Predicting prolonged postoperative ileus in gastric cancer patients based on bowel sounds using intelligent auscultation and machine learning

BACKGROUND Prolonged postoperative ileus(PPOI)delays the postoperative recovery of gastrointestinal function in patients with gastric cancer(GC),leading to longer hospitalization and higher healthcare expenditure.However,effective monitoring of gastrointestinal recovery in patients with GC remains challenging because of AIM To explore the risk factors for delayed postoperative bowel function recovery and evaluate bowel sound indicators collected via an intelligent auscultation system to guide clinical practice.METHODS This study included data from 120 patients diagnosed with GC who had undergone surgical treatment and postoperative bowel sound monitoring in the Department of General Surgery II at Shaanxi Provincial People's Hospital between January 2019 and January 2021.Among them,PPOI was reported in 33 cases.The patients were randomly divided into the training and validation cohorts.Significant variables from the training cohort were identified using univariate and multivariable analyses and were included in the model.RESULTS The analysis identified six potential variables associated with PPOI among the included participants.The incidence rate of PPOI was 27.5%.Age≥70 years,cTNM stage(Ⅰ and Ⅳ),preoperative hypoproteinemia,recovery time of bowel sounds(RTBS),number of bowel sounds(NBS),and frequency of bowel sounds(FBS)were independent risk factors for PPOI.The Bayesian model demonstrated good performance with internal validation:Training cohort[area under the curve(AUC)=0.880,accuracy=0.823,Brier score=0.139]and validation cohort(AUC=0.747,accuracy=0.690,Brier score=0.215).The model showed a good fit and calibration in the decision curve analysis,indicating a significant net benefit.CONCLUSION PPOI is a common complication following gastrectomy in patients with GC and is associated with age,cTNM stage,preoperative hypoproteinemia,and specific bowel sound-related indices(RTBS,NBS,and FBS).To facilitate early intervention and improve patient outcomes,clinicians should consider these factors,optimize preoperative nutritional status,and implement routine postoperative bowel sound monitoring.This study introduces an accessible machine learning model for predicting PPOI in patients with GC.

Diagnostic value of digital continuous bowel sounds in critically ill patients with acute gastrointestinal injury: A prospective observational study

BACKGROUND Acute gastrointestinal injury(AGI)is common in intensive care unit(ICU)and worsens the prognosis of critically ill patients.The four-point grading system proposed by the European Society of Intensive Care Medicine is subjective and lacks specificity.Therefore,a more objective method is required to evaluate and determine the grade of gastrointestinal dysfunction in this patient population.Digital continuous monitoring of bowel sounds and some biomarkers can change in gastrointestinal injuries.We aimed to develop a model of AGI using continuous monitoring of bowel sounds and biomarkers.AIM To develop a model to discriminate AGI by monitoring bowel sounds and biomarker indicators.METHODS We conducted a prospective observational study with 75 patients in an ICU of a tertiary-care hospital to create a diagnostic model for AGI.We recorded their bowel sounds,assessed AGI grading,collected clinical data,and measured biomarkers.We evaluated the model using misjudgment probability and leave-one-out cross-validation.RESULTS Mean bowel sound rate and citrulline level are independent risk factors for AGI.Gastrin was identified as a risk factor for the severity of AGI.Other factors that correlated with AGI include mean bowel sound rate,amplitude,interval time,Sequential Organ Failure Assessment score,Acute Physiology and Chronic Health Evaluation II score,platelet count,total protein level,blood gas potential of hydrogen(pH),and bicarbonate(HCO3-)level.Two discriminant models were constructed with a misclassification probability of<0.1.Leave-one-out cross-validation correctly classified 69.8%of the cases.CONCLUSION Our AGI diagnostic model represents a potentially effective approach for clinical AGI grading and holds promise as an objective diagnostic standard for AGI.

