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


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


Miniaturization effects and node placement for neural decoding in EEG sensor networks

Electroencephalography (EEG) is a non-invasive neurorecording technique, which has the potential to be used for 24/7 neuromonitoring in daily life, e.g., in the context of neural prostheses, brain-computer interfaces, or for improved diagnosis of brain disorders. Although existing mobile wireless EEG headsets are a useful tool for short-term experiments, they are still too heavy, bulky and obtrusive, for long-term EEG-monitoring in daily life. However, we are now witnessing a wave of new miniature EEG sensor devices containing small electrodes embedded in them, which we refer to as Mini-EEGs. Mini-EEGs ideally consist of a wireless node with a small scalp area footprint, in which the electrodes, amplifier and wireless radio are embedded. However, due to their miniaturization, these mini-EEGs have the drawback that only a few EEG channels can be recorded within a small area. The latter also implies that the ...

Mundanad Narayanan, Abhijith — 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


Monitoring Infants by Automatic Video Processing

This work has, as its objective, the development of non-invasive and low-cost systems for monitoring and automatic diagnosing specific neonatal diseases by means of the analysis of suitable video signals. We focus on monitoring infants potentially at risk of diseases characterized by the presence or absence of rhythmic movements of one or more body parts. Seizures and respiratory diseases are specifically considered, but the approach is general. Seizures are defined as sudden neurological and behavioural alterations. They are age-dependent phenomena and the most common sign of central nervous system dysfunction. Neonatal seizures have onset within the 28th day of life in newborns at term and within the 44th week of conceptional age in preterm infants. Their main causes are hypoxic-ischaemic encephalopathy, intracranial haemorrhage, and sepsis. Studies indicate an incidence rate of neonatal seizures of 2‰ live births, 11‰ for preterm ...

Cattani Luca — University of Parma (Italy)


Multimodal epileptic seizure detection : towards a wearable solution

Epilepsy is one of the most common neurological disorders, which affects almost 1% of the population worldwide. Anti-epileptic drugs provide adequate treatment for about 70% of epilepsy patients. The remaining 30% of the patients continue to have seizures, which drastically affects their quality of life. In order to obtain efficacy measures of therapeutic interventions for these patients, an objective way to count and document seizures is needed. However, in an outpatient setting, one of the major problems is that seizure diaries kept by patients are unreliable. Automated seizure detection systems could help to objectively quantify seizures. Those detection systems are typically based on full scalp Electroencephalography (EEG). In an outpatient setting, full scalp EEG is of limited use because patients will not tolerate wearing a full EEG cap for long time periods during daily life. There is a need for ...

Vandecasteele, Kaat — KU Leuven


Localisation of Brain Functions: Stimuling Brain Activity and Source Reconstruction for Classification

A key issue in understanding how the brain functions is the ability to correlate functional information with anatomical localisation. Functional information can be provided by a variety of techniques like positron emission tomography (PET), functional MRI (fMRI), electroencephalography (EEG), magnetoencephalography (MEG) or transcranial magnetic stimulation (TMS). All these methods provide different, but complementary, information about the functional areas of the brain. PET and fMRI provide spatially accurate picture of brain regions involved in a given task. TMS permits to infer the contribution of the stimulated brain area to the task under investigation. EEG and MEG, which reflects brain activity directly, have temporal accuracy of the order of a millisecond. TMS, EEG and MEG are offset by their low spatial resolution. In this thesis, we propose two methods to improve the spatial accuracy of method based on TMS and EEG. The ...

Noirhomme, Quentin — Katholieke Universiteit Leuven


Design and applications of Filterbank structures implementing Reed-Solomon codes

In nowadays communication systems, error correction provides robust data transmission through imperfect (noisy) channels. Error correcting codes are a crucial component in most storage and communication systems – wired or wireless –, e.g. GSM, UMTS, xDSL, CD/DVD. At least as important as the data integrity issue is the recent realization that error correcting codes fundamentally change the trade-offs in system design. High-integrity, low redundancy coding can be applied to increase data rate, or battery life time or by reducing hardware costs, making it possible to enter mass market. When it comes to the design of error correcting codes and their properties, there are two main theories that play an important role in this work. Classical coding theory aims at finding the best code given an available block length. This thesis focuses on the ubiquitous Reed-Solomon codes, one of the major ...

Van Meerbergen, Geert — Katholieke Universiteit Leuven


Tensor-based blind source separation for structured EEG-fMRI data fusion

A complex physical system like the human brain can only be comprehended by the use of a combination of various medical imaging techniques, each of which shed light on only a specific aspect of the neural processes that take place beneath the skull. Electroencephalography (EEG) and functional magnetic resonance (fMRI) are two such modalities, which enable the study of brain (dys)function. While the EEG is measured with a limited set of scalp electrodes which record rapid electrical changes resulting from neural activity, fMRI offers a superior spatial resolution at the expense of only picking up slow fluctuations of oxygen concentration that takes place near active brain cells. Hence, combining these very complementary modalities is an appealing, but complicated task due to their heterogeneous nature. In this thesis, we devise advanced signal processing techniques which integrate the multimodal data stemming from ...

