved through linear discriminant analysis (LDA) before generating the final I-Vectors. This work develops the application of I-Vectors to EEGs by addressing three key research aims. First: can the I-Vector technique be used to classify EEG data with equivalent performance to other machine learning classifiers. Secondly: how should I-Vector parameters be tuned to optimize performance on EEG data. And thirdly: What properties of EEG data do I-Vectors take advantage of, and can this knowledge be used to inform the EEG classification process. I-Vector performance was rigorously evaluated using larger and more diverse data sets than have been used in comparable published literature, specifically various blends of the PhysioNet Motor Movement Database and the Temple University Hospital EEG Corpus. Benchmark comparisons were made against well-known classifiers in the EEG domain, namely the Mahalanobis Distance and Gaussian Mixture Model-Universal Background Model (GMMUBM) classifiers. Performance was also evaluated using three different EEG feature sets as system inputs, namely Power Spectral Density, Spectral Coherence, and Cepstral Coefficients. Ultimately, the I-Vectors exceeded the performance of the MD classifier and reported an equal error rate 5% higher higher than the GMMUBMs. This was achieved using I-Vectors that were one to two orders of magnitude smaller than those in the GMMUBM classifier and half the size of the MD classifier. These results Indicated the technique was robust and has the potential to scale for use on large datasets such as the Temple University Hospital EEG Corpus.
Temple University--Theses
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Löfhede, Johan. "Classification of Burst and Suppression in the Neonatal EEG." Licentiate thesis, Högskolan i Borås, Institutionen Ingenjörshögskolan, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-3448.
Повний текст джерелаАнотація:
The brain requires a continuous supply of oxygen and even a short period ofreduced oxygen supply risks severe and lifelong consequences for theaffected individual. The delivery is a vulnerable period for a baby who mayexperience for example hypoxia (lack of oxygen) that can damage the brain.Babies who experience problems are placed in an intensive care unit wheretheir vital signs are monitored, but there is no reliable way to monitor thebrain directly. Monitoring the brain would provide valuable informationabout the processes going on in it and could influence the treatment and helpto improve the quality of neonatal care. The scope of this project is todevelop methods that eventually can be put together to form a monitoringsystem for the brain that can function as decision-support for the physician incharge of treating the patient.The specific technical problem that is the topic of this thesis is detection ofburst and suppression in the electroencephalogram (EEG) signal. The thesisstarts with a brief description of the brain, with a focus on where the EEGoriginates, what types of activity can be found in this signal and what theymean. 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 certaintypes of characteristics from the data are defined. The next section describesclassification methodology and how it can be used for making decisionsbased on combinations of several features extracted from a signal. Theclassification methods Fisher’s Linear Discriminant, Neural Networks andSupport Vector Machines are described and are finally compared with respectto their ability to discriminate between burst and suppression. An experimentwith different combinations of features in the classification has also beencarried out. The results show similar results for the three methods but it canbe seen that the SVM is the best method with respect to handling multiplefeatures.
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Knoblauch, Vera. "Circadian and homeostatic modulation of sleep spindles in the human electroencephalogram." Basel : Universität Basel, 2004. http://www.unibas.ch/diss/2004/DissB_6791.htm.
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Werth, Esther. "Human Sleep: Homeostatic regulation and topographic differences of the sleep electroencephalogram /." Zürich, 1997. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=12326.
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James, Christopher J. "Detection of epileptiform activity in the electroencephalogram using artificial neural networks." Thesis, University of Canterbury. Electrical and Electronic Engineering, 1997. http://hdl.handle.net/10092/6760.
