Cardiac Arrhythmias

Cardiac arrhythmias are common triggers of emergency admission to cardiology or high-dependency departments. Most cases are easy to diagnose and treat, while others may present a challenge to healthcare professionals. A translational approach to arrhythmias links molecular and cellular scientific research with clinical diagnostics and therapeutic methods, which may include both pharmacological and non-pharmacologic treatments. This book presents a comprehensive overview of specific cardiac arrhythmias and discusses translational approaches to their diagnosis and treatment

Assessing Variability of EEG and ECG/HRV Time Series Signals Using a Variety of Non-Linear Methods

Time series signals, such as Electroencephalogram (EEG) and Electrocardiogram (ECG) represent the complex dynamic behaviours of biological systems. The analysis of these signals using variety of nonlinear methods is essential for understanding variability within EEG and ECG, which potentially could help unveiling hidden patterns related to underlying physiological mechanisms. EEG is a time varying signal, and electrodes for recording EEG at different positions on the scalp give different time varying signals. There might be correlation between these signals. It is important to know the correlation between EEG signals because it might tell whether or not brain activities from different areas are related. EEG and ECG might be related to each other because both of them are generated from one co-ordinately working body. Investigating this relationship is of interest because it may reveal information about the correlation between EEG and ECG signals. This thesis is about assessing variability of time series data, EEG and ECG, using variety of nonlinear measures. Although other research has looked into the correlation between EEGs using a limited number of electrodes and a limited number of combinations of electrode pairs, no research has investigated the correlation between EEG signals and distance between electrodes. Furthermore, no one has compared the correlation performance for participants with and without medical conditions. In my research, I have filled up these gaps by using a full range of electrodes and all possible combinations of electrode pairs analysed in Time Domain (TD). Cross-Correlation method is calculated on the processed EEG signals for different number unique electrode pairs from each datasets. In order to obtain the distance in centimetres (cm) between electrodes, a measuring tape was used. For most of our participants the head circumference range was 54-58cm, for which a medium-sized I have discovered that the correlation between EEG signals measured through electrodes is linearly dependent on the physical distance (straight-line) distance between them for datasets without medical condition, but not for datasets with medical conditions. Some research has investigated correlation between EEG and Heart Rate Variability (HRV) within limited brain areas and demonstrated the existence of correlation between EEG and HRV. But no research has indicated whether or not the correlation changes with brain area. Although Wavelet Transformations (WT) have been performed on time series data including EEG and HRV signals to extract certain features respectively by other research, so far correlation between WT signals of EEG and HRV has not been analysed. My research covers these gaps by conducting a thorough investigation of all electrodes on the human scalp in Frequency Domain (FD) as well as TD. For the reason of different sample rates of EEG and HRV, two different approaches (named as Method 1 and Method 2) are utilised to segment EEG signals and to calculate Pearson’s Correlation Coefficient for each of the EEG frequencies with each of the HRV frequencies in FD. I have demonstrated that EEG at the front area of the brain has a stronger correlation with HRV than that at the other area in a frequency domain. These findings are independent of both participants and brain hemispheres. Sample Entropy (SE) is used to predict complexity of time series data. Recent research has proposed new calculation methods for SE, aiming to improve the accuracy. To my knowledge, no one has attempted to reduce the computational time of SE calculation. I have developed a new calculation method for time series complexity which could improve computational time significantly in the context of calculating a correlation between EEG and HRV. The results have a parsimonious outcome of SE calculation by exploiting a new method of SE implementation. In addition, it is found that the electrical activity in the frontal lobe of the brain appears to be correlated with the HRV in a time domain. Time series analysis method has been utilised to study complex systems that appear ubiquitous in nature, but limited to certain dynamic systems (e.g. analysing variables affecting stock values). In this thesis, I have also investigated the nature of the dynamic system of HRV. I have disclosed that Embedding Dimension could unveil two variables that determined HRV

