Detection of epileptic seizures based on video and accelerometer recordings

Epilepsy is one of the most common neurological diseases, especially in children. And although the majority of patients can be treated through medication or surgery (70%-75%), a significant group of patients cannot be treated. For this latter group of patients it is advisable to follow the evolution of the disease. This can be done through a long-term automatic monitoring, which gives an objective measure of the number of seizures that the patient has, for example during the night. On the other hand, there is a reduced social control overnight and the parents or caregivers can miss some seizures. In severe seizures, it is sometimes necessary, however, to avoid dangerous situations during or after the seizure (e.g. the danger of suffocation caused by vomiting or a position that obstructs breathing, or the risk of injury during violent movements), and to comfort ...

Cuppens, Kris — Katholieke Universiteit Leuven


New approaches for EEG signal processing: Artifact EOG removal by ICA-RLS scheme and Tracks extraction method

Localizing the bioelectric phenomena originating from the cerebral cortex and evoked by auditory and somatosensory stimuli are clear objectives to both understand how the brain works and to recognize different pathologies. Diseases such as Parkinson's, Alzheimer's, schizophrenia and epilepsy are intensively studied to find a cure or accurate diagnosis. Epilepsy is considered the disease with major prevalence within disorders with neurological origin. The recurrent and sudden incidence of seizures can lead to dangerous and possibly life-threatening situations. Since disturbance of consciousness and sudden loss of motor control often occur without any warning, the ability to predict epileptic seizures would reduce patients' anxiety, thus considerably improving quality of life and safety. The common procedure for epilepsy seizure detection is based on brain activity monitorization via electroencephalogram (EEG) data. This process consumes a lot of time, especially in the case of long ...

Carlos Guerrero-Mosquera — University Carlos III of Madrid


Mining the ECG: Algorithms and Applications

This research focuses on the development of algorithms to extract diagnostic information from the ECG signal, which can be used to improve automatic detection systems and home monitoring solutions. In the first part of this work, a generically applicable algorithm for model selection in kernel principal component analysis is presented, which was inspired by the derivation of respiratory information from the ECG signal. This method not only solves a problem in biomedical signal processing, but more importantly offers a solution to a long-standing problem in the field of machine learning. Next, a methodology to quantify the level of contamination in a segment of ECG is proposed. This level is used to detect artifacts, and to improve the performance of different classifiers, by removing these artifacts from the training set. Furthermore, an evaluation of three different methodologies to compute the ECG-derived ...

Varon, Carolina — KU Leuven


Development of an automated neonatal EEG seizure monitor

Brain function requires a continuous flow of oxygen and glucose. An insufficient supply for a few minutes during the first period of life may have severe consequences or even result in death. This happens in one to six infants per 1000 live term births. Therefore, there is a high need for a method which can enable bedside brain monitoring to identify those neonates at risk and be able to start the treatment in time. The most important currently available technology to continuously monitor brain function is electroEncephaloGraphy (or EEG). Unfortunately, visual EEG analysis requires particular skills which are not always present round the clock in the Neonatal Intensive Care Unit (NICU). Even if those skills are available it is laborsome to manually analyse many hours of EEG. The lack of time and skill are the main reasons why EEG is ...

Deburchgraeve, Wouter — KU Leuven


Learning from structured EEG and fMRI data supporting the diagnosis of epilepsy

Epilepsy is a neurological condition that manifests in epileptic seizures as a result of an abnormal, synchronous activity of a large group of neurons. Depending on the affected brain regions, seizures produce various severe clinical symptoms. Epilepsy cannot be cured and in many cases is not controlled by medication either. Surgical resection of the region responsible for generating the epileptic seizures might offer remedy for these patients. Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) measure the changes of brain activity in time over different locations of the brain. As such, they provide valuable information on the nature, the timing and the spatial origin of the epileptic activity. Unfortunately, both techniques record activity of different brain and artefact sources as well. Hence, EEG and fMRI signals are characterised by low signal to noise ratio. Data quality and the vast amount ...

