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

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

doi:10.3390/s18124240

Cited by 26 publications

(31 citation statements)

References 33 publications

(62 reference statements)

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“…Abdominal sounds were recorded using a Zoom H6 Handy recorder (Zoom, Tokyo, Japan) attached to 4 piezo-based sensors placed on the 4 quadrants of the abdomen and held in place with stretchy Tubigrip, together referred to as the “belt.” Software was developed for the initial preprocessing that identified bowel sounds for extraction. This system has previously been described following use for recordings of bowel sounds and detection of the migrating motor complex (MMC) (17).…”

Section: Methodsmentioning

confidence: 99%

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

Allwood

Webberley

et al. 2019

Clin Transl Gastroenterol

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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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Section: Methodsmentioning

confidence: 99%

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

Allwood

Webberley

et al. 2019

Clin Transl Gastroenterol

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“…Thus, this design would be very effective to harvest acoustic energy from surrounding noise. Du et al [148] designed an acoustic piezoelectric sensing device using a lowcost technique (Figure 24c). The device is compared with an electronic stethoscope (Litmann 3200), and an almost similar performance was observed (Figure 24c,i).…”

Section: Png Devices For Acoustic Energy Harvestingmentioning

confidence: 99%

Recent Advances in Self‐Powered Tribo‐/Piezoelectric Energy Harvesters: All‐In‐One Package for Future Smart Technologies

Karan

Maiti

Lee

et al. 2020

Adv Funct Materials

149

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Electric devices has become a necessity in modern smart societies. The energy research community is continually exploring new solutions that can be employed as potential and reliable alternatives to satisfy future energy requirements in this context. Triboelectric/piezoelectric nanogenerators (TNGs/PNGs) are considered promising renewable energy harvesting technologies. TNGs/PNGs are gaining considerable research attention because of their advantages such as ease of fabrication, cost effectiveness, flexibility, lightweightness, long-term-durability, and excellent output performance. The TNGs/PNGs have enormous potential in harvesting energy from water waves (blue energy), airflow (wind energy), sound frequency (acoustic energy), vibrations/mechanical motions (such as body motions activities/sleeping), and in vivo body motions. These technologies are thus an "all-in-one package," which is a unique selling point. The advantage of this technology is that all sources are natural and abundant. This paper provides a review of the potential use of PNGs/TNGs and includes a description of the rise of nanogenerators, their underlying mechanisms, different materials used, various models, possible applications, and future prospects. Stored energy can be used to develop smart/informative technologies (as ultrasensitive sensors) and self-powered biomedical science. Owing to their considerable effects, these harvesting technologies are expected to dominate the future smart world.

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“…In recent years, with the development of artificial intelligence technology and wearable medical devices, a lot of AIassisted diagnoses using medical imaging and electronic medical record data have been proved to be effective in reducing workload for doctors. By contrast, the absence of stereophonic data has prevented the auscultation process from being digitized, with only a small number of datasets collected by electronic stethoscopes for research, such as ICBHI[1], Physionet [2] and the Noisy Guts project [3]. [3] explored the use of bowel sounds to characterize IBS with a view to diagnostic use using a diagnostic case-control study, and Independent testing demonstrated 87% sensitivity and 87% specificity for IBS diagnosis using the 15 IBS and 15 healthy participants.…”

Section: Introductionmentioning

confidence: 99%

“…By contrast, the absence of stereophonic data has prevented the auscultation process from being digitized, with only a small number of datasets collected by electronic stethoscopes for research, such as ICBHI[1], Physionet [2] and the Noisy Guts project [3]. [3] explored the use of bowel sounds to characterize IBS with a view to diagnostic use using a diagnostic case-control study, and Independent testing demonstrated 87% sensitivity and 87% specificity for IBS diagnosis using the 15 IBS and 15 healthy participants. In connection with the work of this article, Our original intention is to mine information about our body and provide helpful information for doctors.…”

Section: Introductionmentioning

confidence: 99%

“…In connection with the work of this article, Our original intention is to mine information about our body and provide helpful information for doctors. As is mentioned in [3], the interpretation of bowel sounds (BS) provides a convenient and non-invasive technique to aid in the diagnosis of gastrointestinal (GI) conditions. However, this approach is limited by the variation between BS and its irregular occurrence.…”

Section: Introductionmentioning

confidence: 99%

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A CRNN System for Sound Event Detection Based on Gastrointestinal Sound Dataset Collected by Wearable Auscultation Devices

Zheng

Zhang

Chen³

et al. 2020

IEEE Access

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In this paper, we set up a novel audio dataset named Gastrointestinal (GI) Sound Set which includes 6 kinds of body sounds Bowel sound, Speech, Snore, Cough, Groan, and Rub. We do sound event detection (SED) based on it, and can accurately detect 6 types of sound events. First, the GI Sound Set is collected by wearable auscultation devices. To ensure generalization, patients from five different hospital departments are recruited for data collection, along with a group of healthy subjects. GI Sound Set refers to Google AudioSet in data format but varies in audio length and sampling rate. Second, we extract Mel-filter features from the recordings and investigate the performance of different activation functions and neural network architectures for detecting sound events. We use data augmentation, class balance to deal with the problem of quantitative imbalance between classes on the dataset. We apply multiple instances learning(MIL) to give out not only bag-level results but also frame-level results. In this work, GI Sound Set is the largest body sound dataset to date, and our approach shows state-of-the-art performance with an average score of F1=81.06% evaluated on the test set. Due to its simple network and conventional processing method, our CRNN system has high universality, which can be used in other audio datasets, such as respiratory sound and heart sound. INDEX TERMS Gastrointestinal (GI) Sound Set, sound event detection(SED), convolutional recurrent neural network (CRNN), multiple instance learning(MIL).

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

Cited by 26 publications

References 33 publications

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

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

Recent Advances in Self‐Powered Tribo‐/Piezoelectric Energy Harvesters: All‐In‐One Package for Future Smart Technologies

A CRNN System for Sound Event Detection Based on Gastrointestinal Sound Dataset Collected by Wearable Auscultation Devices

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