Secure Data Collection and Analysis in Smart Health Monitoring

Smart health monitoring uses real-time monitored data to support diagnosis, treatment, and health decision-making in modern smart healthcare systems and benefit our daily life. The accurate health monitoring and prompt transmission of health data are facilitated by the ever-evolving on-body sensors, wireless communication technologies, and wireless sensing techniques. Although the users have witnessed the convenience of smart health monitoring, severe privacy and security concerns on the valuable and sensitive collected data come along with the merit. The data collection, transmission, and analysis are vulnerable to various attacks, e.g., eavesdropping, due to the open nature of wireless media, the resource constraints of sensing devices, and the lack of security protocols. These deficiencies not only make conventional cryptographic methods not applicable in smart health monitoring but also put many obstacles in the path of designing privacy protection mechanisms. In this dissertation, we design dedicated schemes to achieve secure data collection and analysis in smart health monitoring. The first two works propose two robust and secure authentication schemes based on Electrocardiogram (ECG), which outperform traditional user identity authentication schemes in health monitoring, to restrict the access to collected data to legitimate users. To improve the practicality of ECG-based authentication, we address the nonuniformity and sensitivity of ECG signals, as well as the noise contamination issue. The next work investigates an extended authentication goal, denoted as wearable-user pair authentication. It simultaneously authenticates the user identity and device identity to provide further protection. We exploit the uniqueness of the interference between different wireless protocols, which is common in health monitoring due to devices\u27 varying sensing and transmission demands, and design a wearable-user pair authentication scheme based on the interference. However, the harm of this interference is also outstanding. Thus, in the fourth work, we use wireless human activity recognition in health monitoring as an example and analyze how this interference may jeopardize it. We identify a new attack that can produce false recognition result and discuss potential countermeasures against this attack. In the end, we move to a broader scenario and protect the statistics of distributed data reported in mobile crowd sensing, a common practice used in public health monitoring for data collection. We deploy differential privacy to enable the indistinguishability of workers\u27 locations and sensing data without the help of a trusted entity while meeting the accuracy demands of crowd sensing tasks

Analysis and applications of models of single-cell cardiac electrical excitation

For over a century, cardiac electrophysiology modelling has been widely used for studying various problems of normal or abnormal heart rhythm, which is essential for understanding the disease mechanisms, provide accurate diagnoses and develop a new treatment. This thesis focuses on several analysis and applications of models in single-cell cardiac electrical excitation. In particular, I aim to study some typical challenges present in cardiac electrophysiology modelling, which is, variability in action potentials (AP) and their effects on cardiac anti-arrhythmic drugs, mechanisms of cardiac alternans and efficient numerical solver. To address the problems, I use various action potential models initially a range of biophysically detailed models, then focusing on a single simplified model. This thesis consists of two main parts, excluding the part for background and introductory materials. The first and most important part, in terms of effort and time spent, is devoted to the investigation of action potential variability in a population of rabbit ventricular myocytes and their effects on cardiac anti-arrhythmic drugs. To determine the distributions of ion channel conductance values that capture the electrophysiological heterogeneity measured in large populations of cells, I apply the experimentally-calibrated population of models introduced by Britton et al. (2013), constructing from randomly varied ion conductances combinations. The model population is further used to quantitatively predict the range of response to the application of hERG and L-type calcium channel blocks. I implement the methodologies on three different AP models to study the capability of the cell models in predicting the drug effects. The models are a rabbit AP model by Shannon et al. (2004) and two human AP models by Ten Tusscher et al. (2004) and O’Hara et al. (2011). The AP responses following channel blocks are compared and analysed. The second part of the thesis covers the analysis and application of a simplified ionic cardiac model. The model used is a modified version of caricature Noble model by Biktashev et al. (2008). Our first task is to propose the model as a generic model of cardiac electrophysiology by using a parameter estimation method. The model’s parameters are adjusted so that it can reproduce AP morphologies of various cell types. In particular, the model is fitted to three different AP models which are Purkinje model by Noble (1962), ventricular model by Luo and Rudy (1991) and atrial model by Courtemanche et al. (1998). The action potential duration restitution curve of targeted models are also reproduced. The similar model template now can be used for various regions of the heart by changing the parameter values. Furthermore, the modified caricature Noble model is fitted to experimental measurements of healthy and failing myocytes by McIntosh et al. (2000). I analyse the difference between parameter values from fitting works intending to find the physiological meaning for AP differences shown in experimental recordings. Parameter fitting of modified caricature Noble model demonstrates that it can replace other more complicated models, and it can also be used as a prototype to look for cardiac alternans and to construct an efficient numerical method. The modified caricature Noble model is further used to develop an efficient numerical method for simulation of cardiac action potential model by taking into account the asymptotic solutions of the system. In order to achieve this, I implement the heterogenous multiscale method proposed by Weinan and Engquist (2003). The proposed method exhibits better stability and efficiency compared to other numerical solvers. The drawbacks of the method are also explained. Finally, the application of the model is extended by utilising it to study the mechanisms of cardiac alternans. The objective is to determine parameters and variables in the model that are responsible for generating action potential duration alternans. Using the slow-slow-time system of the model, an explicit discrete restitution map is derived and their equilibrium branches and bifurcations are studied. The bifurcations of equilibria of these maps are studied to identify regions in the parameter space of the model where normal response and alternans exhibit. Also, using the full system of the model, a framework formulated in terms of a boundary value problem is developed, which can be used to construct various branches of the action potential duration restitution map. At the end of the work, I perform some numerical simulations by fitting the caricature Noble model to models of normal response and alternans. The differences in parameter values are analysed and used to understand the onset of alternans. Importantly, the result shows that the magnitude of time-dependent potassium current can induce or suppress alternans

