Automated heartsound analysis/computer-aided auscultation: A cardiologist's perspective and suggestions for future development

Mahnke, C. Becket

doi:10.1109/iembs.2009.5332551

Cited by 12 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are several factors which may improve the chances of adoption of this new technology 33. Firstly, successful CAA systems must have a clearly defined indication: screening, diagnosis, and/or teaching.…”

Section: Challenges and Further Developmentmentioning

confidence: 99%

The promise of computer-assisted auscultation in screening for structural heart disease and clinical teaching : review article

Zühlke

Myer

Mayosi

2012

CVJA

View full text Add to dashboard Cite

Cardiac auscultation has been the central clinical tool for the diagnosis of valvular and other structural heart diseases for over a century. Physicians acquire competence in this technique through considerable training and experience. In Africa, however, we face a shortage of physicians and have the lowest health personnel-to-population ratio in the world. One of the proposed solutions for tackling this crisis is the adoption of health technologies and product innovations to support different cadres of health workers as part of task shifting.Computer-assisted auscultation (CAA) uses a digital stethoscope combined with acoustic neural networking to provide a visual display of heart sounds and murmurs, and analyses the recordings to distinguish between innocent and pathological murmurs. In so doing, CAA may serve as an objective tool for the screening of structural heart disease and facilitate the teaching of cardiac auscultation. This article reviews potential clinical applications of CAA.

show abstract

Section: Challenges and Further Developmentmentioning

confidence: 99%

The promise of computer-assisted auscultation in screening for structural heart disease and clinical teaching : review article

Zühlke

Myer

Mayosi

2012

CVJA

View full text Add to dashboard Cite

show abstract

“…to precisely classify the presence or absence of pathological events in the cardiac cycle [3]. If the presence of such an event is confirmed, an automated system should ideally also identify the type of pathology.…”

Section: Introductionmentioning

confidence: 99%

Robust Denoising of Phonocardiogram Signals using Time-Frequency Analysis and U-Nets

González–rodríguez,

Alonso–arévalo,

García–canseco

2023

IEEE Access

View full text Add to dashboard Cite

Cardiovascular diseases are the cause of many deaths worldwide every day. Automated cardiac auscultation is a promising diagnosis method; however, one of its main disadvantages is that it is prone to receiving too much noise during sound recording, which hinders diagnosis. In the available literature, most phonocardiogram (PCG) denoising methods have been evaluated using only synthetic sources such as white noise; this work proposes a more realistic approach. In this paper, the denoising process occurs in the time-frequency domain. More specifically, we compute the Short-Time Fourier Transform (STFT) of the contaminated PCG signal and train a U-Net to recognize normal and pathological cardiac sounds from noise. We are unaware of previous attempts to develop a robust PCG-denoising algorithm capable of simultaneously removing noise signals from four different sources: additive white Gaussian noise (AWGN), additive pink Gaussian noise (APGN), speech, and real PCG background noise. Since we are limited by the relatively small number of clean PCG signals available, we also propose a method for high-quality phonocardiogram data augmentation. In our tests, the proposed method exhibits high performance even in unfavorable scenarios since it can denoise a PCG signal contaminated with a signal-to-noise ratio (SNR) of −5 dB with average improvements ranging from 11.85 dB up to 17.60 dB, depending on the noise type used to degrade the cardiac signal. This method could significantly improve the performance of automatic cardiac sound classification algorithms in noisy environments but could also be used in electronic stethoscopes.

show abstract

“…Auscultation plays a key role in the diagnosis of VHD and CHD [ 4 , 5 , 6 ]. In the context of analysing heart sound signals, computer-aided detection technology can be a useful and cost-effective tool for acquiring and analysing these signals in a quantitative manner, with the added benefits of speed and efficiency [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Audiological Diagnosis of Valvular and Congenital Heart Diseases in the Era of Artificial Intelligence

Ainiwaer¹,

Kadier²,

Lian³

et al. 2023

Rev. Cardiovasc. Med.

View full text Add to dashboard Cite

In recent years, electronic stethoscopes have been combined with artificial intelligence (AI) technology to digitally acquire heart sounds, intelligently identify valvular disease and congenital heart disease, and improve the accuracy of heart disease diagnosis. The research on AI-based intelligent stethoscopy technology mainly focuses on AI algorithms, and the commonly used methods are end-to-end deep learning algorithms and machine learning algorithms based on feature extraction, and the hot spot for future research is to establish a large standardized heart sound database and unify these algorithms for external validation; in addition, different electronic stethoscopes should also be extensively compared so that the algorithms can be compatible with different. In addition, there should be extensive comparison of different electronic stethoscopes so that the algorithms can be compatible with heart sounds collected by different stethoscopes; especially importantly, the deployment of algorithms in the cloud is a major trend in the future development of artificial intelligence. Finally, the research of artificial intelligence based on heart sounds is still in the preliminary stage, although there is great progress in identifying valve disease and congenital heart disease, they are all in the research of algorithm for disease diagnosis, and there is little research on disease severity, remote monitoring, prognosis, etc., which will be a hot spot for future research.

show abstract

Automated heartsound analysis/computer-aided auscultation: A cardiologist's perspective and suggestions for future development

Cited by 12 publications

References 21 publications

The promise of computer-assisted auscultation in screening for structural heart disease and clinical teaching : review article

The promise of computer-assisted auscultation in screening for structural heart disease and clinical teaching : review article

Robust Denoising of Phonocardiogram Signals using Time-Frequency Analysis and U-Nets

Audiological Diagnosis of Valvular and Congenital Heart Diseases in the Era of Artificial Intelligence

Contact Info

Product

Resources

About