Ultrastructure of stridulating organ of Xylotrechus rusticus L. (Coleoptera, Cerambycidae) and behavioral responses to alarm sounds

We used scanning electron microscope （SEM） to observe the ultrastructure of stridulating organs in Xylotrechus rusticus L. We com- pared the morphological structure and size of stridulating organs, the numbers of a tooth-like part used in stridulation and its presence in fe- males and males of this beetle. The alarm sound of X.. rusticus was re- corded first when it was stimulated, then we tested behavioral responses to this alarm sound. The alarm sound ofX. rusticus has a warning effect on conspecifics.

Sounds Synthesis with Slime Mould of Physarum Polycephalum

This paper introduces a novel application of bionic engineering： a bionic musical instrument using Physarum polycephalum. Physarum polycephalum is a huge single cell with thousands of nuclei, which behaves like a giant amoeba. During its foraging behavior this plasmodium produces electrical activity corresponding to different physiological states. We developed a method to render sounds from such electrical activity and thus represent spatio-temporal behavior of slime mould in a form apprehended auditorily. The electrical activity is captured by various electrodes placed on a Petri dish containing the cultured slime mold. Sounds are synthesized by a bank of parallel sinusoidal oscillators connected to the electrodes. Each electrode is responsible for one partial of the spectrum of the resulting sound. The behavior of the slime mould can be controlled to produce different timbres.

Spectral analysis of bowel sounds in intestinal obstruction using an electronic stethoscope

AIM: To determine the value of bowel sounds analysis using an electronic stethoscope to support a clinical diagnosis of intestinal obstruction. METHODS: Subjects were patients who presented with a diagnosis of possible intestinal obstruction based on symptoms, signs, and radiological findings. A 3MTH Littmann Model 4100 electronic stethoscope was used in this study. With the patients lying supine, six 8-second recordings of bowel sounds were taken from each patient from the lower abdomen. The recordings were analysed for sound duration, soundto-sound interval, dominant frequency, and peak frequency. Clinical and radiological data were reviewed and the patients were classified as having either acute, subacute, or no bowel obstruction. Comparison of bowel sound characteristics was made between these subgroups of patients. In the presence of an obstruction, the site of obstruction was identified and bowel calibre was also measured to correlate with bowel sounds. RESULTS: A total of 71 patients were studied during the period July 2009 to January 2011. Forty patientshad acute bowel obstruction (27 small bowel obstruction and 13 large bowel obstruction), 11 had subacute bowel obstruction (eight in the small bowel and three in large bowel) and 20 had no bowel obstruction (diagnoses of other conditions were made). Twenty-five patients received surgical intervention (35.2%) during the same admission for acute abdominal conditions. A total of 426 recordings were made and 420 recordings were used for analysis. There was no significant difference in sound-to-sound interval, dominant frequency, and peak frequency among patients with acute bowel obstruction, subacute bowel obstruction, and no bowel obstruction. In acute large bowel obstruction, the sound duration was significantly longer (median 0.81 s vs 0.55 s, P = 0.021) and the dominant frequency was significantly higher (median 440 Hz vs 288 Hz, P = 0.003) when compared to acute small bowel obstruction. No significant difference was seen between acute large bowel obstruction and large bowel pseudoobstruction. For patients with small bowel obstruction, the sound-to-sound interval was significantly longer in those who subsequently underwent surgery compared with those treated non-operatively (median 1.29 s vs 0.63 s, P < 0.001). There was no correlation between bowel calibre and bowel sound characteristics in both acute small bowel obstruction and acute large bowel obstruction. CONCLUSION: Auscultation of bowel sounds is nonspecific for diagnosing bowel obstruction. Differences in sound characteristics between large bowel and small bowel obstruction may help determine the likely site of obstruction.

Soundscape Design of Old Urban Residential Districts

This paper introduced the concept of soundscape, investigated sound environment of old urban residential districts, and explored soundscape elements and soundscape designs. In view of current sound environment features of old urban residential districts and different population groups' preferences for different sound elements, this paper proposed soundscape design methods to improve sound environment of old urban residential districts.

