Novel wavelet domain Wiener filtering de-noising techniques: Application to bowel sounds captured by means of abdominal surface vibrations

Prediction method of human defecation based on informer audio data augmentation and improved residual network

Defecation care for disabled patients is a major challenge in health management. Traditional post-defecation treatment will bring physical injury and negative emotions to patients, while existing pre-defecation forecasting care methods are physically intrusive. On the basis of exploring the mechanism of defecation intention generation, and based on the characteristic analysis and clinical application of bowel sounds, it is found that the generation of desire to defecate and bowel sounds are correlated to a certain extent. Therefore, a deep learning-based bowel sound recognition method is proposed for human defecation prediction. The wavelet domain based Wiener filter is used to filter the bowel sound data to reduce other noise. Statistical analysis, fast Fourier transform and wavelet packet transform are used to extract the integrated features of bowel sound in time, frequency and time-frequency domain. In particular, an audio signal expansion data algorithm based on the Informer model is proposed to solve the problem of poor generalization of the training model caused by the difficulty of collecting bowel sound in reality. An improved one-dimensional residual network model (1D-IResNet) for defecation classification prediction is designed based on multi-domain features. The experimental results show that the proposed bowel sound augmentation strategy can effectively improve the data sample size and increase the sample diversity. Under the augmented dataset, the training speed of the 1D-IResNet model is accelerated, and the classification accuracy reaches 90.54 %, the F1 score reaches 83.88 %, which achieves a relatively good classification stability while maintaining a high classification index.

Differences in intestinal motility during different sleep stages based on long-term bowel sounds

BACKGROUND AND OBJECTIVES

This study focused on changes in intestinal motility during different sleep stages based on long-term bowel sounds.

METHODS

A modified higher order statistics algorithm was devised to identify the effective bowel sound segments. Next, characteristic values (CVs) were extracted from each bowel sound segment, which included 4 time-domain, 4 frequency-domain and 2 nonlinear CVs. The statistical analysis of these CVs corresponding to the different sleep stages could be used to evaluate the changes in intestinal motility during sleep.

RESULTS

A total of 6865.81 min of data were recorded from 14 participants, including both polysomnographic data and bowel sound data which were recorded simultaneously from each participant. The average accuracy, sensitivity and specificity of the modified higher order statistics detector were 96.46 ± 2.60%, 97.24 ± 2.99% and 94.13 ± 4.37%. In addition, 217088 segments of effective bowel sound corresponding to different sleep stages were identified using the modified detector. Most of the CVs were statistically different during different sleep stages ([Formula: see text]). Furthermore, the bowel sounds were low in frequency based on frequency-domain CVs, high in energy based on time-domain CVs and low in complexity base on nonlinear CVs during deep sleep, which was consistent with the state of the EEG signals during deep sleep.

CONCLUSIONS

The intestinal motility varies by different sleep stages based on long-term bowel sounds using the modified higher order statistics detector. The study indicates that the long-term bowel sounds can well reflect intestinal motility during sleep. This study also demonstrates that it is technically feasible to simultaneously record intestinal motility and sleep state throughout the night. This offers great potential for future studies investigating intestinal motility during sleep and related clinical applications.

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.

Non-invasive Detection of Bowel Sounds in Real-life Settings Using Spectrogram Zeros and Autoencoding

Gastrointestinal (GI) diseases are amongst the most painful and dangerous clinical cases, due to inefficient recognition of symptoms and thus, lack of early-diagnostic tools. The analysis of bowel sounds (BS) has been fundamental for GI diseases, however their long-term recordings require technical and clinical resources along with the patientt's motionless concurrence throughout the auscultation procedure. In this study, an end-to-end non-invasive solution is proposed to detect BS in real-life settings utilizing a smart-belt apparatus along with advanced signal processing and deep neural network algorithms. Thus, high rate of BS identification and separation from other domestic and urban sounds have been achieved over the realization of an experiment where BS recordings were collected and analyzed out of 10 student volunteers.

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.

