A de-noising method for heart sound signal using Otsu's threshold selection

2011 ◽  
Author(s):  
Haoyu Chen ◽  
R. Gururajan
Keyword(s):  
Heart Sound ◽  
Sound Signal ◽  
Threshold Selection ◽  
Heart Sound Signal

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.

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.

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