Numerical methods for improved signal to noise ratios in spatiotemporal biomedical data

Magnetocardiography (MCG) is a technique to measure the magnetic fields produced by electrical activity in the heart. The interpretation of MCG signals is difficult because of different disturbances and noise. Several methods have been suggested for noise reduction in MCG data such as averaging, pass or stop band filters, and statistical based methods, but a unified framework that takes into account different typologies of MCG signals (rest, stress, and patients with an already ICD– Implanted Cardioverter Defibrillator- implanted) using an adequate number of recordings is still missing. Consequently, the main aim of the thesis is to develop methods for noise and artifacts treatment. Due to the non-stationarity (NS) of the noise, the conventional ensemble averaging of the data does not yield the theoretical improvement. In order to overcome this problem an average procedure that ignores the noisiest beats is applied. The results of this averaging procedure confirms that in case of NS, the Signal to Noise Ratio (SNR) does not behave as expected, but reaches a maximum after a certain number of selected beats. Furthermore, a theoretical proof of this result is given. The second part of the thesis deals with techniques based on Blind Source Separation (BSS), as preprocessing step for the averaging procedure, in case of MCG signals with low SNR. Different BSS algorithms are compared in order to find the best one in terms of noise reduction, separation, and computational time for each data typology. A drawback of BSS techniques is the order of the sources that cannot be determined a priori; for this reason 3 methods (based on different statistical principles) have been developed for the retrieval of cardiac signals. The last part of the thesis deals with the application of BSS methods to a category of signals not yet analyzed: patients with ICD implanted. It is shown that it is possible to extract the cardiac signal also in such noisy data, although not automatically. The Temporal Decorrelation source SEParation (TDSEP) algorithm outperforms the other BSS methods. This thesis shows that, applying novel automatic routines for the removal of noise and artifacts, MCG data could be used in clinical environments

Independent component analysis of magnetoencephalographic signals

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Innovative Processing Algorithms for Fetal Magnetoencephalographic Data

