Analysing nystagmus waveforms: a computational framework

We present a new computational approach to analyse nystagmus waveforms. Our framework is designed to fully characterise the state of the nystagmus, aid clinical diagnosis and to quantify the dynamical changes in the oscillations over time. Both linear and nonlinear analyses of time series were used to determine the regularity and complexity of a specific homogenous phenotype of nystagmus. Two-dimensional binocular eye movement recordings were carried out on 5 adult subjects who exhibited a unilateral, uniplanar, vertical nystagmus secondary to a monocular late-onset severe visual loss in the oscillating eye (the Heimann-Bielschowsky Phenomenon). The non-affected eye held a central gaze in both horizontal and vertical planes (± 10 min. of arc). All affected eyes exhibited vertical oscillations, with mean amplitudes and frequencies ranging from 2.0°–4.0° to 0.25–1.5 Hz, respectively. Unstable periodic orbit analysis revealed only 1 subject exhibited a periodic oscillation. The remaining subjects were found to display quasiperiodic (n = 1) and nonperiodic (n = 3) oscillations. Phase space reconstruction allowed attractor identification and the computation of a time series complexity measure—the permutation entropy. The entropy measure was found to be able to distinguish between a periodic oscillation associated with a limit cycle attractor, a quasiperiodic oscillation associated with a torus attractor and nonperiodic oscillations associated with higher-dimensional attractors. Importantly, the permutation entropy was able to rank the oscillations, thereby providing an objective index of nystagmus complexity (range 0.15–0.21) that could not be obtained via unstable periodic orbit analysis or attractor identification alone. These results suggest that our framework provides a comprehensive methodology for characterising nystagmus, aiding differential diagnosis and also permitting investigation of the waveforms over time, thereby facilitating the quantification of future therapeutic managements. In addition, permutation entropy could provide an additional tool for future oculomotor modelling

MR image based measurement, modelling and diagnostic interpretation of pressure and flow in the pulmonary arteries: applications in pulmonary hypertension

Pulmonary hypertension (PH) is a clinical condition characterised by an increased mean pulmonary arterial pressure (mPAP) of over 25 mmHg measured, at rest, by right heart catheterisation (RHC). RHC is currently considered the gold standard for diagnosis, follow-up and measurement of response to treatment. Although the severe complications and mortality risk associated with the invasive procedure are reduced when it is performed in a specialist centre, finding non-invasive PH diagnosis methods is highly desirable. Non-invasive, non-ionising imaging techniques, based on magnetic resonance imaging (MRI) and on echocardiography, have been integrated into the clinical routine as means for PH assessment. Although the imaging techniques can provide valuable information supporting the PH diagnosis, accurately identifying patients with PH based upon images alone remains challenging. Computationally based models can bring additional insights into the haemodynamic changes occurring under the manifestation of PH. The primary hypothesis of this thesis is that that the physiological status of the pulmonary circulation can be inferred using solely non-invasive flow and anatomy measurements of the pulmonary arteries, measured by MRI and interpreted by 0D and 1D mathematical models. The aim was to implement a series of simple mathematical models, taking the inputs from MRI measurements, and to evaluate their potential to support the non-invasive diagnosis and monitoring of PH. The principal objective was to develop a tool that can readily be translated into the clinic, requiring minimum operator input and time and returning meaningful and accurate results. Two mathematical models, a 3 element Windkessel model and a 1D model of an axisymmetric straight elastic tube for wave reflections were implemented and clinically tested on a cohort of healthy volunteers and of patients who were clinically investigated for PH. The latter group contained some who were normotensive, and those with PH were stratified according to severity. A 2D semi-automatic image segmentation workflow was developed to provide patient specific, simultaneous flow and anatomy measurements of the main pulmonary artery (MPA) as input to the mathematical models. Several diagnostic indices are proposed, and of these distal resistance (Rd), total vascular compliance (C) and the ratio of reflected to total wave power (Wb/Wtot) showed statistically significant differences between the analysed groups, with good accuracy in PH classification. A machine learning classifier using the derived computational metrics and several other PH metrics computed from MRI images of the MPA and of the right ventricle alone, proposed in the literature as PH surrogate markers, was trained and validated with leave-one-out cross-validation to improve the accuracy of non-invasive PH diagnosis. The results accurately classified 92% of the patients, and furthermore the misclassified 8% were patients with mPAP close to the 25 mmHg (at RHC) threshold (within the range of clinical uncertainty). The individual analysis of all PH surrogate markers emphasised that wave reflection quantification, although with lower diagnosis accuracy (75%) than the machine learning model embedding multiple markers, has the potential to distinguish between multiple PH categories. A finite element method (FEM) based model to solve a 1D pulmonary arterial tree linear system, has been implemented to contribute further to the accurate, non-invasive assessment of pulmonary hypertension. The diagnostic protocols, including the analysis work flow, developed and reported in this PhD thesis can be integrated into the clinical process, with the potential to reduce the need for RHC by maximising the use of available MRI data

Advances in Electrocardiograms

Electrocardiograms have become one of the most important, and widely used medical tools for diagnosing diseases such as cardiac arrhythmias, conduction disorders, electrolyte imbalances, hypertension, coronary artery disease and myocardial infarction. This book reviews recent advancements in electrocardiography. The four sections of this volume, Cardiac Arrhythmias, Myocardial Infarction, Autonomic Dysregulation and Cardiotoxicology, provide comprehensive reviews of advancements in the clinical applications of electrocardiograms. This book is replete with diagrams, recordings, flow diagrams and algorithms which demonstrate the possible future direction for applying electrocardiography to evaluating the development and progression of cardiac diseases. The chapters in this book describe a number of unique features of electrocardiograms in adult and pediatric patient populations with predilections for cardiac arrhythmias and other electrical abnormalities associated with hypertension, coronary artery disease, myocardial infarction, sleep apnea syndromes, pericarditides, cardiomyopathies and cardiotoxicities, as well as innovative interpretations of electrocardiograms during exercise testing and electrical pacing

