scholarly journals Heart cardiac’s sounds signals segmentation by using the discrete wavelet transform (DWT)

2021 ◽  
Vol 4 (3) ◽  
pp. 01-15
Author(s):  
Sid Debbal
Keyword(s):  
Wavelet Transform ◽  
Cardiac Cycle ◽  
Simple Technique ◽  
Average Duration ◽  
Heart Sounds ◽  
Discrete Wavelet ◽  
Heart Murmurs ◽  
Shannon Energy ◽  
Processing Techniques ◽  
Signal Processing Techniques

The presence of abnormal sounds in one cardiac cycle, provide valuable information on various diseases.Early detection of various diseases is necessary; it is done by a simple technique known as: phonocardiography. The phonocardiography, based on registration of vibrations or oscillations of different frequencies, audible or not, that correspond to normal and abnormal heart sounds. It provides the clinician with a complementary tool to record the heart sounds heard during auscultation. The advancement of intracardiac phonocardiography, combined with signal processing techniques, has strongly renewed researchers’ interest in studying heart sounds and murmurs. This paper presents an algorithm based on the denoising by wavelet transform (DWT) and the Shannon energy of the PCG signal, for the detection of heart sounds (the first and second sounds, S1 and S2) and heart murmurs. This algorithm makes it possible to isolate individual sounds (S1 or S2) and murmurs to give an assessment of their average duration.

SEGMENTATION OF HEART SOUNDS AND HEART MURMURS

2008 ◽  
Vol 08 (04) ◽  
pp. 549-559 ◽  
Author(s):  
L. HAMZA CHERIF ◽  
S. M. DEBBAL ◽  
F. BEREKSI-REGUIG
Keyword(s):  
Heart Valves ◽  
Average Duration ◽  
Digital Processing ◽  
Heart Sounds ◽  
Pathological Changes ◽  
Pathological Conditions ◽  
Primary Health ◽  
Heart Murmurs ◽  
Shannon Energy ◽  
Processing Techniques

Heart murmurs are often the first signs of pathological changes of the heart valves, and are usually found during auscultation in primary health care. Many pathological conditions of the cardiovascular system cause murmurs and aberrations in heart sounds. Phonocardiography provides the clinician with a complementary tool to record the heart sounds heard during auscultation. The advancement of intracardiac phonocardiography, combined with modern digital processing techniques, has strongly renewed researchers' interest in studying heart sounds and murmurs. This paper presents an algorithm for the detection of heart sounds (the first and second sounds, S1 and S2) and heart murmurs. The segmentation algorithm, which separates the heart signal (or the phonocardiogram (PCG) signal), is based on the normalized average Shannon energy of the PCG signal. This algorithm makes it possible to isolate individual sounds (S1 or S2) and murmurs to give an assessment of their average duration.

COMPARISON BETWEEN DISCRETE AND PACKET WAVELET TRANSFORM ANALYSES IN THE STUDY OF HEARTBEAT CARDIAC SOUNDS

2007 ◽  
Vol 07 (02) ◽  
pp. 199-214 ◽  
Author(s):  
S. M. DEBBAL ◽  
F. BEREKSI-REGUIG
Keyword(s):  
Wavelet Transform ◽  
Heart Sound ◽  
Wavelet Packet ◽  
Extraction Methods ◽  
Heart Sounds ◽  
Discrete Wavelet ◽  
Clinical Tool ◽  
Prognostic Information ◽  
Transform Method ◽  
Time Frequency

This work investigates the study of heartbeat cardiac sounds through time–frequency analysis by using the wavelet transform method. Heart sounds can be utilized more efficiently by medical doctors when they are displayed visually rather through a conventional stethoscope. Heart sounds provide clinicians with valuable diagnostic and prognostic information. Although heart sound analysis by auscultation is convenient as a clinical tool, heart sound signals are so complex and nonstationary that they are very difficult to analyze in the time or frequency domain. We have studied the extraction of features from heart sounds in the time–frequency (TF) domain for the recognition of heart sounds through TF analysis. The application of wavelet transform (WT) for heart sounds is thus described. The performances of discrete wavelet transform (DWT) and wavelet packet transform (WP) are discussed in this paper. After these transformations, we can compare normal and abnormal heart sounds to verify the clinical usefulness of our extraction methods for the recognition of heart sounds.

Vibration Analyses of the Bearing Using the Time Frequency Domain Technique

2014 ◽  
Vol 592-594 ◽  
pp. 2091-2096
Author(s):  
H.N. Sharma ◽  
Santosh Verma
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Energy Levels ◽  
Vibration Signal ◽  
Test Rig ◽  
Time Frequency ◽  
Bearing Vibration ◽  
Processing Techniques ◽  
Signal Processing Techniques ◽  
Bearing System

This work employs the wavelet transform for reading the fault diagnosis in a rotor-bearing system. Initiating with literature review with some relevant studies of bearing fault and the signal processing techniques used followed by the theory of wavelet transform. A bearing test rig is shown which is used for implementing wavelet transform. A faulty bearing vibration signal is measured from the test rig; thereafter the fast Fourier transform is plotted to show the critical frequencies, bearing characteristics frequency and its harmonics. A scalogram showing the energy levels of signal is plotted as result. Faulty signal is analyzed using wavelet transform.

scholarly journals An Automated Optimal Engagement and Attention Detection System Using Electrocardiogram