Повний текст джерелаАнотація:
A system for automated detection of epileptiform activity in the electroencephalogram (EEG) has been developed and tested on prerecorded data from a range of patients. Epileptiform activity is manifest as spikes in the EEG and consequently the automated detection of spikes in the EEG is an important tool in the diagnosis of epilepsy and is a goal sought by many researchers. The system presented herein is centred around artificial neural networks (ANNs), in particular the multi-layer perceptron (MLP) and the self-organising feature map (SOFM). The MLP is used in the form of an adaptive filter to enhance the presence of epileptiform transients in the EEG while the SOFM is used to form a novel pattern classifier. A modification to the 'standard' calibration technique for the SOFM is proposed based on a method involving Bayesian probabilities. The SOFM allows a large quantity of EEG data to be used to form a pattern classifier in an unsupervised manner. Fuzzy logic is introduced in order to incorporate spatial contextual information in the spike detection process. By using fuzzy logic it has been possible to develop an approximate model of the spatial reasoning performed by an electroencephalographer (EEGer) as opposed to a precise biological model.
The human brain is overviewed in terms of its structure, organisation and function. Simplistic mathematical modelling of the neural network of the brain is discussed and ANNs are introduced. After reviewing ANNs in general the perceptron based network is introduced and discussed. The SOFM is introduced and through a number of computer simulations several suggestions are put forward regarding the choice of parameters for training the SOFM. After a review of the literature on spike detection systems, in particular ANN based systems, a multi-stage spike detection system is proposed. There are four stages to the system: spike enhancer, mimetic stage, SOFM and fuzzy logic stage. Each stage of the system is discussed at length and measures of performance are indicated at each stage. The importance of spatial and temporal contextual information is discussed and a method using fuzzy logic is proposed to model the spatial reasoning of an EEGer.
The system was trained on 35 epileptiform EEGs containing in excess of 3000 epileptiform events and was tested on a different set of 7 EEGs (6 containing epileptiform activity and 1 'normal') containing 133 epileptiform events. The EEGs consisted of standard clinical recordings with an average length of 22.9 minutes. Preliminary results show that the system has a sensitivity of 59% and a selectivity of 31% with an average false detection rate of 61 per hour. The performance compares well with other leading systems to be found in the literature once the measures of performance obtained in each are case placed in context. Several aspects in the system have been identified for modification which should lead to considerable improvements in performance (e.g., temporal context, improved mimetic stage).
The new approach to the spike detection problem presented in this thesis shows that it is possible to form an accurate classifier in a self-organised fashion, thus eliminating the need to accurately label large quantities of data - a weak point in many spike detection systems. Furthermore, the importance of spatial contextual analysis is highlighted showing that it is possible to model the spatial reasoning of an EEGer with a fuzzy logic system, thus eliminating the need to produce accurate models of the process.
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Fauvel, Simon. "Energy-efficient compressed sensing frameworks for the compression of electroencephalogram signals." Thesis, University of British Columbia, 2013. http://hdl.handle.net/2429/45359.
Повний текст джерелаАнотація:
The use of wireless body sensor networks (WBSNs) is gaining popularity in monitoring and communicating information about a person's health. In such applications, the amount of data transmitted by the sensor node should be minimized. This is because the energy available in these battery-powered sensors is limited. In this thesis, we study the wireless transmission of electroencephalogram (EEG) signals. We propose novel, energy-efficient compressed sensing (CS) frameworks that take advantage of the inherent structure present in EEG signals (both temporal and spatial correlations) to efficiently compress these signals at the sensor node in WBSNs.
We first present a simple CS-based framework that is adapted to the EEG WBSN setting. We optimize the sparsifying dictionary and demonstrate that using a fixed sparse binary sensing matrix offers similar performances to optimal matrices while requiring far fewer computations.
We then add an energy-efficient Independent Component Analysis (ICA) preprocessing block to the simple CS framework to exploit the spatial correlations among EEG channels. We show that the proposed framework provides significant energy savings as compared to the state-of-the-art method. As well, for a fixed compression ratio, our system achieves similar normalized mean square error performance as the state-of-the-art method, which is better than that achieved by the simple CS framework.
We further improve on the energy performance of the framework by replacing the ICA preprocessing block by a simpler, correlations-based interchannel redundancy module and by using entropy coding. On the energy front, our proposed CS framework is up to 8 times more energy-efficient than the typical wavelet compression method. We also show that our method achieves a better reconstruction quality than the state-of-the art BSBL method. We further demonstrate that our method is robust to measurement noise and to packet loss, and that it is applicable to a wide range of EEG signal types.
