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

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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Phonocardiographic Signal and Electrocardiographic Signal Analysis for the Detection of Cardiovascular Diseases

Biosciences Biotechnology Research Asia ◽

10.13005/bbra/2610 ◽

2018 ◽

Vol 15 (1) ◽

pp. 79-89 ◽

Cited By ~ 1

Author(s):

V. Kalaivani ◽

R. Lakshmi Devi ◽

V. Anusuyadevi

Keyword(s):

Cardiovascular Diseases ◽

Heart Sound ◽

Physiological State ◽

Heart Sounds ◽

Sound Signal ◽

Ecg Signal ◽

Short Term ◽

Rr Interval ◽

Short Term Variability ◽

Heart Sound Signal

The main objective is to develop a novel method for the heart sound analysis for the detection of cardiovascular diseases. It can be considered as one of the important phases in the automated analysis of PCG signals. Heart sounds carry information about mechanical activity of the cardiovascular system. This information includes specific physiological state of the subject and the short term variability related to the respiratory cycle. The interpretation of sounds and extraction of changes in the physiological state while maintaining the short term variability are still an open problem and is subject of this paper. The system deals with the process of de-noising of the heart sound signal(PCG) and the signal is decomposed into several sub-bands and the de-noised heart sound signal is segmented into the basic heart sounds S1 and S2, along with the systolic and diastolic interval.. Also, the ECG signal is de-noised. Meanwhile, the R-peaks are identified from the ECG signal and RR interval is obtained. Extraction of features are done from both the heart sound signal and the ECG signal. From the features, the R-peaks are identified from the ECG signal and RR interval is obtained. The attribute selection is to find the best attribute values that can be used for the classification process. Finally, using classification technique, cardiac diseases are detected. This work is implemented by using MATLAB software.

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Heart Sound Signal Generator Based on LabVIEW

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.121-126.872 ◽

2011 ◽

Vol 121-126 ◽

pp. 872-876

Author(s):

Ye Wei Tao ◽

Xie Feng Cheng ◽

Shu Yang He ◽

Yan Ping Ge ◽

Yan Hong Huang

Keyword(s):

Heart Sound ◽

Signal Generator ◽

Heart Sounds ◽

Sound Signal ◽

Sound Analysis ◽

Analysis Instrument ◽

Heart Sound Signal

A heart sounds signal generator in the heart sound analysis instrument based on the LabVIEW is devised. The instrument is developed in PC. Heart sounds signal generator can according to need to produce a synthetic heart sounds signal for users to learn and use. The parameters setting are also discussed to find out the best for the each part. All the parameters can be set by user and the best ones are default values so that the instrument can fit other environment. The running test of this instrument proves it can generate and play heart sound precisely，and can be used as an assistance to show, play, and analyze heart sound

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

Applied Sciences ◽

10.3390/app11020651 ◽

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.

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Design of Heart Sound Analyzer

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1042.131 ◽

2014 ◽

Vol 1042 ◽

pp. 131-134

Author(s):

Lu Zhang

Keyword(s):

Feature Extraction ◽

Heart Sound ◽

Heart Sounds ◽

Sound Signal ◽

System Acquisition ◽

Heart Sound Signal

There is important physiological and pathological information in heart sound, so the patients’ information can be obtained by detection of their heart sounds. In the hardware of the system, the heart sound sensor HKY06B is used to acquire the heart sound signal, and the DSP chip TMS320VC5416 is used to process the heart sound. De-noising based on wavelet and HHT and other technical are used in the process of heart sound. There are five steps in the system: acquisition, de-noising, segmentation, feature extraction, and finally, heart sounds are classified

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

Entropy ◽

10.3390/e21050472 ◽

2019 ◽

Vol 21 (5) ◽

pp. 472 ◽

Cited By ~ 11

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%.

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Study on the Heart Sound Signal Denoising Technology Based on Integrated Filtering Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.484-485.396 ◽

2014 ◽

Vol 484-485 ◽

pp. 396-399

Author(s):

Le Juan Zhang ◽

Xing Hai Yang ◽

Nian Qiang Li ◽

Shi Yao Cui

Keyword(s):

Heart Sound ◽

Calculation Model ◽

High Order ◽

Heart Sounds ◽

Sound Signal ◽

Signal Denoising ◽

Filtering Algorithm ◽

Signal Sources ◽

Order Algorithm ◽

Heart Sound Signal

In the previous studies of heart sounds, the calculation model of small waveform is often used, and new waveform graph is formed through the decomposition and restructuring of small waveform so as to remove the noise from the new waveform. There are a lot of shortcomings in the use of such a method. The features of new waveform are difficult to be controlled, and thus the noise generated by the wave and the interference of wave will be disturbed by the filter to certain degree. In this paper, the integrated filtering algorithm is introduced, and a wave can be used in the studied use of small waveform, and also the high-order algorithm in mathematics is used, so that the frequency is controlled in a certain range, the frequency of heart sounds to be interfered is effectively reduced, and also the harmonic harm generated by the waveform is considered. After the signal sources are protected with some technologies, the effect of filtering and denoising is eventually achieved.

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