Fetale Magnetenzepahalographie (fMEG) ermöglicht die Untersuchung der Entwicklung des zentralen und des autonomen Nervensystems bei Feten ab der 20. Schwangerschaftswoche. Wie normale Magnetenzephalographie bei Erwachsenen und Kindern ist auch fMEG eine nicht-invasive Methode und in der Anwendung vollkommen harmlos für Mutter und Kind. Die magnetischen Sensoren sind hierbei um das Abdomen der schwangeren Frau angeordnet. Die gute räumliche und zeitliche Auflösung erlaubt es, mütterliche und fetale Magnetokardiogramme gleichzeitig mit der fetalen Hirnaktivität zu messen. Die Signale der fetalen Magnetoenzephalographie werden vor allem zur Messung von auditiven und visuellen ereignisbezogenen Hirnreaktionen oder der spontanen Hirnaktivität verwendet. Wichtige Fragen zum Entwicklungsprozess des fetalen Gehirns und des autonomen Nervensystems sowie der mütterliche Einfluss auf den metabolischen und kognitiven Zustand des Neugeborenen können durch die Analyse der fetalen Magnetoenzephalographie-Signale geklärt werden. Die Auswertung der fetalen Hirnaktivität birgt einige Herausforderungen, da die Signale der fetalen und mütterlichen Herzaktivität etwa 10-1000 mal stärker sind als das fetale Hirnsignal. Daher ist es zwingend erforderlich, die Herzaktivität der Mutter und des Fetus zu erkennen und zu entfernen, bevor die fetale Hirnaktivität analysiert wird. Die derzeit verwendeten Methoden für die Erkennung und Entfernung der Herzaktivität funktionieren für die meisten Datensätze zuverlässig, die Verarbeitung enthält jedoch einige manuelle Schritte, was das Ganze sehr zeitaufwändig macht. Darüber hinaus ist die Signal Redistribution beim Entfernen der Herzaktivität ein bekanntes Problem, welches es schwierig macht, die Hirnaktivität später zu identifizieren. Das Ziel dieser Arbeit war es, die Auswertung der fMEG Daten schneller, besser und trotzdem leicht handhabbar zu machen. In dieser Arbeit werden zwei neue vollautomatisierte Methoden zur Erkennung und Entfernung der Herzaktivität vorgestellt. Der vollautomatisierte R-Peak Erkennungsalgorithmus (FLORA) verbessert die R-Peak Erkennung, indem er die Vorteile der zuvor verwendeten Methoden kombiniert und erweitert. Der Algorithmus zur vollautomatisierten Subtraktion der Herzaktivität (FAUNA) verbessert die Signalqualität und vereinfacht die Erkennung der Hirnaktivität, ohne Redistribution. Die Zuverlässigkeit der Daten wird dadurch erhöht, da keine manuelle Auswahl getroffen werden muss. Die Kombination beider Methoden in einem Programm zur vollautomatisierten Verarbeitung für die fetale Magnetoenzephalographie (FAIRY) macht die Datenauswertung nun einfach und schnell. Damit wird die fMEG Datenverarbeitung auf die "Big Data"- und "Automated Science"-Ära vorbereitet. Des Weiteren wurde eine Studie über die autonome und zentralnervöse Reaktion von Feten und Neugeborenen auf die mütterliche Stimme (AURORA) mit den neuen Datenverarbeitungsmethoden durchgeführt. Die Ergebnisse zeigten eine reduzierte Bewegung der Feten zwischen der 26. und 32. Schwangerschaftswoche und eine niedrigere Herzfrequenz während der ersten 20 Sekunden der Stimulation in den letzten Schwangerschaftswochen, als Reaktion auf die mütterliche Stimme. Zusätzlich fanden wir eine höhere Amplitude der Gehirnreaktion als Reaktion auf eine fremde Frauenstimme bei Neugeborenen.Fetal magnetoencephalography (fMEG) facilitates the investigation of both the nature and development of the fetal central and autonomic nervous system, starting at 20 weeks of gestational age. Like magnetoencephalography in children and adults, fetal magnetoencephalography is a noninvasive method and therefore completely harmless for both the mother and the child. Magnetic sensors in fMEG devices are arranged around the abdomen of the pregnant woman. The good spatial and temporal resolution allows to measure maternal and fetal magnetocardiograms simultaneously with fetal brain activity. The fMEG signals are mainly used to measure the auditory and visual event-related brain responses or the spontaneous brain activity. Important questions concerning the developmental process of the fetal brain, as well as the maternal influence on the metabolic and cognitive state of the newborn, can be clarified by the analysis of fMEG signals. The evaluation of the fetal brain activity poses some challenges, as the signals of fetal and maternal heart activity are 10-1000 times stronger than the fetal brain signal. Therefore, it is mandatory to detect and remove the heart activity of both the mother and the fetus before analyzing the fetal brain activity. The currently used methods for this detection and removal work well for most datasets, but the processing includes numerous manual steps and is therefore very time consuming. Furthermore, signal redistribution is a problem with the current methods, which makes later detection of the fetal brain activity challenging. The aim of this work was to make the evaluation of fMEG data faster, better and nevertheless, easy to use. In this thesis two new fully-automated procedures for the detection and removal of the heart activity are presented. The fully automated R-peak detection algorithm (FLORA) improves R-peak detection by combining and extending the advantages of the previously used methods. The algorithm for the fully automated subtraction of heart activity (FAUNA) improves the signal quality and facilitates detection of brain activity without the problem of redistribution. Furthermore these methods lead to a higher reliability of the data analysis since no manual interventions are necessary. Combining both methods in a tool for fully automated processing for fetal magnetoencephalography (FAIRY) makes data evaluation now easy and fast. This prepares the processing of fMEG data for the era of "Big Data" and "Automated Science". Additionally a study about the fetal and neonatal autonomous and central nervous response to maternal voice (AURORA) was performed using the new data processing methods. The results showed a reduced movement of fetuses between 26 and 32 weeks of pregnancy and a lower heart rate during the fist 20 seconds of stimulation in the last weeks of pregnancy as a reaction to maternal voice. We additionally found a higher amplitude of the brain response to voice onset of a stranger female voice in newborns

