Blind Source Separation for the Processing of Contact-Less Biosignals

(Spatio-temporale) Blind Source Separation (BSS) eignet sich für die Verarbeitung von Multikanal-Messungen im Bereich der kontaktlosen Biosignalerfassung. Ziel der BSS ist dabei die Trennung von (z.B. kardialen) Nutzsignalen und Störsignalen typisch für die kontaktlosen Messtechniken. Das Potential der BSS kann praktisch nur ausgeschöpft werden, wenn (1) ein geeignetes BSS-Modell verwendet wird, welches der Komplexität der Multikanal-Messung gerecht wird und (2) die unbestimmte Permutation unter den BSS-Ausgangssignalen gelöst wird, d.h. das Nutzsignal praktisch automatisiert identifiziert werden kann. Die vorliegende Arbeit entwirft ein Framework, mit dessen Hilfe die Effizienz von BSS-Algorithmen im Kontext des kamera-basierten Photoplethysmogramms bewertet werden kann. Empfehlungen zur Auswahl bestimmter Algorithmen im Zusammenhang mit spezifischen Signal-Charakteristiken werden abgeleitet. Außerdem werden im Rahmen der Arbeit Konzepte für die automatisierte Kanalauswahl nach BSS im Bereich der kontaktlosen Messung des Elektrokardiogramms entwickelt und bewertet. Neuartige Algorithmen basierend auf Sparse Coding erwiesen sich dabei als besonders effizient im Vergleich zu Standard-Methoden.(Spatio-temporal) Blind Source Separation (BSS) provides a large potential to process distorted multichannel biosignal measurements in the context of novel contact-less recording techniques for separating distortions from the cardiac signal of interest. This potential can only be practically utilized (1) if a BSS model is applied that matches the complexity of the measurement, i.e. the signal mixture and (2) if permutation indeterminacy is solved among the BSS output components, i.e the component of interest can be practically selected. The present work, first, designs a framework to assess the efficacy of BSS algorithms in the context of the camera-based photoplethysmogram (cbPPG) and characterizes multiple BSS algorithms, accordingly. Algorithm selection recommendations for certain mixture characteristics are derived. Second, the present work develops and evaluates concepts to solve permutation indeterminacy for BSS outputs of contact-less electrocardiogram (ECG) recordings. The novel approach based on sparse coding is shown to outperform the existing concepts of higher order moments and frequency-domain features

Comparative Study of CV3 Carbonaceous Chondrites Allende and Bali Using Micro-Raman Spectroscopy and SEM/EDS

The birth of the solar system (over 4 billion years) is speculated to have happened from a nebula, swirling and compacting in localized regions to eventually form the Sun and planets. This complex process consists of numerous changes and intermediary steps, yet to be fully understood. Carbonaceous chondritic meteorites are relics of that process and therefore have potential to reveal information about the formation history. Several theories have been formulated linking their composition to planet formation. This study focusses on two carbonaceous chondritic specimens, Allende and Bali, both of the group CV and petrologic type 3. CV meteorites are abundant in inclusions (e.g. chondrules), which are considered the oldest objects to have formed in the solar system. Samples were studied using micro-Raman spectroscopy, scanning electron microscopy and energy dispersive X-ray spectroscopy (SEM/EDS). Chondrules were identified in the Allende sample, while Bali had several irregular inclusions and small crystals. These other inclusions were refractory inclusions rich in Ca and Al. Elements common to both were: O, Si, Fe, Mg, C and S. Ni, Ca and Al were also present but varied in relative percentage weight in each sample. Bali had several Fe-Ni spots, while no such spots were identified in Allende. S-rims were seen around inclusions in both, as was the Fe/Mg complementarity between matrix and inclusion. Minerals found in both were graphitic carbon and Mg-rich olivine. Allende also contained pyroxene and quartz; while Bali had larnite, magnetite and awaruite. Information obtained about the parent bodies of these samples includes their peak metamorphic temperatures and extent of secondary alterations (post-accretion). Allende was found to be more metamorphosed than Bali. Possible implications from the obtained results have been discussed in the light of planet formation models, such as that the formation of the inclusions and surrounding matrix could have taken place in the same local nebular region. Overall, the findings from this study agree well with literature and add to the studies conducted within the planet formation community

