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


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


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


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


Extraction and Denoising of Fetal ECG Signals

Congenital heart defects are the leading cause of birth defect-related deaths. The fetal electrocardiogram (fECG), which is believed to contain much more information as compared with conventional sonographic methods, can be measured by placing electrodes on the mother’s abdomen. However, it has very low power and is mixed with several sources of noise and interference, including the strong maternal ECG (mECG). In previous studies, several methods have been proposed for the extraction of fECG signals recorded from the maternal body surface. However, these methods require a large number of sensors, and are ineffective with only one or two sensors. In this study, state modeling, statistical and deterministic approaches are proposed for capturing weak traces of fetal cardiac signals. These three methods implement different models of the quasi-periodicity of the cardiac signal. In the first approach, the heart rate and its ...

Niknazar, Mohammad — University of Grenoble


Extraction of efficient and characteristic features of multidimensional time series

In numerous signal processing applications one disposes of multiple probes, delivering simultaneously information about one or multiple observed processes. The resulting multidimensional time series are often highly redundant and may contain stochastic contributions. The perception of the useful information becomes therefore very difficult and sometimes impossible. Thus, the major issue of concern of this thesis resides in the development of novel algorithms for the extraction of the salient and characteristic features of multidimensional time series. The proposed algorithms are based on parametric signal processing, namely we assume that the features of the experimental data can be represented efficiently by a specific model. We present a global framework for the selection of a specific model out of the large span of techniques proposed in the literature. For the selection of the model classes we use, in addition to prior knowledge about ...

Vetter, Rolf — Swiss Federal Institute of Technology


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


Study of electrophysiological function of the heart in experimental cardiology

Accurate detection of ischemic patterns in ECG is important for correct diagnosis of myocardial ischemia or infarct and further treatment of the patients. Therefore, this research area is highly topical for the past few decades. The thesis introduces the results of comprehensive study on rabbit isolated heart, where many various aspects of ECG acquisition, processing and analysis have been addressed. Particularly, effects of ischemia, increased left ventricular mass and voltage-sensitive dye di-4-ANEPPS on heart electrical activity were evaluated by analysis of various ECG and VCG parameters. The impact of the last two phenomena (frequent in animal studies) on electrocardiographic ischemia manifestations and detection was described quantitatively. Various approaches for ischemia detection via advanced supervised classification tools and feature selection methods were proposed and evaluated by cross-validation technique considering such important aspects as electrode placement, features and classification models used, definition ...

Ronzhina, Marina — Brno University of Technology


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


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


Polarization and Index Modulations: a Theoretical and Practical Perspective

Radiocommunication systems have evolved significantly in recent years in order to meet present and future demands. Historically, time, frequency and more recently, spatial dimensions have been used to improve capacity and robustness. Paradoxically, radiocommunications that leverage the polarization dimension have not evolved at the same pace. In particular, these communications are widely used by satellites, where several streams are multiplexed in each orthogonal polarization. Current communication trends advocate for simplifying and unifying different frameworks in order to increase flexibility and address future needs. Due to this, systems that do not require channel information are progressively gaining traction, as they help to improve the overall quality of the network instead of that of specific users only. The search for new paradigms aimed at improving the quality of wireless communications is unstoppable. In order to increase the capacity of current communications systems, ...

Henarejos, Pol — Universitat Politècnica de Catalunya


Signal processing for monitoring cerebral hemodynamics in neonates

Disturbances in cerebral hemodynamics are one of the principal causes of cerebral damage in premature infants. Specifically, changes in cerebral blood flow might cause ischemia or hemorrhage that can lead to motor and developmental disabilities. Under normal circumstances, there are several mechanisms that act jointly to preserve cerebral hemodynamics homeostasis. However, in case that one of these mechanisms is disrupted the brain is exposed to damage. Premature infants are susceptible to variations in cerebral circulation due to their fragility. Therefore, monitoring cerebral hemodynamics is of vital importance in order to prevent brain damage in this population and avoid subsequent sequelae. This thesis is oriented to the development of signal processing techniques that can be of help in monitoring cerebral hemodynamics in neonates. There are several problems that hinder the use in clinical practice of monitoring cerebral hemodynamics. On one hand, ...

Caicedo Dorado, Alexander — KU Leuven


Compressive Sensing Based Candidate Detector and its Applications to Spectrum Sensing and Through-the-Wall Radar Imaging

Signal acquisition is a main topic in signal processing. The well-known Shannon-Nyquist theorem lies at the heart of any conventional analog to digital converters stating that any signal has to be sampled with a constant frequency which must be at least twice the highest frequency present in the signal in order to perfectly recover the signal. However, the Shannon-Nyquist theorem provides a worst-case rate bound for any bandlimited data. In this context, Compressive Sensing (CS) is a new framework in which data acquisition and data processing are merged. CS allows to compress the data while is sampled by exploiting the sparsity present in many common signals. In so doing, it provides an efficient way to reduce the number of measurements needed for perfect recovery of the signal. CS has exploded in recent years with thousands of technical publications and applications ...

Lagunas, Eva — Universitat Politecnica de Catalunya


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


Ultra low-power biomedical signal processing: an analog wavelet filter approach for pacemakers

The purpose of this thesis is to describe novel signal processing methodologies and analog integrated circuit techniques for low-power biomedical systems. Physiological signals, such as the electrocardiogram (ECG), the electroencephalogram (EEG) and the electromyogram (EMG) are mostly non-stationary. The main difficulty in dealing with biomedical signal processing is that the information of interest is often a combination of features that are well localized temporally (e.g., spikes) and others that are more diffuse (e.g., small oscillations). This requires the use of analysis methods sufficiently versatile to handle events that can be at opposite extremes in terms of their time-frequency localization. Wavelet Transform (WT) has been extensively used in biomedical signal processing, mainly due to the versatility of the wavelet tools. The WT has been shown to be a very efficient tool for local analysis of nonstationary and fast transient signals due ...

Haddad, Sandro Augusto Pavlík — Delft University of Technology

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