Phonocardiogram segmentation by using an hybrid RBF-HMM model

This paper is concerned to the segmentation of heart sounds by using Radial-Basis Functions for acoustical modelling, combined with a Hidden Markov Model for heart sounds sequence modelling. The idea behind the use of RBF’s is to take advantage of the local approximations using exponentially decaying localized nonlinearities achieved by the Gaussian function, which increases the clustering power relatively to MLP’s. This neural model can be advantageous over the global approximations to nonlinear input-output mappings provided by Multilayer Perceptrons (MLP’s), especially when non-stationary processes need to be accurately modelled. The above described RBF’s properties combined with the non-stationary statistical properties of Hidden Markov Models can help in the detection of the T-wave which is fundamental for the detection of the second heart sound. The feature vectors are based on a MFCC based representation obtained from a spectral normalisation procedure, which showed better performance than the MFCC representation alone, in an Isolated Speech Recognition framework. Experimental results were evaluated on data collected from five different subjects, using CardioLab system and a Dash family patient monitor. The ECG leads I, II and III and an electronic stethoscope signal were sampled at 977 samples per second

Phonocardiogram segmentation by using Hidden Markov Models

This paper is concerned to the segmentation of heart sounds by using state of art Hidden Markov Models technology. Concerning to several heart pathologies the analysis of the intervals between the first and second heart sounds is of utmost importance. Such intervals are silent for a normal subject and the presence of murmurs indicate certain cardiovascular defects and diseases. While the first heart sound can easily be detected if the ECG is available, the second heart sound is much more difficult to be detected given the low amplitude and smoothness of the T-wave. In the scope of this segmentation difficulty the well known non-stationary statistical properties of Hidden Markov Models concerned to temporal signal segmentation capabilities can be adequate to deal with this kind of segmentation problems. The feature vectors are based on a MFCC based representation obtained from a spectral normalisation procedure, which showed better performance than the MFCC representation alone in an Isolated Speech Recognition framework. Experimental results were evaluated on data collected from five different subjects, using CardioLab system and a Dash family patient monitor. The ECG leads I, II and III and an electronic stethoscope signal were sampled at 977 samples per second

Cardiac arrhythmia detection by parameters sharing and MMIE training of hidden Markov models

This paper is concerned to the cardiac arrhythmia classification by using hidden Markov models and maximum mutual information estimation (MMIE) theory. The types of beat being selected are normal (N), premature ventricular contraction (V), and the most common class of supra-ventricular arrhythmia (S), named atrial fibrillation (AF). The approach followed in this paper is based on the supposition that atrial fibrillation and normal beats are morphologically similar except that the former does not exhibit the P wave. In fact there are more differences as the irregularity of the RR interval, but ventricular conduction in AF is normal in morphology. Regarding to the Hidden Markov Models (HMM) modelling this can mean that these two classes can be modelled by HMM's of similar topology and sharing some parameters excepting the part of the HMM structure that models the P wave. This paper shows, under that underlying assumption, how this information can be compacted in only one HMM, increasing the classification accuracy by using MMIE training, and saving computational resources at run-time decoding. The algorithm performance was tested by using the MIT-BIH database. Better performance was obtained comparatively to the case where Maximum Likelihood Estimation training is used alone.Centre Algoritm

Hidden Markov tree model applied to the detection of micro-calcification clusters in mammograms

This paper is concerned to the application of a relatively new image texture segmentation algorithm named Hidden Markov Tree (HMT) to the detection of micro-calcification clusters in mammograms. The HMT is a wavelet-based tree-structured probabilistic graph that can capture the statistical properties of the coefficients of the wavelet transform. The aim of this approach is, on the one hand, to take advantage of the wavelet coefficients in the characterization of different textures, and on the other hand, to link these coefficients by a tree structure enabling texture change to be detected. The application of the method was evaluated using the Digital Database for Screening Mammography (DDSM) for training purposes and a sample of the Nijmegen database for testing purposes

Selective MMIE training of hidden Markov models for cardiac arrhythmia classification

Centre AlgoritmiThis paper is concerned to the cardiac arrhythmia classification by using Hidden Markov Models. The types of beat being selected are normal (N), premature ventricular contraction (V) which is often precursor of ventricular arrhythmia, and two of the most common class of supra-ventricular arrhythmia (S), named atrial fibrillation (AF) and atrial flutter (AFL). The approach followed in this paper is based on the supposition that atrial fibrillation, atrial flutter and normal beats are morphologically similar except that the former does not exhibit the P wave, while the later exhibits several P waves following the QRS. Regarding to the HMM modelling this can mean that these three classes can be modelled by HMM’s of similar topology and sharing some similar parameters excepting the part of the HMM structure that models the P wave. This paper shows, under that underlying assumption, how this information can be compacted in only one HMM, increasing the classification accuracy by using MMI (Maximum Mutual Information) training, and saving computational resources at run-time decoding. This paper also shows that the similarities among normal, atrial fibrillation and atrial flutter beats, which main difference is the lack or repetitions of the P wave, can be taken into consideration to improve the classifier performance by using MMI training, in a single model/triple class framework, which is similar of having three different models sharing several parameters. The algorithm performance was tested by using the MIT-BIH database. Better performance was obtained comparatively to the case where one different HMM models each class when using MLE (Maximum Likelihood Estimation) training alone.Centre Algoritm