Robust Audio Content Classification Using Hybrid-Based SMD and Entropy-Based VAD

A robust approach for the application of audio content classification (ACC) is proposed in this paper, especially in variable noise-level conditions. We know that speech, music, and background noise (also called silence) are usually mixed in the noisy audio signal. Based on the findings, we propose a hierarchical ACC approach consisting of three parts: voice activity detection (VAD), speech/music discrimination (SMD), and post-processing. First, entropy-based VAD is successfully used to segment input signal into noisy audio and noise even if variable-noise level is happening. The determinations of one-dimensional (1D)-subband energy information (1D-SEI) and 2D-textural image information (2D-TII) are then formed as a hybrid feature set. The hybrid-based SMD is achieved because the hybrid feature set is input into the classification of the support vector machine (SVM). Finally, a rule-based post-processing of segments is utilized to smoothly determine the output of the ACC system. The noisy audio is successfully classified into noise, speech, and music. Experimental results show that the hierarchical ACC system using hybrid feature-based SMD and entropy-based VAD is successfully evaluated against three available datasets and is comparable with existing methods even in a variable noise-level environment. In addition, our test results with the VAD scheme and hybrid features also shows that the proposed architecture increases the performance of audio content discrimination.

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

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.

A mathematical model of bowel sound generation

Humans have been interested in bowel sounds and wondered about their origins for millennia. To better understand the phenomenon, a mathematical model of bowel sound generation was developed based on a spring-mass-damping system. This is similar to vocal folds models for speech. The bowel sound model has four parameters that link to bowel activities: the individual wave component, pressure index, component quantity, and component interval time. All types of bowel sound documented previously can be modelled by combining different values for these parameters. Further, a 2500 ms bowel sound incorporating all the common types was simulated to present the model's accuracy.

The potential of computerised analysis of bowel sounds for diagnosis of gastrointestinal conditions: a systematic review

BACKGROUND

Gastrointestinal (GI) conditions are highly prevalent, and their standard diagnostic tests are costly and carry risks. There is a need for new, cost-effective, non-invasive tests. Our main objective was to assess the potential for use of bowel sounds computerised analysis in the diagnosis of GI conditions.

METHODS

The systematic review followed the PRISMA requirements. Searches were made of four databases (PubMed, MEDLINE, Embase, and IEEE Xplore) and the references of included papers. Studies of all types were included. The titles and abstracts were screened by one author. Full articles were reviewed and data collected by two authors independently. A third reviewer decided on inclusion in the event of disagreement. Bias and applicability were assessed via a QUADAS tool adapted to accommodate studies of multiple types.

RESULTS

Two thousand eight hundred eighty-four studies were retrieved; however, only 14 studies were included. Most of these simply assessed associations between a bowel sound feature and a condition. Four studies also included assessments of diagnostic accuracy. We found many significant associations between a bowel sound feature and a GI condition. Receiver operating characteristic curve analyses revealed high sensitivity and specificity for an irritable bowel syndrome test, and a high negative predictive value for a test for post-operative ileus. Assessment of methodological quality identified weaknesses in all studies. We particularly noted a high risk of bias in patient selection. Because of the limited number of trials included and the variety in conditions, technology, and statistics, we were unable to conduct pooled analyses.

CONCLUSIONS

Due to concerns over quality and small sample sizes, we cannot yet recommend an existing BSCA diagnostic test without additional studies. However, the preliminary results found in the included studies and the technological advances described in excluded studies indicate excellent future potential. Research combining sophistical clinical and engineering skills is likely to be fruitful.

SYSTEMATIC REVIEW REGISTRATION

The review protocol (review ID number 42016054028) was developed by three authors (AI, KMW, and JM) and was published in the PROSPERO International prospective register of systematic reviews. It can be accessed from https://www.crd.york.ac.uk/PROSPERO/ .

Enhancement of lung sounds based on empirical mode decomposition and Fourier transform algorithm

BACKGROUND AND OBJECTIVE

There is always heart sound (HS) signal interfering during the recording of lung sound (LS) signals. This obscures the features of LS signals and creates confusion on pathological states, if any, of the lungs. In this work, a new method is proposed for reduction of heart sound interference which is based on empirical mode decomposition (EMD) technique and prediction algorithm.

