scholarly journals Optimization Audicor for Normal and Abnormal Heart Sounds Characteristic

2020 ◽  
Vol 1 (2) ◽  
pp. 99-106
Author(s):  
Dedi Kurniadi ◽  
Surfa Yondri ◽  
Albar ◽  
Roza Susanti ◽  
David Eka Putra ◽  
...  
Keyword(s):  
Signal Processing ◽  
Feature Extraction ◽  
Data Analysis ◽  
Ventricular Septal Defect ◽  
Human Body ◽  
Septal Defect ◽  
Heart Sound ◽  
Heart Sounds ◽  
Normal Case ◽  
Recording Process

Heart Sounds are important things in the human body that can deliver information related to the heart condition. However, a recorded signal such as PCG and ECG that getting through Audicor still contain unexpected components or noise while the recording process happens it makes the result data from Audicor cannot directly use to recognize the condition of the heart. This research presents signal processing and data analysis to suppress the noise of the heart sounds that getting while the process of recording data happens. The cleaned heart sound will be processed in feature extraction by using FFT and PCA that capable to produce the feature both of the normal and abnormal heart sounds. For the normal case, we get the data from some healthy volunteers recorded by using Audicor. While the abnormal heart sound we focus to observe the data that contain Ventricular Septal Defect (VSD) that getting from a partner hospital.  As a result, feature both normal and abnormal heart sounds can be separated.

HEART SOUND CLASSIFICATION AND RECOGNITION BASED ON EEMD AND CORRELATION DIMENSION

2014 ◽  
Vol 14 (04) ◽  
pp. 1450046 ◽  
Author(s):  
WENYING ZHANG ◽  
XINGMING GUO ◽  
ZHIHUI YUAN ◽  
XINGHUA ZHU
Keyword(s):  
Feature Extraction ◽  
Heart Sound ◽  
Linear Time ◽  
Heart Diseases ◽  
Recognition Rate ◽  
Heart Sounds ◽  
Support Vector ◽  
Svm Classifier ◽  
Intrinsic Mode Functions ◽  
Sound Classification

Analysis of heart sound is of great importance to the diagnosis of heart diseases. Most of the feature extraction methods about heart sound have focused on linear time-variant or time-invariant models. While heart sound is a kind of highly nonstationary and nonlinear vibration signal, traditional methods cannot fully reveal its essential properties. In this paper, a novel feature extraction approach is proposed for heart sound classification and recognition. The ensemble empirical mode decomposition (EEMD) method is used to decompose the heart sound into a finite number of intrinsic mode functions (IMFs), and the correlation dimensions of the main IMF components (IMF1~IMF4) are calculated as feature set. Then the classical Binary Tree Support Vector Machine (BT-SVM) classifier is employed to classify the heart sounds which include the normal heart sounds (NHSs) and three kinds of abnormal signals namely mitral stenosis (MT), ventricular septal defect (VSD) and aortic stenosis (AS). Finally, the performance of the new feature set is compared with the correlation dimensions of original signals and the main IMF components obtained by the EMD method. The results showed that, for NHSs, the feature set proposed in this paper performed the best with recognition rate of 98.67%. For the abnormal signals, the best recognition rate of 91.67% was obtained. Therefore, the proposed feature set is more superior to two comparative feature sets, which has potential application in the diagnosis of cardiovascular diseases.

scholarly journals "Fixed" splitting of the second heart sound in ventricular septal defect.

Heart ◽  
1971 ◽  
Vol 33 (4) ◽  
pp. 428-431 ◽  
Author(s):  
C Harris ◽  
J Wise ◽  
C M Oakley
Keyword(s):  
Ventricular Septal Defect ◽  
Septal Defect ◽  
Heart Sound ◽  
Second Heart Sound

scholarly journals Automatic recognition of murmurs of ventricular septal defect using convolutional recurrent neural networks with temporal attentive pooling

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jou-Kou Wang ◽  
Yun-Fan Chang ◽  
Kun-Hsi Tsai ◽  
Wei-Chien Wang ◽  
Chang-Yen Tsai ◽  
...  
Keyword(s):  
Septal Defect ◽  
Heart Sound ◽  
Heart Diseases ◽  
Recognition System ◽  
Heart Sounds ◽  
Septal Defects ◽  
Heart Malformation ◽  
Structural Heart Diseases ◽  
Sensitivity Rate ◽  
Sound Patterns

AbstractRecognizing specific heart sound patterns is important for the diagnosis of structural heart diseases. However, the correct recognition of heart murmur depends largely on clinical experience. Accurately identifying abnormal heart sound patterns is challenging for young and inexperienced clinicians. This study is aimed at the development of a novel algorithm that can automatically recognize systolic murmurs in patients with ventricular septal defects (VSDs). Heart sounds from 51 subjects with VSDs and 25 subjects without a significant heart malformation were obtained in this study. Subsequently, the soundtracks were divided into different training and testing sets to establish the recognition system and evaluate the performance. The automatic murmur recognition system was based on a novel temporal attentive pooling-convolutional recurrent neural network (TAP-CRNN) model. On analyzing the performance using the test data that comprised 178 VSD heart sounds and 60 normal heart sounds, a sensitivity rate of 96.0% was obtained along with a specificity of 96.7%. When analyzing the heart sounds recorded in the second aortic and tricuspid areas, both the sensitivity and specificity were 100%. We demonstrated that the proposed TAP-CRNN system can accurately recognize the systolic murmurs of VSD patients, showing promising potential for the development of software for classifying the heart murmurs of several other structural heart diseases.

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

Differential diagnosis of fetal large ventricular septal defect and tetralogy of Fallot based on big data analysis

2020 ◽  
Vol 37 (4) ◽  
pp. 620-624
Author(s):  
Jing Lv ◽  
Tingyang Yang ◽  
Xiaoyan Gu ◽  
Ye Zhang ◽  
Lin Sun ◽  
...  
Keyword(s):  
Big Data ◽  
Differential Diagnosis ◽  
Data Analysis ◽  
Ventricular Septal Defect ◽  
Tetralogy Of Fallot ◽  
Septal Defect ◽  
Big Data Analysis
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