Detection of Heart Murmurs Using Wavelet Analysis and Artificial Neural Networks

Andrisevic, Nicholas; Ejaz, K.; Rios-Gutiérrez, Fernando; Alba-Flores, Rocio; Nordehn, Glenn; Burns, S.G.

doi:10.1115/1.2049327

Cited by 40 publications

(26 citation statements)

References 13 publications

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“…Their system was able to achieve a sensitivity of 97% and a specificity of 94%. Andrisevic et al [24] developed an autonomous auscultation system with an ANN classifier. They used principal component analysis (PCA) and block processing to prepare features for the network.…”

Section: Classification Of Heart Soundsmentioning

confidence: 99%

Autonomous auscultation of the human heart employing a precordial electro-phonocardiogram and ensemble empirical mode decomposition

Botha

Scheffer

Lubbe

et al. 2010

Australas Phys Eng Sci Med

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The research presented in this paper serves to provide a tool to autonomously screen for cardiovascular disease in the rural areas of Africa. With this tool, cardiovascular disease can potentially be detected in its initial stages, which is essential for effective treatment. The autonomous auscultation system proposed here utilizes recorded heart sounds and electrocardiogram signals to automatically distinguish between normal and abnormal heart conditions. Patients that are identified as abnormal by the system can then be referred to a specialist consultant, which will save a lot of unnecessary referrals. In this study, heart sound and electrocardiogram signals were recorded with the prototype precordial electro-phonocardiogram device, as part of a clinical study to screen patients for cardiovascular disease. These volunteers consisted of 28 patients with a diagnosed cardiovascular disease and, for control purposes, 34 persons diagnosed with healthy hearts. The proposed system employs wavelets to first denoise the recorded signals, which is then followed by segmentation of heart sounds. Frequency spectrum information was extracted as diagnostic features from the heart sounds by means of ensemble empirical mode decomposition and auto regressive modelling. The respective features were then classified with an ensemble artificial neural network. The performance of the autonomous auscultation system used in concert with the precordial electro-phonocardiogram prototype showed a sensitivity of 82% and a specificity of 88%. These results demonstrate the potential benefit of the precordial electro-phonocardiogram device and the developed autonomous auscultation software as a screening tool in a rural healthcare environment where large numbers of patients are often cared for by a small number of inexperienced medical personnel.

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Section: Classification Of Heart Soundsmentioning

confidence: 99%

Autonomous auscultation of the human heart employing a precordial electro-phonocardiogram and ensemble empirical mode decomposition

Botha

Scheffer

Lubbe

et al. 2010

Australas Phys Eng Sci Med

View full text Add to dashboard Cite

show abstract

“…Then, the STFT of wavelet coefficients, performed at seven different frequency intervals, was used to obtain 91 wavelet entropy features 35. Andrisevic et al 33 used the wavelet techniques to de-noise and prefilter heart sounds. The WT plot of a whole cardiac cycle was then reorganised as a vector and taken as input of the classifier 33.…”

Section: Cardiac Sound Analysis and Feature Extractionmentioning

confidence: 99%

“…Andrisevic et al 33 used the wavelet techniques to de-noise and prefilter heart sounds. The WT plot of a whole cardiac cycle was then reorganised as a vector and taken as input of the classifier 33. Choi and Zhongwei32 presented a method in which only two features were extracted from the autoregressive spectral envelope after a wavelet-based analysis of the acoustic signal.…”

Section: Cardiac Sound Analysis and Feature Extractionmentioning

confidence: 99%

Current trends and perspectives for automated screening of cardiac murmurs

Marascio

Modesti

2013

Heart Asia

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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.

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“…Previous authors have also addressed the analysis and classification of heart sounds [9][10][11][12][13] . Signal processing techniques implemented in the analysis of heart sounds include the Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT), Wigner Distribution (WD), ChoiWilliams Distribution (CWD) and the Wavelet Transform (WT).…”

Section: Classification Of Heart Soundsmentioning

confidence: 99%

“…A sensitivity of 100% and a specificity of 100% were obtained. Andrisevic et al 9 implemented an ANN consisting of two hidden layers and one output layer to differentiate between normal and abnormal heart sounds. The network was trained with the back-propagation algorithm and a sensitivity of 64.7% and a specificity of 70.5% was obtained.…”

Section: Classification Of Heart Soundsmentioning

confidence: 99%

Autonomous detection of heart sound abnormalities using an auscultation jacket

Visagie

Scheffer

Lubbe

et al. 2009

Australas. Phys. Eng. Sci. Med.

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This paper presents a study using an auscultation jacket with embedded electronic stethoscopes, and a software classification system capable of differentiating between normal and certain auscultatory abnormalities. The aim of the study is to demonstrate the potential of such a system for semi-automated diagnosis for underserved locations, for instance in rural areas or in developing countries where patients far outnumber the available medical personnel. Using an "auscultation jacket", synchronous data was recorded at multiple chest locations on 31 healthy volunteers and 21 patients with heart pathologies. Electrocardiograms (ECGs) were also recorded simultaneously with phonocardiographic data. Features related to heart pathologies were extracted from the signals and used as input to a feed-forward artificial neural network. The system is able to classify between normal and certain abnormal heart sounds with a sensitivity of 84% and a specificity of 86%. Though the number of training and testing samples presented are limited, the system performed well in differentiating between normal and abnormal heart sounds in the given database of available recordings. The results of this study demonstrate the potential of such a system to be used as a fast and cost-effective screening tool for heart pathologies.

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Detection of Heart Murmurs Using Wavelet Analysis and Artificial Neural Networks

Cited by 40 publications

References 13 publications

Autonomous auscultation of the human heart employing a precordial electro-phonocardiogram and ensemble empirical mode decomposition

Autonomous auscultation of the human heart employing a precordial electro-phonocardiogram and ensemble empirical mode decomposition

Current trends and perspectives for automated screening of cardiac murmurs

Autonomous detection of heart sound abnormalities using an auscultation jacket

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