Adaptive Neuro-Fuzzy Inference System for Prediction of Surgery Time for Ischemic Stroke Patients

With the advent of machine learning techniques, creation and utilization of prediction models for different medical procedures including prediction of diagnosis, treatment and recovery of different medical conditions has become the norm. Recent studies focus on the automation of infarction volume growth rate prediction by the utilization of machine learning techniques. These techniques when effectively applied, could significantly help in reducing the time needed to attend to stroke patients. We propose, in this proposal, a Fuzzy Inference System that can determine when a stroke patient should undergo Decompressive Hemicraniectomy. The second infarction volume growth rate and the decision whether a patient needs to undergo this procedure, both predicted outputs of two trained models, act as inputs to this system. While the initial prediction model, that which predicts the second infarction volume growth rate is adopted from an earlier model, we propose the later model in this paper. Three Machine Learning techniques - Support Vector Machine, Artificial Neural Network and Adaptive Neuro Fuzzy Inference System with and without the feature reduction technique of Principle Component Analysis were modelled and evaluated, the best of which was selected to model the proposed prediction model. We also defined the structure of Fuzzy Inference System along with its rules and obtained an overall accuracy of 95.7% with a precision of 1 showing promising results from the use of fuzzy logic

Application of Machine Learning Techniques for The Prediction of Decompressive Hemicraniectomy Prognosis in Acute Ischemic Stroke

Stroke is one of the leading causes of death in the world with the number of people suffering from it increasing every year. Ischemic strokes, one of the two main types of stroke, occur when blood clots block brain arteries which leads to infarction eventually leading to brain edema. If not addressed quickly enough it may lead to disability and in worst case scenario may even lead to death. In this thesis we proposed a machine learning based MATLAB tool that aids in speeding up the prognosis of acute ischemic stroke patients. From a set of patient medical data such as patient age, blood pressure reading and infarction volume from first CT scan, we created three prediction models which predict second infarction volume, decision for surgery and treatment time. We also experimented with utilizing the technique of feature reduction and implementing Fuzzy Inference System to consider improving the generated models and combined the best performing models into a MATLAB application

Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis

<p>Abstract</p> <p>Background</p> <p>The computer-aided identification of specific gait patterns is an important issue in the assessment of Parkinson's disease (PD). In this study, a computer vision-based gait analysis approach is developed to assist the clinical assessments of PD with kernel-based principal component analysis (KPCA).</p> <p>Method</p> <p>Twelve PD patients and twelve healthy adults with no neurological history or motor disorders within the past six months were recruited and separated according to their "Non-PD", "Drug-On", and "Drug-Off" states. The participants were asked to wear light-colored clothing and perform three walking trials through a corridor decorated with a navy curtain at their natural pace. The participants' gait performance during the steady-state walking period was captured by a digital camera for gait analysis. The collected walking image frames were then transformed into binary silhouettes for noise reduction and compression. Using the developed KPCA-based method, the features within the binary silhouettes can be extracted to quantitatively determine the gait cycle time, stride length, walking velocity, and cadence.</p> <p>Results and Discussion</p> <p>The KPCA-based method uses a feature-extraction approach, which was verified to be more effective than traditional image area and principal component analysis (PCA) approaches in classifying "Non-PD" controls and "Drug-Off/On" PD patients. Encouragingly, this method has a high accuracy rate, 80.51%, for recognizing different gaits. Quantitative gait parameters are obtained, and the power spectrums of the patients' gaits are analyzed. We show that that the slow and irregular actions of PD patients during walking tend to transfer some of the power from the main lobe frequency to a lower frequency band. Our results indicate the feasibility of using gait performance to evaluate the motor function of patients with PD.</p> <p>Conclusion</p> <p>This KPCA-based method requires only a digital camera and a decorated corridor setup. The ease of use and installation of the current method provides clinicians and researchers a low cost solution to monitor the progression of and the treatment to PD. In summary, the proposed method provides an alternative to perform gait analysis for patients with PD.</p

Desenvolvimento de metodologias multivariadas para análise de queijos por espectroscopia Drift

Resumo: O presente trabalho teve como principal objetivo o desenvolvimento de metodologias analíticas orientadas a análise de queijos, utilizando-se espectroscopia no infravermelho e ferramentas de calibração multivariada. Inicialmente, estudos foram realizados para verificar a potencialidade da análise de componentes principais (PCA), em relação à discriminação de diferentes tipos de queijo, utilizando-se como critério a sua composição química. Seguidamente, processos de calibração multivariada, particularmente a regressão de mínimos quadrados parciais (PLSR), foram utilizados para desenvolver modelos orientados à eterminação individual e simultânea de características físico-químicas e índice de maturação de queijo prato, utilizando espectroscopia no infravermelho com transformada de Fourier, no modo de refletância difusa (DRIFTS). Os modelos multivariados individuais e simultâneos foram desenvolvidos correlacionando-se os espectros DRIFT de 16 amostras de queijo prato de diferentes marcas, com os parâmetros de gordura, proteína, umidade, extrato seco, cinzas e pH, determinados conforme método oficial da AOAC (1995). A capacidade preditiva dos modelos foi avaliada por validação externa, utilizandose um conjunto de 5 mostras que não fizeram parte do processo de modelagem. Por sua vez, os modelos multivariados, individuais e simultâneos, orientados à determinação dos parâmetros relacionados com o índice de maturação foram desenvolvidos a partir das informações espectrais de 13 amostras de diferentes marcas e dos parâmetros de índice de extensão (IEP) e profundidade da proteólise (IPP), determinados em função dos teores de nitrogênio total, nitrogênio solúvel em pH 4,6 e nitrogênio solúvel em ácido tricloroacético (TCA). A capacidade preditiva dos modelos foi avaliada por validação externa, utilizando-se um conjunto de 4 amostras que não fizeram parte do processo de modelagem. Em todos os casos, inúmeros modelos foram elaborados, utilizando-se diversas ferramentas destinadas ao pré-processamento de sinais, assim como vários números de variáveis latentes (VL). Os resultados obtidos demonstram a capacidade das ferramentas de calibração multivariada no desenvolvimento de metodologias espectroscópicas (DRIFT) orientadas à discriminação e à determinação da composição físico-química de queijos. A análise de componentes principais permitiu a discriminação entre diversos tipos de queijo, principalmente em função do teor de umidade, enquanto que modelos multivariados, tanto individuais como simultâneos, permitiram a determinação de gordura, proteína, umidade e extrato seco com excelente aproximação, em relação aos resultados obtidos por aplicação de rotinas convencionais por via úmida. Inúmeras vantagens associadas à utilização de rotinas espectroscópicas multivariadas podem ser salientadas, destacando a extrema simplicidade operacional, a elevada velocidade analítica e a ausência de resíduos químicos, características estas que são possíveis graças à inexistência de processos orientados ao preparo ou abertura de amostras. odelos individuais e simultâneos foram considerados insatisfatórios para a avaliação dos parâmetros de proteólise. De maneira geral, erros de previsão da ordem de 15 a 20% foram observados na etapa de previsão, rovavelmente em razão dos dados espectrais serem insuficientes para representar as pequenas mudanças observadas durante a proteólise do queijo

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis