7 research outputs found
Segmentation of heart sounds by Re-Sampled signal energy method
Auscultation, which means listening to heart sounds, is one of the most basic medical methods used by physicians to diagnose heart diseases. These voices provide considerable information about the pathological cardiac condition of arrhythmia, valve disorders, heart failure and other heart conditions. This is why cardiac sounds have a great prominence in the early diagnosis of cardiovascular disease. Heart sounds mainly have two main components, S1 and S2. These components need to be well identified to diagnose heart conditions easily and accurately. In this case, the segmentation of heart sounds comes into play and naturally a lot of work has been done in this regard. The first step in the automatic analysis of heart sounds is the segmentation of heart sound signals. Correct detection of heart sounds components is crucial for correct identification of systolic or diastolic regions. Thus, the pathological conditions in these regions can be clearly demonstrated. In previous studies, frequency domain studies such as Shannon energy and Hilbert transformation method were generally performed for segmentation of heart sounds. These methods involve quite long and exhausting stages. For this reason, in this study, a re-sampled
energy method which can easily segment heart sounds in the time domain has been developed. The results obtained from the experiments show that the proposed method segments S1 and S2 sounds very efficiently
Classification of phonocardiograms with convolutional neural networks
The diagnosis of heart diseases from heart sounds is a matter of many years. This is the effect of having too many people with heart diseases in the world. Studies on heart sounds are usually based on classification for helping doctors. In other words, these studies are a substructure of clinical decision support systems. In this study, three different heart sound data in the PASCAL Btraining data set such as normal, murmur, and extrasystole are classified. Phonocardiograms which were obtained from heart sounds in the data set were used for classification. Both Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) were used for classification to compare obtained results. In these studies, the obtained results show that the CNN classification gives the better result with 97.9% classification accuracy according to the results of ANN. Thus, CNN emerges as the ideal classification tool for the classification of heart sounds with variable characteristics
Classificação de sons cardíacos usando Motifs: desenvolvimento de uma aplicação móvel
Este documento foi redigido no âmbito da Tese, do Mestrado em Engenharia Informática na área de Tecnologias do Conhecimento e Decisão, do Departamento de Engenharia Informática, do ISEP, cujo tema é classificação de sons cardíacos usando motifs.
Neste trabalho, apresenta-se um algoritmo de classificação de sons cardíacos, capaz de identificar patologias cardíacas. A classificação do som cardíaco é um trabalho desafiante dada a dificuldade em separar os sons ambiente (vozes, respiração, contacto do microfone com superfícies como pele ou tecidos) ou de ruído dos batimentos cardíacos.
Esta abordagem seguiu a metodologia de descoberta de padrões SAX (motifs) mais frequentes, em séries temporais relacionando-os com a ocorrência sistólica (S1) e a ocorrência diastólica (S2) do coração. A metodologia seguida mostrou-se eficaz a distinguir sons normais de sons correspondentes a patologia. Os resultados foram publicados na conferência internacional IDEAS’14 [Oliveira, 2014], em Julho deste ano.
Numa fase seguinte, desenvolveu-se uma aplicação móvel, capaz de captar os batimentos cardíacos, de os tratar e os classificar. A classificação dos sons é feita usando o método referido no parágrafo anterior. A aplicação móvel, depois de tratar os sons, envia-os para um servidor, onde o programa de classificação é executado, e recebe a resposta da classificação.
É também descrita a arquitetura aplicacional desenhada e as componentes que a constituem, as ferramentas e tecnologias utilizadas.This document was prepared as part of the Thesis of the MSc in Computer Science in the area of Knowledge and Decision Technologies, Department of Computer Engineering, ISEP.
The theme is classification of heart sounds.
In this dissertation we present an algorithm for heart sounds classification, able to identify cardiac pathologies. The classification of the heart sound is a challenging work due to the difficulty in separating heartbeat sound from the ambient sounds (voices, breathing, microphone contact with surfaces like skin or textiles) or noise.
In this approach we use the methodology of discovery of frequent SAX patterns (motifs) in time series, relating them with systolic (S1) and diastolic (S2) heart events. The methodology was effective to distinguish normal sounds from pathologic sounds. The results were published in international conference IDEAS'14 [Oliveira, 2014], in July.
We have also developed a mobile application, able to capture, process and classify heart beats. The mobile application, captures and processes the sounds, sends them to a server where the classification program is running, and receives the classification result.
We also described the application architecture, its components as well as the tools and technologies used
Diagnóstico cardíaco a partir de dados acústicos e clínicos
Este documento foi redigido no âmbito da dissertação do Mestrado em Engenharia
Informática na área de Arquiteturas, Sistemas e Redes, do Departamento de Engenharia
Informática, do ISEP, cujo tema é diagnóstico cardíaco a partir de dados acústicos e clínicos.
O objetivo deste trabalho é produzir um método que permita diagnosticar
automaticamente patologias cardíacas utilizando técnicas de classificação de data mining.
Foram utilizados dois tipos de dados: sons cardíacos gravados em ambiente hospitalar e dados
clínicos. Numa primeira fase, exploraram-se os sons cardíacos usando uma abordagem baseada
em motifs. Numa segunda fase, utilizamos os dados clínicos anotados dos pacientes. Numa
terceira fase, avaliamos a combinação das duas abordagens. Na avaliação experimental os
modelos baseados em motifs obtiveram melhores resultados do que os construídos a partir dos
dados clínicos. A combinação das abordagens mostrou poder ser vantajosa em situações
pontuais.This document was written as part of the Thesis of the MSc in computer science in the
area of Architecture, System and Network, Department of Computer Engineering in ISEP. The
main theme of this Thesis is to diagnose cardiac diseases, through acoustic and clinical data.
The goal of this work is to produce a process for automatically diagnosing heart
problems using data mining classification techniques. Two types of data were used: heart
sounds recorded in hospitals and clinical data. Initially, we explored the heart sounds using an
approach based on motifs. In a second stage, we used the clinical data of the patients. In a third
phase, we evaluated the combination of both approaches. Experimental evaluation showed
that models based on motifs performed better than those built from clinical data. The
combination of approaches has shown to be advantageous in specific situations