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Ashwin Belle ◽  
Rosalyn Hobson Hargraves ◽  
Kayvan Najarian
Keyword(s):  
Detection System ◽  
Machine Learning Algorithms ◽  
Discrete Wavelet ◽  
Physiological Signal ◽  
Cognitive Attention ◽  
Primary Focus ◽  
Stockwell Transform ◽  
Processing Techniques ◽  
Signal Processing Techniques

This research proposes to develop a monitoring system which uses Electrocardiograph (ECG) as a fundamental physiological signal, to analyze and predict the presence or lack of cognitive attention in individuals during a task execution. The primary focus of this study is to identify the correlation between fluctuating level of attention and its implications on the cardiac rhythm recorded in the ECG. Furthermore, Electroencephalograph (EEG) signals are also analyzed and classified for use as a benchmark for comparison with ECG analysis. Several advanced signal processing techniques have been implemented and investigated to derive multiple clandestine and informative features from both these physiological signals. Decomposition and feature extraction are done using Stockwell-transform for the ECG signal, while Discrete Wavelet Transform (DWT) is used for EEG. These features are then applied to various machine-learning algorithms to produce classification models that are capable of differentiating between the cases of a person being attentive and a person not being attentive. The presented results show that detection and classification of cognitive attention using ECG are fairly comparable to EEG.

scholarly journals Noise Suppression in ECG Signals through Efficient One-Step Wavelet Processing Techniques

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
E. Castillo ◽  
D. P. Morales ◽  
A. García ◽  
F. Martínez-Martí ◽  
L. Parrilla ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Pregnant Women ◽  
Discrete Wavelet Transform ◽  
Noise Suppression ◽  
Wavelet Denoising ◽  
Discrete Wavelet ◽  
Ecg Signals ◽  
One Step ◽  
Exhaustive Study ◽  
Processing Techniques

This paper illustrates the application of the discrete wavelet transform (DWT) for wandering and noise suppression in electrocardiographic (ECG) signals. A novel one-step implementation is presented, which allows improving the overall denoising process. In addition an exhaustive study is carried out, defining threshold limits and thresholding rules for optimal wavelet denoising using this presented technique. The system has been tested using synthetic ECG signals, which allow accurately measuring the effect of the proposed processing. Moreover, results from real abdominal ECG signals acquired from pregnant women are presented in order to validate the presented approach.

scholarly journals Intelligent Diagnosis of Heart Murmurs in Children with Congenital Heart Disease

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Jiaming Wang ◽  
Tao You ◽  
Kang Yi ◽  
Yaqin Gong ◽  
Qilian Xie ◽  
...  
Keyword(s):  
Congenital Heart Disease ◽  
Heart Disease ◽  
Congenital Heart ◽  
Heart Diseases ◽  
Heart Sounds ◽  
Discrete Wavelet ◽  
Intelligent Diagnosis ◽  
Heart Murmurs ◽  
Neural Network Classifiers ◽  
Heart Auscultation

Heart auscultation is a convenient tool for early diagnosis of heart diseases and is being developed to be an intelligent tool used in online medicine. Currently, there are few studies on intelligent diagnosis of pediatric murmurs due to congenital heart disease (CHD). The purpose of the study was to develop a method of intelligent diagnosis of pediatric CHD murmurs. Phonocardiogram (PCG) signals of 86 children were recorded with 24 children having normal heart sounds and 62 children having CHD murmurs. A segmentation method based on the discrete wavelet transform combined with Hadamard product was implemented to locate the first and the second heart sounds from the PCG signal. Ten features specific to CHD murmurs were extracted as the input of classifier after segmentation. Eighty-six artificial neural network classifiers were composed into a classification system to identify CHD murmurs. The accuracy, sensitivity, and specificity of diagnosis for heart murmurs were 93%, 93.5%, and 91.7%, respectively. In conclusion, a method of intelligent diagnosis of pediatric CHD murmurs is developed successfully and can be used for online screening of CHD in children.

scholarly journals Techniques of Acquisition and Processing of Electrocardiographic Signals in the Detection of Cardiac Arrhythmias

Respuestas ◽  
2019 ◽  
Vol 24 (2) ◽  
pp. 91-100 ◽  
Author(s):  
Giovanny Barbosa Casanova ◽  
Darwin Orlando Cardozo Sarmiento ◽  
Mario Joaquin Illera Bustos ◽  
Andrés Orozco Duque ◽  
Henry Andrade Caicedo
Keyword(s):  
Signal Processing ◽  
Adaptive Filters ◽  
Current Trend ◽  
Computational Cost ◽  
Ambulatory Monitoring ◽  
Digital Signal ◽  
Discrete Wavelet ◽  
Ecg Signal Processing ◽  
Processing Techniques ◽  
Signal Processing Techniques

The development of ambulatory monitoring systems and its electrocardiographic (ECG) signal processing techniques has become an important field of investigation, due to its relevance in the early detection of cardiovascular diseases such as the arrhythmias. The current trend of this technology is oriented to the use of portable equipment and mobile devices such as Smartphones, which have been widely accepted due to the technical characteristics and common integration in daily life. A fundamental characteristic of these systems is their ability to reduce the most common types of noise by means of digital signal processing techniques.  Among the most used techniques are the adaptive filters and the Discrete Wavelet Transform (DWT) which have been successfully implemented in several studies. There are systems that integrate classification stages based on artificial intelligence, which increases the performance in the process of arrhythmias detection. These techniques are not only evaluated for their functionality but for their computational cost, since they will be used in real-time applications, and implemented in embedded systems. This paper shows a review of each of the stages in the construction of a standard ambulatory monitoring system, for the contextualization of the reader in this type of technology.

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