Automatic signal and image-based assessments of spinal cord injury and treatments.

Spinal cord injury (SCI) is one of the most common sources of motor disabilities in humans that often deeply impact the quality of life in individuals with severe and chronic SCI. In this dissertation, we have developed advanced engineering tools to address three distinct problems that researchers, clinicians and patients are facing in SCI research. Particularly, we have proposed a fully automated stochastic framework to quantify the effects of SCI on muscle size and adipose tissue distribution in skeletal muscles by volumetric segmentation of 3-D MRI scans in individuals with chronic SCI as well as non-disabled individuals. We also developed a novel framework for robust and automatic activation detection, feature extraction and visualization of the spinal cord epidural stimulation (scES) effects across a high number of scES parameters to build individualized-maps of muscle recruitment patterns of scES. Finally, in the last part of this dissertation, we introduced an EMG time-frequency analysis framework that implements EMG spectral analysis and machine learning tools to characterize EMG patterns resulting in independent or assisted standing enabled by scES, and identify the stimulation parameters that promote muscle activation patterns more effective for standing. The neurotechnological advancements proposed in this dissertation have greatly benefited SCI research by accelerating the efforts to quantify the effects of SCI on muscle size and functionality, expanding the knowledge regarding the neurophysiological mechanisms involved in re-enabling motor function with epidural stimulation and the selection of stimulation parameters and helping the patients with complete paralysis to achieve faster motor recovery

Automating the multimodal analysis of musculoskeletal imaging in the presence of hip implants

In patients treated with hip arthroplasty, the muscular condition and presence of inflammatory reactions are assessed using magnetic resonance imaging (MRI). As MRI lacks contrast for bony structures, computed tomography (CT) is preferred for clinical evaluation of bone tissue and orthopaedic surgical planning. Combining the complementary information of MRI and CT could improve current clinical practice for diagnosis, monitoring and treatment planning. In particular, the different contrast of these modalities could help better quantify the presence of fatty infiltration to characterise muscular condition after hip replacement. In this thesis, I developed automated processing tools for the joint analysis of CT and MR images of patients with hip implants. In order to combine the multimodal information, a novel nonlinear registration algorithm was introduced, which imposes rigidity constraints on bony structures to ensure realistic deformation. I implemented and thoroughly validated a fully automated framework for the multimodal segmentation of healthy and pathological musculoskeletal structures, as well as implants. This framework combines the proposed registration algorithm with tailored image quality enhancement techniques and a multi-atlas-based segmentation approach, providing robustness against the large population anatomical variability and the presence of noise and artefacts in the images. The automation of muscle segmentation enabled the derivation of a measure of fatty infiltration, the Intramuscular Fat Fraction, useful to characterise the presence of muscle atrophy. The proposed imaging biomarker was shown to strongly correlate with the atrophy radiological score currently used in clinical practice. Finally, a preliminary work on multimodal metal artefact reduction, using an unsupervised deep learning strategy, showed promise for improving the postprocessing of CT and MR images heavily corrupted by metal artefact. This work represents a step forward towards the automation of image analysis in hip arthroplasty, supporting and quantitatively informing the decision-making process about patient’s management

