scholarly journals A benchmark of heart sound classification systems based on sparse decompositions

2018 ◽  
Author(s):  
Roilhi Frajo Ibarra-Hernández ◽  
Nancy Bertin ◽  
Miguel Angel Alonso-Arévalo ◽  
Hugo Armando Guillén-Ramírez
Keyword(s):  
Heart Sound ◽  
Classification Systems ◽  
Sound Classification

Unsegmented Heart Sound Classification Using Hybrid CNN-LSTM Neural Networks

2021 ◽  
Author(s):  
Drishti Ramesh Megalmani ◽  
Shailesh B G ◽  
Achuth Rao M V ◽  
Satish S Jeevannavar ◽  
Prasanta Kumar Ghosh
Keyword(s):  
Neural Networks ◽  
Heart Sound ◽  
Sound Classification

HEART SOUND CLASSIFICATION AND RECOGNITION BASED ON EEMD AND CORRELATION DIMENSION

2014 ◽  
Vol 14 (04) ◽  
pp. 1450046 ◽  
Author(s):  
WENYING ZHANG ◽  
XINGMING GUO ◽  
ZHIHUI YUAN ◽  
XINGHUA ZHU
Keyword(s):  
Feature Extraction ◽  
Heart Sound ◽  
Linear Time ◽  
Heart Diseases ◽  
Recognition Rate ◽  
Heart Sounds ◽  
Support Vector ◽  
Svm Classifier ◽  
Intrinsic Mode Functions ◽  
Sound Classification

Analysis of heart sound is of great importance to the diagnosis of heart diseases. Most of the feature extraction methods about heart sound have focused on linear time-variant or time-invariant models. While heart sound is a kind of highly nonstationary and nonlinear vibration signal, traditional methods cannot fully reveal its essential properties. In this paper, a novel feature extraction approach is proposed for heart sound classification and recognition. The ensemble empirical mode decomposition (EEMD) method is used to decompose the heart sound into a finite number of intrinsic mode functions (IMFs), and the correlation dimensions of the main IMF components (IMF1~IMF4) are calculated as feature set. Then the classical Binary Tree Support Vector Machine (BT-SVM) classifier is employed to classify the heart sounds which include the normal heart sounds (NHSs) and three kinds of abnormal signals namely mitral stenosis (MT), ventricular septal defect (VSD) and aortic stenosis (AS). Finally, the performance of the new feature set is compared with the correlation dimensions of original signals and the main IMF components obtained by the EMD method. The results showed that, for NHSs, the feature set proposed in this paper performed the best with recognition rate of 98.67%. For the abnormal signals, the best recognition rate of 91.67% was obtained. Therefore, the proposed feature set is more superior to two comparative feature sets, which has potential application in the diagnosis of cardiovascular diseases.

Combining sparse coding and time-domain features for heart sound classification

2017 ◽  
Vol 38 (8) ◽  
pp. 1701-1713 ◽  
Author(s):  
Bradley M Whitaker ◽  
Pradyumna B Suresha ◽  
Chengyu Liu ◽  
Gari D Clifford ◽  
David V Anderson
Keyword(s):  
Time Domain ◽  
Sparse Coding ◽  
Heart Sound ◽  
Sound Classification

scholarly journals Heart sound classification from unsegmented phonocardiograms

2017 ◽  
Vol 38 (8) ◽  
pp. 1658-1670 ◽  
Author(s):  
Philip Langley ◽  
Alan Murray
Keyword(s):  
Heart Sound ◽  
Sound Classification

scholarly journals Multi-level basis selection of wavelet packet decomposition tree for heart sound classification

2013 ◽  
Vol 43 (10) ◽  
pp. 1407-1414 ◽  
Author(s):  
Fatemeh Safara ◽  
Shyamala Doraisamy ◽  
Azreen Azman ◽  
Azrul Jantan ◽  
Asri Ranga Abdullah Ramaiah
Keyword(s):  
Heart Sound ◽  
Wavelet Packet ◽  
Wavelet Packet Decomposition ◽  
Decomposition Tree ◽  
Sound Classification ◽  
Multi Level ◽  
Selection Of
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