Classification of heart sound signal using curve fitting and fractal dimension

2018 ◽  
Vol 39 ◽  
pp. 351-359 ◽  
Author(s):  
Maryam Hamidi ◽  
Hassan Ghassemian ◽  
Maryam Imani
Keyword(s):  
Fractal Dimension ◽  
Curve Fitting ◽  
Heart Sound ◽  
Sound Signal ◽  
Heart Sound Signal

scholarly journals Classification of Heart Sound Signal Using Multiple Features

2018 ◽  
Vol 8 (12) ◽  
pp. 2344 ◽  
Author(s):  
Yaseen ◽  
Gui-Young Son ◽  
Soonil Kwon
Keyword(s):  
Heart Sound ◽  
Sound Signal ◽  
Support Vector ◽  
Multiple Features ◽  
Electronic Stethoscope ◽  
Heart Disorders ◽  
Discrete Wavelets ◽  
Mel Frequency Cepstral Coefficient ◽  
Heart Sound Signal

Cardiac disorders are critical and must be diagnosed in the early stage using routine auscultation examination with high precision. Cardiac auscultation is a technique to analyze and listen to heart sound using electronic stethoscope, an electronic stethoscope is a device which provides the digital recording of the heart sound called phonocardiogram (PCG). This PCG signal carries useful information about the functionality and status of the heart and hence several signal processing and machine learning technique can be applied to study and diagnose heart disorders. Based on PCG signal, the heart sound signal can be classified to two main categories i.e., normal and abnormal categories. We have created database of 5 categories of heart sound signal (PCG signals) from various sources which contains one normal and 4 are abnormal categories. This study proposes an improved, automatic classification algorithm for cardiac disorder by heart sound signal. We extract features from phonocardiogram signal and then process those features using machine learning techniques for classification. In features extraction, we have used Mel Frequency Cepstral Coefficient (MFCCs) and Discrete Wavelets Transform (DWT) features from the heart sound signal, and for learning and classification we have used support vector machine (SVM), deep neural network (DNN) and centroid displacement based k nearest neighbor. To improve the results and classification accuracy, we have combined MFCCs and DWT features for training and classification using SVM and DWT. From our experiments it has been clear that results can be greatly improved when Mel Frequency Cepstral Coefficient and Discrete Wavelets Transform features are fused together and used for classification via support vector machine, deep neural network and k-neareast neighbor(KNN). The methodology discussed in this paper can be used to diagnose heart disorders in patients up to 97% accuracy. The code and dataset can be accessed at “https://github.com/yaseen21khan/Classification-of-Heart-Sound-Signal-Using-Multiple-Features-/blob/master/README.md”.

scholarly journals Heart Murmur Detection∕Classification System Using Cochlea-Like Pre-Processing

2008 ◽  
Vol 2 (2) ◽  
Author(s):  
W. Ahmad ◽  
M. I. Hayee ◽  
Glenn Nordehn ◽  
S. Burns ◽  
Janet. L. Fitzakerley
Keyword(s):  
Coming Out ◽  
Heart Sound ◽  
Heart Association ◽  
Electrical Signal ◽  
Sound Signal ◽  
Heart Murmur ◽  
Permanent Disability ◽  
Novel Approach ◽  
Heart Sound Signal

According to the most recent report of American Heart Association (AHA), heart disease, stroke and other cardiovascular diseases continue to remain not only the no.1 killer of Americans but also a major cause of permanent disability among American workers. Recently, many research efforts have been carried out to apply artificial intelligence (AI) to auscultation based method for rigorous detection/classification of heart murmurs but accuracy rates are not always high. All of the proposed AI techniques rely on converting the heart sound to an electrical signal and processing that signal to optimize the AI for murmur detection and classification. However, all these techniques fail to recognize that the electrical signal coming out of the cochlea is very different than the electrical signal coming out of the microphone or any other electrical sensor which is commonly used for converting heart sound to electrical signal. In this research paper, we want to take a novel approach to pre-process the electrical heart sound signal before it goes to AI for murmur detection/classification by altering the electrical signal in a similar way as is done by the human cochlea before sending the signals to the brain. Our hypothesis is that cochlea like pre-processing will change the spectral contents of the heart sound signal to enhance the murmur information which can then be efficiently detected and classified by AI circuitry. Using this approach, we plan to develop an AI based system for heart murmur classification/ detection with success rate comparable to that of an expert cardiologist.

scholarly journals Research on Segmentation and Classification of Heart Sound Signals Based on Deep Learning

2021 ◽  
Vol 11 (2) ◽  
pp. 651
Author(s):  
Yi He ◽  
Wuyou Li ◽  
Wangqi Zhang ◽  
Sheng Zhang ◽  
Xitian Pi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Heart Sound ◽  
Original Data ◽  
Heart Sounds ◽  
Sound Signal ◽  
Accuracy Rate ◽  
Heart Research ◽  
Data Set ◽  
Heart Sound Signal

The heart sound signal is one of the signals that reflect the health of the heart. Research on the heart sound signal contributes to the early diagnosis and prevention of cardiovascular diseases. As a commonly used deep learning network, convolutional neural network (CNN) has been widely used in images. In this paper, the method of analyzing heart sound through using CNN has been studied. Firstly, the original data set was preprocessed, and then the heart sounds were segmented on U-net, based on the deep CNN. Finally, the classification of heart sounds was completed through CNN. The data from 2016 PhysioNet/CinC Challenge was utilized for algorithm validation, and the following results were obtained. When the heart sound segmented, the overall accuracy rate was 0.991, the accuracy of the first heart sound was 0.991, the accuracy of the systolic period was 0.996, the accuracy of the second heart sound was 0.996, and the accuracy of the diastolic period was 0.997, and the average accuracy rate was 0.995; While in classification, the accuracy was 0.964, the sensitivity was 0.781, and the specificity was 0.873. These results show that deep learning based on CNN shows good performance in the segmentation and classification of the heart sound signal.