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas

Previsão e geração de sinais EMG de Parkison com redes neurais

Orientador: Esther Luna ColombiniDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: A doença de Parkinson é uma desordem neurodegenerativa que afeta aproximadamente 2% da população mundial acima de 60 anos (7-10 milhões de pessoas), e é caracterizada por sintomas como tremor em repouso e em movimento, que podem causar graves restrições na vida dos pacientes e também estão associados a sintomas não motores como dificuldade para dormir, depressão e fadiga. Apenas no Brasil, existem mais de 200.000 pessoas diagnosticadas com doença de Parkinson, número que pode duplicar até 2030 devido ao envelhecimento da população brasileira. Neste contexto, o desenvolvimento de novos tratamentos e formas de assistência que possam melhorar a qualidade de vida e a autonomia de pacientes é extremamente importante. Neste trabalho, são propostas novas técnicas baseadas em Redes Neurais para a previsão e geração de sinais de eletromiografia (EMG) do tremor em pacientes, para o suporte ao desenvolvimento de novos dispositivos e técnicas para assistência a pacientes. Primeiro, comparamos diferentes modelos de Redes Neuras, utilizando perceptron multicamadas (MLP) e redes neurais recorrentes (RNN) para a previsão dos sinais EMG de doença de Parkinson, antecipando os padrões de tremor em repouso. Os resultados experimentais indicam que os modelos propostos adaptam-se aos padrões específicos de cada paciente, gerando previsões acuradas para os sinais puros ou envelopes EMG. Segundo, são propostos duas novas técnicas para aumento de dados baseadas em redes adversárias generativas convolucionais profundas (DCGANs) e transferência de estilo (ST) para aumentar sinais EMG, cujos resultados mostram que os modelos propostos conseguem adaptar-se aos diferentes formatos, frequências e amplitudes de tremor, simulando os padrões específicos de cada paciente e estendendo as bases de dados existentes para diferentes protocolos de movimento. Ambos resultados sugerem que o emprego de redes neurais na geração e previsão de sinais biológicos complexos como sinais EMG pode ser bem-sucedido, permitindo o uso de tais modelos para a extensão dos dados de pacientes e para geração de sinais de tremor que auxiliem no desenvolvimento e validação de novas técnicas de supressão de tremor em pacientesAbstract: Parkinson¿s Disease (PD) is a neurodegenerative disorder that affects approximately 2% of the world¿s population over 60 years old (7-10 million people). It is characterized by symptoms like resting and action tremors, which cause severe impairments to the patient¿s life and may also cause non-motor symptoms such as difficulty to sleep, depression, and fatigue. Only in Brazil, there are more than 200,000 people with PD, a number that might double by 2030 due to the aging of the population. In such a context, developing new treatments and assistance techniques that can improve PD patient¿s life quality and autonomy are extremely important. In this work, we propose novel methods based on Neural Networks (NN) for predicting and generating patient-specific PD electromyography (EMG) tremor signals, to support the development of new assisting devices and techniques. First, we compare different NN models, using the multi-layer perceptron (MLPs) and recurrent neural network (RNN) for predicting PD EMG signals, to anticipate resting tremor patterns. The experimental results indicate that the proposed models can adapt to the patient¿s specific tremor patterns and provide reasonable predictions for both EMG envelopes and EMG raw signals. Next, we propose two new data augmentation approaches based on Deep Convolutional Generative Adversarial Networks (DCGANs) and Style Transfer (ST) for augmenting EMG signals. Results show that the proposed models can adapt to different shapes, frequencies, and amplitudes of tremor, simulating each patient¿s specific tremor patterns and extending them to different sets of movement protocols. All results suggest that Neural Networks can successfully be used for predicting and generating complex biological signals like EMG, allowing these models to be used for extending patients¿ datasets and generating tremor signals. These new data could help to validate treatment approaches on different movement scenarios, contributing to the development of new techniques for tremor suppression on patientsMestradoCiência da ComputaçãoMestre em Ciência da Computaçã

Generalisable FPCA-based Models for Predicting Peak Power in Vertical Jumping using Accelerometer Data