Dynamical Systems

Complex systems are pervasive in many areas of science integrated in our daily lives. Examples include financial markets, highway transportation networks, telecommunication networks, world and country economies, social networks, immunological systems, living organisms, computational systems and electrical and mechanical structures. Complex systems are often composed of a large number of interconnected and interacting entities, exhibiting much richer global scale dynamics than the properties and behavior of individual entities. Complex systems are studied in many areas of natural sciences, social sciences, engineering and mathematical sciences. This special issue therefore intends to contribute towards the dissemination of the multifaceted concepts in accepted use by the scientific community. We hope readers enjoy this pertinent selection of papers which represents relevant examples of the state of the art in present day research. [...

Modeling diversity by strange attractors with application to temporal pattern recognition

This thesis belongs to the general discipline of establishing black-box models from real-word data, more precisely, from measured time-series. This is an old subject and a large amount of papers and books has been written about it. The main difficulty is to express the diversity of data that has essentially the same origin without creating confusion with data that has a different origin. Normally, the diversity of time-series is modeled by a stochastic process, such as filtered white noise. Often, it is reasonable to assume that the time series is generated by a deterministic dynamical system rather than a stochastic process. In this case, the diversity of the data is expressed by the variability of the parameters of the dynamical system. The parameter variability itself is then, once again, modeled by a stochastic process. In both cases the diversity is generated by some form of exogenous noise. In this thesis a further step has been taken. A single chaotic dynamical system is used to model the data and their diversity. Indeed, a chaotic system produces a whole family of trajectories that are different but nonetheless very similar. It is believed that chaotic dynamics not only are a convenient means to represent diversity but that in many cases the origin of diversity stems actually from chaotic dynamic. Since the approach of this thesis explores completely new grounds the most suitable kind of data is considered, namely approximately periodic signals. In nature such time-series are rather common, in particular the physiological signal of living beings, such as the electrocardiograms (ECG), parts of speech signals, electroencephalograms (EEG), etc. Since there are strong arguments in favor of the chaotic nature of these signals, they appear to be the best candidates for modeling diversity by chaos. It should be stressed however, that the modeling approach pursued in this thesis is thought to be quite general and not limited to signals produced by chaotic dynamics in nature. The intended application of the modeling effort in this thesis is temporal signal classification. The reason for this is twofold. Firstly, classification is one of the basic building block of any cognitive system. Secondly, the recently studied phenomenon of synchronization of chaotic systems suggests a way to test a signal against its chaotic model. The essential content of this work can now be formulated as follows. Thesis: The diversity of approximately periodic signals found in nature can be modeled by means of chaotic dynamics. This kind of modeling technique, together with selective properties of the synchronization of chaotic systems, can be exploited for pattern recognition purposes. This Thesis is advocated by means of the following five points. Models of randomness (Chapter 2) It is argued that the randomness observed in nature is not necessarily the result of exogenous noise, but it could be endogenally generated by deterministic chaotic dynamics. The diversity of real signals is compared with signals produced by the most common chaotic systems. Qualitative resonance (Chapter 3) The behavior of chaotic systems forced by periodic or approximately periodic input signals is studied theoretically and by numerical simulation. It is observed that the chaotic system "locks" approximately to an input signal that is