The application of furniture manufacturing residues in wood pellets: Assessment of the combustion efficiency

The European Union aims to fulfill 20% of the primary energy consumption with renewable energy sources, by 2020. Biomass has a great potential for domestic and industrial heating applications and these are amongst the most promising uses for biomass as they combine high efficiency and ease of use. Pellets are a good method for biomass distribution due to its quality standards and energy density which make them attractive to distribution and handling. In addition to the traditional use of sawdust as a raw material, there has been a growing interest in the incorporation of residues from industry processing such as the furniture sector. This paper reports the application of pellets made of residues from the furniture industry as a fuel source in domestic heating applications. Pellets were characterized according to their chemical and physical composition. They were subsequently burned in an automatic boiler rated at 15 kW. A probe in the exhaust chimney was used to continuously analyze the flue gases. In addition, the ashes chemical composition was also analyzed. The results show that these pellets have a good thermal efficiency in domestic boilers, releasing however large emissions of NOx, originated from the high concentration of nitrogen in its chemical composition. The ash analysis confirms the slagging and fouling prediction, and these problems were verified in the grate, chamber combustion and heat exchanger. In conclusion, these pellets can be explored for industrial applications, with better control of its chemical composition. For domestic boilers however, these can cause serious ash problems.One of authors (P. T. Ferreira) acknowledges the scholarship (SFRH/BD/73101/2010) sponsored by FCT

Energy and exergy analysis of a biomass based ceramic plant

The manufacture of relatively low commercial value ceramic products for construction is an energy intensive industry. It is important to improve and optimize the energy equation of the plant operation while simultaneously introducing renewable primary energy sources for the heat supply. The present paper concerns the analysis of the energy usage in a brick plant. This unit operates continuously on a 3 shift schedule. The overall annual production of five types of bricks is over 62 kton and the main energy consumption unit is the furnace. For this unit, the thermal load is supplied mainly by biomass coupled with fuel oil (80%–20% split, respectively) which yield a maximum temperature of 950 °C. The process is controlled by adjusting the air mixing in the kiln. A secondary furnace provides the heat for a rotating dryer for biomass drying which is supplied to the main furnace. The fuel is a mixture of various sources and its characteristics were determined by means of an elemental analysis, ash content and the measurement of the heat value. Measurements of mass fluxes along with the operating temperature on critical elements of the plant and chemical composition of the flue gases were used to calculate the energy balances to the plant. Because of the diversity of the product mix the production was normalized using the mass/surface area ratio of the various types of bricks. From the results, the energy intensity is 44 kg of oil equivalent per ton. The exergy analysis of the plant shows that most of the energy degradation occurs in the kiln. The analysis also enabled to assess the influence of the replacing fossil fuel by biomass on the increase of exergy efficiency of the plant.This work was financed by FCT, under the Strategic Project UID/SEM/04077/2013. The authors acknowledge the contribution of Amaro de Macedo S.A., for providing access to the plant

Interaction of wine mannoproteins and arabinogalactans with anthocyanins

Wine polymeric material (WPM), which includes polysaccharides, proteins, and polyphenolic compounds, interacts with anthocyanins. To determine the contribution of polysaccharides in these interactions, the diffusion performance of anthocyanins along a dialysis membrane was determined in the presence and absence of isolated mannoproteins (MP) and arabinogalactans (AG) from WPM. Furthermore, to estimate the extent of the interaction between WPM and polyphenolic compounds, the activation energy (Ea) required for their diffusion in the presence of WPM was determined. AG, generally more abundant than MP in wine, interact in a greater extent with anthocyanins, showing their relevant contribution for WPM/anthocyanins interactions. The Ea for the diffusion of polyphenolic compounds in presence of WPM indicated the occurrence of interactions with relative weak to strong intensities (2.6–50.8 kJ/mol). As not all polyphenolic compounds were able to be released from WPM, stronger interactions, possibly by covalent linkages, are involved, providing new insights on WPM/polyphenolic compounds relationships.info:eu-repo/semantics/acceptedVersio

Abstract computation in schizophrenia detection through artificial neural network based systems

Schizophrenia stands for a long-lasting state of mental uncertainty that may bring to an end the relation among behavior, thought, and emotion; that is, it may lead to unreliable perception, not suitable actions and feelings, and a sense of mental fragmentation. Indeed, its diagnosis is done over a large period of time; continuos signs of the disturbance persist for at least 6 (six) months. Once detected, the psychiatrist diagnosis is made through the clinical interview and a series of psychic tests, addressed mainly to avoid the diagnosis of other mental states or diseases. Undeniably, the main problem with identifying schizophrenia is the difficulty to distinguish its symptoms from those associated to different untidiness or roles. Therefore, this work will focus on the development of a diagnostic support system, in terms of its knowledge representation and reasoning procedures, based on a blended of Logic Programming and Artificial Neural Networks approaches to computing, taking advantage of a novel approach to knowledge representation and reasoning, which aims to solve the problems associated in the handling (i.e., to stand for and reason) of defective information.This work is funded by National Funds through the FCT, Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within projects PEstOE/EEI/UI0752/2014 and PEst-OE/QUI/UI0619/2012