An Accurate and Real-time Method for Resolving Superimposed Action Potentials in MultiUnit Recordings

Objective: Spike sorting of muscular and neural recordings requires separating action potentials that overlap in time (superimposed action potentials (APs)). We propose a new algorithm for resolving superimposed action potentials, and we test it on intramuscular EMG (iEMG) and intracortical recordings. Methods: Discrete-time shifts of the involved APs are first selected based on a heuristic extension of the peel-off algorithm. Then, the time shifts that provide the minimal residual Euclidean norm are identified (Discrete Brute force Correlation (DBC)). The optimal continuous-time shifts are then estimated (High-Resolution BC (HRBC)). In Fusion HRBC (FHRBC), two other cost functions are used. A parallel implementation of the DBC and HRBC algorithms was developed. The performance of the algorithms was assessed on 11,000 simulated iEMG and 14,000 neural recording superpositions, including two to eight APs, and eight experimental iEMG signals containing four to eleven active motor units. The performance of the proposed algorithms was compared with that of the Branch-and-Bound (BB) algorithm using the Rank-Product (RP) method in terms of accuracy and efficiency. Results: The average accuracy of the DBC, HRBC and FHRBC methods on the entire simulated datasets was 92.16\ub117.70, 93.65\ub116.89, and 94.90\ub115.15 (%). The DBC algorithm outperformed the other algorithms based on the RP method. The average accuracy and running time of the DBC algorithm on 10.5 ms superimposed spikes of the experimental signals were 92.1\ub121.7 (%) and 2.3\ub115.3 (ms). Conclusion and Significance: The proposed algorithm is promising for real-time neural decoding, a central problem in neural and muscular decoding and interfacing

Robust processing of diffusion weighted image data

The work presented in this thesis comprises a proposed robust diffusion weighted magnetic resonance imaging (DW-MRI) pipeline, each chapter detailing a step designed to ultimately transform raw DW-MRI data into segmented bundles of coherent fibre ready for more complex analysis or manipulation. In addition to this pipeline we will also demonstrate, where appropriate, ways in which each step could be optimized for the maxillofacial region, setting the groundwork for a wider maxillofacial modelling project intended to aid surgical planning. Our contribution begins with RESDORE, an algorithm designed to automatically identify corrupt DW-MRI signal elements. While slower than the closest alternative, RESDORE is also far more robust to localised changes in SNR and pervasive image corruptions. The second step in the pipeline concerns the retrieval of accurate fibre orientation distribution functions (fODFs) from the DW-MRI signal. Chapter 4 comprises a simulation study exploring the application of spherical deconvolution methods to `generic' fibre; finding that the commonly used constrained spherical harmonic deconvolution (CSHD) is extremely sensitive to calibration but, if handled correctly, might be able to resolve muscle fODFs in vivo. Building upon this information, Chapter 5 conducts further simulations and in vivo image experimentation demonstrating that this is indeed the case, allowing us to demonstrate, for the first time, anatomically plausible reconstructions of several maxillofacial muscles. To complete the proposed pipeline, Chapter 6 then introduces a method for segmenting whole volume streamline tractographies into anatomically valid bundles. In addition to providing an accurate segmentation, this shape-based method does not require computationally expensive inter-streamline comparisons employed by other approaches, allowing the algorithm to scale linearly with respect to the number of streamlines within the dataset. This is not often true for comparison based methods which in the best case scale in higher linear time but more often by O(N2) complexity

Advanced robust non-invasive foetal heart detection techniques during active labour using one pair of transabdominal electrodes

