Decorrelation of Lung and Heart Sound

Abstract— Signal separation is very useful where several signals have been mixed together to form combined signal and our objective is to recover individual original component signals from that combined signal. One of the major problem in neural network and research in other disciplines is finding a suitable representation of multivariate data, i.e. random vectors. For concept and computational simplicity representation is in terms of linear transformation of the original data. This means that each component of the representation is a linear combination of the original variables. There are linear transformation methods such as principal component analysis and Independent Component Analysis (ICA). ICA is a recently developed method in which the goal is to find a linear representation of non-gaussian data so that the components are statistically independent or as independent as possible. DOI: 10.17762/ijritcc2321-8169.150615

Current trends and perspectives for automated screening of cardiac murmurs

Although in high income countries rheumatic heart disease is now rare, it remains a major burden in low and middle income countries. In these world areas, physicians and expert sonographers are rare, and screening campaigns are usually performed by nomadic caregivers who can only recognise patients in an advanced phase of heart failure with high economic and social costs. Therefore, great interest exists regarding the possibility of developing a simple, low-cost procedure for screening valvular heart disease. With the development of computer science, the cardiac sound signal can be analysed in an automatic way. More precisely, a panel of features characterising the acoustic signal are extracted and sent to a decision-making software able to provide the final diagnosis. Although no system is currently available in the market, the rapid evolution of these technologies recently led to the activation of clinical trials. The aim of this note is to review the state of advancement of this technology (trends in feature selection and automatic diagnostic strategies), data available regarding performance of the technology in the clinical setting and finally what obstacles still need to be overcome before automated systems can be clinically/commercially viable

PCGCleaner: Development and implementation of an R package for heart sound signal preprocessing

In our present study, we focused on developing an R package, PCGCleaner, for the preprocessing of PCG signals. We replicated parts of a well-established algorithm for heart sound analysis in MATLAB code and translated them into R. We also implemented this tool on a heart sounds database established by the University of Michigan.Master of ScienceInformation, School ofUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/162560/1/Fu_Mingzhou_Final_MTOP_Thesis_20200527.pd

An expert system for diagnose of the heart valve diseases

In this paper, an expert diagnosis system is presented for interpretation of the Doppler signals of the heart valve diseases based on the pattern recognition. This paper especially deals with the feature extraction from measured Doppler signal waveforms at the heart valve using the Doppler Ultrasound. Wavelet transforms and short time Fourier transform methods are used to feature extract from the Doppler signals on the time–frequency domain. Wavelet entropy method is applied to these features. The back-propagation neural network is used to classify the extracted features. The performance of the developed system has been evaluated in 215 samples. The test results showed that this system was effective to detect Doppler heart sounds. The correct classification rate was about 94% for normal subjects and 95.9% for abnormal subjects.We want to thank, the Cardiology Department of the Firat Medicine Center, Elazig, Turkey for providing the DHS signals to us. This work was supported by Firat University Research Fund. (Project No: 527)

A fuzzy expert system for diagnosis and treatment of musculoskeletal disorders in wrist

