Caracterización beta, gamma y de oscilaciones de alta frecuencia para localización de diana en procedimientos de Estimulación Cerebral Profunda

Deep Brain Stimulation (DBS) has been successfully used to treat patients with Parkinson’s Disease. DBS employs an electrode that regulates the oscillatory activity of the basal ganglia, such as the subthalamic nucleus (STN). A critical point during the surgical implantation of such electrode is the precise localization of the target. This is done using presurgical images, stereotactic frames, and microelectrode recordings (MER). The latter allows neurophysiologists to visualize the electrical activity of different structures along the surgical track, each of them with well-defined variations in the frequency pattern; however, this is far from an automatic or semi-automatic method to help these specialists make decisions concerning the surgical target. To pave the way to automation, we analyzed three frequency bands in MER signals acquired from 11 patients undergoing DBS: beta (13-40 Hz), gamma (40-200 Hz), and high-frequency oscillations (HFO – 201-400 Hz). In this study, we propose and assess five indexes in order to detect the STN: variations in autoregressive parameters and their derivative along the surgical track, the energy of each band calculated using the Yule-Walker power spectral density, the high-to-low (H/L) ratio, and its derivative. We found that the derivative of one parameter of the beta band and the H/L ratio of the HFO/gamma bands produced errors in STN targeting like those reported in the literature produced by image-based methods (<2 mm). Although the indexes introduced here are simple to compute and could be applied in real time, further studies must be conducted to be able to generalize their results.La estimulación cerebral profunda (DBS por sus siglas en inglés) ha sido usada exitosamente en el tratamiento de pacientes con enfermedad de Párkinson. La DBS tiene un electrodo que regula la actividad oscilatoria de los ganglios basales involucrados, como el núcleo subtalámico (STN). Un aspecto crítico en el implante de dicho electrodo es la localización precisa de la diana quirúrgica. Esta se realiza mediante imágenes pre-quirúrgicas, marcos estereotácticos y registros de micro-electrodos (MER). Este último permite visualizar la actividad eléctrica de diferentes estructuras a través del recorrido quirúrgico, cada una de ellas con un patrón de variaciones bien definidas en frecuencia; sin embargo, esto dista de ser un método automático o semi-automático que ayude al neurofisiólogo a tomar decisiones en cuanto a la diana quirúrgica. Con el ánimo de contribuir a la automatización, analizamos tres bandas de frecuencias de señales MER adquiridas en 11 pacientes sometidos a DBS: beta (13-40 Hz), gamma (40-200 Hz) y oscilaciones de alta frecuencia (HFO – 201-400 Hz). Se propusieron y evaluaron 5 índices para detectar el STN: variaciones de parámetros auto-regresivos y su derivada a lo largo del recorrido quirúrgico, la energía de cada banda a partir de la densidad espectral de potencia mediante el método de Yule-Walker, la relación de frecuencias altas a bajas y su derivada. Encontramos que la derivada de un parámetro de la banda beta y la relación alta-bajas de las bandas HFO/gamma alcanzaron errores en la localización del STN, similares a los reportados en la literatura (<2mm). Aunque los índices propuestos son sencillos de calcular y de fácil implementación en tiempo real, se deben seguir explorando para incrementar la capacidad de generalización de los resultados obtenidos

Advanced Signal Processing in Wearable Sensors for Health Monitoring

Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods

Intravascular Ultrasound

Intravascular ultrasound (IVUS) is a cardiovascular imaging technology using a specially designed catheter with a miniaturized ultrasound probe for the assessment of vascular anatomy with detailed visualization of arterial layers. Over the past two decades, this technology has developed into an indispensable tool for research and clinical practice in cardiovascular medicine, offering the opportunity to gather diagnostic information about the process of atherosclerosis in vivo, and to directly observe the effects of various interventions on the plaque and arterial wall. This book aims to give a comprehensive overview of this rapidly evolving technique from basic principles and instrumentation to research and clinical applications with future perspectives

An inverse transmission line model of the lower limb arterial system

Includes bibliography. Includes disk in pocket at back of book

Statistical and Graph-Based Signal Processing: Fundamental Results and Application to Cardiac Electrophysiology