METHOD

In this approach, first the mixed signal is split into several components in terms of intrinsic mode functions (IMFs). Thereafter, HS-included segments are localized and removed from them. The missing values of the gap thus produced, is predicted by a new Fast Fourier Transform (FFT) based prediction algorithm and the time domain LS signal is reconstructed by taking an inverse FFT of the estimated missing values.

RESULTS

The experiments have been conducted on simulated and recorded HS corrupted LS signals at three different flow rates and various SNR levels. The performance of the proposed method is evaluated by qualitative and quantitative analysis of the results.

CONCLUSIONS

It is found that the proposed method is superior to the baseline method in terms of quantitative and qualitative measurement. The developed method gives better results compared to baseline method for different SNR levels. Our method gives cross correlation index (CCI) of 0.9488, signal to deviation ratio (SDR) of 9.8262, and normalized maximum amplitude error (NMAE) of 26.94 for 0 dB SNR value.

Usefulness of a real-time bowel sound analysis system in patients with severe sepsis (pilot study)

Healthy bowel function is an important factor when judging the advisability of early enteral nutrition in critically ill patients, but long-term observation and objective evaluation of gastrointestinal motility are difficult. In the study, real-time continuous measurement of gastrointestinal motility was performed in patients with severe sepsis using a developed bowel sound analysis system, and the correlation between bowel sounds and changes over time in blood concentrations of interleukin (IL)-6, which is associated with sepsis severity, was evaluated. The subjects were five adult patients in the acute phase of severe sepsis on a mechanical ventilator, with IL-6 blood concentrations ≥100 pg/mL, who had consented to participate in the study. Gastrointestinal motility was measured for a total of 62,399 min: 31,544 min in 3 subjects in the no-steroids group and 30,855 min in 2 subjects in the steroid treatment group. In the no-steroids group, the bowel sound counts were negatively correlated with IL-6 blood concentration, suggesting that gastrointestinal motility was suppressed as IL-6 blood concentration increased. However, in the steroid treatment group, gastrointestinal motility showed no correlation with IL-6 blood concentration (r = -0.25, p = 0.27). The IL-6 blood concentration appears to have decreased with steroid treatment irrespective of changes in the state of sepsis, whereas bowel sound counts with the monitoring system reflected the changes in the state of sepsis, resulting in no correlation. This monitoring system provides a useful method of continuously, quantitatively, and non-invasively evaluating gastrointestinal motility in patients with severe sepsis. Gastrointestinal motility might be useful as a parameter reflecting disease severity, particularly in patients treated with steroids.

Recovery of gastrointestinal tract motility detection using Naive Bayesian and minimum statistics

Loss of gastrointestinal motility is a significant medical setback for patients who experience abdominal surgery and contributes to the most common reason for prolonged hospital stays. Recent clinical studies suggest that initiating feeding early after abdominal surgery is beneficial. Early feeding is possible when the patients demonstrate bowel motility in the form of bowel sounds (BS). This work provides a data collection, processing and analysis methodology for detection of recovery of gastrointestinal track motility by observing BSs in auscultation recordings. The approach is suitable for real-time long-term continuous monitoring in clinical environments. The system was developed using a Naive Bayesian algorithm for pattern classification, and Minimum Statistics and spectral subtraction for noise attenuation. The solution was tested on 59h of recordings and 94.15% recognition accuracy was observed.

Estimation algorithm of the bowel motility based on regression analysis of the jitter and shimmer of bowel sounds

Bowel sound (BS) signals can be used clinically as useful indicators of bowel motility. In this study, we devised a modified iterative kurtosis-based detector algorithm, in order to enhance the de-noising performance of BS signals, and an estimation algorithm of bowel motility based on the regression modeling of the jitter and shimmer of BS signals obtained by auscultation. The correlation coefficient, coefficient of determination and errors between the colon transit times measured by a conventional radiograph and the corresponding values estimated by our method were 0.987, 0.974 and 3.5 ± 3.3h, respectively. These results demonstrated that our method could be used as a complementary tool for the non-invasive diagnosis and monitoring of bowel motility, instead of conventional radiography.

