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

Phono-spectrographic analysis of heart murmur in children

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Diagnostic accuracy of machine learning models to identify congenital heart disease: A meta-analysis

Background: With the dearth of trained care providers to diagnose congenital heart disease (CHD) and a surge in machine learning (ML) models, this review aims to estimate the diagnostic accuracy of such models for detecting CHD. Methods: A comprehensive literature search in the PubMed, CINAHL, Wiley Cochrane Library, and Web of Science databases was performed. Studies that reported the diagnostic ability of ML for the detection of CHD compared to the reference standard were included. Risk of bias assessment was performed using Quality Assessment for Diagnostic Accuracy Studies-2 tool. The sensitivity and specificity results from the studies were used to generate the hierarchical Summary ROC (HSROC) curve. Results: We included 16 studies (1217 participants) that used ML algorithm to diagnose CHD. Neural networks were used in seven studies with overall sensitivity of 90.9% (95% CI 85.2-94.5%) and specificity was 92.7% (95% CI 86.4-96.2%). Other ML models included ensemble methods, deep learning and clustering techniques but did not have sufficient number of studies for a meta-analysis. Majority (n=11, 69%) of studies had a high risk of patient selection bias, unclear bias on index test (n=9, 56%) and flow and timing (n=12, 75%) while low risk of bias was reported for the reference standard (n=10, 62%). Conclusion: ML models such as neural networks have the potential to diagnose CHD accurately without the need for trained personnel. The heterogeneity of the diagnostic modalities used to train these models and the heterogeneity of the CHD diagnoses included between the studies is a major limitation

An open access database for the evaluation of heart sound algorithms

This is an author-created, un-copyedited version of an article published in Physiological Measurement. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at https://doi.org/10.1088/0967-3334/37/12/2181In the past few decades, analysis of heart sound signals (i.e. the phonocardiogram or PCG), especially for automated heart sound segmentation and classification, has been widely studied and has been reported to have the potential value to detect pathology accurately in clinical applications. However, comparative analyses of algorithms in the literature have been hindered by the lack of high-quality, rigorously validated, and standardized open databases of heart sound recordings. This paper describes a public heart sound database, assembled for an international competition, the PhysioNet/Computing in Cardiology (CinC) Challenge 2016. The archive comprises nine different heart sound databases sourced from multiple research groups around the world. It includes 2435 heart sound recordings in total collected from 1297 healthy subjects and patients with a variety of conditions, including heart valve disease and coronary artery disease. The recordings were collected from a variety of clinical or nonclinical (such as in-home visits) environments and equipment. The length of recording varied from several seconds to several minutes. This article reports detailed information about the subjects/patients including demographics (number, age, gender), recordings (number, location, state and time length), associated synchronously recorded signals, sampling frequency and sensor type used. We also provide a brief summary of the commonly used heart sound segmentation and classification methods, including open source code provided concurrently for the Challenge. A description of the PhysioNet/CinC Challenge 2016, including the main aims, the training and test sets, the hand corrected annotations for different heart sound states, the scoring mechanism, and associated open source code are provided. In addition, several potential benefits from the public heart sound database are discussed.This work was supported by the National Institutes of Health (NIH) grant R01-EB001659 from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) and R01GM104987 from the National Institute of General Medical Sciences.Liu, C.; Springer, DC.; Li, Q.; Moody, B.; Abad Juan, RC.; Li, Q.; Moody, B.... (2016). An open access database for the evaluation of heart sound algorithms. Physiological Measurement. 37(12):2181-2213. doi:10.1088/0967-3334/37/12/2181S21812213371

Detection of Pathologic Heart Murmurs Using a Piezoelectric Sensor

This study aimed to evaluate the capability of a piezoelectric sensor to detect a heart murmur in patients with congenital heart defects. Heart sounds and murmurs were recorded using a piezoelectric sensor and an electronic stethoscope in healthy neonates (n = 9) and in neonates with systolic murmurs caused by congenital heart defects (n = 9) who were born at a hospital. Signal data were digitally filtered by high-pass filtering, and the envelope of the processed signals was calculated. The amplitudes of systolic murmurs were evaluated using the signal-to-noise ratio and compared between healthy neonates and those with congenital heart defects. In addition, the correlation between the amplitudes of systolic murmurs recorded by the piezoelectric sensor and electronic stethoscope was determined. The amplitudes of systolic murmurs detected by the piezoelectric sensor were significantly higher in neonates with congenital heart defects than in healthy neonates (p < 0.01). Systolic murmurs recorded by the piezoelectric sensor had a strong correlation with those recorded by the electronic stethoscope (rho = 0.899 and p < 0.01, respectively). The piezoelectric sensor can detect heart murmurs objectively. Mechanical improvement and automatic analysis algorithms are expected to improve recording in the future