High-temperature superconducting magnetometers for on-scalp MEG

In the growing field of on-scalp magnetoencephalography (MEG), brain activity is studied by non-invasively mapping the magnetic fields generated by neuronal currents with sensors that are flexibly placed in close proximity to the subject\u27s head. This thesis focuses on high-temperature superconducting magnetometers made from YBa2Cu3Ox-7 (YBCO), which enables a reduction in the sensor-to-room temperature standoff distance from roughly 2 cm (for conventional MEG systems) down to 1 mm. Because of the higher neuromagnetic signal magnitudes available to on-scalp sensors, simulations predict that even a relatively low-sensitivity (higher noise) full-head on-scalp MEG system can extract more information about brain activity than conventional systems.In the first part of this thesis, the development of high critical temperature (high-Tc) superconducting quantum interference device (SQUID) magnetometers for a 7-channel on-scalp MEG system is described. The sensors are single layer magnetometers with a directly coupled pickup loop made on 10 mm 7 10 mm substrates using bicrystal grain boundary Josephson junctions. We found that the kinetic inductance strongly varies with film quality and temperature. Determination of all SQUID parameters by combining measurements and inductance simulations led to excellent agreement between experimental results and theoretical predictions. This allowed us to perform an in-depth magnetometer optimization. The best magnetometers achieve a magnetic field noise level of 44 fT/√Hz at 78 K. Fabricated test SQUIDs provide evidence that noise levels below 30 fT/√Hz are possible for high quality junctions with fairly low critical currents and in combination with the optimized pickup loop design. Different feedback methods for operation in a densely-packed on-scalp MEG system were also investigated. Direct injection of current into the SQUID loop was identified as the best on-chip feedback method with feedback flux crosstalk below 0.5%. By reducing the operation temperature, the noise level can be further reduced, however, the effective area also decreases because of the decreasing kinetic inductance contribution. We present a method that allows for one-time sensor calibration independent of temperature.In the second part, the design, operation, and performance of the constructed 7-channel on-scalp MEG system based on the fabricated magnetometers is presented. With a dense (2 mm edge-to-edge) hexagonal head-aligned array, the system achieves a small sensor-to-head standoff distance of 1-3 mm and dense spatial sampling. The magnetic field noise levels are 50-130 fT/√Hz and the sensor-to-sensor feedback flux crosstalk is below 0.6%. MEG measurements with the system demonstrate the feasibility of the approach and indicate that our on-scalp MEG system allows retrieval of information unavailable to conventional MEG.In the third part, two alternative magnetometer types are studied for the next generation system. The first alternative is magnetometers based on Dayem bridge junctions instead of bicrystal grain boundary junctions. With a magnetometer based on the novel grooved Dayem bridge junctions, a magnetic field noise level of 63 fT/√Hz could be achieved, which shows that Dayem bridge junctions are starting to become a viable option for single layer magnetometers. The second alternative are high-Tc SQUID magnetometers with an inductively coupled flux transformer. The best device with bicrystal grain boundary junctions reaches a magnetic field noise level below 11 fT/√Hz and outperforms the best single layer device for frequencies above 20 Hz.In the last part, the potential of kinetic inductance magnetometers (KIMs) is investigated. We demonstrate the first high-Tc KIMs, which can be operated in fields of 9-28 \ub5T and achieve a noise level of 4 pT/√Hz at 10 kHz

Extraction et débruitage de signaux ECG du foetus.

Les malformations cardiaques congénitales sont la première cause de décès liés à une anomalie congénitale. L electrocardiogramme du fœtus (ECGf), qui est censé contenir beaucoup plus d informations par rapport aux méthodes échographiques conventionnelles, peut être mesuré e par des électrodes sur l abdomen de la mère. Cependant, il est tres faible et mélangé avec plusieurs sources de bruit et interférence y compris l ECG de la mère (ECGm) dont le niveau est très fort. Dans les études précédentes, plusieurs méthodes ont été proposées pour l extraction de l ECGf à partir des signaux enregistrés par des électrodes placées à la surface du corps de la mère. Cependant, ces méthodes nécessitent un nombre de capteurs important, et s avèrent inefficaces avec un ou deux capteurs. Dans cette étude trois approches innovantes reposant sur une paramétrisation algébrique, statistique ou par variables d état sont proposées. Ces trois méthodes mettent en œuvre des modélisations différentes de la quasi-périodicité du signal cardiaque. Dans la première approche, le signal cardiaque et sa variabilité sont modélisés par un filtre de Kalman. Dans la seconde approche, le signal est découpé en fenêtres selon les battements, et l empilage constitue un tenseur dont on cherchera la décomposition. Dans la troisième approche, le signal n est pas modélisé directement, mais il est considéré comme un processus Gaussien, caractérisé par ses statistiques à l ordre deux. Dans les différentes modèles, contrairement aux études précédentes, l ECGm et le (ou les) ECGf sont modélisés explicitement. Les performances des méthodes proposées, qui utilisent un nombre minimum de capteurs, sont évaluées sur des données synthétiques et des enregistrements réels, y compris les signaux cardiaques des fœtus jumeaux.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 variability are modeled by a Kalman filter. In the second approach, the signal is divided into windows according to the beats. Stacking the windows constructs a tensor that is then decomposed. In a third approach, the signal is not directly modeled, but it is considered as a Gaussian process characterized by its second order statistics. In all the different proposed methods, unlike previous studies, mECG and fECG(s) are explicitly modeled. The performances of the proposed methods, which utilize a minimal number of electrodes, are assessed on synthetic data and actual recordings including twin fetal cardiac signals.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF

Signal processing algorithms for enhanced image fusion performance and assessment

The dissertation presents several signal processing algorithms for image fusion in noisy multimodal conditions. It introduces a novel image fusion method which performs well for image sets heavily corrupted by noise. As opposed to current image fusion schemes, the method has no requirements for a priori knowledge of the noise component. The image is decomposed with Chebyshev polynomials (CP) being used as basis functions to perform fusion at feature level. The properties of CP, namely fast convergence and smooth approximation, renders it ideal for heuristic and indiscriminate denoising fusion tasks. Quantitative evaluation using objective fusion assessment methods show favourable performance of the proposed scheme compared to previous efforts on image fusion, notably in heavily corrupted images. The approach is further improved by incorporating the advantages of CP with a state-of-the-art fusion technique named independent component analysis (ICA), for joint-fusion processing based on region saliency. Whilst CP fusion is robust under severe noise conditions, it is prone to eliminating high frequency information of the images involved, thereby limiting image sharpness. Fusion using ICA, on the other hand, performs well in transferring edges and other salient features of the input images into the composite output. The combination of both methods, coupled with several mathematical morphological operations in an algorithm fusion framework, is considered a viable solution. Again, according to the quantitative metrics the results of our proposed approach are very encouraging as far as joint fusion and denoising are concerned. Another focus of this dissertation is on a novel metric for image fusion evaluation that is based on texture. The conservation of background textural details is considered important in many fusion applications as they help define the image depth and structure, which may prove crucial in many surveillance and remote sensing applications. Our work aims to evaluate the performance of image fusion algorithms based on their ability to retain textural details from the fusion process. This is done by utilising the gray-level co-occurrence matrix (GLCM) model to extract second-order statistical features for the derivation of an image textural measure, which is then used to replace the edge-based calculations in an objective-based fusion metric. Performance evaluation on established fusion methods verifies that the proposed metric is viable, especially for multimodal scenarios

Brain and Human Body Modeling

This open access book describes modern applications of computational human modeling with specific emphasis in the areas of neurology and neuroelectromagnetics, depression and cancer treatments, radio-frequency studies and wireless communications. Special consideration is also given to the use of human modeling to the computational assessment of relevant regulatory and safety requirements. Readers working on applications that may expose human subjects to electromagnetic radiation will benefit from this book’s coverage of the latest developments in computational modelling and human phantom development to assess a given technology’s safety and efficacy in a timely manner. Describes construction and application of computational human models including anatomically detailed and subject specific models; Explains new practices in computational human modeling for neuroelectromagnetics, electromagnetic safety, and exposure evaluations; Includes a survey of modern applications for which computational human models are critical; Describes cellular-level interactions between the human body and electromagnetic fields

Brain and Human Body Modeling

This open access book describes modern applications of computational human modeling with specific emphasis in the areas of neurology and neuroelectromagnetics, depression and cancer treatments, radio-frequency studies and wireless communications. Special consideration is also given to the use of human modeling to the computational assessment of relevant regulatory and safety requirements. Readers working on applications that may expose human subjects to electromagnetic radiation will benefit from this book’s coverage of the latest developments in computational modelling and human phantom development to assess a given technology’s safety and efficacy in a timely manner. Describes construction and application of computational human models including anatomically detailed and subject specific models; Explains new practices in computational human modeling for neuroelectromagnetics, electromagnetic safety, and exposure evaluations; Includes a survey of modern applications for which computational human models are critical; Describes cellular-level interactions between the human body and electromagnetic fields