@Article{ujs737,
AUTHOR = {Montlouis, Webert},
TITLE = {Individual Wave Component Signal Modeling, Parameters Extraction, and Analysis},
JOURNAL = {Universal Journal of Stomatology},
VOLUME = {1},
YEAR = {2023},
NUMBER = {1},
PAGES = {26-39},
URL = {https://www.scipublications.com/journal/index.php/UJGH/article/view/737},
ISSN = {ISSN Pending},
DOI = {10.31586/ujgh.2023.737},
ABSTRACT = {The accurate estimation of Individual Wave Components (IWC) is crucial for automated diagnosis of the human digestive system in a clinical setting. However, this process can be challenging due to signal contamination by other signal sources in the body, such as the lungs and heart, as well as environmental noise. To address this issue, various denoising techniques are commonly employed in bowel sound signal processing. While denoising is important, it can increase computational complexity, making it challenging for portable devices. Therefore, signal processing algorithms often require a trade-off between fidelity and computational complexity. This study aims to evaluate an IWC parameter extraction algorithm that was previously developed and reconstruct the IWC without denoising using synthetic and clinical data. To that end, the role of a reliable model in creating synthetic data is paramount. The rigorous testing of the algorithm is limited by the availability of quality and quantity recorded data. To overcome this challenge, a mathematical model has been proposed to generate synthetic bowel sound data that can be used to test new algorithms. The proposed algorithm’s robust performance is evaluated using both synthetic and clinically recorded data. We perform time-frequency analysis of original and reconstructed bowel sound signals in various digestive system states and characterize the performance using Monte Carlo simulation when denoising is not applied. Overall, our study presents a promising algorithm for accurate IWC estimation that can be useful for predicting anomalies in the digestive system.},
}

%0 Journal Article
%A Montlouis, Webert
%D 2023
%J Universal Journal of Stomatology

%@ ISSN Pending
%V 1
%N 1
%P 26-39

%T Individual Wave Component Signal Modeling, Parameters Extraction, and Analysis
%M doi:10.31586/ujgh.2023.737
%U https://www.scipublications.com/journal/index.php/UJGH/article/view/737

TY  - JOUR
AU  - Montlouis, Webert
TI  - Individual Wave Component Signal Modeling, Parameters Extraction, and Analysis
T2  - Universal Journal of Stomatology
PY  - 2023
VL  - 1
IS  - 1
SN  - ISSN Pending
SP  - 26
EP  - 39
UR  - https://www.scipublications.com/journal/index.php/UJGH/article/view/737
AB  - The accurate estimation of Individual Wave Components (IWC) is crucial for automated diagnosis of the human digestive system in a clinical setting. However, this process can be challenging due to signal contamination by other signal sources in the body, such as the lungs and heart, as well as environmental noise. To address this issue, various denoising techniques are commonly employed in bowel sound signal processing. While denoising is important, it can increase computational complexity, making it challenging for portable devices. Therefore, signal processing algorithms often require a trade-off between fidelity and computational complexity. This study aims to evaluate an IWC parameter extraction algorithm that was previously developed and reconstruct the IWC without denoising using synthetic and clinical data. To that end, the role of a reliable model in creating synthetic data is paramount. The rigorous testing of the algorithm is limited by the availability of quality and quantity recorded data. To overcome this challenge, a mathematical model has been proposed to generate synthetic bowel sound data that can be used to test new algorithms. The proposed algorithm’s robust performance is evaluated using both synthetic and clinically recorded data. We perform time-frequency analysis of original and reconstructed bowel sound signals in various digestive system states and characterize the performance using Monte Carlo simulation when denoising is not applied. Overall, our study presents a promising algorithm for accurate IWC estimation that can be useful for predicting anomalies in the digestive system.
DO  - Individual Wave Component Signal Modeling, Parameters Extraction, and Analysis
TI  - 10.31586/ujgh.2023.737
ER  -