Data driven filtering of bowel sounds using multivariate empirical mode decomposition

Kölle, Konstanze; Aftab, Muhammad Faisal; Andersson, Leif Erik; Fougner, Anders Lyngvi; Stavdahl, Øyvind

doi:10.1186/s12938-019-0646-1

Cited by 11 publications

(7 citation statements)

References 22 publications

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“…In terms of analytics, Kölle et al devised an intrinsic mode function-fractal dimension method for filtering artifacts, including the frequency range where BS typically occur [ 109 ]. Chen and Montlouis explored individual wave components as a basic building block for synthesizing artificial bowel sounds in the absence of large publicly available datasets, and confirmed its utility in extracting information by comparing it with a clinical sample [ 110 ].…”

Section: Resultsmentioning

confidence: 99%

Automated Bowel Sound Analysis: An Overview

Nowak

Radzikowski

et al. 2021

Sensors

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Despite technological progress, we lack a consensus on the method of conducting automated bowel sound (BS) analysis and, consequently, BS tools have not become available to doctors. We aimed to briefly review the literature on BS recording and analysis, with an emphasis on the broad range of analytical approaches. Scientific journals and conference materials were researched with a specific set of terms (Scopus, MEDLINE, IEEE) to find reports on BS. The research articles identified were analyzed in the context of main research directions at a number of centers globally. Automated BS analysis methods were already well developed by the early 2000s. Accuracy of 90% and higher had been achieved with various analytical approaches, including wavelet transformations, multi-layer perceptrons, independent component analysis and autoregressive-moving-average models. Clinical research on BS has exposed their important potential in the non-invasive diagnosis of irritable bowel syndrome, in surgery, and for the investigation of gastrointestinal motility. The most recent advances are linked to the application of artificial intelligence and the development of dedicated BS devices. BS research is technologically mature, but lacks uniform methodology, an international forum for discussion and an open platform for data exchange. A common ground is needed as a starting point. The next key development will be the release of freely available benchmark datasets with labels confirmed by human experts.

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Section: Resultsmentioning

confidence: 99%

Automated Bowel Sound Analysis: An Overview

Nowak

Radzikowski

et al. 2021

Sensors

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“…An increased sound power around meal time may partly even be caused by meal-related activities (such as moving arms and hands, scratching cutlery, etc.). Typical artifacts cause a different power distribution over the frequency range than bowel sounds which can be exploited to eliminate artifacts [18], [29]. Motivated by this, power ratios were included as features in this study.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Feasibility of Early Meal Detection Based on Abdominal Sound

Kölle

Fougner

Ellingsen

et al. 2019

IEEE J. Transl. Eng. Health Med.

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In classical approaches for an artificial pancreas, continuous glucose monitoring (CGM) is the only measured variable used for insulin dosing and additional control functions. The CGM values are subject to time delays and slow dynamics between blood and the sensing location. These time lags compromise the controller's performance in maintaining (near to) normal glucose levels. Meal information could enhance the control outcome. However, meal announcement by the user is not reliable, and it takes 30 min to 40 min from meal onset until a meal is detected by methods based on CGM. In this pilot study, the use of bowel sounds for meal detection was investigated. In particular, we focused on whether bowel sounds change qualitatively during or shortly after meal ingestion. After fasting for at least 4 h, 11 healthy volunteers ingested a lunch meal at their usual time. Abdominal sound was recorded by a condenser microphone that was attached to the right upper quadrant of the abdomen by medical tape. Features that describe the power distribution over the frequency spectrum were extracted and used for classification by support vector machines. These classifiers were trained in a leave-one-out cross-validation scheme. Meals could be detected on average 10 min (std: 4.4 min) after they had started. Half of these were detected without false alarms. This shows that abdominal sound monitoring could provide an early meal detection. Further studies should investigate this possibility on a larger population in more general settings.

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“…The studies between 1967 and 2022 on this data set have been summarized in Table 1 [ 21 , 22 , 28 , 42 , 61 , 65 , 83 , 87 , 88 , 89 , 91 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 ].…”

Section: Auscultation and Recording Technologiesmentioning

confidence: 99%

Practicing Digital Gastroenterology through Phonoenterography Leveraging Artificial Intelligence: Future Perspectives Using Microwave Systems

Redij

Kaur

Muddaloor

et al. 2023

Sensors

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Production of bowel sounds, established in the 1900s, has limited application in existing patient-care regimes and diagnostic modalities. We review the physiology of bowel sound production, the developments in recording technologies and the clinical application in various scenarios, to understand the potential of a bowel sound recording and analysis device—the phonoenterogram in future gastroenterological practice. Bowel sound production depends on but is not entirely limited to the type of food consumed, amount of air ingested and the type of intestinal contractions. Recording technologies for extraction and analysis of these include the wavelet-based filtering, autoregressive moving average model, multivariate empirical mode decompression, radial basis function network, two-dimensional positional mapping, neural network model and acoustic biosensor technique. Prior studies evaluate the application of bowel sounds in conditions such as intestinal obstruction, acute appendicitis, large bowel disorders such as inflammatory bowel disease and bowel polyps, ascites, post-operative ileus, sepsis, irritable bowel syndrome, diabetes mellitus, neurodegenerative disorders such as Parkinson’s disease and neonatal conditions such as hypertrophic pyloric stenosis. Recording and analysis of bowel sounds using artificial intelligence is crucial for creating an accessible, inexpensive and safe device with a broad range of clinical applications. Microwave-based digital phonoenterography has huge potential for impacting GI practice and patient care.

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Data driven filtering of bowel sounds using multivariate empirical mode decomposition

Cited by 11 publications

References 22 publications

Automated Bowel Sound Analysis: An Overview

Automated Bowel Sound Analysis: An Overview

Feasibility of Early Meal Detection Based on Abdominal Sound

Practicing Digital Gastroenterology through Phonoenterography Leveraging Artificial Intelligence: Future Perspectives Using Microwave Systems

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