Medicinski ekspertni sustav je aplikacija koja može značajno pomoći pri odlučivanju o dijagnozi i liječenju bolesti. Veći dio znanja koje se odnosi na dijagnozu simptoma i liječenje bolesti je eksperimentalan. Problem je kako to znanje ekstrahirati i učiniti dostupnim drugima. Svrha ovoga rada je napraviti ekspertni sustav za dijagnozu i liječenje mišićno skeletnih poremećaja u ručnom zglobu u fuzzy okruženju. Iskustvo se steklo od 15 elitnih stručnjaka iz područja mišićnoskeletnih poremećaja. Za prikupljanje podataka o simptomima poremećaja primijenjena je fuzzy Delphi metoda. Ta se metoda primijenila i za dobivanje podataka o liječenju. Konačno, razvijen je fuzzy ekspertni sustav za dijagnosticiranje i liječenje mišićno skeletnih poremećaja u ručnom zglobu primjenom softvera MATLAB. Moguće je dijagnosticirati sedam poremećaja zgloba primjenom toga sustava. Ulaz u sustav je magnituda simptoma, a izlaz su rezultati koje je sustav pronašao za te poremećaje; konačno se prikazuje poremećaj s najviše prikupljenih bodova kao sustavna dijagnoza za koju se predlaže liječenje. Ta se dijagnoza uspoređuje s elitnom dijagnozom uz primjenu statističke analize dobivene SPSS softverom. Rezultati pokazuju značajnu korelaciju između dviju varijabli. Usporedbom varijabli ustanovilo se da je 86,7 % sustavnih dijagnoza slično elitnim dijagnozama. U nedostatku elita, dijagnoza i liječenje se mogu postaviti relativno pouzdano. Rezultati ovog istraživanja su pokazali da se studenti medicine mogu koristiti medicinskim ekspertnim sustavima u znanstvene svrhe. Oni mogu također pomoći korisnicima kao pomoćni dijagnostički sustav u dijagnozi i liječenju bolesti.Medical expert system is an application which can effectively contribute to decisions on diagnosis and treatment of diseases. A major part of the knowledge related to diagnosis of symptoms and treatment of illnesses is experimental. The problem is that how this knowledge can be extracted and made available to others.The purpose of this study is to design an expert system for diagnosis and treatment of musculoskeletal disorders in wrist in fuzzy environment.The knowledge is achieved from 15 elites in the field of musculoskeletal disorders. For data gathering related to symptoms of the disorders, a fuzzy Delphi method is used. A Delphi method is also used for gathering data related to treatments. Finally, a fuzzy expert system is developed for diagnosis and treatment of musculoskeletal disorders in wrist using MATLAB software. The designed system is able to diagnose seven disorders of the wrist. The input to the system is the magnitude of symptoms and the output is the scores given by the system to disorders; finally, the disorder with the highest score is displayed as the systemic diagnosis for which treatments are suggested.The systemic diagnosis is compared to elite diagnosis using statistical analysis conducted by SPSS software. Results show a significant correlation between two variables. By comparing the variables, it is found that 86,7 % of the systemic diagnoses were similar to elite diagnoses. In the absence of elites, diagnosis and treatment can be performed to a relatively reliable level. As results of current research, medical expert systems can be used as a scientific source by medical students. It can also help users as an auxiliary diagnostic system to diagnose and treat diseases

Algoritmos de Enjambre para la Optimización de HMM en la Detección de Soplos Cardíacos en Señales Fonocardiográficas Usando Representaciones Derivadas del Análisis de Vibraciones

Este trabajo presenta una metodología para desarrollar un sistema automático de apoyo en la clasificación de señales fonocardiográficos (PCG). En primer lugar, las señales PCG fueron pre-procesadas. Luego descompuestas por medio de la técnica descomposición modo empírico (EMD) con algunas de sus variantes y el análisis de vibración por descomposición de Hilbert (HVD) de forma independiente, donde se comparó el costó computacional y el error en la reconstrucción de la señal original generando constructos a partir de las IMFs. A continuación, se extrajeron las características con los momentos estadísticos de los datos generados por la transformada de Hilbert-Huang (HHT), además de los coeficientes cepstrales en las frecuencias de Mel (MFCC) y cuatro de sus variantes. Por último, un subconjunto de características fue seleccionado usando conjuntos de aproximación difusos (FRS), análisis de componentes principales (PCA) y selección secuencial flotante hacia adelante (SFFS) de manera simultánea para ser utilizadas como entradas del modelo oculto de Markov (HMM) ergódico ajustado con optimización por enjambre de partículas (PSO), con el fin de proporcionar un mecanismo objetivo y preciso para mejorar la fiabilidad en la detección de soplos en el corazón, obteniendo resultados en la clasificación de alrededor del 96% con valores de sensibilidad superiores a 0.8 y de especificidad mayores a 0.9, utilizando validación cruzada (70/30 con 30 fold)This study presents a methodology for developing an automated support system in the classification of phonographic signals (PCG). First, the PCG signals were preprocessed. You then decomposed by the decomposition technique empirically (EMD) with some of its variants and vibration analysis by decomposition of Hilbert (HVD) independently, where the computational cost and the error was compared in the reconstruction of the original signal generating constructs from IMFs. Then the characteristics of the statistical moments data generated by the Hilbert-Huang Transform (HHT), plus cepstral coeffcients at frequencies of Mel (MFCC) and four of its variants were extracted. Finally, a subset of features was selected using sets of fuzzy approximation (FRS), principal component analysis (PCA) and floating sequential forward selection (SFFS) simultaneously to be used as inputs to the hidden Markov model (HMM) ergodic adjusted particle swarm optimization (PSO), in order to provide an objective and accurate to improve reliability in detecting heart murmurs mechanism, obtaining results in the classification of about 96% with sensitivity values higher 0.8 and higher specificity to 0.9, using cross-validation (70/30 split with 30 fold)Magister en Automatización y Contro