The thesis proposes and evaluates three state-of-the-art signal processing techniques to detect fetal heartbeats within each maternal cardiac cycle, during labour contractions, using only a pair of transabdominal electrodes. The first and second techniques are, namely, the structured third- order cumulant-slice-template matching and the bispectral-contours-template matching for fetal QRS identification, respectively. The third technique is based on the modified and appropriately weighted spectral multiple signal classification (MUSIC) with incorporated covariance matrix for uterine contraction noise-like interfering signals also contaminated with noise. Essentially, two modifications to the standard MUSIC have been developed in order to enhance the performance of the spectral estimator in our applied work. The first modification involves the introduction of an optimised weighting function to the segmented ECG covariance matrix, and is chiefly aimed at enhancing the fetal QRS major spectral peak which occurs at around 30 Hz against the mother QRS major spectral peak usually occurring around 17 Hz and all other noise contributions. Additional optional pseudo-bispectral enhancement to sharpen the maternal and fetal spectral peaks, in particular when the mother and fetal R-waves are temporally coincident, have been achieved. The second modification to the spectral MUSIC is the removal of the unjustified assumption that only white Gaussian noise is present and the incorporation of the actual measured labour uterine contraction covariance matrix in reconfigured subspace analysis. This inevitably leads to the generalised eigenvectors - eigenvalues decomposition modern signal processing. This is now coined the modified, interference incorporated pseudo-spectral MUSIC. The above mentioned first and second techniques are higher-order statistics-based (HOS) and hybrid involving both signal processing and NN classifiers. The third technique is second-order statistics-based (SOS). In all techniques, the removal of signal non-linearity with the aid of non-linear Volterra synthesisers plays a crucial part in the fetal detection integrity. Accurately assessed fetal heart classification rates as high as 95% have been achieved during labour, thus helping to provide non-invasive transparency to fetal intrapartum welfare. Performance analysis and evaluation processes involved more than 30 critical cases classified as “fetal under stress in labour” recorded in a London hospital database and used both transbadominal ECG electrodes and fetal scalp electrodes. The latter facilitates detection of the instantaneous fetal heart rate which is then used as the Reference Fetal Heart Rate in the assessment of the classification rate of each of the above mentioned techniques. It will be shown that the fetal heartbeats are completely masked by uterine activity and noise artefacts in all the recorded transabdominal maternal ECG signals. The fetal scalp electrode was, therefore, deemed necessary to provide the highest accurate measure of fetal heart functionality (from the hospital viewpoint), and in the assessment of the three non-invasive techniques presented in this thesis. The techniques may also be used during gestation and as early as 10 weeks

Single Epoch Analysis and Bi-hemisphere Study of Magnetoencephalographic (MEG) Signals using Vector Signal Transformation V3 and Magnetic Field Tomography (MFT)

The biomagnetic inverse problem has no unique solution, nevertheless even a cursory look at the features shown in raw signal can often suffice to highlight strong superficial activity. To do a proper single epoch analysis is normally prohibitively expensive in terms of computing demands. Hence the original aim of this thesis was to use simple efficient signal transformations to characterize superficial generators and contrast the single epoch signature with that extracted from the average signal. The results have intrigued us sufficiently to go beyond the original goal and extract very preliminary estimates of activity across the cerebral hemisphere in single trials. The original tool, and one that we have used for much of the work, is a simple vector signal transformation called V3. This signal transformation highlights nearby sources; it is a crude but quick estimator of generators directly from the raw MEG signals. Together with Magnetic Field Tomography (MFT), which relies on distributed source analysis of the MEG signals, we have tackled the following specific problems relating to aspects of normal brain function: efficient estimation of generators of magnetic fields; relationship between the average signal and single trials; and interhemispheric differences and relationship between the activity in the left and right hemispheres of the brain. During the project, we have used as examples auditory evoked MEG measurements obtained from two multichannel systems and applied the V3 and MFT analysis to both the average and single trial signals. In particular, we chose the 40-Hz (or gamma band) auditory response as the study subject. We found that in single epochs similar patterns of high frequency activity are observed in the area around the auditory cortex well before, close to and well after stimulus onset; the sequence of events observed in the average can only represent the evolution of events in single trials in a statistical way; and deep and central areas of the brain may be the seeds for the main deflections observed in the auditory responses

Multi-component MRI transverse-relaxation parameter estimation to detect and monitor neuromuscular disease

We aimed to optimise the estimation of skeletal muscle-water spin-spin relaxation time (T2m), and fat fraction estimated from multi-echo MRI, as potential biomarkers, by accounting for instrumental factors such as B1 errors, non-Gaussian noise and non-ideal echo train evolution. A multi-component slice-profile-compensated extended phase graph (sEPG) model for multi-echo Carr-Purcell-Meiboom-Gill (CPMG) spin-echo sequence signals was implemented, modelling the fat signal as two empirically calibrated sEPG components with fixed parameters, and the remaining unknown parameters (B1 field factor, T2m, fat fraction (ffa), global amplitude and Rician noise SD) determined by maximum likelihood estimation. After validation using a calibrated test object the algorithm was used to analyse clinical muscle study data from patient groups with amyotrophic lateral sclerosis (ALS), Kennedy’s disease (KD) and Duchenne muscular dystrophy (DMD) and matched healthy controls. Parameter maps were generated using quality control steps to reject pixels failing fit quality or physical meaningfulness criteria. Muscle fat-fraction was also determined independently by 3-point Dixon MRI (ffd). In ALS and KD median T2m were significantly elevated compared with healthy controls in varied patterns and time courses, whereas it was decreased in DMD; other T2m distribution histogram metrics such as the skewness and full width at quarter maximum also differed significantly between patients and healthy volunteers. Quantitative comparison of ffa and ffd in the same muscles revealed a monotonic relationship deviating from linearity due to differing deviations from the assumed ideal signal behaviour in each method. Finally, the effects upon estimation accuracy and precision of practically realisable pulse sequence parameter choices were explored in simulations and with real data. Recommendations are presented for optimal choices. Clinically practical conventional CPMG sequences, combined with an appropriate signal model and parameter estimation method can provide robust T2m and ffa measures which change in disease and may sensitively reflect different aspects of neuromuscular pathology

Multivariate methods for interpretable analysis of magnetic resonance spectroscopy data in brain tumour diagnosis