We finally apply our CS framework to compress EEG signals in the context of a brain computer interface application and evaluate its impact on the performance of the system. We show that interesting energy savings can be realized at the cost of a mild decrease in classification accuracy.
42
Kander, Veena. "Validation of a pediatric guideline on basic electroencephalogram interpretation for clinicians." Thesis, Bloemfontein : Central University of Technology, Free State, 2013. http://hdl.handle.net/11462/172.
Повний текст джерелаАнотація:
Thesis (M. Tech. (Clinical technology )) - Central University of technology, Free State, 2013The incidence of epilepsy is high in sub-Saharan Africa and resource poor countries (RPCs). There are few neurologists and paediatric neurologists to manage people with epilepsy (PWE). Health care is often limited, particularly technological services, including electroencephalogram (EEG), video EEG monitoring, and Neuroradiology services. All these are important in the management of PWE. Since 2008, informal electrophysiology training has been provided at the Red Cross War Memorial Hospital, in the Department of Paediatric Neurology. The Principal Investigator (PI) elected to develop a formal teaching course on EEG interpretation at the Red Cross War Memorial Hospital. A study was designed to evaluate the practical use of a handbook entitled “Handbook of Paediatric Electroencephalography: A guide to basic paediatric electroencephalogram interpretation.” This has been developed to fulfill the need for basic understanding and interpretation of EEG amongst clinicians caring for children in sub-Saharan Africa who may not have access to, or be able to afford, training at a recognized facility or on-line. In 2008, the department of Paediatric Neurology at the Red Cross War Memorial Hospital had their first African fellow from Kenya. By 2011, seven participants had undergone EEG training. A quantitative research approach and design was used in order to evaluate the handbook in terms of the accessibility of the contents and its practical use. Quantification included the recruitment of participants who constituted the population sample, a pilot study, and the collection of data from comparative assessments of participants’ use of the handbook, and from questionnaires completed by participants. This provided the researcher with the opportunity to improve and validate her knowledge of training in EEG interpretation. The researcher was able to quantify and compare the scores of participants using the handbook, as well as to compare their evaluative responses to its content and practical use. Eleven of thirteen participants completed the study. The pre-training results showed a median percentage of 50 which increased to 70 percent post-test. A comparison of the scores of trained versus not-trained revealed that those participants who had undergone one-on-one training on site at the unit fared much better both in their interpretations, conclusions, and reporting of EEG findings. The responses from the evaluative and comparative survey between the two groups showed no significant difference across all questions, the majority of the questions on the relative usefulness of the handbook being rated ‘agree’ and ‘strongly agree’, thus supporting the finding that all participants found the handbook useful whether they had received one-on-one training or not. The post-training results in EEG interpretation showed a stronger trend towards statistical significance (p<0.06) with trained participants and with the not-trained. These findings lend support to the success and usefulness of the handbook as a basic guide to paediatric EEG interpretation. The handbook was not aimed at making the electroencephalography reader an expert at a specialist level, but rather to maximize the reliability of the reading of EEG when screening electroencephalograms for important key diagnostic markers which would alter the child’s management. This is the first published handbook on paediatric EEG in South Africa. The results of this study strongly suggest that the handbook is useful as a learning and reference tool in interpretation of paediatric EEG, both for individuals with access to one-on-one training as well as those without. It is intended that the handbook, in conjunction with one-on-one training, will form part of a post-graduate diploma course offered by the University of Cape Town on “basic electrophysiology and the management of children with epilepsy” for training neurologists and child neurologists, paediatricians and health care workers in sub-Saharan Africa.
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Estepp, Justin Ronald. "An improved adaptive filtering approach for removing artifact from the electroencephalogram." Wright State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=wright1433244703.
