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 ...

This thesis focuses on the design of various automatic signal processing algorithms to extract information from physiological signals of preterm infants. Overall, the aim was to improve the neurodevelopmental outcome of the neonate. More specifically, three main research objectives were carried out. The first objective was to describe the maturation of neonates during their stay in the neonatal intensive care unit. The second objective was to assess the stress and pain in premature infants and their impact on the development of neonates. The third objective was to predict developmental disabilities, such as autism. The first part of this thesis presents an extensive overview of various developmental models to describe the maturation of premature infants. Three main strategies were proposed. The first strategy proposed an investigation of EEG connectivity networks. A variety of functional and effective connectivity methods were combined with ...

The rate at which our heart beats, is a dynamical process enabling adaptive changes according to the demands of our body. These variations in heart rate are widely studied in so-called heart rate variability (HRV) analyses, as they contain much information about the activity of our autonomic nervous system. Variability in the heart rate arises from several processes, such as thermoregulation, hormones, arterial blood pressure, respiration, etc. One of the main short-term modulators of the heart rate is respiration. This phenomenon is called respiratory sinus arrhythmia (RSA) and comprises the rhythmic fluctuation of the heart rate at respiratory frequency. It has also widely been used as an index of vagal outflow. However, this has been widely debated as some studies have shown that the magnitude of RSA changes with respiratory rate and the depth of breathing, independently of parasympathetic activity. ...

This thesis contributes to motion characterization through inertial and physiological signals captured by wearable devices and analyzed using signal processing and deep learning techniques. This research leverages the possibilities of motion analysis for three main applications: to know what physical activity a person is performing (Human Activity Recognition), to identify who is performing that motion (user identification) or know how the movement is being performed (motor anomaly detection). Most previous research has addressed human motion modeling using invasive sensors in contact with the user or intrusive sensors that modify the user’s behavior while performing an action (cameras or microphones). In this sense, wearable devices such as smartphones and smartwatches can collect motion signals from users during their daily lives in a less invasive or intrusive way. Recently, there has been an exponential increase in research focused on inertial-signal processing to ...

Preterm birth is a major risk factor for neurodevelopment impairments often only appearing later in life. The brain is still at a high rate of development during adolescence, making this a promising window for intervention. It is thus crucial to understand the mechanisms of altered brain function in this population. The aim of this thesis is to investigate how the brain dynamically reconfigures its own organisation over time in preterm-born young adolescents. Research to date has mainly focused on structural disturbances or in static features of brain function in this population. However, recent studies have shown that brain activity is highly dynamic, both spontaneously and during performance of a task, and that small disruptions in its complex architecture may interfere with normal behaviour and cognitive abilities. This thesis explores the dynamic nature of brain function in preterm-born adolescents in three ...

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 ...

Every year, an estimated 15 million babies are born preterm, that is, before 37 weeks of gestation. This number is rising in all countries and currently represents more than 1 in 10 babies, affecting families all over the world. During the last decades, the survival rate of prematurely born neonates has steadily increased, mainly as a result of medical and technical progress in neonatal intensive care. The very preterm infants, which represent up to 10% of the preterm infants in the EU, remain at risk for adverse outcome and neurodevelopmental disability. These maladaptive outcomes have a severe effect on the children’s quality of life and a huge economic impact on society. In order to reduce this burden and improve neonatal care in general, appropriate tools need to be developed to identify the neonates with a higher risk of adverse outcomes. ...

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 ...

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 ...

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 ...

Obstructive sleep apnea (OSA) is the most prevalent sleep related breathing disorder, nevertheless subjects suffering from it often remain undiagnosed due to the cumbersome diagnosis procedure. Moreover, the prevalence of OSA is increasing and a better phenotyping of patients is needed in order to prioritize treatment. The goal of this thesis was to tackle those challenges in OSA diagnosis. Additionally, two main algorithmic contributions which are generally applicable were proposed within this thesis. The binary interval coded scoring algorithm was extended to multilevel problems and novel monotonicity constraints were introduced. Moreover, improvements to the random-forest based feature selection were proposed including the use of the Cohen’s kappa value, patient independent validation, and further feature pruning steered by the correlation between features. These novel methods were applied together with classification and feature selection methods from the literature to improve the OSA ...

In recent years, a huge body of data generated by various applications in domains like social networks and healthcare have paved the way for the development of high performance models. Deep learning has transformed the field of data analysis by dramatically improving the state of the art in various classification and prediction tasks. Combined with advancements in electromyography it has given rise to new hand gesture recognition applications, such as human computer interfaces, sign language recognition, robotics control and rehabilitation games. The purpose of this thesis is to develop novel methods for electromyography signal analysis based on deep learning for the problem of hand gesture recognition. Specifically, we focus on methods for data preparation and developing accurate models even when few data are available. Electromyography signals are in general one-dimensional time-series with a rich frequency content. Various feature sets have ...

The electrocardiogram or ECG is a relatively easy-to-record signal that contains an enormous amount of potentially useful information. It is currently mostly being used for screening purposes. For example, pre-participation cardiovascular screening of young athletes has been endorsed by both scientific organisations and sporting governing bodies. A typical cardiac examination is taken in a hospital environment and lasts 10 seconds. This is often sufficient to detect major pathologies, yet this small sample size of the heart’s functioning can be deceptive when used to evaluate one’s general condition. A solution for this problem is to monitor the patient outside of the hospital, during a longer period of time. Due to the extension of the analysis period, the detection rate of cardiac events can be highly increased, compared to the cardiac exam in the hospital. However, it also increases the likelihood of ...

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 ...

The subject of this PhD thesis is the efficient and robust processing and analysis of the audio recordings that are derived from a call center. The thesis is comprised of two parts. The first part is dedicated to dialogue/non-dialogue detection and to speaker segmentation. The systems that are developed are prerequisite for detecting (i) the audio segments that actually contain a dialogue between the system and the call center customer and (ii) the change points between the system and the customer. This way the volume of the audio recordings that need to be processed is significantly reduced, while the system is automated. To detect the presence of a dialogue several systems are developed. This is the first effort found in the international literature that the audio channel is exclusively exploited. Also, it is the first time that the speaker utterance ...