The goal of cardiac electrophysiology is to obtain information about the mechanism, function, and performance of the electrical activities of the heart, the identification of deviation from normal pattern and the design of treatments. Offering a better insight into cardiac arrhythmias comprehension and management, signal processing can help the physician to enhance the treatment strategies, in particular in case of atrial fibrillation (AF), a very common atrial arrhythmia which is associated to significant morbidities, such as increased risk of mortality, heart failure, and thromboembolic events. Catheter ablation of AF is a therapeutic technique which uses radiofrequency energy to destroy atrial tissue involved in the arrhythmia sustenance, typically aiming at the electrical disconnection of the of the pulmonary veins triggers. However, recurrence rate is still very high, showing that the very complex and heterogeneous nature of AF still represents a challenging problem. Leveraging the tools of non-stationary and statistical signal processing, the first part of our work has a twofold focus: firstly, we compare the performance of two different ablation technologies, based on contact force sensing or remote magnetic controlled, using signal-based criteria as surrogates for lesion assessment. Furthermore, we investigate the role of ablation parameters in lesion formation using the late-gadolinium enhanced magnetic resonance imaging. Secondly, we hypothesized that in human atria the frequency content of the bipolar signal is directly related to the local conduction velocity (CV), a key parameter characterizing the substrate abnormality and influencing atrial arrhythmias. Comparing the degree of spectral compression among signals recorded at different points of the endocardial surface in response to decreasing pacing rate, our experimental data demonstrate a significant correlation between CV and the corresponding spectral centroids. However, complex spatio-temporal propagation pattern characterizing AF spurred the need for new signals acquisition and processing methods. Multi-electrode catheters allow whole-chamber panoramic mapping of electrical activity but produce an amount of data which need to be preprocessed and analyzed to provide clinically relevant support to the physician. Graph signal processing has shown its potential on a variety of applications involving high-dimensional data on irregular domains and complex network. Nevertheless, though state-of-the-art graph-based methods have been successful for many tasks, so far they predominantly ignore the time-dimension of data. To address this shortcoming, in the second part of this dissertation, we put forth a Time-Vertex Signal Processing Framework, as a particular case of the multi-dimensional graph signal processing. Linking together the time-domain signal processing techniques with the tools of GSP, the Time-Vertex Signal Processing facilitates the analysis of graph structured data which also evolve in time. We motivate our framework leveraging the notion of partial differential equations on graphs. We introduce joint operators, such as time-vertex localization and we present a novel approach to significantly improve the accuracy of fast joint filtering. We also illustrate how to build time-vertex dictionaries, providing conditions for efficient invertibility and examples of constructions. The experimental results on a variety of datasets suggest that the proposed tools can bring significant benefits in various signal processing and learning tasks involving time-series on graphs. We close the gap between the two parts illustrating the application of graph and time-vertex signal processing to the challenging case of multi-channels intracardiac signals

Anatomo-functional magnetic resonance imaging of the spinal cord and its application to the characterization of spinal lesions in cats

