An automatic wavelet denoising scheme for heart sounds

2015 ◽  
Vol 13 (03) ◽  
pp. 1550016 ◽  
Author(s):  
Tahar Omari ◽  
Fethi Bereksi-Reguig
Keyword(s):  
Heart Diseases ◽  
Heart Sounds ◽  
Automated System ◽  
Mother Wavelet ◽  
Electronic Stethoscope ◽  
Novel Approach ◽  
Breath Sounds ◽  
Many Sources ◽  
Pcg Signals ◽  
Selection Of

Phonocardiograms (PCGs), recording of heart sounds, have many advantages over traditional auscultation in that they may be replayed and analyzed for spectral and frequency information. PCG is not a widely used diagnostic tool as it could be. One of the major problems with PCG is noise corruption. Many sources of noise may pollute a PCG signal including lung and breath sounds, environmental noise and blood flow noises which are known as murmurs. These murmurs contain many information on heart hemodynamic which can be used particularly in detecting of heart valve diseases. An automated system for heart murmurs processing can be an important tool in diagnostic of heart diseases using a simple electronic stethoscope. However, the first step before developing any automated system is the segmentation of the PCG signals from which the murmurs can be separated. A robust segmentation algorithm must have a robust denoising technique, where, wavelet transform (WT) is among the ones which exhibits very high satisfactory results in such situations. However, the selection of level of decomposition and the mother wavelet are the major challenges. This paper proposes a novel approach for an automatic selection of mother wavelet and level of decomposition that can be used in heart sounds denoising. The obtained results on both simulative and real PCG signals showed that the proposed approach can successfully select the best level of decomposition with the best mother wavelet that can be used in extraction operation of main PCG sound components (S1 and S2) from various types of murmurs.

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.

Optimal level and order detection in wavelet decomposition for PCG signal denoising

2019 ◽  
Vol 64 (2) ◽  
pp. 163-176 ◽  
Author(s):  
Mohamed Rouis ◽  
Abdelkrim Ouafi ◽  
Salim Sbaa
Keyword(s):  
Signal To Noise Ratio ◽  
Optimal Level ◽  
Signal Denoising ◽  
Discrete Wavelet ◽  
Clinical Environment ◽  
Mother Wavelet ◽  
New Approach ◽  
The Mean ◽  
Pcg Signals ◽  
Selection Of

Abstract The recorded phonocardiogram (PCG) signal is often contaminated by different types of noises that can be seen in the frequency band of the PCG signal, which may change the characteristics of this signal. Discrete wavelet transform (DWT) has become one of the most important and powerful tools of signal representation, but its effectiveness is influenced by the issue of the selected mother wavelet and decomposition level (DL). The selection of the DL and the mother wavelet are the main challenges. This work proposes a new approach for finding an optimal DL and optimal mother wavelet for PCG signal denoising. Our approach consists of two algorithms designed to tackle the problems of noise and variability caused by PCG acquisition in a real clinical environment for different categories of patients. The results obtained are evaluated by examining the coherence analysie (Coh) correlation coefficient (Corr) and the mean square error (MSE) and signal-to-noise ratio (SNR) in simulated noisy PCG signals. The experimental results show that the proposed method can effectively reduce noise.

scholarly journals Detection of pathological heart murmurs by feature extraction of phonocardiogram signals

2017 ◽  
Vol 2 (4) ◽  
Author(s):  
Antra Ganguly ◽  
Manisha Sharma
Keyword(s):  
Heart Diseases ◽  
Cost Effective ◽  
Heart Sounds ◽  
Cardiac Auscultation ◽  
Non Invasive ◽  
Hearing Ability ◽  
Detection And Diagnosis ◽  
Electrocardiogram Ecg ◽  
Pcg Signals ◽  
Heart Condition

Cardiac auscultation can be perceived as method of determining the human heart condition by listening to the heart sounds. These heart sounds contain vital information related to a person’s heart condition. Any departure from the normal cardiac auscultation readings in terms of presence of additional heart sounds is indicative of an unhealthy heart. The use of Phonocardiogram (PCG)signals (i.e. the electronic recording of heart sounds) completely dismisses the limitation of relying solely on the physician’s hearing ability. At the same time, they provide with a high-fidelity representation of the heart sounds in the most cost-effective way as compared to the methods like Electrocardiogram (ECG). In this paper, a method of detection of heart ailments by extracting the features of PCG signals is proposed. The normal heart sounds, gallop rhythms and the most common pathological murmurs have been used for analysis. By analyzing these signals, early detection and diagnosis of heart diseases can be done reliably. This will not only confirm health and longevity by early diagnosis and pin-pointed prognosis, but will also be economically suitable for those who can hardly afford tests like ECG. It can also be practicable in the case of infants wherein the other non-invasive diagnosis techniques like ECG fail.

scholarly journals Development of a Low-Cost Ear-Contactless Stethoscope Powered by Raspberry Pi: A Solution to the Auscultation Difficulty on Patients with COVID-19 (Preprint)

2020 ◽  
Author(s):  
Chuan Yang ◽  
Wei Zhang ◽  
Zhixuan Pang ◽  
Jing Zhang ◽  
Deling Zou ◽  
...  
Keyword(s):  
Real Time ◽  
Safety Concern ◽  
Low Cost ◽  
Severe Pneumonia ◽  
Heart Sounds ◽  
Raspberry Pi ◽  
Respiratory Dysfunction ◽  
Electronic Stethoscope ◽  
Breath Sounds ◽  
Data Files