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Snyder, Selena Tyr. "Time Series Modeling of Clinical Electroencephalogram Data - An Information Theory Approach." Ohio University Honors Tutorial College / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ouhonors1524830090342372.
Повний текст джерела
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Zak, Francis Anthony Jr. "Effects of lithium on auditory evoked potential and electroencephalogram spectral edges." Diss., The University of Arizona, 1992. http://hdl.handle.net/10150/185782.
Повний текст джерелаАнотація:
The effects of lithium on auditory evoked potential and electroencephalogram spectral edges are examined. The nature of the relationship between spectral edge and serum lithium concentration is investigated, with the goal of establishing a unique and functional relationship, thus enabling the spectral edge to be used as an index, or surrogate measurement, of serum lithium concentration.
46
Gale, Amy Ash 1960. "An analytical study of the electroencephalogram in sevoflurane and enflurane anesthesia." Thesis, The University of Arizona, 1993. http://hdl.handle.net/10150/278297.
Повний текст джерелаАнотація:
The objective of this thesis is to investigate how the human Electroencephalogram(EEG) is affected by anesthetic agents. The ultimate goal of the research is to improve clinical understanding of the EEG in anesthesia, and to determine the value of quantitative analytical techniques for generalizing or differentiating among anesthetic agents. Power spectrum and time domain analysis were conducted on EEG waveforms from 30 human, male volunteer subjects during sevoflurane and enflurane general anesthesia. Univariate parametric statistics and Discriminant Function Analysis (DFA) were performed to analyze and classify EEG spectral content. Statistically significant differences were found between the two anesthetics, duration of anesthetic period, and anesthetic depth levels. DFA classification of EEG epochs by anesthetic condition group was performed with a high degree of accuracy, especially when the stepwise analysis method was used.
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Levan, Pierre. "A system for automatic artifact removal in ictal scalp electroencephalograms /." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=98986.
Повний текст джерелаАнотація:
Scalp electroencephalograms (EEGs) constitute a well-established modality in the diagnosis of epilepsy. EEGs are frequently contaminated by artifacts originating from various sources such as scalp muscles, ocular activity, or patient movement. Recently, independent component analysis (ICA) has been applied to separate and remove statistically independent artifactual sources from scalp EEG recorded during seizures. However, this method requires a trained electroencephalographer to visually identify the artifacts among the components extracted by ICA.Proposed is a system to automate this process, using a Bayesian framework to classify the components as either brain activity or artifact. The system identified EEG components with 87.6% sensitivity and 70.2% specificity. Most misclassified components were mixtures of EEG and artifactual activity. The classification error rate was comparable to the human intra-expert variability observed in EEG classification tasks. The value of system lies in its ability to remove simultaneously and automatically several types of artifacts from the EEG.
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El, Sayed Hussein Jomaa Mohamad. "Signal processing of electroencephalograms with 256 sensors in epileptic children." Thesis, Angers, 2019. http://www.theses.fr/2019ANGE0028.
