Identificação de estertores em sons respiratórios utilizando transformada wavelet e análise de discriminante linear

Crackles are adventitious and discontinuous breath sounds that occur in lung diseases. Time domain parameters classify the crackles as fine, medium, and coarse, and may have positive or negative polarity. This work investigates methods and tools to characterize and classify crackles. Samples of breath sounds containing crackles were normalized and resampled at 8 kHz. Several experiments using the discrete wavelet transform (DWT), linear discriminant analysis (LDA), and k-NN have been performed, and evaluated with ROC analysis. A pattern recognition system was implemented with DWT, LDA and k-NN to classify fine and coarse crackles, and normal breath sounds. The experiment with different signal border extension methods during DWT decomposition showed the influence on the results of the characterization. The results indicate that the methods ZPD, SP0, SYMH, SYMW, ASYMH, PPD and PER are recommended, while SP1 and ASYMW methods are not recommended for the decomposition and characterization of crackles because they generate different characteristics in the higher subbands. Another experiment showed that the characterization of crackles using DWT can be made using certain decomposition subbands (D3, D4, and D5 with signal sampled at 8 kHz), thus reducing the computational effort. Another classification system implemented using LDA and DWT showed that crackles can be classified by their polarity indicating a high degree of accuracy (AUC rate up to 0.9943 for Symlet 19). Two experiments were conducted for mother-wavelet selection that best characterizes crackles. The first one quantitatively evaluated the similarity between the crackle and several mother-wavelets using Pearson's correlation coefficient. The mother-wavelet that resulted a strong correlation with the crackles, being most indicated for use were: Reverse Biorthogonal 3.7, 5.5 Biorthogonal Reverse, Reverse Biorthogonal 3.5, Daubechies 5, Symlet 5, Daubechies 6, 7, and Symlet Daubechies 7. The second experiment selected mother-wavelets by the power concentration in subbands. Previous trials already shown that the energy of the crackles decomposed by DWT are concentrated in a few subbands, so mothers-wavelet that concentrate larger percentage of the energy in a specific subband were selected, which were Daubechies 7, Symlet 7, Coiflet 3 and Symlet 12. The final experiment performed was a combination of mother-wavelets to improve the separability of crackles and normal breath sounds. The experiment showed that a classification system using DWT, LDA, and a linear classifier may totally separate the two classes (AUC ratio = 1) when the combination of mother-wavelets to generate the feature vector of the signals is used.CAPESEstertores são sons respiratórios adventícios e descontínuos que ocorrem em patologias pulmonares. Parâmetros no domínio do tempo classificam os estertores como finos, médios e grossos, e podem ter polaridade positiva ou negativa. Este trabalho investiga métodos e ferramentas para caracterizar e classificar estertores. Amostras de sons respiratórios contendo estertores foram normalizadas e reamostradas em 8 kHz. Foram realizados diversos ensaios utilizando a transformada wavelet discreta (DWT) e a análise de discriminante linear (LDA), e avaliados com análise ROC. Um sistema de reconhecimento de padrões foi implementado com DWT, LDA e k-NN para classificar estertores finos, grossos e sons respiratórios normais. O ensaio com diferentes métodos de extensão de borda do sinal durante a decomposição DWT mostrou a influência nos resultados da caracterização. Os resultados indicam que os métodos ZPD, SP0, SYMH, SYMW, ASYMH, PPD e PER são recomendados, enquanto que os métodos SP1 e ASYMW não são recomendados para a decomposição e caracterização de estertores, pois geram características diferentes nas sub-bandas mais altas. Outro ensaio mostrou que a caracterização dos estertores utilizando DWT pode ser feita utilizando-se algumas sub-bandas de decomposição (D3, D4 e D5, no caso de sinais amostrados em 8 kHz), reduzindo-se desta forma o esforço computacional. Outro sistema de classificação implementado utilizando DWT e LDA mostrou que os estertores podem ser classificados indicando a polaridade com elevado grau de acerto (AUC de até 0,9943 para Symlet 19). Dois ensaios foram realizados para seleção da wavelet-mãe que melhor caracterize estertores. O primeiro ensaio avaliou quantitativamente a semelhança entre o estertor e diversas wavelets-mães através do índice de correlação de Pearson. As wavelets-mães que resultaram uma forte correlação com o estertores, se mostrando mais indicadas para serem utilizadas, foram: Reverse Biorthogonal 3.7, Reverse Biorthogonal 5.5, Reverse Biorthogonal 3.5, Daubechies 5, Symlet 5, Daubechies 6, Symlet 7 e Daubechies 7. O segundo ensaio selecionou a wavelet-mãe pela concentração de energia nas sub-bandas. Ensaios anteriores já mostravam que a energia dos estertores decompostos pela DWT se concentra em poucas sub-bandas, então foram selecionadas wavelets-mães que concentrassem maior porcentagem da energia em uma sub-banda específica, que foram: Daubechies 7, Symlet 7, Coiflet 3 e Symlet 12. O último ensaio realizado foi uma combinação de wavelets-mães para melhorar a separabilidade de estertores e sons respiratórios normais. O ensaio mostrou que um sistema de classificação utilizando DWT, LDA e um classificador linear pode separar totalmente as duas classes (índice AUC = 1) quando é utilizada a combinação de wavelets-mães para gerar o vetor de características dos sinais

