scholarly journals Detection and Classification of Abnormities of First Heart Sound Using Empirical Wavelet Transform

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 139643-139652 ◽  
Author(s):  
Haixia Li ◽  
Yongfeng Ren ◽  
Guojun Zhang ◽  
Renxin Wang ◽  
Jiangong Cui ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Heart Sound ◽  
First Heart Sound ◽  
Empirical Wavelet Transform

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

2020 ◽  
Vol 10 (14) ◽  
pp. 4791 ◽  
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.

Empirical Wavelet Transform Based Features for Classification of Parkinson’s Disease Severity

2017 ◽  
Vol 42 (2) ◽  
Author(s):  
Qi Wei Oung ◽  
Hariharan Muthusamy ◽  
Shafriza Nisha Basah ◽  
Hoileong Lee ◽  
Vikneswaran Vijean
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Wavelet Transform ◽  
Disease Severity ◽  
Empirical Wavelet Transform

scholarly journals Synthesis of Normal Heart Sounds Using Generative Adversarial Networks and Empirical Wavelet Transform

2020 ◽  
Vol 10 (19) ◽  
pp. 7003 ◽  
Author(s):  
Pedro Narváez ◽  
Winston S. Percybrooks
Keyword(s):  
Wavelet Transform ◽  
Intelligent Systems ◽  
Heart Sound ◽  
State Of The Art ◽  
Computational Cost ◽  
Heart Sounds ◽  
Generative Adversarial Networks ◽  
Sound Classification ◽  
Adversarial Networks ◽  
Empirical Wavelet Transform

Currently, there are many works in the literature focused on the analysis of heart sounds, specifically on the development of intelligent systems for the classification of normal and abnormal heart sounds. However, the available heart sound databases are not yet large enough to train generalized machine learning models. Therefore, there is interest in the development of algorithms capable of generating heart sounds that could augment current databases. In this article, we propose a model based on generative adversary networks (GANs) to generate normal synthetic heart sounds. Additionally, a denoising algorithm is implemented using the empirical wavelet transform (EWT), allowing a decrease in the number of epochs and the computational cost that the GAN model requires. A distortion metric (mel–cepstral distortion) was used to objectively assess the quality of synthetic heart sounds. The proposed method was favorably compared with a mathematical model that is based on the morphology of the phonocardiography (PCG) signal published as the state of the art. Additionally, different heart sound classification models proposed as state-of-the-art were also used to test the performance of such models when the GAN-generated synthetic signals were used as test dataset. In this experiment, good accuracy results were obtained with most of the implemented models, suggesting that the GAN-generated sounds correctly capture the characteristics of natural heart sounds.

Classification of epileptic EEG signals using sparse spectrum based empirical wavelet transform

2020 ◽  
Vol 56 (25) ◽  
pp. 1370-1372
Author(s):  
A. Nishad ◽  
A. Upadhyay ◽  
G. Ravi Shankar Reddy ◽  
V. Bajaj
Keyword(s):  
Wavelet Transform ◽  
Eeg Signals ◽  
Empirical Wavelet Transform
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