Automatic analysis and classification of cardiac acoustic signals for long term monitoring

Objective: Cardiovascular diseases are the leading cause of death worldwide resulting in over 17.9 million deaths each year. Most of these diseases are preventable and treatable, but their progression and outcomes are significantly more positive with early-stage diagnosis and proper disease management. Among the approaches available to assist with the task of early-stage diagnosis and management of cardiac conditions, automatic analysis of auscultatory recordings is one of the most promising ones, since it could be particularly suitable for ambulatory/wearable monitoring. Thus, proper investigation of abnormalities present in cardiac acoustic signals can provide vital clinical information to assist long term monitoring. Cardiac acoustic signals, however, are very susceptible to noise and artifacts, and their characteristics vary largely with the recording conditions which makes the analysis challenging. Additionally, there are challenges in the steps used for automatic analysis and classification of cardiac acoustic signals. Broadly, these steps are the segmentation, feature extraction and subsequent classification of recorded signals using selected features. This thesis presents approaches using novel features with the aim to assist the automatic early-stage detection of cardiovascular diseases with improved performance, using cardiac acoustic signals collected in real-world conditions. Methods: Cardiac auscultatory recordings were studied to identify potential features to help in the classification of recordings from subjects with and without cardiac diseases. The diseases considered in this study for the identification of the symptoms and characteristics are the valvular heart diseases due to stenosis and regurgitation, atrial fibrillation, and splitting of fundamental heart sounds leading to additional lub/dub sounds in the systole or diastole interval of a cardiac cycle. The localisation of cardiac sounds of interest was performed using an adaptive wavelet-based filtering in combination with the Shannon energy envelope and prior information of fundamental heart sounds. This is a prerequisite step for the feature extraction and subsequent classification of recordings, leading to a more precise diagnosis. Localised segments of S1 and S2 sounds, and artifacts, were used to extract a set of perceptual and statistical features using wavelet transform, homomorphic filtering, Hilbert transform and mel-scale filtering, which were then fed to train an ensemble classifier to interpret S1 and S2 sounds. Once sound peaks of interest were identified, features extracted from these peaks, together with the features used for the identification of S1 and S2 sounds, were used to develop an algorithm to classify recorded signals. Overall, 99 features were extracted and statistically analysed using neighborhood component analysis (NCA) to identify the features which showed the greatest ability in classifying recordings. Selected features were then fed to train an ensemble classifier to classify abnormal recordings, and hyperparameters were optimized to evaluate the performance of the trained classifier. Thus, a machine learning-based approach for the automatic identification and classification of S1 and S2, and normal and abnormal recordings, in real-world noisy recordings using a novel feature set is presented. The validity of the proposed algorithm was tested using acoustic signals recorded in real-world, non-controlled environments at four auscultation sites (aortic valve, tricuspid valve, mitral valve, and pulmonary valve), from the subjects with and without cardiac diseases; together with recordings from the three large public databases. The performance metrics of the methodology in relation to classification accuracy (CA), sensitivity (SE), precision (P+), and F1 score, were evaluated. Results: This thesis proposes four different algorithms to automatically classify fundamental heart sounds – S1 and S2; normal fundamental sounds and abnormal additional lub/dub sounds recordings; normal and abnormal recordings; and recordings with heart valve disorders, namely the mitral stenosis (MS), mitral regurgitation (MR), mitral valve prolapse (MVP), aortic stenosis (AS) and murmurs, using cardiac acoustic signals. The results obtained from these algorithms were as follows: • The algorithm to classify S1 and S2 sounds achieved an average SE of 91.59% and 89.78%, and F1 score of 90.65% and 89.42%, in classifying S1 and S2, respectively. 87 features were extracted and statistically studied to identify the top 14 features which showed the best capabilities in classifying S1 and S2, and artifacts. The analysis showed that the most relevant features were those extracted using Maximum Overlap Discrete Wavelet Transform (MODWT) and Hilbert transform. • The algorithm to classify normal fundamental heart sounds and abnormal additional lub/dub sounds in the systole or diastole intervals of a cardiac cycle, achieved an average SE of 89.15%, P+ of 89.71%, F1 of 89.41%, and CA of 95.11% using the test dataset from the PASCAL database. The top 10 features that achieved the highest weights in classifying these recordings were also identified. • Normal and abnormal classification of recordings using the proposed algorithm achieved a mean CA of 94.172%, and SE of 92.38%, in classifying recordings from the different databases. Among the top 10 acoustic features identified, the deterministic energy of the sound peaks of interest and the instantaneous frequency extracted using the Hilbert Huang-transform, achieved the highest weights. • The machine learning-based approach proposed to classify recordings of heart valve disorders (AS, MS, MR, and MVP) achieved an average CA of 98.26% and SE of 95.83%. 99 acoustic features were extracted and their abilities to differentiate these abnormalities were examined using weights obtained from the neighborhood component analysis (NCA). The top 10 features which showed the greatest abilities in classifying these abnormalities using recordings from the different databases were also identified. The achieved results demonstrate the ability of the algorithms to automatically identify and classify cardiac sounds. This work provides the basis for measurements of many useful clinical attributes of cardiac acoustic signals and can potentially help in monitoring the overall cardiac health for longer duration. The work presented in this thesis is the first-of-its-kind to validate the results using both, normal and pathological cardiac acoustic signals, recorded for a long continuous duration of 5 minutes at four different auscultation sites in non-controlled real-world conditions.Open Acces

