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

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 audio processing pipeline for acquiring diagnostic quality heart sounds via mobile phone

Recently, heart sound signals captured using mobile phones have been employed to develop data-driven heart disease detection systems. Such signals are generally captured in person by trained clinicians who can determine if the recorded heart sounds are of diagnosable quality. However, mobile phones have the potential to support heart health diagnostics, even where access to trained medical professionals is limited. To adopt mobile phones as self-diagnostic tools for the masses, we would need to have a mechanism to automatically establish that heart sounds recorded by non-expert users in uncontrolled conditions have the required quality for diagnostic purposes. This paper proposes a quality assessment and enhancement pipeline for heart sounds captured using mobile phones. The pipeline analyzes a heart sound and determines if it has the required quality for diagnostic tasks. Also, in cases where the quality of the captured signal is below the required threshold, the pipeline can improve the quality by applying quality enhancement algorithms. Using this pipeline, we can also provide feedback to users regarding the cause of low-quality signal capture and guide them towards a successful one. We conducted a survey of a group of thirteen clinicians with auscultation skills and experience. The results of this survey were used to inform and validate the proposed quality assessment and enhancement pipeline. We observed a high level of agreement between the survey results and fundamental design decisions within the proposed pipeline. Also, the results indicate that the proposed pipeline can reduce our dependency on trained clinicians for capture of diagnosable heart sounds

Respiratory sound analysis as a diagnosis tool for breathing disorders

This paper provides an overview of respiratory sound analysis (RSA) and its functionality as a diagnostic tool for breathing disorders. A number of respiratory conditions and the techniques used to diagnose them, including sleep apnoea, lung sound analysis (LSA), wheeze detection and phase estimation are discussed. The technologies used, from multi-channel bespoke recording systems to using a smart phone application are explained. A new study that focusses on developing a non-invasive tool for the detection and characterisation of inducible laryngeal obstruction (ILO) is presented. ILO is a debilitating condition, caused by malfunctioning structures of the upper airway, commonly triggered by exertion, leaving children feeling out of breath and unable to exercise normally. In rare cases it can lead to critical laryngeal obstruction and admission to intensive care for endotracheal intubation. The current definitive method of diagnosis is by inserting a camera through the nose while the person is exercising. This approach is invasive, uncomfortable (in particular for young children) subjective and relies on the consultant's expertise. There are only a handful of consultants with the appropriate level of expertise in the UK to diagnose this condition

DigiScope Collector - Unobtrosive collection and annotating of auscultations in real hospital environments

Mestrado em Informática MédicaMaster Programme in Medical Informatic

Computer-assisted auscultation as a screening tool for cardiovascular disease : a cross-sectional study

Includes synopsis.Includes bibliographical references.Cardiac auscultation is inherently qualitative, highly subjective and requires considerable skill and experience. Computer- assisted auscultation (CAA) is an objective referral-decision support tool that aims to minimise inappropriate referrals. This study evaluated the sensitivity and specificity of 2 CAA systems, Cardioscan® and Sensi®, in detecting echo-confirmed cardiac abnormalities in 79 consecutive patients referred for assessment to a tertiary cardiac clinic. CAA demonstrated suboptimal sensitivity and specificity in detecting cardiac abnormalities in children and adults. As both systems demonstrate 100% sensitivity in detecting acyanotic heart disease, and theoretically carry significant potential in resource-limited settings, further development of current technologies to improve sensitivity and specificity for clinical applications is still warranted

Exploring the Impact of Noise and Degradations on Heart Sound Classification Models

The development of data-driven heart sound classification models has been an active area of research in recent years. To develop such data-driven models in the first place, heart sound signals need to be captured using a signal acquisition device. However, it is almost impossible to capture noise-free heart sound signals due to the presence of internal and external noises in most situations. Such noises and degradations in heart sound signals can potentially reduce the accuracy of data-driven classification models. Although different techniques have been proposed in the literature to address the noise issue, how and to what extent different noise and degradations in heart sound signals impact the accuracy of data-driven classification models remains unexplored. To answer this question, we produced a synthetic heart sound dataset including normal and abnormal heart sounds contaminated with a variety of noise and degradations. We used this dataset to investigate the impact of noise and degradation in heart sound recordings on the performance of different classification models. The results show different noises and degradations affect the performance of heart sound classification models to a different extent; some are more problematic for classification models, and others are less destructive. Comparing the findings of this study with the results of a survey we previously carried out with a group of clinicians shows noise and degradations that are more detrimental to classification models are also more disruptive to accurate auscultation. The findings of this study can be leveraged to develop targeted heart sound quality enhancement approaches — which adapt the type and aggressiveness of quality enhancement based on the characteristics of noise and degradation in heart sound signals

Distinguishing Between Asthma and Pneumonia Through Automated Lung Sound Analysis

This project attempts to distinguish between two pulmonary disorders, asthma and pneumonia, using automated analysis of lung sounds. Such an approach minimizes the subjectivity of diagnosis inherent to current practices by physicians. Breath sounds are recorded by a physiological microphone and hardware acquisition system, and then analyzed in software using a two stage algorithm. The first stage detects abnormal lung sounds and second stage makes a diagnosis. A clinical trial was conducted at a pediatric clinic to validate the system

Noise reduction method for the heart sound records from digital stethoscope

In recent years, digital instruments have been widely used in the medical area with the rapid development of digital technology. The digital stethoscope, which converts the acoustic sound waves in to electrical signals and then amplifies them, is gradually replacing the conventional acoustic stethoscope with the advantage of additional usage such as restoring, replaying and processing the signals for optimal listening. As the sounds are transmitted in to electrical form, they can be recorded for further signal processing. One of the major problems with recording heart sounds is noise corruption. Although there are many solutions available to noise reduction problems, it was found that most of them are based on the assumption that the noise is an additive white noise [1]. More research is required to find different de-noising techniques based on the specific noise present. Therefore, this study is motivated to answer the research question: ‘How might the noise be reduced from the heart sound records collected from digital stethoscope with suitable noise reduction method’. This research question is divided into three sub-questions, including the identification of the noise spectrum, the design of noise reduction method and the assessment of the method. In the identification stage, five main kinds of noise were chosen and their characteristics and spectrums were discussed. Compared with different kinds of adaptive filters, the suitable noise reduction filter for this study was confirmed. To assess the effect of the method, 68 pieces of sound resources were collected for the experiment. These sounds were selected based on the noise they contain. A special noise reduction method was developed for the noise. This method was tested and assessed with those sound samples by two factors: the noise level and the noise kind. The results of the experiment showed the effect of the noise reduction method for each kind of noise. The outcomes indicated that this method was suitable for heart sound noise reduction. The findings of this study, including the analysis of noise level and noise kind, indicated and concluded that the chosen method for heart sound noise reduction performed well. This is perhaps the first attempt to understand and assess the noise reduction method with classified heart sound signals which are collected from the real healthcare environment. This noise reduction method may provide a de-noising solution for the specific noise present in heart sound