BACKGROUND In the battle against COVID-19, auscultation examination was essential, especially to patients with poor respiratory conditions, such as severe pneumonia, respiratory dysfunction, and intensive cases who were intubated and assisted with ventilators. However, auscultation was hard to be accomplished on the infected patients due to the safety concern and unavailability for medical workers wearing personal protective suits. OBJECTIVE The objective of our study was to design and develop an electronic stethoscope with the characteristics of ear-contactless auscultation, low-cost property, and digital storage for further analysis. An assessment of its clinical feasibility should also be made with the comparison to the electronic stethoscope currently in use. METHODS We developed a prototype of the electronic stethoscope without ear-contact, Auscul Pi, powered by Raspberry Pi and Python, which can make real-time auscultation sounds played with a micro-speaker instead of ear pieces, and it can also store data files for further analysis. We utilized this stethoscope to assess the feasibility by detecting abnormal heart and breath sounds from 8 patients by comparing it with 3M Littmann electronic stethoscope, and 2 healthy volunteers were included for controls. We then plotted the phonocardiography of heart sounds for visualization for the comparisons. RESULTS We were able to operate Auscul Pi conveniently and record the auscultation sounds precisely in practice from the aspects of ergonomics and information technology. A total of 10 participants were recruited to receive auscultation examination with Auscul Pi and 3M Littmann electronic stethoscope. In terms of the real-time playout and recorded audio of heart sounds and breath sounds, the Auscul Pi showed consistency to 3M Littmann. As for the heart sounds, we also plotted phonocardiograph based on the data generated by Auscul Pi and 3M Littmann, and aligned them with the cardiac cycle of ECG respectively. The phonocardiography showed good conformity between Auscul Pi and 3M Littmann according to the waveforms. CONCLUSIONS Auscul Pi is feasible to perform the auscultation in clinical practice by applying real-time ear-contactless playout and later quantified analysis of auscultation sounds. So, it is expected to benefit the patients with COVID-19 examined by medical employees wearing protective suits and having difficulties in auscultation. CLINICALTRIAL ChiCTR.org.cn ChiCTR2000033830; http://www.chictr.org.cn/showproj.aspx?proj=54971

Development of a Performance-Related Framework for Asphalt Mixture Design for the Illinois Tollway

2021 ◽  
pp. 036119812110148
Author(s):  
Behnam Jahangiri ◽  
Punyaslok Rath ◽  
Hamed Majidifard ◽  
William G. Buttlar
Keyword(s):  
Asphalt Mixture ◽  
Field Performance ◽  
Compact Tension ◽  
T Test ◽  
Novel Approach ◽  
Final Adjustment ◽  
Development And Validation ◽  
High Degree ◽  
Selection Of

Various agencies have begun to research and introduce performance-related specifications (PRS) for the design of modern asphalt paving mixtures. The focus of most recent studies has been directed toward simplified cracking test development and evaluation. In some cases, development and validation of PRS has been performed, building on these new tests, often by comparison of test values to accelerated pavement test studies and/or to limited field data. This study describes the findings of a comprehensive research project conducted at Illinois Tollway, leading to a PRS for the design of mainline and shoulder asphalt mixtures. A novel approach was developed, involving the systematic establishment of specification requirements based on: 1) selection of baseline values based on minimally acceptable field performance thresholds; 2) elevation of thresholds to account for differences between short-term lab aging and expected long-term field aging; 3) further elevation of thresholds to account for variability in lab testing, plus variability in the testing of field cores; and 4) final adjustment and rounding of thresholds based on a consensus process. After a thorough evaluation of different candidate cracking tests in the course of the project, the Disk-shaped Compact Tension—DC(T)—test was chosen to be retained in the Illinois Tollway PRS and to be presented in this study for the design of crack-resistant mixtures. The DC(T) test was selected because of its high degree of correlation with field results and its excellent repeatability. Tailored Hamburg rut depth and stripping inflection point thresholds were also established for mainline and shoulder mixes.

Selection of mother wavelet and decomposition level for energy management in electrical vehicles including a fuel cell

2015 ◽  
Vol 40 (45) ◽  
pp. 15823-15833 ◽  
Author(s):  
Mona Ibrahim ◽  
Samir Jemei ◽  
Geneviève Wimmer ◽  
Nadia Yousfi Steiner ◽  
Célestin C. Kokonendji ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Energy Management ◽  
Mother Wavelet ◽  
Decomposition Level ◽  
Electrical Vehicles ◽  
Selection Of

Novel approach for Recanalization and Limb Saving Following Acute Thrombosis of Upper Limb Arteries in Two Neonates After Congenital Heart Surgery

2020 ◽  
Author(s):  
Jameel Al- Ata ◽  
Gaser Abdelmohsen ◽  
Saud Bahaidarah ◽  
Naif Alkhushi ◽  
Zaher Zaher
Keyword(s):  
Congenital Heart Disease ◽  
Cardiac Output ◽  
Congenital Heart ◽  
Heart Surgery ◽  
Heart Diseases ◽  
Congenital Heart Surgery ◽  
Low Cardiac Output ◽  
Vascular Thrombosis ◽  
Acute Thrombosis ◽  
Novel Approach

IntroductionNeonates with congenital heart disease are at a high risk of vascular thrombosis. Thrombosis may occur due to vascular injury, increased blood viscosity secondary to polycythemia associated with congenital cyanotic heart diseases, or stasis of blood flow associated with low cardiac output (Schmidt B & Andrew M., Pediatrics 1995; 96: 939–943. Veldman A et al.,Vasc Health Risk Manag 2008; 4: 1337–1348).

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