Peak power in the countermovement jump is correlated with various measures of sports performance and can be used to monitor athlete training. The gold standard method for determining peak power uses force platforms, but they are unsuitable for field-based testing favoured by practitioners. Alternatives include predicting peak power from jump flight times, or using Newtonian methods based on body-worn inertial sensor data, but so far neither has yielded sufficiently accurate estimates. This thesis aims to develop a generalisable model for predicting peak power based on Functional Principal Component Analysis applied to body-worn accelerometer data. Data was collected from 69 male and female adults, engaged in sports at recreational, club or national levels. They performed up to 16 countermovement jumps each, with and without arm swing, 696 jumps in total. Peak power criterion measures were obtained from force platforms, and characteristic features from accelerometer data were extracted from four sensors attached to the lower back, upper back and both shanks. The best machine learning algorithm, jump type and sensor anatomical location were determined in this context. The investigation considered signal representation (resultant, triaxial or a suitable transform), preprocessing (smoothing, time window and curve registration), feature selection and data augmentation (signal rotations and SMOTER). A novel procedure optimised the model parameters based on Particle Swarm applied to a surrogate Gaussian Process model. Model selection and evaluation were based on nested cross validation (Monte Carlo design). The final optimal model had an RMSE of 2.5 W·kg-1, which compares favourably to earlier research (4.9 ± 1.7 W·kg-1 for flight-time formulae and 10.7 ± 6.3 W·kg-1 for Newtonian sensor-based methods). Whilst this is not yet sufficiently accurate for applied practice, this thesis has developed and comprehensively evaluated new techniques, which will be valuable to future biomechanical applications

Automated analysis of ultrasound imaging of muscle and tendon in the upper limb using artificial intelligence methods

Accurate estimation of geometric musculoskeletal parameters from medical imaging has a number of applications in healthcare analysis and modelling. In vivo measurement of key morphological parameters of an individual’s upper limb opens up a new era for the construction of subject-specific models of the shoulder and arm. These models could be used to aid diagnosis of musculoskeletal problems, predict the effects of interventions and assist in the design and development of medical devices. However, these parameters are difficult to evaluate in vivo due to the complicated and inaccessible nature of structures such as muscles and tendons. Ultrasound, as a non-invasive and low-cost imaging technique, has been used in the manual evaluation of parameters such as muscle fibre length, cross sectional area and tendon length. However, the evaluation of ultrasound images depends heavily on the expertise of the operator and is time-consuming. Basing parameter estimation on the properties of the image itself and reducing the reliance on the skill of the operator would allow for automation of the process, speeding up parameter estimation and reducing bias in the final outcome. Key barriers to automation are the presence of speckle noise in the images and low image contrast. This hinders the effectiveness of traditional edge detection and segmentation methods necessary for parameter estimation. Therefore, addressing these limitations is considered pivotal to progress in this area.The aims of this thesis were therefore to develop new methods for the automatic evaluation of these geometric parameters of the upper extremity, and to compare these with manual evaluations. This was done by addressing all stages of the image processing pipeline, and introducing new methods based on artificial intelligence.Speckle noise of musculoskeletal ultrasound images was reduced by successfully applying local adaptive median filtering and anisotropic diffusion filtering. Furthermore, low contrast of the ultrasound image and video was enhanced by developing a new method based on local fuzzy contrast enhancement. Both steps contributed to improving the quality of musculoskeletal ultrasound images to improve the effectiveness of edge detection methods.Subsequently, a new edge detection method based on the fuzzy inference system was developed to outline the necessary details of the musculoskeletal ultrasound images after image enhancement. This step allowed automated segmentation to be used to estimate the morphological parameters of muscles and tendons in the upper extremity.Finally, the automatically estimated geometric parameters, including the thickness and pennation angle of triceps muscle and the cross-sectional area and circumference of the flexor pollicis longus tendon were compared with manually taken measurements from the same ultrasound images.The results show successful performance of the novel methods in the sample population for the muscles and tendons chosen. A larger dataset would help to make the developed methods more robust and more widely applicable.Future work should concentrate on using the developed methods of this thesis to evaluate other geometric parameters of the upper and lower extremities such as automatic evaluation of the muscle fascicle length

Intelligent Sensors for Human Motion Analysis

The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems

Biochemical Biomarkers and Neurodegenerative Diseases

In this book, we collected scientific articles, including reviews and research articles, showcasing the lastest literature on the importance of biochemical biomarkers in the management of neurodegenerative diseases, from screening to diagnosis, prognosis, and treatment