Handbook on clinical neurology and neurosurgery

HANDBOOKNEUROLOGYNEUROSURGERYКЛИНИЧЕСКАЯ НЕВРОЛОГИЯНЕВРОЛОГИЯНЕЙРОХИРУРГИЯThis handbook includes main parts of clinical neurology and neurosurgery

Detection of higher visual function deficits and validation of multifocal pupillography in stroke, chiasmal compression and anterior ischemic optic neuropathy.

It is well established that neural damage can result in visual dysfunction, both visual field loss and in higher visual function (HVF) loss such as perceptions of depth, colour, motion and faces. This thesis examines these visual deficits in the common neurological diseases of stroke, chiasmal compression, and anterior ischemic optic neuropathy (AION). While it is established that isolated complete HVF deficits do occur in stroke, they are also known to be rare. However, as HVFs are not routinely tested in clinical practice, it is unknown how common more subtle defects are, and what tools are effective in detecting these. Chapter 3 explores these questions, outlining that colour and depth perceptions are the most commonly affected, that Ishihara (colour) and stereofly and randot (depth), are the most useful tests, and outlines recommendations for improvement in some of these tools. The relatively new invention of multifocal pupillographic objective perimetry (mfPOP) provides a number of benefits from other forms of perimetry. It measures both eyes at once, allowing measures of direct and consensual responses, it is objective, and it allows repeat measures of each region giving a measure of error. This advancement opens up new opportunities to investigate pupillary physiology in neurological disorders and adds new challenges in how to combine these signals into a single meaningful measure. Chapter 4 investigates the physiology of the pupil in stroke, chiasmal compression, and AION, and investigates how these components can be appropriately combined into a single measure. Results show naso-temporal differences are consistent with known physiology in control subjects and provides evidence that denser nasal retinal input may underpin the greater contraction anisocoria seen in temporal fields than in nasal fields. With the intention that mfPOP be used in clinical practice, it must demonstrate it can perform as well as traditional perimetry, such as Humphrey and Matrix devices, in a wide range of disorders. Currently mfPOP testing neurological disorders has been very limited, and this represents a large gap in the literature. Chapter 5 compares mfPOP to Humphrey and Matrix perimeters, showing they mfPOP does not correlate well with these devices, and compares their utility in neurological disease. It shows that Humphrey appears the most useful device overall, with Matrix being exceptionally good in chiasmal compression, while mfPOP does not appear effective in these disorders. With the first mfPOP approach having limitations in its diagnostic ability, a second stimulus protocol was designed using colour opponency with the measure of response latency (rather than amplitude), thought to preferentially stimulate cortical input to the pupil response, and may allow detection of cortical lesions. Chapter 6 investigates this new colour exchange protocol and latency measure, contrasting with the more common luminance approach used in chapter 5. It shows that the colour protocol shows a number of subtle differences compared to the luminance protocol, but does not show any greater utility in neurological disease. It reveals that latency and amplitude appear to have a weak positive relationship, and that mfPOP repeats appear to correlate well, but all measures have substantial variation. These finding open up a number of future directions, from a larger and more focused HVF study into colour and depth perception, to considering retinal density as contributing towards biases in pupillary components, exploring hemifield ratios as a measure of early detection of chiasmal compression, and trialling other mfPOP methods to determine whether neurological disorders can be detected through pupillometry

Risk Factors for Cardiovascular Disease

Risk Factors for Cardiovascular Disease raises awareness about the importance of early recognition and prevention of modifiable cardiovascular risk factors. Some non-modifiable factors, like diabetes, can even be impacted by lifestyle modification (like weight loss) early in the disease. This book also describes cardiovascular risk factors in different patient populations and work settings

Multiscale Modelling of Neuronal Dynamics and Their Dysfunction in the Developing Brain

Over the last few decades, an increasing number of neurodevelopmental disorders has been associated with molecular causes – such as genetic mutations, or autoantibodies affecting synaptic transmission. Yet understanding the pathophysiology that leads from particular molecular disruptions at the synapse to patients’ signs and symptoms remains challenging, even today. The work presented in this thesis illustrates how computational models can help bridge the explanatory gap between disruptions at the molecular scale and brain dysfunction at the level of integrated circuits. I utilise computational models at different scales of neuronal function, ranging from the neuronal membrane, to integrated cortical microcircuits and whole-brain sensory processing networks. These computational models are informed with, and further constrained by both empirical data derived from a number of model systems of neurodevelopmental disorders, and clinical patient data. The worked examples in this thesis include the biophysical characterisation of an epilepsy-causing mutation in the voltage-gated sodium channel gene SCN1A, calcium imaging in a larval zebrafish model of epileptic seizures in the immature brain, electrophysiological recordings from patients with NMDA receptor antibody encephalitis as well as from a mouse model of the disorder, and pharmacologically induced NMDA receptor blockade in young adults that captures features of acute psychosis and schizophrenia. The combination of this diverse range of empirical data and different computational models offers a mechanistic, multi-scale account of how specific phenotypic features in neurodevelopmental disorders emerge. This provides novel insights both in regard to the specific conditions included here, but also concerning the link between molecular determinants and their neurodevelopmental phenotypes more broadly