related to its internal chaotic dynamic. In contrast to this, its chaotic behavior is reinforced when the input signal has nothing to do with its internal dynamics. This new phenomenon is called "qualitative resonance". Modeling and recognizing (Chapter 4) In this chapter qualitative resonance is used for pattern recognition. The core of the method is a chaotic dynamical system that is able to reproduce the class of time-series that is to be recognized. This model is excited in a suitable way by an input signal such that qualitative resonance is realized. This means that if the input signal belongs to the modeled class of time-series, the system approximately "locks" into it. If not, the trajectory of the system and the input signal remain unrelated. Automated design of the recognizer (Chapters 5 and 6) For the kind of signals considered in this thesis a systematic design method of the recognizer is presented. The model used is a system of Lur'e type, i.e. a model where the linear dynamic and nonlinear static part are separated. The identification of the model parameters from the given data proceed iteratively, adapting in turn the linear and the nonlinear part. Thus, the difficult nonlinear dynamical system identification task is decomposed into the easier problems of linear dynamical and nonlinear static system identification. The way to apply the approximately periodic input signal in order to realize qualitative resonance is chosen with the help of periodic control theory. Validation (Chapter 7) The pattern recognition method has been validated on the following examples — A synthetic example — Laboratory measurement from Colpitts oscillator — ECG — EEG — Vowels of a speech signals In the first four cases a binary classification and in the last example a classification with five classes was performed. To the best of the knowledge of the author the recognition method is original. Chaotic systems have been already used to produce pseudo-noise and to model signal diversity. Also, parameter identification of chaotic systems has been already carried out. However, the direct establishment of the model from the given data and its subsequent use for classification based on the phenomenon of qualitative resonance is entirely new

Interventional Electrophysiology in Advanced Heart Disease Atrial Fibrillation and Heart Failure

The optimal therapy for atrial fibrillation (AF) associated with heart failure (HF) is unclear. Drug-based rhythm control has not proved clinically beneficial. Catheter ablation-based rhythm control improves cardiac function in HF patients, but impact on physiological performance has not been formally evaluated in a randomised trial. A randomised trial was designed and conducted, comparing catheter ablation with rate control in adults with symptomatic heart failure, radionuclide left ventricular ejection fraction (EF) ≤35%, and persistent AF. The primary outcome was change in peak oxygen consumption (VO2) at cardiopulmonary exercise test. Secondary endpoints included change in quality of life (Minnesota), 6-minute walk, BNP, and EF. Patients were followed-up for 12 months, and results analysed by intention-to-treat. 52 patients (63±9y, EF 24±8%, VO2 17.3±5.1ml/kg/min) were randomised, 26 to each arm. In the ablation arm, at 12 month follow up, 88% maintained SR, with a single procedure success of 69%. In the rate control arm, rate criteria were achieved in 96% at 12 months. At 12 months, peak VO2 had increased by 2.13 (95%CI -0.1 to 4.36) ml/kg/min in the ablation arm, compared with a decrease (-0.94ml/kg/min, 95%CI -2.21 to 0.32) under rate control: mean benefit of ablation +3.07ml/kg/min, 95% CI 0.56-5.59, p=0.018. The change appeared progressive, with a difference of only 0.79ml/kg/min at 3 months (95% CI -1.01 to 2.60, p=0.38). Compared with rate control, ablation reduced 12-month Minnesota score (p=0.019) and BNP (p=0.045), and showed trends toward increased 6 min walk distance (p=0.095) and EF (p=0.055). LA size fell significantly after ablation (p=0.001). Catheter ablation of persistent AF in patients with HF, with the ablation strategy achieving sinus rhythm in the majority, improves prognostically important objective cardiopulmonary exercise performance, symptoms and neurohormonal status. The effects are clear at 1 year but less distinct earlier, suggesting a period of cardiac remodelling and recovery

