Due to aging of the population, some studies predict that the burden of Parkinson Disease (PD) will grow substantially in future decades. The rapid increase of PD will place a substantial burden on individuals, society, and health systems. In recent years, a series of works have been published on the use of mobile devices, equipped with sensors, such as accelerometers, gyroscopes and magnetometers to diagnosis and monitor PD outpatients . In this work, the influence of a series of factors on the diagnosis of Parkinson disease were evaluated, using walking activity data obtained from an mPower study. Through constructing several databases, the following factors were evaluated: dependent individual and independent individual approach, input record size, interleaved and non-interleaved data. In addition to these factors, the effect of the complexity of the CNN network on its performance was also evaluated. Databases with large records provided models with better performance in PD diagnosis than databases with small records. CNN's complexity also had a great impact on PD diagnosis performance. In this work, the best results achieved for the independent individual approach and for the dependent individual approach were an AUCROC of 0.511 and 0.861, respectively.
For the gait phases characterization and for the gait classification (slow, normal, fast, upstairs, downstairs), it is of fundamental importance to determine the Heel-Strike and Toe-Off gait events, which correspond to the moment when the ankle hits the ground, and to the instant when the toes leave the ground, respectively. These two events allow calculating various quantitative parameters of the gait, such as number of steps and cadence, and qualitative parameters, such as the symmetry indices. In this work, we present a method for detecting the above-mentioned events, using simple LSTM neural network architectures and windows filtering through a rule set. Mean accuracies obtained for Heel-Strike and Toe-Off 97 events, respectively, were 98,50% and 97,47%.
Studies in intravascular optical coherence tomography (IV-OCT) have demonstrated the importance of coronary bifurcation regions in intravascular medical imaging analysis, as plaques are more likely to accumulate in this region leading to coronary disease. A typical IV-OCT pullback acquires hundreds of frames, thus developing an automated tool to classify the OCT frames as bifurcation or non-bifurcation can be an important step to speed up OCT pullbacks analysis and assist automated methods for atherosclerotic plaque quantification. In this work, we evaluate the performance of two state-of-the-art classifiers, SVM and Neural Networks in the bifurcation classification task. The study included IV-OCT frames from 9 patients. In order to improve classification performance, we trained and tested the SVM with different parameters by means of a grid search and different stop criteria were applied to the Neural Network classifier: mean square error, early stop and regularization. Different sets of features were tested, using feature selection techniques: PCA, LDA and scalar feature selection with correlation. Training and test were performed in sets with a maximum of 1460 OCT frames. We quantified our results in terms of false positive rate, true positive rate, accuracy, specificity, precision, false alarm, f-measure and area under ROC curve. Neural networks obtained the best classification accuracy, 98.83%, overcoming the results found in literature. Our methods appear to offer a robust and reliable automated classification of OCT frames that might assist physicians indicating potential frames to analyze. Methods for improving neural networks generalization have increased the classification performance.
According to the World Health Organization, cardiovascular diseases are the leading cause of death worldwide, accounting for 17.3 million deaths per year, a number that is expected to grow to more than 23.6 million by 2030. Most cardiac pathologies involve the left ventricle; therefore, estimation of several functional parameters from a previous segmentation of this structure can be helpful in diagnosis. Manual delineation is a time consuming and tedious task that is also prone to high intra and inter-observer variability. Thus, there exists a need for automated cardiac segmentation method to help facilitate the diagnosis of cardiovascular diseases. In this work we propose a deep fully convolutional neural network architecture to address this issue and assess its performance. The model was trained end to end in a supervised learning stage from whole cardiac MRI images input and ground truth to make a per pixel classification. For its design, development and experimentation was used Caffe deep learning framework over an NVidia Quadro K4200 Graphics Processing Unit. The net architecture is: Conv64-ReLU (2x) – MaxPooling – Conv128-ReLU (2x) – MaxPooling – Conv256-ReLU (2x) – MaxPooling – Conv512-ReLu-Dropout (2x) – Conv2-ReLU – Deconv – Crop – Softmax. Training and testing processes were carried out using 5-fold cross validation with short axis cardiac magnetic resonance images from Sunnybrook Database. We obtained a Dice score of 0.92 and 0.90, Hausdorff distance of 4.48 and 5.43, Jaccard index of 0.97 and 0.97, sensitivity of 0.92 and 0.90 and specificity of 0.99 and 0.99, overall mean values with SGD and RMSProp, respectively.
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