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

Acoustic biomarkers in asthma: a systematic review

OBJECTIVE

Current monitoring methods of asthma, such as peak expiratory flow testing, have important limitations. The emergence of automated acoustic sound analysis, capturing cough, wheeze, and inhaler use, offers a promising avenue for improving asthma diagnosis and monitoring. This systematic review evaluated the validity of acoustic biomarkers in supporting the diagnosis of asthma and its monitoring.

DATA SOURCES

A search was performed using two databases (PubMed and Embase) for all relevant studies published before November 2023.

STUDY SELECTION

27 studies were included for analysis. Eligible studies focused on acoustic signals as digital biomarkers in asthma, utilizing recording devices to register or analyze sound.

RESULTS

Various respiratory acoustic signal types were analyzed, with cough and wheeze being predominant. Data collection methods included smartphones, custom sensors and digital stethoscopes. Across all studies, automated acoustic algorithms achieved average accuracy of cough and wheeze detection of 88.7% (range: 61.0 - 100.0%) with a median of 92.0%. The sensitivity of sound detection ranged from 54.0 to 100.0%, with a median of 90.3%; specificity ranged from 67.0 to 99.7%, with a median of 95.0%. Moreover, 70.4% (19/27) studies had a risk of bias identified.

CONCLUSIONS

This systematic review establishes the promising role of acoustic biomarkers, particularly cough and wheeze, in supporting the diagnosis of asthma and monitoring. The evidence suggests the potential for clinical integration of acoustic biomarkers, emphasizing the need for further validation in larger, clinically-diverse populations.

[Snoring noise removal method for bowel sound signal during sleep]

Monitoring of bowel sounds is an important method to assess bowel motility during sleep, but it is seriously affected by snoring noise. In this paper, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method was applied to remove snoring noise from bowel sounds during sleep. Specifically, the noisy bowel sounds were first band-pass filtered, then decomposed by the CEEMDAN method, and finally the appropriate components were selected to reconstruct the pure bowel sounds. The results of semi-simulated and real data showed that the CEEMDAN method was better than empirical mode decomposition and wavelet denoising method. The CEEMDAN method is used to remove snoring noise from bowel sounds during sleep, which lays an important foundation for using bowel sounds to assess the intestinal motility during sleep.

Wireless broadband acousto-mechanical sensing system for continuous physiological monitoring

The human body generates various forms of subtle, broadband acousto-mechanical signals that contain information on cardiorespiratory and gastrointestinal health with potential application for continuous physiological monitoring. Existing device options, ranging from digital stethoscopes to inertial measurement units, offer useful capabilities but have disadvantages such as restricted measurement locations that prevent continuous, longitudinal tracking and that constrain their use to controlled environments. Here we present a wireless, broadband acousto-mechanical sensing network that circumvents these limitations and provides information on processes including slow movements within the body, digestive activity, respiratory sounds and cardiac cycles, all with clinical grade accuracy and independent of artifacts from ambient sounds. This system can also perform spatiotemporal mapping of the dynamics of gastrointestinal processes and airflow into and out of the lungs. To demonstrate the capabilities of this system we used it to monitor constrained respiratory airflow and intestinal motility in neonates in the neonatal intensive care unit (n = 15), and to assess regional lung function in patients undergoing thoracic surgery (n = 55). This broadband acousto-mechanical sensing system holds the potential to help mitigate cardiorespiratory instability and manage disease progression in patients through continuous monitoring of physiological signals, in both the clinical and nonclinical setting.

The Potential Use of Artificial Intelligence in Irritable Bowel Syndrome Management

Irritable bowel syndrome (IBS) has a global prevalence of around 4.1% and is associated with a low quality of life and increased healthcare costs. Current guidelines recommend that IBS is diagnosed using the symptom-based Rome IV criteria. Despite this, when patients seek medical attention, they are usually over-investigated. This issue might be resolved by novel technologies in medicine, such as the use of Artificial Intelligence (AI). In this context, this paper aims to review AI applications in IBS. AI in colonoscopy proved to be useful in organic lesion detection and diagnosis and in objectively assessing the quality of the procedure. Only a recently published study talked about the potential of AI-colonoscopy in IBS. AI was also used to study biofilm characteristics in the large bowel and establish a potential relationship with IBS. Moreover, an AI algorithm was developed in order to correlate specific bowel sounds with IBS. In addition to that, AI-based smartphone applications have been developed to facilitate the monitoring of IBS symptoms. From a therapeutic standpoint, an AI system was created to recommend specific diets based on an individual's microbiota. In conclusion, future IBS diagnosis and treatment may benefit from AI.

Automated Bowel Sound and Motility Analysis with CNN Using a Smartphone

Bowel sound (BS) is receiving more attention as an indicator of gut health since it can be acquired non-invasively. Current gut health diagnostic tests require special devices that are limited to hospital settings. This study aimed to develop a prototype smartphone application that can record BS using built-in microphones and automatically analyze the sounds. Using smartphones, we collected BSs from 100 participants (age 37.6 ± 9.7). During screening and annotation, we obtained 5929 BS segments. Based on the annotated recordings, we developed and compared two BS recognition models: CNN and LSTM. Our CNN model could detect BSs with an accuracy of 88.9% andan F measure of 72.3% using cross evaluation, thus displaying better performance than the LSTM model (82.4% accuracy and 65.8% F measure using cross validation). Furthermore, the BS to sound interval, which indicates a bowel motility, predicted by the CNN model correlated to over 98% with manual labels. Using built-in smartphone microphones, we constructed a CNN model that can recognize BSs with moderate accuracy, thus providing a putative non-invasive tool for conveniently determining gut health and demonstrating the potential of automated BS research.

A High-Sensitivity Bowel Sound Electronic Monitor Based on Piezoelectric Micromachined Ultrasonic Transducers

Bowel sounds contain some important human physiological parameters which can reflect information about intestinal function. In this work, in order to realize real-time monitoring of bowel sounds, a portable and wearable bowel sound electronic monitor based on piezoelectric micromachined ultrasonic transducers (PMUTs) is proposed. This prototype consists of a sensing module to collect bowel sounds and a GUI (graphical user interface) based on LabVIEW to display real-time bowel sound signals. The sensing module is composed of four PMUTs connected in parallel and a signal conditioning circuit. The sensitivity, noise resolution, and non-linearity of the bowel sound monitor are measured in this work. The result indicates that the designed prototype has high sensitivity (-142.69 dB), high noise resolution (50 dB at 100 Hz), and small non-linearity. To demonstrate the characteristic of the designed electronic monitor, continuous bowel sound monitoring is performed using the electronic monitor and a stethoscope on a healthy human before and after a meal. Through comparing the experimental results and analyzing the signals in the time domain and frequency domain, this bowel sound monitor is demonstrated to record bowel sounds from the human intestine. This work displays the potential of the sensor for the daily monitoring of bowel sounds.

Graphene-based strain sensor with sandwich structure and its application in bowel sounds monitoring

Surgery is one of the primary treatment modalities for gastrointestinal tumors but can lead to postoperative ileus (POI), which can aggravate pain and increase costs. The incidence of POI can be effectively reduced by monitoring bowel sounds to assist doctors in deciding the timing of transoral feeding. In this study, we prepared a flexible strain sensor based on a graphene composite material and tested the feasibility of sensor monitoring of bowel sounds using simultaneous stethoscope and sensor monitoring. We found that the time of hearing the bowel sounds (12.0–12.1 s) corresponded to the time of waveform change monitored by the sensor (12.036 s), and the sound tone magnitude corresponded to the waveform amplitude. This proves that the application of sensors to monitor bowel sounds is feasible, which opens up a new field for the application of graphene sensors and provides a new way for clinicians to judge the condition of the intestine.