A Human-Machine Framework for the Classification of Phonocardiograms

In this thesis, we present and evaluate a framework for combining machine learning algo- rithms, crowd workers, and experts in the classification of heart sound recordings. The development of a hybrid human-machine framework for heart sound recordings is moti- vated by the past success in utilizing human computation to solve problems in medicine as well as the use of human-machine frameworks in other domains. We describe the methods that decide when and how to escalate the analysis of heart sound recordings to different resources and incorporate their decision into a final classification. We present and discuss the results of the framework which was tested with a number of different machine classi- fiers and a group of crowd workers from Amazon’s Mechanical Turk. We also provide an evaluation of how crowd workers perform in various different heart sound analysis tasks, and how they compare with machine classifiers. In addition, we investigate how machine and human analysis are effected by different types of heart sounds and provide a strategy for involving experts when these methods are uncertain. We conclude that the use of a hybrid framework is a viable method for heart sound classification

Contribution to non-invasive diagnostic methods by adaptive approach for the detection of mitral valve prolapse in pediatrics patients

Rano prepoznavanje bolesti srca je od posebne važnosti u pedijatriji. Visoka prevalenca (77-90%) šuma na srcu je značajan problem u ovoj populaciji. Klasičnaa dijagnostika u pedijatrijskoj praksi je baziranaa na neinvazivnim metodama (auskultacija, EKG, Rtg.) koje imaju relativno niske performanse. Zato se procena težine šuma određuje ehokardiografski. Ehokardiografija je uglavnom dostupna u zdravstvenim centrima većih gradova. Cilj ove doktorske disertacije je razvoj jeftine dijagnostičke metode, bazirane na automatskoj detekciji prolapsa mitralne valvule (lokalizacija klik sindroma na fonokardiogramu), kao podrške u razlikovanju benignih i patoloških šumova srca korišćeljem fonokardiografije i auskultacije. Metode: Predloženi metod je baziran na akustičnim signalima srca. Izabrani metod koristi višeslojni perceptron (MLP) sa algoritmom serijskog obučanja i postepnim dolaskom do rešenja propagacijom greške unazad. MLP neuralna mreža se sastoji od feedforward mreže neurona. Neuroni su organizovani u tri sloja (ulazni,skriveni i izlazni sloj). MLP obezbeđuje nelinearno mapiranje između ulaza i izlaza. Svaki neuron MLP koristi nelinearnu sigmoidalnu aktivacionu funkciju. Fonokardiogrami na ulazu u mrežu su automatski klasifikovani u jednu od tri moguće klase (PMV,zdravi,ostali- niti PMV niti zdravi). Prvi korak je kreiranje trening i test skupa za svaku iteraciju krosvalidacije i inicializaciju svih parametara VNM. Drugi korak je backpropagation algoritam: uključuje proračun cost funkcije, postupni dolazak do rešenja i podešavanje težina. Podešavanjem težina minimalizuje se cost funkcija sa ciljem smanjenja greške u klasifikaciji. Poslednji korak je klasifikacija korišćenjem algoritma one-versus-all. Rezultati: VNM sadrži 64.033 neurona u ulaznom sloju (ukučujući jedan neuron kao bias ulaz), 95 neurona u skrivenom sloju i tri neurona u izlaznom sloju ( po jedan za svaku klasu). Ovo je bilo kompromisno rešenje između performansi i tačnosti mreže. Skup ulaznih podataka sadrži 135 fonokardiograma podeljenih u tri klase PMV (48),zdravi (49) i ostali (38). Za svaki skup