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The aim of this work is to present an automated method that assists in the diagnosis of Alzheimer's disease and also supports the monitoring of the progression of the disease. The method is based on features extracted from the data acquired during an fMRI experiment. It consists of six stages: (a) preprocessing of fMRI data, (b) modeling of fMRI voxel time(More)
The heart rate signal contains valuable information about cardiac health, which cannot be extracted without the use of appropriate computerized methods. This paper presents an analysis of various electrocardiograms, the aim of which is to categorize them into two distinct groups. Group A represents young male subjects with no prior occurrence of coronary(More)
The aim of this work is the development of a method for the automatic determination of the optimum number of base classifiers which consists of the Random Forests. The novelty of the proposed method is that it doesn't need to select the classifiers to be in the final ensemble from a pool of classifiers which is known in advance, but determines the number of(More)
In this study, heartbeat time series are classified using support vector machines (SVMs). Statistical methods and signal analysis techniques are used to extract features from the signals. The SVM classifier is favorably compared to other neural network-based classification approaches by performing leave-one-out cross validation. The performance of the SVM(More)
BACKGROUND Venous thromboembolism (VTE) is a significant risk in trauma patients. Although low-molecular weight heparin (LMWH) is effective in VTE prophylaxis, its use for patients with traumatic intracranial hemorrhage remains controversial. The purpose of this study was to evaluate the safety of LMWH for VTE prophylaxis in blunt intracranial injury. (More)
  • George Manis
  • 2008
The approximate entropy (ApEn) is a measure of systems complexity. The implementation of the method is computationally expensive and requires execution time analogous to the square of the size of the input signal. We propose here a fast algorithm which speeds up the computation of approximate entropy by detecting early some vectors that are not similar and(More)
The goal of this paper is to examine the classification capabilities of various prediction and approximation methods and suggest which are most likely to be suitable for the clinical setting. Various prediction and approximation methods are applied in order to detect and extract those which provide the better differentiation between control and patient(More)
In this study, we discuss the use of support vector machine (SVM) learning to classify heart rate signals. Each signal is represented by an attribute vector containing a set of statistical measures for the respective signal. At first, the SVM classifier is trained by data (attribute vectors) with known ground truth. Then, the classifier learnt parameters(More)