Towards a Phenomenological Theory of the Visceral in the Interactive Arts

This is a digitised version of a thesis that was deposited in the University Library. If you are the author and you have a query about this item please contact PEARL Admin ([email protected])Metadata merged with duplicate record (http://hdl.handle.net/10026.1/2319) on 20.12.2016 by CS (TIS).This thesis explores the ways in which certain forms of interactive art may and do elicit visceral responses. The term "visceral" refers to the cardiovascular, respiratory, uro-genital and especially excretory systems that affect mind and body on a continuum of awareness. The "visceral" is mentioned in the field of interactive arts, but it remains systematically unexplored and undefined. Further, interactive artworks predominantly focus on the exteroceptive (stimuli from outside) rather than the interoceptive (stimuli arising within the body, especially the viscera) senses. The existentialist phenomenology of Maurice Merleau-Ponty forms the basis for explorations of the visceral dimension of mind/body. New approaches to understanding interactive art, design and the mind/body include: attunements to the world; intertwinings of mind/body, technology and world; and of being in the world. Each artwork within utilizes a variation of the phenomenological methods derived from Merl eau-Ponty's; these are discussed primarily in Chapters One and Three. Because subjective, first-person, experiences are a major aspect of a phenomenological approach, the academic writing is interspersed with subjective experiences of the author and others. This thesis balances facets of knowledge from diverse disciplines that account for visceral phenomena and subjective experience. Along with the textual exegesis, one major work of design and two major works of art were created. These are documented on the compact disc (CDROM) bound within. As an essential component of each artwork, new technological systems were created or co-created by the author. User surveys comprise Appendices Two, Three and Four, and are also online at: www. sfu. ca/-dgromala/thesis. To access the URL: login as , and use the password . Numerous talks, exhibitions and publications that directly relate to the thesis work is in Appendix One. This work begins with an introduction to Merleau-Ponty's ideas of flesh and reversibility. Chapter Two is the review of the literature, while Chapter Three is an explication of the hypothesis, an overview of the field, and a framing of the problem. Discussions of each artwork are in Chapter Four (The Meditation Chamber), Chapter Five (BioMorphic Typography) and Chapter Six (The MeatBook). Chapter Seven forms the conclusion. References to the documentation on the CD are found throughout the thesis, and italicized paragraphs provide an artistic context for each chapter

Socio-Cognitive and Affective Computing

Social cognition focuses on how people process, store, and apply information about other people and social situations. It focuses on the role that cognitive processes play in social interactions. On the other hand, the term cognitive computing is generally used to refer to new hardware and/or software that mimics the functioning of the human brain and helps to improve human decision-making. In this sense, it is a type of computing with the goal of discovering more accurate models of how the human brain/mind senses, reasons, and responds to stimuli. Socio-Cognitive Computing should be understood as a set of theoretical interdisciplinary frameworks, methodologies, methods and hardware/software tools to model how the human brain mediates social interactions. In addition, Affective Computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects, a fundamental aspect of socio-cognitive neuroscience. It is an interdisciplinary field spanning computer science, electrical engineering, psychology, and cognitive science. Physiological Computing is a category of technology in which electrophysiological data recorded directly from human activity are used to interface with a computing device. This technology becomes even more relevant when computing can be integrated pervasively in everyday life environments. Thus, Socio-Cognitive and Affective Computing systems should be able to adapt their behavior according to the Physiological Computing paradigm. This book integrates proposals from researchers who use signals from the brain and/or body to infer people's intentions and psychological state in smart computing systems. The design of this kind of systems combines knowledge and methods of ubiquitous and pervasive computing, as well as physiological data measurement and processing, with those of socio-cognitive and affective computing