primenjena je metoda krosvalidacije. Klasifikaciona tačnost predložene VNM iznosi 79,85%, senzitivnost 90% i specifičnost 75%. Zaključak: Auskultacija je važan dijagnostički indikator hemodinamskih poremećaja Razvoj precizne dijagnostičke metode je od posebnog značaja za rano prepoznavanje oboljenja srca i smanjenje troškova u zdravstvu. VNM su vredan alat za nelinearno adaptivno filtriranje, prepoznavanje i klasifikaciju. Dobijeni rezultati se mogu smatrati korisnim za kliničku podršku u ranom otkrivanju prolapsa mitralne valvule u pedijatrijskoj populaciji.Early recognition of heart disease is an important goal in pediatrics. This is a significant problem in pediatric cardiology because of the high rate of prevalence (77- 90%) of heart murmurs in this population. Classical diagnosis of heart murmurs in pediatric practice is based on non-invasive methods (auscultation, ECG, X-ray), which have relatively low performance. Thus, patients with heart murmurs are frequently assessed by echocardiography. However, echocardiography is usually only available in healthcare centers in major cities. The objective of the present doctoral dissertations is to develop an inexpensive diagnostic method, based on automatically detection of mitral valve prolapse ( localization of click syndrom in phonocardiograms),that can assist in the differentiation between innocent and pathological heart murmurs via phonocardiography and auscultation. Methods: The proposed method is heart signal-based. Selected method uses a multilayer perceptron (MLP) with backpropagation batch gradient descent learning algorithm. MLP neural network consists of a feed-forward, layered network of neurons. Neurons are organized in three layers (input layer, hidden layer, and output layer). An MLP provides a nonlinear mapping between its input and output. Each neuron in an MLP has a nonlinear sigmoid activation function. Input phonocardiograms are autamatically classified in one of three possible classes (MVP, healthy, others – neither MVP nor healthy). The first step is to create training and test sets for each iteration of cross-validation method and to initialize all parameters for ANN. The second step is back-propagation algorithm; it includes calculation of cost function, descent gradients and adjusting weights of ANN. With adjusting weights we try to minimize cost function with goals to minimize error in classification. Final step is classification using one-versus-all algorithm. Results: ANN consists of 64.033 neurons in input layers (including one neuron for bias input), 95 neurons in hidden layer, and three neurons in output layer (one for each class). This was a compromise solution between performance and accuracy. The resulting data comprised 135 phonocardiograms; the three data sets are labeled as MVP(48), healthy(49) and others (38). Cross-validation method is applied on every dataset. The proposed ANN showed 79,85% classification accuracy, 90% sensitivity and 75% specificity. Conclusion: The auscultation method is an important diagnostic indicator for hemodynamic anomalies. Developing a more accurate screening and diagnostic method is vital in early recognition of heart disease and reducing health care costs. Artificial neural networks (ANNs) are valuable tools used in nonlinear adaptive filtering, complex pattern recognition and classification tasks. Obtained result can be cosidered as a quite useful tool for clinical support and early detection of mitral valve prolapse in pediatric population