Recent Developments in Multichannel Raman Microprobing

This paper reviews the capabilities of multichannel Raman microprobing instruments with emphasis on some major innovative progress recently proposed which looks promising for the development of new fields of application. This new progress includes: confocal Raman microanalysis that provides better spatial resolution and background rejection, confocal Raman mapping that revitalizes the field of Raman imaging pioneered by us 20 years ago, the coupling of molecular Raman microanalysis to elemental electron microprobe, and finally, Raman spectroscopy with near-infrared excitation which permits avoiding the fluorescence limitation of visible Raman spectroscopy

Analysis of Atrial Electrograms

This work provides methods to measure and analyze features of atrial electrograms - especially complex fractionated atrial electrograms (CFAEs) - mathematically. Automated classification of CFAEs into clinical meaningful classes is applied and the newly gained electrogram information is visualized on patient specific 3D models of the atria. Clinical applications of the presented methods showed that quantitative measures of CFAEs reveal beneficial information about the underlying arrhythmia

Toward a Noninvasive Automatic Seizure Control System in Rats With Transcranial Focal Stimulations via Tripolar Concentric Ring Electrodes

Epilepsy affects approximately 1% of the world population. Antiepileptic drugs are ineffective in approximately 30% of patients and have side effects. We are developing a noninvasive, or minimally invasive, transcranial focal electrical stimulation system through our novel tripolar concentric ring electrodes to control seizures. In this study, we demonstrate feasibility of an automatic seizure control system in rats with pentylenetetrazole-induced seizures through single and multiple stimulations. These stimulations are automatically triggered by a real-time electrographic seizure activity detector based on a disjunctive combination of detections from a cumulative sum algorithm and a generalized likelihood ratio test. An average seizure onset detection accuracy of 76.14% was obtained for the test set (n = 13). Detection of electrographic seizure activity was accomplished in advance of the early behavioral seizure activity in 76.92% of the cases. Automatically triggered stimulation significantly (p = 0.001) reduced the electrographic seizure activity power in the once stimulated group compared to controls in 70% of the cases. To the best of our knowledge this is the first closed-loop automatic seizure control system based on noninvasive electrical brain stimulation using tripolar concentric ring electrode electrographic seizure activity as feedback

Magnetic cluster formation in Al₂O₃.

Master of Science in Chemistry and Physics. University of KwaZulu-Natal, Durban, 2017.Abstract available in PDF file

Extracellular stimulation system for the modification of network parameters in cultured neural networks

Este proyecto se centra en el uso de dispositivos de microelectrodos MEAs (Multi Electrode Arrays) de última generación para el estudio y la manipulación de redes neuronales en cultivo. Chips MEA, con 26400 electrodos situados en una superficie de 3.85x2.10mm^2, fueron utilizados para registrar la actividad eléctrica de dos cultivos de neuronas corticales disociadas obtenidas de embriones de rata. En las mismas plataformas MEA, se implementó un protocolo de estimulación en bucle cerrado, de manera que se pudieran enviar pulsos eléctricos de estimulación a determinados electrodos en respues a potenciales de acción detectados en otro electrodo. Uno de los cultivos de neuronas fue sometido al protocolo de estimulación en bucle cerrado mientras que el segundo cultivo fue utilizado como control. Se desarrollaron diferentes métodos con el fin de hacer una caracterización funcional de los cultivos. El análisis funcional de los registros obtenidos en los experimentos indican que la estimulación en bucle cerrado provocó perdidas significativas y generalizadas de actividad y conectividad en la red neuronal en cultivo