Hunyadi, Borbála — KU Leuven


Automated quantification of preterm brain maturation using electroencephalography

Around 10 percent of all human births is premature, which means that annually about 15 million babies are born before 37 completed weeks of gestation. About one third of the admissions to the Neonatal Intensive Care Unit (NICU) consists of this patient group. Due to complications, 1 million babies die from premature delivery, and it is therefore the most important cause of neonatal death. In general, premature and immature babies have a high risk for neurological abnormalities by maturation in extra-uterine life. Even though improved health care has increased the survival changes of these neonates, they are sensitive to brain damage and consequently, neurocognitive disabilities. Nowadays, critical information about the brain development can be extracted from the electroencephalography (EEG). Clinical experts visually assess evolving EEG characteristics over both short and long periods to evaluate maturation of patients at risk and, ...

Koolen, Ninah — KU Leuven


EEG-Biofeedback and Epilepsy: Concept, Methodology and Tools for (Neuro)therapy Planning and Objective Evaluation

Objective diagnosis and therapy evaluation are still challenging tasks for many neurological disorders. This is highly related to the diversity of cases and the variety of treatment modalities available. Especially in the case of epilepsy, which is a complex disorder not well-explained at the biochemical and physiological levels, there is the need for investigations for novel features, which can be extracted and quantified from electrophysiological signals in clinical practice. Neurotherapy is a complementary treatment applied in various disorders of the central nervous system, including epilepsy. The method is subsumed under behavioral medicine and is considered an operant conditioning in psychological terms. Although the application areas of this promising unconventional approach are rapidly increasing, the method is strongly debated, since the neurophysiological underpinnings of the process are not yet well understood. Therefore, verification of the efficacy of the treatment is one ...

Kirlangic, Mehmet Eylem — Technische Universitaet Ilmenau


Heart rate variability : linear and nonlinear analysis with applications in human physiology

Cardiovascular diseases are a growing problem in today’s society. The World Health Organization (WHO) reported that these diseases make up about 30% of total global deaths and that heart diseases have no geographic, gender or socioeconomic boundaries. Therefore, detecting cardiac irregularities early-stage and a correct treatment are very important. However, this requires a good physiological understanding of the cardiovascular system. The heart is stimulated electrically by the brain via the autonomic nervous system, where sympathetic and vagal pathways are always interacting and modulating heart rate. Continuous monitoring of the heart activity is obtained by means of an ElectroCardioGram (ECG). Studying the fluctuations of heart beat intervals over time reveals a lot of information and is called heart rate variability (HRV) analysis. A reduction of HRV has been reported in several cardiological and noncardiological diseases. Moreover, HRV also has a prognostic ...

Vandeput, Steven — KU Leuven


Audio-visual processing and content management techniques, for the study of (human) bioacoustics phenomena

The present doctoral thesis aims towards the development of new long-term, multi-channel, audio-visual processing techniques for the analysis of bioacoustics phenomena. The effort is focused on the study of the physiology of the gastrointestinal system, aiming at the support of medical research for the discovery of gastrointestinal motility patterns and the diagnosis of functional disorders. The term "processing" in this case is quite broad, incorporating the procedures of signal processing, content description, manipulation and analysis, that are applied to all the recorded bioacoustics signals, the auxiliary audio-visual surveillance information (for the monitoring of experiments and the subjects' status), and the extracted audio-video sequences describing the abdominal sound-field alterations. The thesis outline is as follows. The main objective of the thesis, which is the technological support of medical research, is presented in the first chapter. A quick problem definition is initially ...

Dimoulas, Charalampos — Department of Electrical and Computer Engineering, Faculty of Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece


Biomechanics based analysis of sleep

The fact that a third of a human life is spent in a bed indicates the essential character of sleep. While some people might opt voluntarily for sleep deprivation, others don’t get to choose. Their healthy pattern of sleep is disrupted due to sleep disorders such as sleep apnea, insomnia and restless legs syndrome. Most clinical diagnoses revolve around complaints of excessive daytime sleepiness. People usually wait quite long however before contacting professional help, and might only do so when complaints have gone from minor to serious. It can be argued that people with minor complaints will have negligible compliance to rather obtrusive therapies, and should not be treated with pharmaceuticals. However, cognitive and behavioral therapy has proven its effectiveness for clinically diagnosed patients in different domains, and might thus also enhance the quality of life for people with minor ...