Combining medicine and materials science. First application of graphene strain sensors for monitoring bowel sounds

Research on a Defecation Pre-Warning Algorithm for the Disabled Elderly Based on a Semi-Supervised Generative Adversarial Network

The elderly population in China is continuously increasing, and the disabled account for a large proportion of the elderly population. An effective solution is urgently needed for incontinence among disabled elderly people. Compared with disposable adult diapers, artificial sphincter implantation and medication for incontinence, the defecation pre-warning method is more flexible and convenient. However, due to the complex human physiology and individual differences, its development is limited. Based on the aging trend of the population and clinical needs, this paper proposes a bowel sound acquisition system and a defecation pre-warning method and system based on a semi-supervised generative adversarial network. A network model was established to predict defecation using bowel sounds. The experimental results show that the proposed method can effectively classify bowel sounds with or without defecation tendency, and the accuracy reached 94.4%.

Simulating human gastrointestinal motility in dynamic in vitro models

The application of dynamic in vitro gastrointestinal (GI) models has grown in popularity to understand the impact of food structure and composition on human health. Given that GI motility is integral to digestion and absorption, a predictive in vitro model should faithfully replicate the motility patterns and motor functions in vivo. In this review, typical characteristics of gastric and small intestinal motility in humans as well as the biomechanical and hydrodynamic events pertinent to gut motility are summarized. The simulation of GI motility in the presently existing dynamic in vitro models is discussed from an engineering perspective and categorized into hydraulic, piston/probe-driven, roller-driven, pneumatic, and other systems. Each system and its representative models are evaluated in terms of their motility patterns, the key hydrodynamic characteristics concerning gut motility, their performance in simulating the key physiological events, and their ability to establish in vitro-in vivo correlations. Practical Application: The review paper provided useful information in the design of dynamic GI models and the simulation of human gastric and small intestinal motility which are important for understanding food and health.

Flexible dual-channel digital auscultation patch with active noise reduction for bowel sound monitoring and application

Bowel sounds (BSs) have important clinical value in the auxiliary diagnosis of digestive diseases, but due to the inconvenience of long-term monitoring and too much interference from environmental noise, they have not been well studied. Most of the current electronic stethoscopes are hard and bulky without the function of noise reduction, and their application for long-term wearable monitoring of BS in noisy clinical environments is very limited. In this paper, a flexible dual-channel digital auscultation patch with active noise reduction is designed and developed, which is wireless, wearable, and conformably attached to abdominal skin to record BS more accurately. The ambient noise can be greatly reduced through active noise reduction based on the adaptive filter. At the same time, some nonstationary noises appearing intermittently (e.g., frictional noise) can also be removed from BS by the cross validation of multichannel simultaneous acquisition. Then, two kinds of typical BS signals are taken as examples, and the feature parameters of the BS in the time domain and frequency domain are extracted through the time-frequency analysis algorithm. Furthermore, based on the short-term energy ratio between the four channels of dual patches, the two-dimensional localization of BS on the abdomen mapping plane is realized. Finally, the continuous wearable monitoring of BS for patients with postoperative ileus (POI) in the noisy ward from pre-operation (POD0) to postoperative Day 7 (POD7) was carried out. The obtained change curve of the occurrence frequency of BS provides guidance for doctors to choose a reasonable feeding time for patients after surgery and accelerate their recovery. Therefore, flexible dual-channel digital auscultation patches with active noise reduction will have promising applications in the clinical auxiliary diagnosis of digestive diseases.

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

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.

Analysis of Gastrointestinal Acoustic Activity Using Deep Neural Networks

Automated bowel sound (BS) analysis methods were already well developed by the early 2000s. Accuracy of ~90% had been achieved by several teams using various analytical approaches. Clinical research on BS had revealed their high potential in the non-invasive investigation of irritable bowel syndrome to study gastrointestinal motility and in a surgical setting. This article proposes a novel methodology for the analysis of BS using hybrid convolutional and recursive neural networks. It is one of the first methods of using deep learning to be widely explored. We have developed an experimental pipeline and evaluated our results with a new dataset collected using a device with a dedicated contact microphone. Data have been collected at night-time, which is the most interesting period from a neurogastroenterological point of view. Previous works had ignored this period and instead kept brief records only during the day. Our algorithm can detect bowel sounds with an accuracy >93%. Moreover, we have achieved a very high specificity (>97%), crucial in diagnosis. The results have been checked with a medical professional, and they successfully support clinical diagnosis. We have developed a client-server system allowing medical practitioners to upload the recordings from their patients and have them analyzed online. This system is available online. Although BS research is technologically mature, it still lacks a uniform methodology, an international forum for discussion, and an open platform for data exchange, and therefore it is not commonly used. Our server could provide a starting point for establishing a common framework in BS research.