Computational models for stuctural analysis of retinal images

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonThe evaluation of retina structures has been of great interest because it could be used as a non-intrusive diagnosis in modern ophthalmology to detect many important eye diseases as well as cardiovascular disorders. A variety of retinal image analysis tools have been developed to assist ophthalmologists and eye diseases experts by reducing the time required in eye screening, optimising the costs as well as providing efficient disease treatment and management systems. A key component in these tools is the segmentation and quantification of retina structures. However, the imaging artefacts such as noise, intensity homogeneity and the overlapping tissue of retina structures can cause significant degradations to the performance of these automated image analysis tools. This thesis aims to provide robust and reliable automated retinal image analysis technique to allow for early detection of various retinal and other diseases. In particular, four innovative segmentation methods have been proposed, including two for retinal vessel network segmentation, two for optic disc segmentation and one for retina nerve fibre layers detection. First, three pre-processing operations are combined in the segmentation method to remove noise and enhance the appearance of the blood vessel in the image, and a Mixture of Gaussians is used to extract the blood vessel tree. Second, a graph cut segmentation approach is introduced, which incorporates the mechanism of vectors flux into the graph formulation to allow for the segmentation of very narrow blood vessels. Third, the optic disc segmentation is performed using two alternative methods: the Markov random field image reconstruction approach detects the optic disc by removing the blood vessels from the optic disc area, and the graph cut with compensation factor method achieves that using prior information of the blood vessels. Fourth, the boundaries of the retinal nerve fibre layer (RNFL) are detected by adapting a graph cut segmentation technique that includes a kernel-induced space and a continuous multiplier based max-flow algorithm. The strong experimental results of our retinal blood vessel segmentation methods including Mixture of Gaussian, Graph Cut achieved an average accuracy of 94:33%, 94:27% respectively. Our optic disc segmentation methods including Markov Random Field and Compensation Factor also achieved an average sensitivity of 92:85% and 85:70% respectively. These results obtained on several public datasets and compared with existing methods have shown that our proposed methods are robust and efficient in the segmenting retinal structures such the blood vessels and the optic disc.Brunel University Londonhttp://bura.brunel.ac.uk/bitstream/2438/10387/1/FulltextThesis.pd

Advanced Statistical Machine Learning Methods for the Analysis of Neurophysiologic Data with Medical Application

Transcranial magnetic stimulation procedures use a magnetic field to carry a short-lasting electrical current pulse into the brain, where it stimulates neurons, particularly in superficial regions of the cerebral cortex. It is a powerfull tool to calculate several parameters related to the intracortical excitability and inhibition of the motor cortex. The cortical silent period (CSP), evoked by magnetic stimulation, corresponds to the suppression of muscle activity for a short period after a muscle response to a magnetic stimulation. The duration of the CSP is paramount to assess intracortical inhibition, and it is known to be correlated with the prognosis of stroke patients’ motor ability. Current mechanisms to estimate the duration of the CSP are mostly based on the analysis of raw electromyographical (EMG) signal and they are very sensitive to the presence of noise. This master thesis is devoted to the analysis of the EMG signal of stroke patients under rehabilitation. The use of advanced statistical machine learning techniques that behave robustly in the presence of noise for this analysis allows us to accurately estimate signal parameters such as the CSP. The research reported in this thesis provides us with a first evidence about their applicability in other areas of neuroscience

Advanced Statistical Machine Learning Methods for the Analysis of Neurophysiologic Data with Medical Application

Transcranial magnetic stimulation procedures use a magnetic field to carry a short-lasting electrical current pulse into the brain, where it stimulates neurons, particularly in superficial regions of the cerebral cortex. It is a powerfull tool to calculate several parameters related to the intracortical excitability and inhibition of the motor cortex. The cortical silent period (CSP), evoked by magnetic stimulation, corresponds to the suppression of muscle activity for a short period after a muscle response to a magnetic stimulation. The duration of the CSP is paramount to assess intracortical inhibition, and it is known to be correlated with the prognosis of stroke patients’ motor ability. Current mechanisms to estimate the duration of the CSP are mostly based on the analysis of raw electromyographical (EMG) signal and they are very sensitive to the presence of noise. This master thesis is devoted to the analysis of the EMG signal of stroke patients under rehabilitation. The use of advanced statistical machine learning techniques that behave robustly in the presence of noise for this analysis allows us to accurately estimate signal parameters such as the CSP. The research reported in this thesis provides us with a first evidence about their applicability in other areas of neuroscience