Van Eyndhoven, Simon — KU Leuven


Methods for functional connectivity and morphometry in neonatal neuroimaging to study neurodevelopment

Preterm birth is a major pediatric health problem that perturbs the genetically determined program of corticogenesis of the developing brain. As a consequence, prematurity has been strongly associated with adverse long-term neurodevelopmental outcome that may persist even into adulthood. Early characterization of the underlying neuronal mechanisms and early identification of infants at risk is of paramount importance since it allows better development of early therapeutic interventions aiming to prevent adverse outcomes through resilience. This dissertation aims to investigate the consequences of preterm birth on brain function and structure and their relation to adverse neurodevelopmental outcome, as well as to unveil the effect of an early music intervention on brain function. Research to date has mainly focused on the effect of early interventions on the long-term outcome but not on the effect of those interventions on brain function in preterm populations. ...

Loukas, Serafeim — Swiss Federal Institute of Technology Lausanne (EPFL)


Automated detection of epileptic seizures in pediatric patients based on accelerometry and surface electromyography

Epilepsy is one of the most common neurological diseases that manifests in repetitive 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. There is no cure for epilepsy and sometimes even medication and other therapies, like surgery, vagus nerve stimulation or ketogenic diet, do not control the number of seizures. In that case, long-term (home) monitoring and automatic seizure detection would enable the tracking of the evolution of the disease and improve objective insight in any responses to medical interventions or changes in medical treatment. Especially during the night, supervision is reduced; hence a large number of seizures is missed. In addition, an alarm should be integrated into the automated seizure detection algorithm for severe seizures in order to help the ...

Milošević, Milica — KU Leuven


Signal Processing Algorithms for EEG-based Auditory Attention Decoding

One in five experiences hearing loss. The World Health Organization estimates that this number will increase to one in four in 2050. Luckily, effective hearing devices such as hearing aids and cochlear implants exist with advanced speaker enhancement algorithms that can significantly improve the quality of life of people suffering from hearing loss. State-of-the-art hearing devices, however, underperform in a so-called `cocktail party' scenario, when multiple persons are talking simultaneously (such as at a family dinner or reception). In such a situation, the hearing device does not know which speaker the user intends to attend to and thus which speaker to enhance and which other ones to suppress. Therefore, a new problem arises in cocktail party problems: determining which speaker a user is attending to, referred to as the auditory attention decoding (AAD) problem. The problem of selecting the attended ...

Geirnaert, Simon — KU Leuven


Advanced solutions for neonatal analysis and the effects of maturation

Worldwide approximately 11% of the babies are born before 37 weeks of gestation. The survival rates of these prematurely born infants have steadily increased during the last decades as a result of the technical and medical progress in the neonatal intensive care units (NICUs). The focus of the NICUs has therefore gradually evolved from increasing life chances to improving quality of life. In this respect, promoting and supporting optimal brain development is crucial. Because these neonates are born during a period of rapid growth and development of the brain, they are susceptible to brain damage and therefore vulnerable to adverse neurodevelopmental outcome. In order to identify patients at risk of long-term disabilities, close monitoring of the neurological function during the first critical weeks is a primary concern in the current NICUs. Electroencephalography (EEG) is a valuable tool for continuous noninvasive ...

De Wel, Ofelie — KU Leuven


Unsupervised and semi-supervised Non-negative Matrix Factorization methods for brain tumor segmentation using multi-parametric MRI data

Gliomas represent about 80% of all malignant primary brain tumors. Despite recent advancements in glioma research, patient outcome remains poor. The 5 year survival rate of the most common and most malignant subtype, i.e. glioblastoma, is about 5%. Magnetic resonance imaging (MRI) has become the imaging modality of choice in the management of brain tumor patients. Conventional MRI (cMRI) provides excellent soft tissue contrast without exposing the patient to potentially harmful ionizing radiation. Over the past decade, advanced MRI modalities, such as perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI) and magnetic resonance spectroscopic imaging (MRSI) have gained interest in the clinical field, and their added value regarding brain tumor diagnosis, treatment planning and follow-up has been recognized. Tumor segmentation involves the imaging-based delineation of a tumor and its subcompartments. In gliomas, segmentation plays an important role in treatment planning as well ...

Sauwen, Nicolas — KU Leuven


Video Processing for Remote Respiration Monitoring

Monitoring of vital signs is a key tool in medical diagnostics to asses the onset and the evolution of several diseases. Among fundamental vital parameters, such as the hearth rate, blood pressure and body temperature, the Respiratory Rate (RR) plays an important role. For this reason, respiration needs to be carefully monitored in order to detect potential signs or events indicating possible changes of health conditions. Monitoring of the respiration is generally carried out in hospital and clinical environments by the use of expensive devices with several sensors connected to the patient's body. A new research trend, in order to reduce healthcare service costs and make monitoring of vital signs more comfortable, is the development of low-cost systems which may allow remote and contactless monitoring; in such a context, an appealing method is to rely on video processing-based solutions. In ...

Alinovi, Davide — University of Parma

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