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%.

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.

scholarly journals An Irregularity Measurement Based Cardiac Status Recognition Using Support Vector Machine

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Poulami Banerjee ◽  
Ashok Mondal
Keyword(s):  
Support Vector Machine ◽  
Heart Sound ◽  
Structural Complexity ◽  
Sample Entropy ◽  
Sound Signal ◽  
Waveform Analysis ◽  
Support Vector ◽  
Data Set ◽  
Cardiac Status ◽  
Nonlinear Signal

An automated robust feature extraction technique is proposed in this paper based on inherent structural distribution of heart sound to analyze the phonocardiogram signal in presence of environmental noise and interference of lung sound signal. The structural complexity of the heart sound signal is estimated in terms of sample entropy using a nonlinear signal processing framework. The effectiveness of the feature is evaluated using a support vector machine under two different circumstances which include Gaussian noise and pulmonary perturbation. The analysis framework has been executed on a composite data set of 60 healthy and 60 pathological individuals for different SNR levels (−5 to 10 dB) and the performance accuracy is close to that of the clean signal. In addition, a comparative study has been done with conventional approaches which includes waveform analysis, spectral domain inspection, and spectrogram evaluation. The experimental results show that sample entropy based classification method gives an accuracy of 96.67% for clean data and 91.66% for noisy data of SNR 10 dB. The result suggests that the proposed method performs significantly well over the visual and audio test.

scholarly journals Phishing Website Classification using Least Square Twin Support Vector Machine

2019 ◽  
Vol 9 (1) ◽  
pp. 2063-2068
Keyword(s):  
Support Vector Machine ◽  
Cyber Security ◽  
Classification Accuracy ◽  
Personal Information ◽  
Classification Problem ◽  
Least Square ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Security Issue

Phishing is one among the luring procedures used by phishing attackers in the means to abuse the personal details of clients. Phishing is earnest cyber security issue that includes facsimileing legitimate website to apostatize online users so as to purloin their personal information. Phishing can be viewed as special type of classification problem where the classifier is built from substantial number of website's features. It is required to identify the best features for improving classifiers accuracy. This study, highlights on the important features of websites that are used to classify the phishing website and form the legitimate ones by presenting a scheme Decision Tree Least Square Twin Support Vector Machine (DT-LST-SVM) for the classification of phishing website. UCI public domain benchmark website phishing dataset was used to conduct the experiment on the proposed classifier with different kernel function and calculate the classification accuracy of the classifiers. Computational results show that DT-LST-SVM scheme yield the better classification accuracy with phishing websites classification dataset

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”.

Simulation and Performance Analysis of Tilted Time Window and Support Vector Machine Based Learning Object Ranking Method

2020 ◽  
Vol 13 (2) ◽  
pp. 153-164
Author(s):  
Narina Thakur ◽  
Deepti Mehrotra ◽  
Abhay Bansal ◽  
Manju Bala
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Time Window ◽  
Optimal Solution ◽  
Similarity Score ◽  
Learning Objects ◽  
Retrieval Algorithm ◽  
Support Vector ◽  
Computationally Efficient ◽  
User Query

Objective: Since the adequacy of Learning Objects (LO) is a dynamic concept and changes in its use, needs and evolution, it is important to consider the importance of LO in terms of time to assess its relevance as the main objective of the proposed research. Another goal is to increase the classification accuracy and precision. Methods: With existing IR and ranking algorithms, MAP optimization either does not lead to a comprehensively optimal solution or is expensive and time - consuming. Nevertheless, Support Vector Machine learning competently leads to a globally optimal solution. SVM is a powerful classifier method with its high classification accuracy and the Tilted time window based model is computationally efficient. Results: This paper proposes and implements the LO ranking and retrieval algorithm based on the Tilted Time window and the Support Vector Machine, which uses the merit of both methods. The proposed model is implemented for the NCBI dataset and MAT Lab. Conclusion: The experiments have been carried out on the NCBI dataset, and LO weights are assigned to be relevant and non - relevant for a given user query according to the Tilted Time series and the Cosine similarity score. Results showed that the model proposed has much better accuracy.

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
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