Automatic Bowel Motility Evaluation Technique for Noncontact Sound Recordings

Sato, Ryunosuke; Emoto, Takahiro; Gojima, Yuki; Akutagawa, Masatake

doi:10.3390/app8060999

Cited by 13 publications

(16 citation statements)

References 22 publications

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“…The team achieved automated BS identification through an autoregressive moving average of 91% accuracy and concluded that BS duration reflects intestinal motility [ 73 ]. The group successfully applied unsupervised BS detection in non-contact recordings and determined three clinically pertinent parameters: sound-to-sound interval, which was associated with frequency (per minute), as well as the ratio of signal to noise [ 74 ]. These achievements can be considered a successful application of artificial intelligence-based BS detection in non-contact recordings.…”

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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“…Microphone-based sensors are popular for BS recording [14,15,16,17,18,19,20]. In 2013, Sakata et al developed a silicon microphone-based sensor for long-term BS recording [15].…”

Section: Introductionmentioning

confidence: 99%

“…Since microphone-based sensors are sensitive to airborne noise, the ambient noise might easily contaminate the BS signal. Emoto et al proposed a non-contact microphone for BS recorded and used their system to identify gut motility before and after soda intake [19,20]. However, their participants were required to lie down on a bed for recordings, which is inconvenient for long recordings.…”

Section: Introductionmentioning

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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“…Rekanos and Hadjileontiadis proposed an iterative kurtosis-based technique for the detection of bowel sound in 2006 [11], and Ulusar developed a gastrointestinal motility monitoring system using a Naive Bayesian algorithm for BS observation in 2014 [12]. Subsequently, several advanced machine learning techniques were employed for BS detection, including support vector machine (SVM) [13], artificial neural network (ANN) [14,15], and long short term memory (LSTM) [16] approaches, and have achieved better results than the traditional methods. However, even these advanced models have some limitations.…”

mentioning

confidence: 99%

Development of a bowel sound detector adapted to demonstrate the effect of food intake

Wang

Testa²,

Marshall

2022

BioMed Eng OnLine

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Objective Bowel sounds (BS) carry useful information about gastrointestinal condition and feeding status. Interest in computerized bowel sound-based analysis has grown recently and techniques have evolved rapidly. An important first step for these analyses is to extract BS segments, whilst neglecting silent periods. The purpose of this study was to develop a convolutional neural network-based BS detector able to detect all types of BS with accurate time stamps, and to investigate the effect of food consumption on some acoustic features of BS with the proposed detector. Results Audio recordings from 40 volunteers were collected and a BS dataset consisting of 6700 manually labelled segments was generated for training and testing the proposed BS detector. The detector attained 91.06% and 90.78% accuracy for the validation dataset and across-subject test dataset, respectively, with a well-balanced sensitivity and specificity. The detection rates evaluated on different BS types were also satisfactory. Four acoustic features were evaluated to investigate the food effect. The total duration and spectral bandwidth of BS showed significant differences before and after food consumption, while no significant difference was observed in mean-crossing rate values. Conclusion We demonstrated that the proposed BS detector is effective in detecting all types of BS, and providing an accurate time stamp for each BS. The characteristics of BS types and the effect on detection accuracy is discussed. The proposed detector could have clinical application for post-operative ileus prognosis, and monitoring of food intake.

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Automatic Bowel Motility Evaluation Technique for Noncontact Sound Recordings

Cited by 13 publications

References 22 publications

Automated Bowel Sound Analysis: An Overview

Automated Bowel Sound Analysis: An Overview

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

Development of a bowel sound detector adapted to demonstrate the effect of food intake

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