A Robust Interpretable Deep Learning Classifier for Heart Anomaly Detection Without Segmentation

Traditionally, abnormal heart sound classification is framed as a three-stage process. The first stage involves segmenting the phonocardiogram to detect fundamental heart sounds; after which features are extracted and classification is performed. Some researchers in the field argue the segmentation step is an unwanted computational burden, whereas others embrace it as a prior step to feature extraction. When comparing accuracies achieved by studies that have segmented heart sounds before analysis with those who have overlooked that step, the question of whether to segment heart sounds before feature extraction is still open. In this study, we explicitly examine the importance of heart sound segmentation as a prior step for heart sound classification, and then seek to apply the obtained insights to propose a robust classifier for abnormal heart sound detection. Furthermore, recognizing the pressing need for explainable Artificial Intelligence (AI) models in the medical domain, we also unveil hidden representations learned by the classifier using model interpretation techniques. Experimental results demonstrate that the segmentation plays an essential role in abnormal heart sound classification. Our new classifier is also shown to be robust, stable and most importantly, explainable, with an accuracy of almost 100% on the widely used PhysioNet dataset

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

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

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Classification of segmented phonocardiograms by convolutional neural networks

One of the first causes of human deaths in recent years in our world is heart diseases or cardiovascular diseases. Phonocardiograms (PCG) and electrocardiograms (ECG) are usually used for the detection of heart diseases. Studies on cardiac signals focus especially on the classification of heart sounds. Naturally, researches generally try to increase accuracy of classification. For this purpose, many studies use for the segmentation of heart sounds into S1 and S2 segments by methods such as Shannon energy, discreet wavelet transform and Hilbert transform. In this study, two different heart sounds data in the PhysioNet Atraining data set such as normal, and abnormal are classified with convolutional neural networks. For this purpose, the S1 and S2 parts of the heart sounds were segmented by the resampled energy method. The images of Phonocardiograms which were obtained from S1 and S2 parts in the heart sounds were used for classification. The resized small images of phonocardiogram were classified by convolutional neural networks. The obtained results were compared with the results from previous studies. The classification with CNN has performance as classification accuracy of 97.21%, sensitivity of 94.78%, and specificity of 99.65%. According to this, CNN classification with segmented S1-S2 sounds showed better results than the results of previous studies. In studies carried out, it has been seen that segmentation and convolutional neural networks increases the accuracy of classification and contributes to the classification studies efficiently

Prediction of Cardiovascular Diseases by Integrating Electrocardiogram (ECG) and Phonocardiogram (PCG) Multi-Modal Features using Hidden Semi Morkov Model

Because the health care field generates a large amount of data, we must employ modern ways to handle this data in order to give effective outcomes and make successful decisions based on data. Heart diseases are the major cause of mortality worldwide, accounting for 1/3th of all fatalities. Cardiovascular disease detection can be accomplished by the detection of disturbance in cardiac signals, one of which is known as phonocardiography. The aim of this project is for using machine learning to categorize cardiac illness based on electrocardiogram (ECG) and phonocardiogram (PCG) readings. The investigation began with signal preprocessing, which included cutting and normalizing the signal, and was accompanied by a continuous wavelet transformation utilizing a mother wavelet analytic morlet. The results of the decomposition are shown using a scalogram, and the outcomes are predicted using the Hidden semi morkov model (HSMM). In the first phase, we submit the dataset file and choose an algorithm to run on the chosen dataset. The accuracy of each selected method is then predicted, along with a graph, and a modal is built for the one with the max frequency by training the dataset to it. In the following step, input for each cardiac parameter is provided, and the sick stage of the heart is predicted based on the modal created. We then take measures based on the patient's condition. When compared to current approaches, the suggested HSMM has 0.952 sensitivity, 0.92 specificity, 0.94 F-score, 0.91 ACC, and 0.96 AUC

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

Classification of heart disease based on PCG signal using CNN

Cardiovascular disease is the leading cause of death in the world, so early detection of heart conditions is very important. Detection related to cardiovascular disease can be conducted through the detection of heart signals interference, one of which is called phonocardiography. This study aims to classify heart disease based on phonocardiogram (PCG) signals using the convolutional neural networks (CNN). The study was initiated with signal preprocessing by cutting and normalizing the signal, followed by a continuous wavelet transformation process using a mother wavelet analytic morlet. The decomposition results are visualized using a scalogram, then the results are used as CNN input. In this study, the PCG signals used were classified into normal, angina pectoris (AP), congestive heart failure (CHF), and hypertensive heart disease (HHD). The total data used, classified into 80 training data and 20 testing data. The obtained model shows the level of accuracy, sensitivity, and diagnostic specificity of 100%, 100%, and 100% for training data, respectively, while the prediction results for testing data indicate the level of accuracy, sensitivity, and specificity of 85%, 80%, and 100%, respectively. This result proved to be better than the mother wavelet or other classifier methods, then the model was deployed into the graphical user interface (GUI)

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%