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. ...

Electrocardiography (ECG) is the standard method for assessing the state of the cardiovascular system non-invasively. In the context of magnetic resonance imaging (MRI) the ECG signal is used for cardiac monitoring and triggering, i.e., the acquisition of images synchronized to the cardiac cycle. However, ECG acquisition is impeded by the static and dynamic magnetic fields which alter the measured voltages and may reduce signal-to-noise ratio (SNR), leading to false alarms during cardiac monitoring or to image artifacts during cardiac triggering. A major source of noise is the magnetohydrodynamic (MHD) effect as it is proportional to field strength and represents a key challenge in application of ultra-high-field (UHF) MRI >=7 T. In this work, two approaches for overcoming these limitations are proposed: i) Development of a hardware and software system based on the principal of photoplethysmography imaging (PPGi) as an optical ...

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 ...

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 ...

The respiratory sinus arrhythmia (RSA) is a form of cardiorespiratory coupling in which the heart rate accelerates during inhalation and decelerates during exhalation. Its quantification has been suggested as a tool to assess different diseases and conditions. However, whilst the potential of the RSA estimation as a diagnostic tool is shown in research works, its use in clinical practice and mobile applications is rather limited. This can be attributed to the lack of understanding of the mechanisms generating the RSA. To try to explain the RSA, studies are done using noninvasive signals, namely, respiration and heart rate variability (HRV), which are combined using different algorithms. Nevertheless, the algorithms are not standardized, making it difficult to draw solid conclusions from these studies. Therefore, the first aim of this thesis was to develop a framework to evaluate algorithms for RSA estimation. To ...

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 ...

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 ...

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 ...

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 ...

Acutely-ill hospitalised patients are at risk of clinical deteriorations in health leading to adverse events such as cardiac arrests. Deteriorations are currently detected by manually measuring physiological parameters every 4-6 hours. Consequently, deteriorations can remain unrecognised between assessments, delaying clinical intervention. It may be possible to provide earlier detection of deteriorations by using wearable sensors for continuous physiological monitoring. Respiratory rate (RR) is not commonly monitored by wearable sensors, despite being a sensitive marker of deteriorations. This thesis presents investigations to identify an algorithm suitable for estimating RR from two signals commonly acquired by wearable sensors: the electrocardiogram (ECG) and photoplethysmogram (PPG). A suitable algorithm was then used to estimate RRs retrospectively from a physiological dataset acquired from acutely-ill patients to assess the potential utility of wearable sensors for detecting deteriorations. Existing RR algorithms were identi ed through a systematic ...

Obstructive sleep apnea (OSA) is among the most prevalent sleep disorders, which is estimated to affect 6 %−19 % of women and 13 %−33 % of men. Besides daytime sleepiness, impaired cognitive functioning and an increased risk for accidents, OSA may lead to obesity, diabetes and cardiovascular diseases (CVD) on the long term. Its prevalence is only expected to rise, as it is linked to aging and excessive body fat. Nevertheless, many patients remain undiagnosed and untreated due to the cumbersome clinical diagnostic procedures. For this, the patient is required to sleep with an extensive set of body attached sensors. In addition, the recordings only provide a single night perspective on the patient in an uncomfortable, and often unknown, environment. Thus, large scale monitoring at home is desired with comfortable sensors, which can stay in place for several nights. To ...

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 ...

The electrocardiogram (ECG) signals bear fundamental information for medical experts to make decisions about heart diseases. Therefore, in the past decades the scientific community has made great efforts to develop methods for the heartbeat features extraction via ECG records with the highest accuracy and efficiency using different strategies. It should be noted that noise and artifacts induced by external factors make it difficult to learn specific patterns of ECG signals, which play an important role to find abnormalities. Using filtering techniques such as the unbiased finite impulse response FIR (UFIR) filtering approach promises better results. Aimed at extracting the features with the highest accuracy, in this dissertation, we have designed and applied to ECG signals the adaptive UFIR filter and smoother. We also compared the proposed technique with the traditional method such as UFIR predictors, standard filters (e.g. low-pass filter), ...

Single-channel speech enhancement refers to the reduction of noise signal components in a single-channel signal composed of both speech and noise. Spectral speech enhancement methods are among the most popular approaches to solving this problem. Since the short-time spectral amplitude has been identified as a highly perceptually relevant quantity, most conventional approaches rely on processing the amplitude spectrum only, ignoring any information that may be contained in the spectral phase. As a consequence, the noisy short-time spectral phase is neither enhanced for the purpose of signal reconstruction nor is it used for refining short-time spectral amplitude estimates. This thesis investigates the use of the spectral phase and its structure in algorithms for single-channel speech enhancement. This includes the analysis of the spectral phase in the context of theoretically optimal speech estimators. The resulting knowledge is exploited in formulating single-channel speech ...

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 ...