Повний текст джерелаАнотація:
Dans cette thèse, nous proposons des méthodes de traitement du signal et les appliquons à des signaux d’électro-encéphalographie (EEG) enregistrés chez des patients épileptiques. L’objectif est de pouvoir quantifier l’état du patient et d’étudier l’évolution du trouble neurologique au cours du temps. Les méthodes que nous avons développées sont basées sur des mesures d’entropie. Ainsi, nous introduisons la « multivariate Improved Weighted Multi-scale Permutation Entropy» (mvIWMPE) que nous appliquons à des signaux EEG d’enfants sains et épileptiques. Elle donne des résultats prometteurs. Nous proposons également une approche multivariée pour la « Sample Entropy». Les résultats montrent qu’elle permet de traiter correctement un plus grand nombre de canaux que la méthode existante. Nous présentons aussi une mesure de complexité temps-fréquence variable dans le temps, basée sur la « Singular Value Decomposition » et la « Rényi Entropy ». Ces mesures, appliquées sur l’EEG d’enfants épileptiques avant et 4-6 semaines après un traitement, conduisent à des résultats qui sont en accord avec le diagnostic clinique quant à l’évolution de la pathologie. La dernière partie de la thèse porte sur les mesures de connectivité fonctionnelle. Nous proposons une méthode de connectivité fonctionnelle basée sur la mvIWMPE et l’information mutuelle. Elle est appliquée sur des signaux EEG d’enfants sains au repos. A l’aide de mesures de réseau, nous pouvons identifier des régions cérébrales actives dans des réseaux précédemment découverts grâce à l’imagerie par résonance magnétique fonctionnelle. La méthode est également utilisée pour étudier les réseaux chez des enfants épileptiquesIn this thesis, our focus is to develop signal processing methods to be used on electroencephalography (EEG) signals recorded from epileptic patients. The aim of these methods is to be able to quantify the state of the patient with epilepsy and to study the progress of the neurological disorder over time. The methods we developed are based on entropy. From previous permutation entropy methods we introduce the multivariate Improved Weighted Multi-scale Permutation Entropy (mvIWMPE). This method is applied on EEG signals of both healthy and epileptic children and gives promising results. We also introduce a new multivariate approach for sample entropy and, when tested and compared with the existing multivariate approach, we find that the introduced approach is much betterin handling a larger numbers of channels. We also introduce a time-varying time frequency complexity measure based on Singular Value Decomposition and Rényi Entropy. These measures are applied on EEG of epileptic children before and after 4-6 weeks of treatment. The results come in correspondence with the clinical diagnosis from the hospital on whether the patients improve or not. The final part of the thesis focuses on functional connectivity measures. We introduce a new functional connectivity method based on mvIWMPE and Mutual Information. The method is applied on EEG signals of healthy children at rest. Using network measures, we are able to identify regions in the brain that are active in networks previously found using functional magnetic resonance imaging. The method is also used to study the networks of epileptic children at several points throughout the treatment
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Orellana, Marco Antônio Pinto. "Seizure detection in electroencephalograms using data mining and signal processing." Universidade Federal de Viçosa, 2017. http://www.locus.ufv.br/handle/123456789/11589.
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Agencia Boliviana Espacial
A epilepsia é uma das doenças neurológicas mais comuns definida como a predisposição a sofrer convulsões não provocadas. A Organização Mundial da Saúde estima que 50 milhões de pessoas estão sofrendo esta condição no mundo inteiro. O diagnóstico de epilepsia implica em um processo caro e longo baseado na opinião de especialistas com base em eletroencefalogramas (EEGs) e gravações de vídeo. Neste trabalho, foram desenvolvidos dois métodos para a predição automática de convulsões usando EEG e mineração de dados. O primeiro sistema desenvolvido é um método específico para cada paciente (patient-specific) que consiste em extrair características espectro-temporais de todos os canais de EEG, aplicar um algoritmo de redução de dimensão, recuperar o envelope do sinal e criar um modelo usando um classificador random forest. Testando este sistema com um grande banco de dados de epilepsia, atingimos 97% de especificidade e 99% de sensibilidade. Assim, a primeira proposta mostrou ter um grande potencial para colaborar com o diagnóstico em um contexto clínico. O segundo sistema desenvolvido é um método não específico do paciente (non-patient specific) que consiste em selecionar o sinal diferencial de dois eletrodos, aplicar um vetor de bancos de filtros para esse sinal, extrair atributos de séries temporais e criar um modelo preditivo usando uma árvore de decisão CART. O desempenho deste método foi de 95% de especificidade e 87% de sensibilidade. Estes valores não são tão altos quanto os de métodos propostos anteriormente. No entanto, a abordagem que propomos apresenta uma viabilidade muito maior para implementação em dispositivos que possam ser efetivamente utilizados por pacientes em larga escala. Isto porque somente dois elétrodos são utilizados e o modelo de predição é computacionalmente leve. Note-se que, ainda assim, o modelo xigerado apresenta um poder preditivo satisfatório e generaliza melhor que em trabalhos anteriores já que pode ser treinado com dados de um conjunto de pacientes e utilizado em pacientes distintos (non-patient specific). Ambas as propostas apresentadas aqui, utilizando abordagens distintas, demonstram ser alternativas de predição de convulsões com performances bastante satisfatórias sob diferentes circunstâncias e requisitos.
