A novel heart-mobile interface for detection and classification of heart sounds

Analysis and classification of heart sounds and murmurs based on the instantaneous energy and frequency estimations

2000 TENCON Proceedings. Intelligent Systems and Technologies for the New Millennium (Cat. No.00CH37119) ◽

10.1109/tencon.2000.888404 ◽

2002 ◽

Cited By ~ 7

Author(s):

Z. Sharif ◽

M.S. Zainal ◽

A.Z. Sha'ameri ◽

S.H.S. Salleh

Keyword(s):

Heart Sounds ◽

Instantaneous Energy

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Classification of heart sounds using fractional fourier transform based mel-frequency spectral coefficients and traditional classifiers

Biomedical Signal Processing and Control ◽

10.1016/j.bspc.2019.101788 ◽

2020 ◽

Vol 57 ◽

pp. 101788 ◽

Cited By ~ 2

Author(s):

Zaid Abduh ◽

Ebrahim Ameen Nehary ◽

Manal Abdel Wahed ◽

Yasser M. Kadah

Keyword(s):

Fourier Transform ◽

Fractional Fourier Transform ◽

Heart Sounds ◽

Spectral Coefficients

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Automatic Segmentation and Classification of Heart Sounds Using Modified Empirical Wavelet Transform and Power Features

Applied Sciences ◽

10.3390/app10144791 ◽

2020 ◽

Vol 10 (14) ◽

pp. 4791 ◽

Cited By ~ 1

Author(s):

Pedro Narváez ◽

Steven Gutierrez ◽

Winston S. Percybrooks

Keyword(s):

Wavelet Transform ◽

Heart Sound ◽

State Of The Art ◽

Automatic Segmentation ◽

Automatic Classification ◽

The State ◽

Heart Sounds ◽

Empirical Wavelet Transform ◽

Art Methods

A system for the automatic classification of cardiac sounds can be of great help for doctors in the diagnosis of cardiac diseases. Generally speaking, the main stages of such systems are (i) the pre-processing of the heart sound signal, (ii) the segmentation of the cardiac cycles, (iii) feature extraction and (iv) classification. In this paper, we propose methods for each of these stages. The modified empirical wavelet transform (EWT) and the normalized Shannon average energy are used in pre-processing and automatic segmentation to identify the systolic and diastolic intervals in a heart sound recording; then, six power characteristics are extracted (three for the systole and three for the diastole)—the motivation behind using power features is to achieve a low computational cost to facilitate eventual real-time implementations. Finally, different models of machine learning (support vector machine (SVM), k-nearest neighbor (KNN), random forest and multilayer perceptron) are used to determine the classifier with the best performance. The automatic segmentation method was tested with the heart sounds from the Pascal Challenge database. The results indicated an error (computed as the sum of the differences between manual segmentation labels from the database and the segmentation labels obtained by the proposed algorithm) of 843,440.8 for dataset A and 17,074.1 for dataset B, which are better values than those reported with the state-of-the-art methods. For automatic classification, 805 sample recordings from different databases were used. The best accuracy result was 99.26% using the KNN classifier, with a specificity of 100% and a sensitivity of 98.57%. These results compare favorably with similar works using the state-of-the-art methods.

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