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.

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.

Heart Sound Signal Generator Based on LabVIEW

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

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

2018 ◽  
Vol 15 (1) ◽  
pp. 79-89 ◽  
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.

scholarly journals Automated Signal Quality Assessment for Heart Sound Signal by Novel Features and Evaluation in Open Public Datasets

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Hong Tang ◽  
Miao Wang ◽  
Yating Hu ◽  
Binbin Guo ◽  
Ting Li
Keyword(s):  
Quality Assessment ◽  
Heart Sound ◽  
Binary Classification ◽  
Classification Performance ◽  
Sound Signal ◽  
Signal Quality ◽  
Potential Candidate ◽  
Accuracy Rate ◽  
Public Datasets ◽  
Heart Sound Signal

Automated heart sound signal quality assessment is a necessary step for reliable analysis of heart sound signal. An unavoidable processing step for this objective is the heart sound segmentation, which is still a challenging task from a technical viewpoint. In this study, ten features are defined to evaluate the quality of heart sound signal without segmentation. The ten features come from kurtosis, energy ratio, frequency-smoothed envelope, and degree of sound periodicity, where five of them are novel in signal quality assessment. We have collected a total of 7893 recordings from open public heart sound databases and performed manual annotation for each recording as gold standard quality label. The signal quality is classified based on two schemes: binary classification (“unacceptable” and “acceptable”) and triple classification (“unacceptable”, “good,” and “excellent”). Sequential forward feature selection shows that the feature “the degree of periodicity” gives an accuracy rate of 73.1% in binary SVM classification. The top five features dominate the classification performance and give an accuracy rate of 92%. The binary classifier has excellent generalization ability since the accuracy rate reaches to ( 90.4 ± 0.5 ) % even if 10% of the data is used to train the classifier. The rate increases to ( 94.3 ± 0.7 ) % in 10-fold validation. The triple classification has an accuracy rate of ( 85.7 ± 0.6 ) % in 10-fold validation. The results verify the effectiveness of the signal quality assessment, which could serve as a potential candidate as a preprocessing in future automatic heart sound analysis in clinical application.

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.

Design of Heart Sound Analyzer

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

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

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.

Sign in / Sign up

Export Citation Format

Share Document