Proposta de Solução para a Mensuração de Peso por Superfície de Contato com Objetivo de Prevenir Lesões por Pressão em Pacientes Acamados

Pressure injuries (LPP) are one of the biggest adverse events foundin health services and consist of damage to the body tissues ofbedridden patients, resulting from prolonged pressure on the skin.This situation impacts on the quality of life of people who developthe condition, causing physical and emotional damage to the bedridden,in addition to increasing the time and costs of hospitalization.Based on this problem, software was developed that shows thepoints of greatest pressure between the body of a bedridden patientand the bed in which he is. This software receives information fromhardware, under development, built specifically for this project.The points of greatest pressure are made available on the screenof a monitoring application, in an organized and intuitive manner.For each person, a pressure map image is generated with the valuesread and decubitus change times are suggested through alarms. Inaddition, this image can be analyzed by a health professional whocan take steps to relieve pressure points and prevent the appearanceof LPP. As a result, in tests carried out during the research, the systembuilt showed the information successfully and the objectiveswere achieved

Dispositivo Vestível para Monitoramento de Pessoas Idosas

ABSTRACTDue to medical advances, life expectancy is getting longer andlonger. Consequently the elderly population is growing and newchallenges will be faced in the coming decades. In this context, thispaper presents the development of a monitoring system for theelderly. The system includes a wearable device to detect falls, apanic button to be activated if the user feels unwell, and a speakerthat beeps when medication is required. If the wearable deviceidentifies a fall or detects panic button activation, an alert messageis automatically sent to the mobile device of the responsible for theelderly. The expected result of this project is to efficiently monitorthe elderly and to promote the quality of life and safety for elderlyover 60 years

Gerenciador de Pílulas Inteligente

In Brazil, as in other countries in the world, the percentage of elderlypeople has been increasing in recent decades. This fact ismainly caused by the drop in the birth rate and the increase in theaverage life span. As a result, health care for the elderly becomesincreasingly important. Adherence to drug treatment is a clinicalchallenge for doctors who serve this population. Very old peoplehave multiple illnesses and, consequently, they use multiple medications.Some also have impaired cognitive or sensory functions,such as visual pathologies or decreased motor skills. However, nonadherenceto drug treatment can lead to the emergence of newdiseases, hospitalizations or even death. This article presents the developmentof an intelligent device that has the function of assistingdrug management, seeking the well-being of the elderly. The deviceallows the registration of the time and interval of medication intakethrough a list of products, regulated by Anvisa, pre-registeredin the system and selected according to its format. After audibleand visual warnings, the medication is dispensed automatically,allowing the medication to be ingested at the appropriate time. Itis also possible to view a drug consumption report and registrationtimes. After testing, the developed prototype was effective for thepurpose for which it was designed. The encoder system, responsiblefor monitoring and controlling the position of the medicationcompartment disc, presented an average deviation of 0.0263 mm,which prevents the overlapping of pills. At the same time, samplesof medication availability at the scheduled times showed a zeroerror rate

Pulmonary crackle characterization: approaches in the use of discrete wavelet transform regarding border effect, mother-wavelet selection, and subband reduction

Introduction Crackles are discontinuous, non-stationary respiratory sounds and can be characterized by their duration and frequency. In the literature, many techniques of filtering, feature extraction, and classification were presented. Although the discrete wavelet transform (DWT) is a well-known tool in this area, issues like signal border extension, mother-wavelet selection, and its subbands were not properly discussed. Methods In this work, 30 different mother-wavelets 8 subbands were assessed, and 9 border extension modes were evaluated. The evaluations were done based on the energy representation of the crackle considering the mother-wavelet and the border extension, allowing a reduction of not representative subbands. Results Tests revealed that the border extension mode considered during the DWT affects crackle characterization, whereas SP1 (Smooth-Padding of order 1) and ASYMW (Antisymmetric-Padding (whole-point)) modes shall not be used. After DWT, only 3 subbands (D3, D4, and D5) were needed to characterize crackles. Finally, from the group of mother-wavelets tested, Daubechies 7 and Symlet 7 were found to be the most adequate for crackle characterization. Discussion DWT can be used to characterize crackles when proper border extension mode, mother-wavelet, and subbands are taken into account