Epilepsy is one of the most common neurological diseases and is defined as the pre- disposition to suffer unprovoked seizures. The World Health Organization estimates that 50 million people are suffering this condition worldwide. Epilepsy diagnosis im- plies an expensive and long process based on the opinion of specialist personnel about electroencephalograms (EEGs) and video recordings. We have developed two meth- ods for automatic seizure detection using EEG and data mining. The first system is a patient-specific method that consists of extracting spectro-temporal features of 23 EEG channels, applying a dimension reduction algorithm, recovering the envelope of the signal, and creating a model using a random forest classifier. Testing this system against a large dataset, we reached 97% of specificity and 99% of sensitivity. Thus, our first proposal showed to have a great potential for diagnosis support in clinical context. The other developed system is a non-patient specific method that consists of selecting the differential signal of two electrodes, applying an array of filter banks to that signal, extracting time series features, and creating a predictive model using a decision tree. The performance of this method was 95% of specificity, and 87% of sensitivity. Although the performance is lower than previous propos- als, due to the design conditions and characteristics, our method allows an easier implementation with low hardware requirements. Both proposals presented here, using distinct approaches, demonstrate to be seizure prediction alternatives with very satisfactory performances under different circumstances and requirements.
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Zelmann, Rina. "Automatic detection and analysis of high frequency oscillations in the human electroencephalogram." Thesis, McGill University, 2013. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=114313.
Повний текст джерелаАнотація:
High Frequency Oscillations (HFOs; 80-500Hz) are spontaneous short-duration small-amplitude EEG patterns that are emerging as a biomarker of tissue capable of generating epileptic seizures. In order to propel the clinical utilization and systematic study of HFOs, it is important to develop robust automatic detectors and to provide a framework to ensure stability in their identification; this is the first goal of this thesis. Although HFOs have mostly been studied with intracranial electrodes, they have also been recorded on the scalp. A fundamental question is to understand how is it possible to see these small events on the scalp given the powerful skull attenuation; this is the second goal of this thesis. The first goal is addressed by designing a procedure to systematize the study of HFOs and by developing an automatic detector. A procedure that allows to control for consistency among readers and to evaluate if a selected interval provides stable information, for automatic and visual identification of HFOs, is first presented. This procedure is now routinely used when identifying interictal HFOs. This study is the first to evaluate the minimum duration needed to obtain consistent information when marking the EEG and showed that analyzing 5min of interictal EEG provided the same information as longer intervals. The approach is applicable to any type of EEG event.An automatic detector of HFOs is then described, which takes an original approach in first detecting baseline segments free of oscillatory activity and then using a statistical threshold obtained from these local baselines to detect HFOs. The detector performs better than other detectors, in particular in active channels and in channels without clear baseline. A comparison of existing detectors on the same dataset is presented to analyze their performance, to show that optimizing on a particular type of data improves performance in any detector, and to emphasize the issues involved in validation. The second goal of this thesis is the study of the spatial distribution of cortical activity at the time of scalp HFOs. As HFOs are produced by small brain regions, and since the EEG is greatly attenuated before reaching the scalp, HFOs are mostly recorded with intracranial electrodes. Surprisingly, HFOs have been recently observed also on the scalp EEG. Using simultaneous scalp and intracranial recordings, we showed that even though the generators of HFOs have small spatial extent, they can be observed on the scalp with small amplitude and focal