Les lésions de la moelle épinière ont un impact significatif sur la qualité de la vie car elles peuvent induire des déficits moteurs (paralysie) et sensoriels. Ces déficits évoluent dans le temps à mesure que le système nerveux central se réorganise, en impliquant des mécanismes physiologiques et neurochimiques encore mal connus. L'ampleur de ces déficits ainsi que le processus de réhabilitation dépendent fortement des voies anatomiques qui ont été altérées dans la moelle épinière. Il est donc crucial de pouvoir attester l'intégrité de la matière blanche après une lésion spinale et évaluer quantitativement l'état fonctionnel des neurones spinaux. Un grand intérêt de l'imagerie par résonance magnétique (IRM) est qu'elle permet d'imager de façon non invasive les propriétés fonctionnelles et anatomiques du système nerveux central. Le premier objectif de ce projet de thèse a été de développer l'IRM de diffusion afin d'évaluer l'intégrité des axones de la matière blanche après une lésion médullaire. Le deuxième objectif a été d'évaluer dans quelle mesure l'IRM fonctionnelle permet de mesurer l'activité des neurones de la moelle épinière. Bien que largement appliquées au cerveau, l'IRM de diffusion et l'IRM fonctionnelle de la moelle épinière sont plus problématiques. Les difficultés associées à l'IRM de la moelle épinière relèvent de sa fine géométrie (environ 1 cm de diamètre chez l'humain), de la présence de mouvements d'origine physiologique (cardiaques et respiratoires) et de la présence d'artefacts de susceptibilité magnétique induits par les inhomogénéités de champ, notamment au niveau des disques intervertébraux et des poumons. L'objectif principal de cette thèse a donc été de développer des méthodes permettant de contourner ces difficultés. Ce développement a notamment reposé sur l'optimisation des paramètres d'acquisition d'images anatomiques, d'images pondérées en diffusion et de données fonctionnelles chez le chat et chez l'humain sur un IRM à 3 Tesla. En outre, diverses stratégies ont été étudiées afin de corriger les distorsions d'images induites par les artefacts de susceptibilité magnétique, et une étude a été menée sur la sensibilité et la spécificité de l'IRM fonctionnelle de la moelle épinière. Les résultats de ces études démontrent la faisabilité d'acquérir des images pondérées en diffusion de haute qualité, et d'évaluer l'intégrité de voies spinales spécifiques après lésion complète et partielle. De plus, l'activité des neurones spinaux a pu être détectée par IRM fonctionnelle chez des chats anesthésiés. Bien qu'encourageants, ces résultats mettent en lumière la nécessité de développer davantage ces nouvelles techniques. L'existence d'un outil de neuroimagerie fiable et robuste, capable de confirmer les paramètres cliniques, permettrait d'améliorer le diagnostic et le pronostic chez les patients atteints de lésions médullaires. Un des enjeux majeurs serait de suivre et de valider l'effet de diverses stratégies thérapeutiques. De telles outils représentent un espoir immense pour nombre de personnes souffrant de traumatismes et de maladies neurodégénératives telles que les lésions de la moelle épinière, les tumeurs spinales, la sclérose en plaques et la sclérose latérale amyotrophique.Spinal cord injury has a significant impact on quality of life since it can lead to motor (paralysis) and sensory deficits. These deficits evolve in time as reorganisation of the central nervous system occurs, involving physiological and neurochemical mechanisms that are still not fully understood. Given that both the severity of the deficit and the successful rehabilitation process depend on the anatomical pathways that have been altered in the spinal cord, it may be of great interest to assess white matter integrity after a spinal lesion and to evaluate quantitatively the functional state of spinal neurons. The great potential of magnetic resonance imaging (MRI) lies in its ability to investigate both anatomical and functional properties of the central nervous system non invasively. To address the problem of spinal cord injury, this project aimed to evaluate the benefits of diffusion-weighted MRI to assess the integrity of white matter axons that remain after spinal cord injury. The second objective was to evaluate to what extent functional MRI can measure the activity of neurons in the spinal cord. Although widely applied to the brain, diffusion-weighted MRI and functional MRI of the spinal cord are not straightforward. Various issues arise from the small cross-section width of the cord, the presence of cardiac and respiratory motions, and from magnetic field inhomogeneities in the spinal region. The main purpose of the present thesis was therefore to develop methodologies to circumvent these issues. This development notably focused on the optimization of acquisition parameters to image anatomical, diffusion-weighted and functional data in cats and humans at 3T using standard coils and pulse sequences. Moreover, various strategies to correct for susceptibility-induced distortions were investigated and the sensitivity and specificity in spinal cord functional MRI was studied. As a result, acquisition of high spatial and angular diffusion-weighted images and evaluation of the integrity of specific spinal pathways following spinal cord injury was achieved. Moreover, functional activations in the spinal cord of anaesthetized cats was detected. Although encouraging, these results highlight the need for further technical and methodological development in the near-future. Being able to develop a reliable neuroimaging tool for confirming clinical parameters would improve diagnostic and prognosis. It would also enable to monitor the effect of various therapeutic strategies. This would certainly bring hope to a large number of people suffering from trauma and neurodegenerative diseases such as spinal cord injury, tumours, multiple sclerosis and amyotrophic lateral sclerosis

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 267, January 1985

This publication is a cumulative index to the abstracts contained in the Supplements 255 through 266 of Aerospace Medicine and Biology: A Continuing Bibliography. It includes seven indexes--subject, personal author, corporate source, foreign technology, contract number, report number, and accession number

Improved 3D MR Image Acquisition and Processing in Congenital Heart Disease

Congenital heart disease (CHD) is the most common type of birth defect, affecting about 1% of the population. MRI is an essential tool in the assessment of CHD, including diagnosis, intervention planning and follow-up. Three-dimensional MRI can provide particularly rich visualization and information. However, it is often complicated by long scan times, cardiorespiratory motion, injection of contrast agents, and complex and time-consuming postprocessing. This thesis comprises four pieces of work that attempt to respond to some of these challenges. The first piece of work aims to enable fast acquisition of 3D time-resolved cardiac imaging during free breathing. Rapid imaging was achieved using an efficient spiral sequence and a sparse parallel imaging reconstruction. The feasibility of this approach was demonstrated on a population of 10 patients with CHD, and areas of improvement were identified. The second piece of work is an integrated software tool designed to simplify and accelerate the development of machine learning (ML) applications in MRI research. It also exploits the strengths of recently developed ML libraries for efficient MR image reconstruction and processing. The third piece of work aims to reduce contrast dose in contrast-enhanced MR angiography (MRA). This would reduce risks and costs associated with contrast agents. A deep learning-based contrast enhancement technique was developed and shown to improve image quality in real low-dose MRA in a population of 40 children and adults with CHD. The fourth and final piece of work aims to simplify the creation of computational models for hemodynamic assessment of the great arteries. A deep learning technique for 3D segmentation of the aorta and the pulmonary arteries was developed and shown to enable accurate calculation of clinically relevant biomarkers in a population of 10 patients with CHD