Malignant tumours of the brain represent one of the most difficult to treat types of cancer due to the sensitive organ they affect. Clinical management of the pathology becomes even more intricate as the tumour mass increases due to proliferation, suggesting that an early and accurate diagnosis is vital for preventing it from its normal course of development. The standard clinical practise for diagnosis includes invasive techniques that might be harmful for the patient, a fact that has fostered intensive research towards the discovery of alternative non-invasive brain tissue measurement methods, such as nuclear magnetic resonance. One of its variants, magnetic resonance imaging, is already used in a regular basis to locate and bound the brain tumour; but a complementary variant, magnetic resonance spectroscopy, despite its higher spatial resolution and its capability to identify biochemical metabolites that might become biomarkers of tumour within a delimited area, lags behind in terms of clinical use, mainly due to its difficult interpretability. The interpretation of magnetic resonance spectra corresponding to brain tissue thus becomes an interesting field of research for automated methods of knowledge extraction such as machine learning, always understanding its secondary role behind human expert medical decision making. The current thesis aims at contributing to the state of the art in this domain by providing novel techniques for assistance of radiology experts, focusing on complex problems and delivering interpretable solutions. In this respect, an ensemble learning technique to accurately discriminate amongst the most aggressive brain tumours, namely glioblastomas and metastases, has been designed; moreover, a strategy to increase the stability of biomarker identification in the spectra by means of instance weighting is provided. From a different analytical perspective, a tool based on signal source separation, guided by tumour type-specific information has been developed to assess the existence of different tissues in the tumoural mass, quantifying their influence in the vicinity of tumoural areas. This development has led to the derivation of a probabilistic interpretation of some source separation techniques, which provide support for uncertainty handling and strategies for the estimation of the most accurate number of differentiated tissues within the analysed tumour volumes. The provided strategies should assist human experts through the use of automated decision support tools and by tackling interpretability and accuracy from different anglesEls tumors cerebrals malignes representen un dels tipus de càncer més difícils de tractar degut a la sensibilitat de l’òrgan que afecten. La gestió clínica de la patologia esdevé encara més complexa quan la massa tumoral s'incrementa degut a la proliferació incontrolada de cèl·lules; suggerint que una diagnosis precoç i acurada és vital per prevenir el curs natural de desenvolupament. La pràctica clínica estàndard per a la diagnosis inclou la utilització de tècniques invasives que poden arribar a ser molt perjudicials per al pacient, factor que ha fomentat la recerca intensiva cap al descobriment de mètodes alternatius de mesurament dels teixits del cervell, tals com la ressonància magnètica nuclear. Una de les seves variants, la imatge de ressonància magnètica, ja s'està actualment utilitzant de forma regular per localitzar i delimitar el tumor. Així mateix, una variant complementària, la espectroscòpia de ressonància magnètica, malgrat la seva alta resolució espacial i la seva capacitat d'identificar metabòlits bioquímics que poden esdevenir biomarcadors de tumor en una àrea delimitada, està molt per darrera en termes d'ús clínic, principalment per la seva difícil interpretació. Per aquest motiu, la interpretació dels espectres de ressonància magnètica corresponents a teixits del cervell esdevé un interessant camp de recerca en mètodes automàtics d'extracció de coneixement tals com l'aprenentatge automàtic, sempre entesos com a una eina d'ajuda per a la presa de decisions per part d'un metge expert humà. La tesis actual té com a propòsit la contribució a l'estat de l'art en aquest camp mitjançant l'aportació de noves tècniques per a l'assistència d'experts radiòlegs, centrades en problemes complexes i proporcionant solucions interpretables. En aquest sentit, s'ha dissenyat una tècnica basada en comitè d'experts per a una discriminació acurada dels diferents tipus de tumors cerebrals agressius, anomenats glioblastomes i metàstasis; a més, es proporciona una estratègia per a incrementar l'estabilitat en la identificació de biomarcadors presents en un espectre mitjançant una ponderació d'instàncies. Des d'una perspectiva analítica diferent, s'ha desenvolupat una eina basada en la separació de fonts, guiada per informació específica de tipus de tumor per a avaluar l'existència de diferents tipus de teixits existents en una massa tumoral, quantificant-ne la seva influència a les regions tumorals veïnes. Aquest desenvolupament ha portat cap a la derivació d'una interpretació probabilística d'algunes d'aquestes tècniques de separació de fonts, proporcionant suport per a la gestió de la incertesa i estratègies d'estimació del nombre més acurat de teixits diferenciats en cada un dels volums tumorals analitzats. Les estratègies proporcionades haurien d'assistir els experts humans en l'ús d'eines automatitzades de suport a la decisió, donada la interpretabilitat i precisió que presenten des de diferents angles

Ultra-fast Imaging of Two-Phase Flow in Structured Monolith Reactors; Techniques and Data Analysis

This thesis will address the use of nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI) techniques to probe the “monolith reactor”, which consists of a structured catalyst over which reactions may occur. This reactor has emerged as a potential alternative to more traditional chemical engineering systems such as trickle bed and slurry reactors. However, being a relatively new design, its associated flow phenomena and design procedures are not rigorously understood, which is retarding its acceptance in industry. Traditional observations are unable to provide the necessary information for design since the systems are opaque and dynamic. Therefore, NMR is proposed as an ideal tool to probe these systems in detail. The theory of NMR is summarised and the development of novel NMR techniques is presented. Novel techniques are validated in simple systems, and tested in more complex systems to ascertain their quantitative nature, and to find their limitations. These techniques are improvements over existing techniques in that they either decrease the acquisition time (allowing the observation of dynamically-changing systems) or allow us to probe systems in different ways to extract useful information. The goal of this research is to better understand the flow phenomena present in such systems, and to use this information to design better, more efficient, more controllable industrial reactors. The analysis of the NMR data acquired is discussed in detail, and several novel image-processing techniques have been developed to aid in the quantification of features within the images, and also to measure quantities such as holdup and velocity. These novel techniques are validated, and then applied to the systems of interest. Various configurations of monolith reactor, ranging from low flow rate systems to more challenging (and more industrially relevant) turbulent systems, are probed using these methods, and the contrasting flow phenomena and performance of these systems are discussed, with a view to optimisation of the choice of design parameters