Willemen, Tim — KU Leuven


Transformation methods in signal processing

This dissertation is concerned with the application of the theory of rational functions in signal processing. The PhD thesis summarizes the corresponding results of the author’s research. Since the systems of rational functions are defined by the collection of inverse poles with multiplicities, the following parameters should be determined: the number, the positions and the multiplicities of the inverse poles. Therefore, we develop the hyperbolic variant of the so-called Nelder–Mead and the particle swarm optimization algorithm. In addition, the latter one is integrated into a more general multi-dimensional framework. Furthermore, we perform a detailed stability and error analysis of these methods. We propose an electrocardiogram signal generator based on spline interpolation. It turns to be an efficient tool for testing and evaluating signal models, filtering techniques, etc. In this thesis, the synthesized heartbeats are used to test the diagnostic distortion ...

Kovács, Péter — Eötvös L. University, Budapest, Hungary


Decomposition methods with applications in neuroscience

The brain is the most important signal processing unit in the human body. It is responsible for receiving, processing and storing information. One of the possibilities to study brain functioning is by placing electrodes on the scalp and recording the synchronous neuronal activity of the brain. Such a recording measures a combination of active processes in the whole brain. Unfortunately, it is also contaminated by artifacts. By extracting the artifacts and removing them, cleaned recordings can be investigated. Furthermore, it is easier to look at specific brain activities, like an epileptic seizure, than at a combination. In this thesis, we present different mathematical techniques that can be used to extract individual contributing sources from the measured signals for this purpose. We focused on Canonical Correlation Analysis (CCA), Independent Component Analysis (ICA) and Canonical/ Parallel Factor Analysis (CPA). We show that ...

De Vos, Maarten — Katholieke Universiteit Leuven


Analysis of electrophysiological measurements during stress monitoring

Work-related musculoskeletal disorders are a growing problem in todays society. These musculoskeletal disorders are caused by, amongst others, repetitive movements and mental stress. Stress is defined as the mismatch between a perceived demand and the perceived capacities to meet this demand. Although stress has a subjective origin, several physiological manifestations (e.g. cardiovascular and muscular) occur during periods of perceived stress. New insight and algorithms to extract information, related to stress are beneficial. Therefore, two series of stress experiments are executed in a laboratory environment, where subjects underwent different tasks inducing physical strain, mental stress and a combination of both. In this manuscript, new and modified algorithms for electromyography signals are presented that improve the individual analysis of electromyography signals. A first algorithm removes the interference of the electrical activity of the heart on singlechannel electromyography measurements. This interference signal is ...

Taelman, Joachim — KU Leuven


Spike train discrimination and analysis in neural and surface electromyography (sEMG) applications

The term "spike" is used to describe a short-time event that is the result of the activity of its source. Spikes can be seen in different signal modalities. In these modalities, often more than one source generates spikes. Classification algorithms can be used to group similar spikes, ideally spikes from the same source. This work examines the classification of spikes generated from neurons and muscles. When each detected spike is assigned to its source, the spike trains of these sources can provide information on complex brain network functioning, muscle disorders, and other applications. During the past several decades, there were many attempts to create and improve spike classification algorithms. No matter how advanced these methods are today, errors in classification cannot be avoided. Therefore, methods that would determine and improve reliability of classification are very desirable. In this work, it ...

Gligorijevic, Ivan — KU Leuven


Digital Processing Based Solutions for Life Science Engineering Recognition Problems

The field of Life Science Engineering (LSE) is rapidly expanding and predicted to grow strongly in the next decades. It covers areas of food and medical research, plant and pests’ research, and environmental research. In each research area, engineers try to find equations that model a certain life science problem. Once found, they research different numerical techniques to solve for the unknown variables of these equations. Afterwards, solution improvement is examined by adopting more accurate conventional techniques, or developing novel algorithms. In particular, signal and image processing techniques are widely used to solve those LSE problems require pattern recognition. However, due to the continuous evolution of the life science problems and their natures, these solution techniques can not cover all aspects, and therefore demanding further enhancement and improvement. The thesis presents numerical algorithms of digital signal and image processing to ...

Hussein, Walid — Technische Universität München

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