extent. We showed that these small extent events are undersampled on the scalp with the density of standard electrode systems, and on cortical grids with the standard inter-electrode spacing of 1cm. A dense distribution of scalp electrodes seems necessary to fully spatially sample HFOs on the scalp. This opens the possibility of systematically studying HFOs non-invasively. By developing methods for the detection and analysis of HFOs, we expect to improve the systematic study of intracranial and scalp HFOs, moving towards their clinical application as a biomarker of epileptogenic tissue.Les oscillations de haute fréquence (OHF; 80-500 Hz) constituent des évènements EEG spontanés de courte durée et de faible amplitude qui émergent en tant que biomarqueur du tissu pouvant générer les crises épileptiques. Afin de promouvoir l'utilisation clinique et l'étude systématique des OHF, il est important de développer des détecteurs automatiques fiables et de fournir un cadre visant à garantir la stabilité de leurs résultats. Il s'agit là du premier objectif de la présente thèse. Les OHF ont principalement été étudiées à partir d'électrodes intracrâniennes, mais elles ont également été enregistrées à l'aide d'électrodes placées sur le cuir chevelu. Il convient alors de comprendre comment l'on peut observer ces évènements de faible envergure du fait de l'atténuation importante du crâne, ce qui constitue le second objectif de cette thèse. Pour répondre au premier objectif, nous avons conçu une procédure visant à systématiser l'étude des OHF et avons élaboré un détecteur automatique. Ainsi, nous présentons d'abord une procédure permettant d'assurer l'uniformité entre les lecteurs et d'évaluer si un intervalle choisi offre des renseignements stables pour un repérage visuel et automatique des OHF. À l'heure actuelle, cette procédure est communément utilisée quand les OHF interictales sont repérées. Cette étude est la première à évaluer la durée minimale nécessaire à l'obtention de renseignements cohérents pour le marquage des EEG et elle a démontré que l'analyse de 5 minutes d'EEG interictal offre la même information que des intervalles de plus longue durée. Cette approche est applicable à tout type d'évènements EEG. Nous avons ensuite décrit un détecteur automatique d'OHF, qui suit une approche originale en détectant d'abord des segments de base dénués d'activités oscillatoires avant d'utiliser un seuil statistique obtenu à partir de ces valeurs de base locales pour déterminer les OHF. Ce détecteur est plus efficace que d'autres détecteurs, notamment pour les canaux actifs et les canaux sans valeur de base claire. Une comparaison entre les détecteurs existants pour le même ensemble de données est présentée afin d'analyser leur performance respective, de démontrer que l'optimisation d'un certain type de données améliore l'efficacité de tous les détecteurs et de mettre en évidence les problèmes en jeu dans la validation. Le second objectif de la présente thèse est d'étudier la distribution spatiale de l'activité corticale au moment des OHF enregistrées sur le cuir chevelu. Dans la mesure où les OHF sont produites par de petites régions cérébrales et que l'EEG est fortement atténué avant d'arriver au cuir chevelu, les OHF sont surtout enregistrées à l'aide d'électrodes intracrâniennes. Il est étonnant que dernièrement, des OHF aient également été observées sur des EEG enregistrés sur le cuir chevelu. En se basant sur les enregistrements simultanés sur le cuir chevelu et intracrâniens, nous avons démontré que, même si les régions génératrices d'OHF sont faiblement étendues sur le plan spatial, les OHF peuvent être observées à l'aide d'électrodes placées sur le cuir chevelu avec une faible amplitude et une étendue focale. Nous avons établi que ces évènements de faible étendue sont sous-échantillonnés sur le cuir chevelu avec la densité des systèmes standards d'électrodes et sur les grilles corticales avec l'espacement standard de 1 cm entre les électrodes. Il semble nécessaire d'avoir une répartition dense des électrodes sur le cuir chevelu afin de représenter spatialement de façon exhaustive les OHF enregistrées sur le cuir chevelu. Cela ouvrirait la voie à une étude systématique non invasive des OHF. Avec l'élaboration de méthodes de détection et d'analyse des OHF, nous souhaitons améliorer l'étude systématique des OHF intracrâniennes et du cuir chevelu, dans l'optique d'une application clinique en tant que biomarqueur du tissu épileptogène.