Exploiting Sparsity for Efficient Compression and Analysis of ECG and Fetal-ECG Signals

Over the last decade there has been an increasing interest in solutions for the continuous monitoring of health status with wireless, and in particular, wearable devices that provide remote analysis of physiological data. The use of wireless technologies have introduced new problems such as the transmission of a huge amount of data within the constraint of limited battery life devices. The design of an accurate and energy efficient telemonitoring system can be achieved by reducing the amount of data that should be transmitted, which is still a challenging task on devices with both computational and energy constraints. Furthermore, it is not sufficient merely to collect and transmit data, and algorithms that provide real-time analysis are needed. In this thesis, we address the problems of compression and analysis of physiological data using the emerging frameworks of Compressive Sensing (CS) and sparse ...

Da Poian, Giulia — University of Udine


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


Visual Analysis of Faces with Application in Biometrics, Forensics and Health Informatics

Computer vision-based analysis of human facial video provides information regarding to expression, diseases symptoms, and physiological parameters such as heartbeat rate, blood pressure and respiratory rate. It also provides a convenient source of heartbeat signal to be used in biometrics and forensics. This thesis is a collection of works done in five themes in the realm of computer vision-based facial image analysis: Monitoring elderly patients at private homes, Face quality assessment, Measurement of physiological parameters, Contact-free heartbeat biometrics, and Decision support system for healthcare. The work related to monitoring elderly patients at private homes includes a detailed survey and review of the monitoring technologies relevant to older patients living at home by discussing previous reviews and relevant taxonomies, different scenarios for home monitoring solutions for older patients, sensing and data acquisition techniques, data processing and analysis techniques, available datasets for ...

Haque, Mohammad Ahsanul — Aalborg Univeristy


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


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)


Modulation Spectrum Analysis for Noisy Electrocardiogram Signal Processing and Applications

Advances in wearable electrocardiogram (ECG) monitoring devices have allowed for new cardiovascular applications to emerge beyond diagnostics, such as stress and fatigue detection, athletic performance assessment, sleep disorder characterization, mood recognition, activity surveillance, biometrics, and fitness tracking, to name a few. Such devices, however, are prone to artifacts, particularly due to movement, thus hampering heart rate and heart rate variability measurement and posing a serious threat to cardiac monitoring applications. To address these issues, this thesis proposes the use of a spectro-temporal signal representation called “modulation spectrum”, which is shown to accurately separate cardiac and noise components from the ECG signals, thus opening doors for noise-robust ECG signal processing tools and applications. First, an innovative ECG quality index based on the modulation spectral signal representation is proposed. The representation quantifies the rate-of-change of ECG spectral components, which are shown to ...

Tobon Vallejo, Diana Patricia — INRS-EMT


Robust Estimation and Model Order Selection for Signal Processing

In this thesis, advanced robust estimation methodologies for signal processing are developed and analyzed. The developed methodologies solve problems concerning multi-sensor data, robust model selection as well as robustness for dependent data. The work has been applied to solve practical signal processing problems in different areas of biomedical and array signal processing. In particular, for univariate independent data, a robust criterion is presented to select the model order with an application to corneal-height data modeling. The proposed criterion overcomes some limitations of existing robust criteria. For real-world data, it selects the radial model order of the Zernike polynomial of the corneal topography map in accordance with clinical expectations, even if the measurement conditions for the videokeratoscopy, which is the state-of-the-art method to collect corneal-height data, are poor. For multi-sensor data, robust model order selection selection criteria are proposed and applied ...

Muma, Michael — Technische Universität Darmstadt


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


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


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


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


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


Spatio-temporal characterization of the surface electrocardiogram for catheter ablation outcome prediction in persistent atrial fibrillation

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia encountered in clinical practice, and one of the main causes of ictus and strokes. Despite the advances in the comprehension of its mechanisms, its thorough characterization and the quantification of its effects on the human heart are still an open issue. In particular, the choice of the most appropriate therapy is frequently a hard task. Radiofrequency catheter ablation (CA) is becoming one of the most popular solutions for the treatment of the disease. Yet, very little is known about its impact on heart substrate during AF, thus leading to an inaccurate selection of positive responders to therapy and a low success rate; hence, the need for advanced signal processing tools able to quantify AF impact on heart substrate and assess the effectiveness of the CA therapy in an objective and ...

Marianna Meo — Université Nice Sophia Antipolis


Modelling of the respiratory parameters in non-invasive ventilation

In this study, the respiratory system are modelled by three linear and one non-linear lumped parameter respiratory model, the equations of the models are driven and the parameters are estimated by using statistical signal processing methods. Linear RIC, Viscoelastic and Mead models and proposed basic non-linear RC model are used to resemble the respiratory system of the patient with Chronic Obstructive Pulmonary Disease (COPD) under non-invasive ventilation. Statistical signal processing methods such as Minimum Variance Unbiased Estimation (MVUE), Maximum Likelihood Estimation (MLE), Kalman Filter (KF), Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF) are very powerful methods to estimate the parameters of the systems embedded in the unknown noise. In the first part of this thesis, artificial respiratory signals (airway flow and airway pressure) are used for the performance measurement criteria. Posterior Cramer Rao Lower Bound (PCRLB) is computed ...

Saatci, Esra — Istanbul University


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

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