Non-invasive algorithm for bowel motility estimation using a back-propagation neural network model of bowel sounds

Background

Radiological scoring methods such as colon transit time (CTT) have been widely used for the assessment of bowel motility. However, these radiograph-based methods need cumbersome radiological instruments and their frequent exposure to radiation. Therefore, a non-invasive estimation algorithm of bowel motility, based on a back-propagation neural network (BPNN) model of bowel sounds (BS) obtained by an auscultation, was devised.

Methods

Twelve healthy males (age: 24.8 ± 2.7 years) and 6 patients with spinal cord injury (6 males, age: 55.3 ± 7.1 years) were examined. BS signals generated during the digestive process were recorded from 3 colonic segments (ascending, descending and sigmoid colon), and then, the acoustical features (jitter and shimmer) of the individual BS segment were obtained. Only 6 features (J_{1, 3}, J_{3, 3}, S_{1, 2}, S_{2, 1}, S_{2, 2}, S_{3, 2}), which are highly correlated to the CTTs measured by the conventional method, were used as the features of the input vector for the BPNN.

Results

As a results, both the jitters and shimmers of the normal subjects were relatively higher than those of the patients, whereas the CTTs of the normal subjects were relatively lower than those of the patients (p < 0.01). Also, through k-fold cross validation, the correlation coefficient and mean average error between the CTTs measured by a conventional radiograph and the values estimated by our algorithm were 0.89 and 10.6 hours, respectively.

Conclusions

The jitter and shimmer of the BS signals generated during the peristalsis could be clinically useful for the discriminative parameters of bowel motility. Also, the devised algorithm showed good potential for the continuous monitoring and estimation of bowel motility, instead of conventional radiography, and thus, it could be used as a complementary tool for the non-invasive measurement of bowel motility.

Digestive activity evaluation by multichannel abdominal sounds analysis

This paper introduces a complete methodology for abdominal sounds analysis, from signal acquisition to statistical data analysis. The goal is to evaluate if and how phonoenterograms can be used to detect different functioning modes of the normal gastrointestinal tract, both in terms of localization and of time evolution during the digestion. After the description of the acquisition protocol and the employed instrumentation, several signal processing steps are presented: wavelet denoising and segmentation, artifact suppression, and source localization. Next, several physiological features are extracted from the processed signals issued from a database of 14 healthy volunteers, recorded during 3 h after a standardized meal. Data analysis is performed using a multifactorial statistical method. Based on the introduced approach, we show that the abdominal regions of healthy volunteers present statistically significant phonoenterographic characteristics, which evolve differently during the normal digestion. The most significant feature allowing us to distinguish regions and time differences is the number of recorded sounds, but important information is also carried by sound amplitudes, frequencies, and durations. Depending on the considered feature, the sounds produced by different abdominal regions (especially stomach, ileocaecal, and lower abdomen regions) present a specific distribution over space and time. This information, statistically validated, is usable in further studies as a comparison term with other normal or pathological conditions.

Long-term signal detection, segmentation and summarization using wavelets and fractal dimension: a bioacoustics application in gastrointestinal-motility monitoring

The current paper describes a wavelet-based method for long-term processing and analysis of gastrointestinal sounds (GIS). Windowing techniques are used to select sequential blocks of the prolonged multi-channel recordings and proceed to various wavelet-domain processing stages. De-noising, significant-activity detection, automated segmentation and extraction of summary curves are applied in an integrated mode, allowing for enhanced content manipulation and analysis. The proposed analysis scheme combines flexible long-term graphical representation tools, while maintaining the ability of quick browsing via visualization and auralization of the detected short-term events. This work is part of a project aiming to implement non-invasive diagnosis over gastrointestinal-motility (GIM) physiology. However, the proposed techniques might be applied to any study of long-term bioacoustics time series.