Advances in Acoustic Signal Processing Techniques for Enhanced Bowel Sound Analysis

Allwood, Gary; Du, Xuhao; Webberley, K. Mary; Osseiran, Adam; Marshall, Barry J.

doi:10.1109/rbme.2018.2874037

Cited by 40 publications

(23 citation statements)

References 63 publications

(73 reference statements)

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“…Using the 20 h of recordings from ten participants, we identified five typical types of BS with our proposed sensing device. They were identified according to their time and spectrogram information expanded based on short time Fourier analysis [31]. These five types of BS are classified as a single burst (SB), multiple bursts (MB), continuous random sound (CRS), harmonic sound (HS) and a combination sound (CS), as shown in Figure 2.…”

Section: Resultsmentioning

confidence: 99%

Bowel Sounds Identification and Migrating Motor Complex Detection with Low-Cost Piezoelectric Acoustic Sensing Device

Allwood

Webberley

et al. 2018

Sensors

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Interpretation of bowel sounds (BS) provides a convenient and non-invasive technique to aid in the diagnosis of gastrointestinal (GI) conditions. However, the approach’s potential is limited by variation between BS and their irregular occurrence. A short, manual auscultation is sufficient to aid in diagnosis of only a few conditions. A longer recording has the potential to unlock additional understanding of GI physiology and clinical utility. In this paper, a low-cost and straightforward piezoelectric acoustic sensing device was designed and used for long BS recordings. The migrating motor complex (MMC) cycle was detected using this device and the sound index as the biomarker for MMC phases. This cycle of recurring motility is typically measured using expensive and invasive equipment. We also used our recordings to develop an improved categorization system for BS. Five different types of BS were extracted: the single burst, multiple bursts, continuous random sound, harmonic sound, and their combination. Their acoustic characteristics and distribution are described. The quantities of different BS during two-hour recordings varied considerably from person to person, while the proportions of different types were consistent. The sensing devices provide a useful tool for MMC detection and study of GI physiology and function.

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Section: Resultsmentioning

confidence: 99%

Bowel Sounds Identification and Migrating Motor Complex Detection with Low-Cost Piezoelectric Acoustic Sensing Device

Allwood

Webberley

et al. 2018

Sensors

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“…As we demonstrate in this review, modern technologies circumvent some of these limitations. Automated identification of bowel sounds already enables their use as a vital sign [ 6 ]. Moreover, it seems that extraction and analysis of more complex data (patterns, variability, spectra) may uncover in more detail the basic physiology of the gastrointestinal tract and the nature of its maladies.…”

Section: Introductionmentioning

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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“…Subsequently, frequency-domain and time-domain features were extracted from each bowel sound, and the approximate location of origin of each bowel sound was determined. The features included many previously identified in the medical (18) and biomedical engineering literature (19) and novel features we developed through our modeling of bowel sound generation (20). The basic process flow of the methodology for gathering the data and creating a machine learning model is presented in Figure 1.…”

Section: Methodsmentioning

confidence: 99%

Noninvasive Diagnosis of Irritable Bowel Syndrome via Bowel Sound Features: Proof of Concept

Allwood

Webberley

et al. 2019

Clinical and Translational Gastroenterology

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INTRODUCTION: Irritable bowel syndrome (IBS) is a common and debilitating disorder estimated to affect approximately 11% of the world's population. Typically, IBS is a diagnosis of exclusion after patients undergo a costly and invasive colonoscopy to exclude organic disease. Clinician's and researchers have identified a need for a new cost-effective, accurate, and noninvasive diagnostic test for IBS. METHODS: Using a diagnostic case-control study, we explored the use of bowel sounds to characterize IBS with a view to diagnostic use. We recruited participants with an existing clinical diagnosis of IBS or healthy (asymptomatic) digestive systems. We recorded bowel sounds for 2 hours after fasting and then for 40 minutes after a standard meal. RESULTS: We here report our results including our accuracy in characterizing IBS-related bowel sounds and differentiation between participants with IBS and healthy participants. Leave-one-out cross-validation of our model developed using the first 31 IBS and 37 healthy participants gave 90% sensitivity and 92% specificity for IBS diagnosis. Independent testing using the next 15 IBS and 15 healthy participants demonstrated 87% sensitivity and 87% specificity for IBS diagnosis. CONCLUSIONS: These preliminary results provide proof of concept for the use of bowel sound analysis to identify IBS. A prospective study is needed to confirm these findings. TRANSLATIONAL IMPACT: Our belt and model offer hope of a new approach for IBS diagnosis in primary practice. Combined with screening tests for organic disease, it would offer greater confidence to patients and could reduce the burden of unnecessary colonoscopies for health care systems and patients.

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Advances in Acoustic Signal Processing Techniques for Enhanced Bowel Sound Analysis

Cited by 40 publications

References 63 publications

Bowel Sounds Identification and Migrating Motor Complex Detection with Low-Cost Piezoelectric Acoustic Sensing Device

Bowel Sounds Identification and Migrating Motor Complex Detection with Low-Cost Piezoelectric Acoustic Sensing Device

Automated Bowel Sound Analysis: An Overview

Noninvasive Diagnosis of Irritable Bowel Syndrome via Bowel Sound Features: Proof of Concept

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