Predicting Cardiovascular Disorders Through Stethoscope Audio Using Convolutional Neural Network

Cardiovascular disorders pose a significant global health challenge, resulting in a substantial number of annual deaths. Early and accurate prediction of heart disorders is crucial to mitigate their impact on individuals and healthcare systems. In this study, we explore the potential of Convolutional Neural Network in automating heart disease prediction using spectrogram data. The dataset comprises audio recordings collected from the general public via an iPhone app and a clinical trial using a digital stethoscope. We preprocess the data to obtain spectrograms and design a Convolutional Neural Network architecture to classify heart sounds into distinct categories. The Convolutional Neural Network exhibits promising performance, achieving an accuracy of approximately 77%. Our research highlights the opportunity to leverage Convolutional Neural Network in this context, paving the way for advanced automated cardiac diagnostics. Keywords: Cardiovascular disorders, Convolutional neural network DOI: 10.7176/RHSS/13-14-02 Publication date:August 31st 202

Deteção de patologia cardíaca usando machine learning

Segundo a Organização Mundial da Saúde, as doenças cardiovasculares (DCV) representam 32% do número de mortes no mundo. A redução deste valor pode ser atingida através da deteção precoce que pode levar a um tratamento mais preciso, melhorando a expectativa de vida do paciente. A ausculta cardíaca é a principal técnica utilizada pelos profissionais de saúde para identificar muitas DCV. No entanto, a auscultação dos sons cardíacos é um procedimento difícil, já que muitos sons são fracos e difíceis de detetar, sendo necessário um processo de treino contínuo. Os estetoscópios modernos podem amplificar os sons cardíacos, reduzir o ruído de ambiente, melhorar a percepção do usuário e, mais importante, converter um sinal acústico em digital. Isto permitiu o desenvolvimento de sistemas de decisão assistidos por computador baseados na auscultação. Este documento apresenta uma metodologia que pode detectar automaticamente a existência de DCV através de sons cardíacos obtidos de diferentes partes do coração. Diversas tecnologias foram analisadas, assim como projetos que tentam resolver parte do problema em questão e a partir deles, três alternativas diferentes foram elaboradas e documentadas, assim como a divisão do dataset e métricas a serem usadas nos testes. Essas alternativas visam classificar anomalias na auscultação cardíaca dos pacientes. Vários modelos das duas primeiras alternativas foram implementados e seus resultados apresentados. Também é feita uma comparação entre as experiências desenvolvidas entre si, também com experiências básicas que não utilizam mecanismos inteligentes e com outros trabalhos que tenham o mesmo objetivo. O melhor resultado obtido foi pela primeira abordagem com uma exatidão de 94%, precisão de 81% e recall de 67%.According to World Health Organization, the cardiovascular diseases (CVD) represent 32% of the number of deaths worldwide. Early detection leads to a more accurate treatment plan and improves the patient’s life expectancy. Cardiac auscultation is the main technique used by health professionals to identify many CVD. Nevertheless, heart sound auscultation is a difficult procedure, since it requires continuous training and many heart sounds are faint and hard to detect. However, modern stethoscopes can amplify heart sounds, reduce the environment noise, improve the user’s perception and, more importantly, convert an acoustic signal to a digital one. This allowed, the development of computer assisted decision systems based on auscultation. This document presents a methodology that can automatically detect the existence of CVD through cardiac sounds obtained from different parts of the heart. Several technologies were analysed, as well as projects that try to solve part of the problem in question and from them, three different alternatives were elaborated and documented, as well as the division of test data and the metrics for their evaluation. These alternatives are intended to classify anomalies in patients' cardiac auscultation. Several models of the first two alternatives were implemented and their results presented. A comparison is also made between the experiences developed among themselves, also with basic experiments that do not use intelligent mechanisms and with other works that have the same objective. The best result obtained was by the first approach with an accuracy of 94%, precision of 81% and recall of 67%

Evaluation of Pre-Trained CNN Models for Cardiovascular Disease Classification: A Benchmark Study

In this paper, we present an up-to-date benchmarking of the most commonly used pre-trained CNN models using a merged set of three available public datasets to have a large enough sample range. From the 18th century up to the present day, cardiovascular diseases, which are considered among the most significant health risks globally, have been diagnosed by the auscultation of heart sounds using a stethoscope. This method is elusive, and a highly experienced physician is required to master it. Artificial intelligence and, subsequently, machine learning are being applied to equip modern medicine with powerful tools to improve medical diagnoses. Image and audio pre-trained convolution neural network (CNN) models have been used for classifying normal and abnormal heartbeats using phonocardiogram signals. We objectively benchmark more than two dozen image-pre-trained CNN models in addition to two of the most popular audio-based pre-trained CNN models: VGGish and YAMnet, which have been developed specifically for audio classification. The experimental results have shown that audio-based models are among the best- performing models. In particular, the VGGish model had the highest average validation accuracy and average true positive rate of 87% and 85%, respectively

A Comprehensive Survey on Heart Sound Analysis in the Deep Learning Era

Heart sound auscultation has been demonstrated to be beneficial in clinical usage for early screening of cardiovascular diseases. Due to the high requirement of well-trained professionals for auscultation, automatic auscultation benefiting from signal processing and machine learning can help auxiliary diagnosis and reduce the burdens of training professional clinicians. Nevertheless, classic machine learning is limited to performance improvement in the era of big data. Deep learning has achieved better performance than classic machine learning in many research fields, as it employs more complex model architectures with stronger capability of extracting effective representations. Deep learning has been successfully applied to heart sound analysis in the past years. As most review works about heart sound analysis were given before 2017, the present survey is the first to work on a comprehensive overview to summarise papers on heart sound analysis with deep learning in the past six years 2017--2022. We introduce both classic machine learning and deep learning for comparison, and further offer insights about the advances and future research directions in deep learning for heart sound analysis

The Usage of Data Augmentation Strategies on the Detection of Murmur Waves in a Pcg Signal

Cardiac auscultation is a key screening tool used for cardiovascular evaluation. When used properly, it speeds up treatment and thus improving the patient’s life quality. However, the analysis and interpretation of the heart sound signals is subjective and dependent of the physician’s experience and domain knowledge. A computer assistant decision (CAD) system that automatically analyse heart sound signals, can not only support physicians in their clinical decisions but also release human resources to other tasks. In this paper, and to the best of our knowledge, for the first time a SMOTE strategy is used to boost a Convolutional Neural Network performance on the detection of murmur waves. Using the SMOTE strategy, a CNN achieved an overall of 88.43%.info:eu-repo/semantics/publishedVersio

Phono-spectrographic analysis of heart murmur in children

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