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The Empirical Mode Decomposition (EMD) is a method to decompose non linear, non stationary time series into a sum of different modes, named Intrinsical Mode Functions each one having a characteristic frequency. In the present work we used the EMD to investigate the properties of the recorded sounds from the Arteriovenous fistula on hemodialysis patients. Phonoangiographic signals coming from two different...
Currently, the best way to reduce the mortality of cancer is to detect and treat it in its early stages. Automatic decision support systems, such as automatic diagnosis systems, are very helpful in this task but their performance is constrained by the integrity of the clinical input data. This could be a problem since clinical databases, in which these systems are based on, are commonly built up containing...
Information generated by sensors that collect a patient's vital signals are continuous and unlimited data sequences. Traditionally, this information requires special equipment and programs to monitor them. These programs process and react to the continuous entry of data from different origins. Thus, the purpose of this study is to analyze the data produced by these biomedical devices, in this case...
The purpose of this study is to develop an automatic classifier based on Artificial Neural Networks (ANNs) to help the diagnostic of Chronic Obstructive Pulmonary Disease (COPD) using forced oscillation measurements (FOT). The classifier inputs are the parameters provided by the FOT and the output is the indication if the parameters indicate COPD or not. The available dataset consists of 7 possible...
This paper examines the feasibility of accurate state classification of autonomic nervous activity (ANA) based on the power spectral pattern of the heart rate fluctuations (HRFs). Some attempts have been made to utilize artificial neural networks (ANNs) to classify HRFs for clinical diagnoses such as ischemic cardiomyopathy, arrhythmia or sleep apnea. To establish the firm bases for making such clinical...
An algorithm to detect automatically drowsiness episodes has been developed. It uses only one EEG channel to differentiate the stages of alertness and drowsiness. In this work the vectors features are building combining Power Spectral Density (PDS) and Wavelet Transform (WT). The feature extracted from the PSD of EEG signal are: Central frequency, the First Quartile Frequency, the Maximum Frequency,...
Wall artery viscoelastic properties (WAVP) are correlated with structural and functional state of the arterial system. An accurate estimation of these properties is achieved measuring wall instantaneous diameter and pressure signals. The aim of this work was to evaluate a new non invasive estimation method of the instantaneous arterial diameter (D), and consequently, WAVP. Ten common carotid arteries...
Vertical ground reaction force (vGRF) has been commonly used in human gait analysis making possible the study of mechanical overloads in the locomotor system. This study aimed at applying the principal component (PC) analysis and two Artificial Neural Networks (ANN), multi-layer feed forward (FF) and self organized maps (SOM), for classifying and clustering gait patterns from normal subjects (CG)...
Spontaneous very low frequency oscillations (<;0.5 Hz) occurring within widely distributed neuroanatomical systems have been increasingly analyzed in brain imaging studies. Whilst being more prominent in the resting brain, these slow waves also persist into task sessions and may potentially interfere with active goal-directed attention, leading to periodic lapses in attention during task execution...
Melting temperature is an important characteristic feature of a protein and is used for various purposes such as in drug development. Currently protein melting temperature is determined by laboratory methods such as Differential Scanning Calorimetry, Circular Dichroism, Fourier transform infrared spectroscopy and several other methods. These methods are laborious and costly. Therefore, we propose...
Since ECG is huge in size sending large volume data over resource constrained wireless networks is power consuming and will reduce the energy of nodes in Body Sensor Networks (BSN). Therefore, compression of ECGs and diagnosis of diseases from compressed ECGs will play key roles in enhancing the life-time of body sensor networks. Moreover, discrimination between ventricular Tachycardia and Ventricular...
The main goal of this study was to develop a new method of estimating the angle of the passively stretched ankle joint, based on structural muscle spindle models of the tibial and peroneal electroneurograms (ENG). Passive ramp-and-hold and alternating stretches of the ankle joint were performed in a rabbit. Simultaneously, two cuff electrodes were used to record the ENGs of peroneal and tibial nerves...
This paper presents a generic methodology for time series prediction, based on a wavelet decomposition/ reconstruction technique, together with a feedforward neural networks structure. The proposed methodology combines the flexibility and learning abilities of neural networks with a compact description of the signals, inherent to wavelets. In a first phase a wavelet decomposition of the signal is...
The purpose of this study was to analyze morphological characteristics of electroencephalogram (EEG) signals in order to define a representation of epileptiform events that can distinguish them from other events occurring in the signal. There are several studies on parameterization of EEG signals, particularly for automatic detection of paroxysms related to epilepsy. Considering that during the automatic...
The paper describes a feature selection process applied to electrogastrogram (EGG) processing. The data set is formed by 42 EGG records from functional dyspeptic (FD) patients and 22 from healthy controls. A wrapper configuration classifier was implemented to discriminate between both classes. The aim of this work is to compare artificial neural networks (ANN) and support vector machines (SVM) when...
This work focuses on the development of models to support the assessment of a patient's global cardiovascular condition. Three types of models, based on different types of information, have been developed: long term cardiovascular risk models, that evaluate the risk of occurring of cardiovascular event within a long period of time (years); short term cardiovascular risk models, to assess the risk...
This paper presents a set of technologies developed to exploit Grid infrastructures for breast cancer CAD, that include (1) federated repositories of mammography images and clinical data over Grid storage, (2) a workstation for mammography image analysis and diagnosis and (3) a framework for data analysis and training machine learning classifiers over Grid computing power specially tuned for medical...
Given High Resolution Magic Angle Spinning (HR-MAS) signals from several glioblastoma tumor subjects, the goal is to differentiate between tumor tissue types by separating the different sources that contribute to the profile of each spectrum. Blind source separation techniques are applied for obtaining characteristic profiles for necrosis, high cellular tumor and border tumor tissue, and providing...
This work aims at studying the autonomic nervous system (ANS) response to hemodialysis (HD) treatment in a population of end stage renal disease (ESRD) patients. ECG Holter recordings and whole body bioimpedance spectroscopy measurements were performed for each patient. Patients were classified according to the fluid overload (FO) values and the systolic blood pressure (SBP) measured before HD. Time...
This paper proposes the identification of regions of interest in biospeckle patterns using unsupervised neural networks of the type Self-Organizing Maps. Segmented images are obtained from the acquisition and processing of laser speckle sequences. The dynamic speckle is a phenomenon that occurs when a beam of coherent light illuminates a sample in which there is some type of activity, not visible,...
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