scholarly journals Classification of Heart Sounds Based on the Wavelet Fractal and Twin Support Vector Machine

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 472 ◽  
Author(s):  
Jinghui Li ◽  
Li Ke ◽  
Qiang Du
Keyword(s):  
Support Vector Machine ◽  
Fractal Dimension ◽  
Classification Accuracy ◽  
Heart Sound ◽  
Heart Diseases ◽  
Sound Signal ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Box Dimension ◽  
Heart Sound Signal

Heart is an important organ of human beings. As more and more heart diseases are caused by people’s living pressure or habits, the diagnosis and treatment of heart diseases also require technical improvement. In order to assist the heart diseases diagnosis, the heart sound signal is used to carry a large amount of cardiac state information, so that the heart sound signal processing can achieve the purpose of heart diseases diagnosis and treatment. In order to quickly and accurately judge the heart sound signal, the classification method based on Wavelet Fractal and twin support vector machine (TWSVM) is proposed in this paper. Firstly, the original heart sound signal is decomposed by wavelet transform, and the wavelet decomposition coefficients of the signal are extracted. Then the two-norm eigenvectors of the heart sound signal are obtained by solving the two-norm values of the decomposition coefficients. In order to express the feature information more abundantly, the energy entropy of the decomposed wavelet coefficients is calculated, and then the energy entropy characteristics of the signal are obtained. In addition, based on the fractal dimension, the complexity of the signal is quantitatively described. The box dimension of the heart sound signal is solved by the binary box dimension method. So its fractal dimension characteristics can be obtained. The above eigenvectors are synthesized as the eigenvectors of the heart sound signal. Finally, the twin support vector machine (TWSVM) is applied to classify the heart sound signals. The proposed algorithm is verified on the PhysioNet/CinC Challenge 2016 heart sound database. The experimental results show that this proposed algorithm based on twin support vector machine (TWSVM) is superior to the algorithm based on support vector machine (SVM) in classification accuracy and speed. The proposed algorithm achieves the best results with classification accuracy 90.4%, sensitivity 94.6%, specificity 85.5% and F1 Score 95.2%.

scholarly journals Automated Heart Sound Signal Segmentation and Identification using Abrupt Changes and Peak Finding Detection

2021 ◽  
Vol 179 ◽  
pp. 260-267
Author(s):  
Norezmi Jamal ◽  
Nabilah Ibrahim ◽  
MNAH Sha’abani ◽  
Farhanahani Mahmud ◽  
N. Fuad
Keyword(s):  
Heart Sound ◽  
Sound Signal ◽  
Signal Segmentation ◽  
Peak Finding ◽  
Abrupt Changes ◽  
Heart Sound Signal

Detection and localization of S1 and S2 heart sounds by 3rd order normalized average Shannon energy envelope algorithm

2021 ◽  
pp. 095441192199810
Author(s):  
Madhwendra Nath ◽  
Subodh Srivastava ◽  
Niharika Kulshrestha ◽  
Dilbag Singh
Keyword(s):  
Success Rate ◽  
Heart Sound ◽  
Heart Diseases ◽  
Heart Sounds ◽  
Sound Signal ◽  
Ecg Signal ◽  
Shannon Energy ◽  
Detection And Localization ◽  
Pcg Signals ◽  
Heart Sound Signal

Adults born after 1970s are more prone to cardiovascular diseases. Death rate percentage is quite high due to heart related diseases. Therefore, there is necessity to enquire the problem or detection of heart diseases earlier for their proper treatment. As, Valvular heart disease, that is, stenosis and regurgitation of heart valve, are also a major cause of heart failure; which can be diagnosed at early-stage by detection and analysis of heart sound signal, that is, HS signal. In this proposed work, an attempt has been made to detect and localize the major heart sounds, that is, S1 and S2. The work in this article consists of three parts. Firstly, self-acquisition of Phonocardiogram (PCG) and Electrocardiogram (ECG) signal through a self-assembled, data-acquisition set-up. The Phonocardiogram (PCG) signal is acquired from all the four auscultation areas, that is, Aortic, Pulmonic, Tricuspid and Mitral on human chest, using electronic stethoscope. Secondly, the major heart sounds, that is, S1 and S2are detected using 3rd Order Normalized Average Shannon energy Envelope (3rd Order NASE) Algorithm. Further, an auto-thresholding has been used to localize time gates of S1 and S2 and that of R-peaks of simultaneously recorded ECG signal. In third part; the successful detection rate of S1 and S2, from self-acquired PCG signals is computed and compared. A total of 280 samples from same subjects as well as from different subjects (of age group 15–30 years) have been taken in which 70 samples are taken from each auscultation area of human chest. Moreover, simultaneous recording of ECG has also been performed. It was analyzed and observed that detection and localization of S1 and S2 found 74% successful for the self-acquired heart sound signal, if the heart sound data is recorded from pulmonic position of Human chest. The success rate could be much higher, if standard data base of heart sound signal would be used for the same analysis method. The, remaining three auscultations areas, that is, Aortic, Tricuspid, and Mitral have smaller success rate of detection of S1 and S2 from self-acquired PCG signals. So, this work justifies that the Pulmonic position of heart is most suitable auscultation area for acquiring PCG signal for detection and localization of S1 and S2 much accurately and for analysis purpose.

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