Transparent and Flexible Vibration Sensor Based on a Wheel-Shaped Hybrid Thin Membrane

With the advent of human–machine interaction and the Internet of Things, wearable and flexible vibration sensors have been developed to detect human voices and surrounding vibrations transmitted to humans. However, previous wearable vibration sensors have limitations in the sensing performance, such as frequency response, linearity of sensitivity, and esthetics. In this study, a transparent and flexible vibration sensor was developed by incorporating organic/inorganic hybrid materials into ultrathin membranes. The sensor exhibited a linear and high sensitivity (20 mV/g) and a flat frequency response (80–3000 Hz), which are attributed to the wheel-shaped capacitive diaphragm structure fabricated by exploiting the high processability and low stiffness of the organic material SU-8 and the high conductivity of the inorganic material ITO. The sensor also has sufficient esthetics as a wearable device because of the high transparency of SU-8 and ITO. In addition, the temperature of the post-annealing process after ITO sputtering was optimized for the high transparency and conductivity. The fabricated sensor showed significant potential for use in transparent healthcare devices to monitor the vibrations transmitted from hand-held vibration tools and in a skin-attachable vocal sensor.

Automated Bowel Sound Analysis: An Overview

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.

Are Classic Bedside Exam Findings Required to Initiate Enteral Nutrition in Critically Ill Patients: Emphasis on Bowel Sounds and Abdominal Distension

The general physical examination of a patient is an axiom of critical care medicine, but evidence to support this practice remains sparse. Given the lack of evidence for a comprehensive physical examination of the entire patient on admission to the intensive care unit, which most clinicians consider an essential part of care, should clinicians continue the practice of a specialized gastrointestinal system physical examination when commencing enteral nutrition in critically ill patients? In this review of literature related to gastrointestinal system examination in critically ill patients, the focus is on gastrointestinal sounds and abdominal distension. There is a summary of what these physical features represent, an evaluation of the evidence regarding use of these physical features in patients after abdominal surgery, exploration of the rationale for and against using the physical findings in routine practice, and detail regarding what is known about each feature in critically ill patients. Based on the available evidence, it is recommended that an isolated symptom, sign, or bedside test does not provide meaningful information. However, it is submitted that a comprehensive physical assessment of the gastrointestinal system still has a role when initiating or administering enteral nutrition: specifically, when multiple features are present, clinicians should consider further investigation or intervention.

Long-Term Bowel Sound Monitoring and Segmentation by Wearable Devices and Convolutional Neural Networks

Bowel sounds (BSs), typically generated by the intestinal peristalses, are a significant physiological indicator of the digestive system's health condition. In this study, a wearable BS monitoring system is presented for long-term BS monitoring. The system features a wearable BS sensor that can record BSs for days long and transmit them wirelessly in real-time. With the system, a total of 20 subjects' BS data under the hospital environment were collected. Each subject is recorded for 24 hours. Through manual screening and annotation, from every subject's BS data, 400 segments were extracted, in which half are BS event-contained segments. Thus, a BS dataset that contains 20 × 400 sound segments is formed. Afterwards, CNNs are introduced for BS segment recognition. Specifically, this study proposes a novel CNN design method that makes it possible to transfer the popular CNN modules in image recognition into the BS segmentation domain. Experimental results show that in holdout evaluation with corrected labels, the designed CNN model achieves a moderate accuracy of 91.8% and the highest sensitivity of 97.0% compared with the similar works. In cross validation with noisy labels, the designed CNN delivers the best generability. By using a CNN visualizing technique-class activation maps, it is found that the designed CNN has learned the effective features of BS events. Finally, the proposed CNN design method is scalable to different sizes of datasets.

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

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.