Music and Performance as Sonic Acts of Political Struggle; Counter-Political Soundscapes in Urban Realm

The paper takes the relation between soundscapes and power struggles as its problem area and focuses on the role of music that is performed in public protests. It argues that music and street performances are conceived and therefore utilised as sonic acts of political struggle in urban realm. Starting with a general understanding of hearing mechanisms, the study elucidates the relationships among territoriality of soundscape, identity construction, social segregation and polarisation, and finally, power struggle. Within the framework of the intersection area of these concepts, the paper discusses the processes of politisation of soundscape through music as a form of protest event that is performed in public realm. Throughout the paper, it is focused on the significant cases of public protests as well as political events that occured in public space. The main emphasis is on the use of sound technologies to impose power on masses of people. The paper tackles the question of how the salient characteristics of soundscape are sonically adopted as means for counter-political acts in public realm.

Wavelet packet feature selection for lung sounds based on optimization

In this paper, a wavelet packet feature selection method for lung sounds based on optimization is proposed to obtain the best feature set which maximizes the differences between normal lung sounds and abnormal lung sounds(sounds with wheezes or rales). The proposed method includes two main steps: Firstly, the wavelet packet transform(WPT) is used to extract the original features of lung sounds; then the genetic algorithm(GA) is used to select the best feature set. The obtained optimal feature set is sent to four different classifiers to evaluate the performance of the proposed method. Experimental results show that the feature set obtained by the proposed method provides a higher classification accuracy of 94.6% in comparison with the best wavelet packet basis approach and multi-scale principal component analysis(PCA) approach. Meanwhile, the proposed method has effective generalization performance and can obtain the best feature set without priori knowledge of lung sounds.

A new method of lung sounds filtering using modulated least mean square—Adaptive noise cancellation

Advanced processing of lung sound (LS) recording is a significant means to separate heart sounds (HS) and combined low frequency noise from instruments (NI), with saving its characteristics. This paper proposes a new method of LS filtering which separates HS and NI simultaneously. It focuses on the application of least mean squares (LMS) algorithm with adaptive noise cancelling (ANC) technique. The second step of the new method is to modulate the reference input r1(n) of LMS-ANC to acquiesce combining HS and NI signals. The obtained signal is removed from primary signal (original lung sound recording-LS). The original signal is recorded from subjects and derived HS from it and it is modified by a band pass filter. NI is simulated by generating approximately periodic white gaussian noise (WGN) signal. The LMS-ANC designed algorithm is controlled in order to determine the optimum values of the order L and the coefficient convergence μ. The output results are measured using power special density (PSD), which has shown the effectiveness of our suggested method. The result also has shown visual difference PSD (to) normal and abnormal LS recording. The results show that the method is a good technique for heart sound and noise reduction from lung sounds recordings simultaneously with saving LS characteristics.

Fast Detection and Classification of Dangerous Urban Sounds Using Deep Learning

Video analytics is an integral part of surveillance cameras. Comparedto video analytics, audio analytics offers several benefits, includingless expensive equipment and upkeep expenses. Additionally, the volume ofthe audio datastream is substantially lower than the video camera datastream,especially concerning real-time operating systems, which makes it lessdemanding of the data channel’s bandwidth needs. For instance, automaticlive video streaming from the site of an explosion and gunshot to the policeconsole using audio analytics technologies would be exceedingly helpful forurban surveillance. Technologies for audio analytics may also be used toanalyze video recordings and identify occurrences. This research proposeda deep learning model based on the combination of convolutional neuralnetwork (CNN) and recurrent neural network (RNN) known as the CNNRNNapproach. The proposed model focused on automatically identifyingpulse sounds that indicate critical situations in audio sources. The algorithm’saccuracy ranged from 95% to 81% when classifying noises from incidents,including gunshots, explosions, shattered glass, sirens, cries, and dog barking.The proposed approach can be applied to provide security for citizens in openand closed locations, like stadiums, underground areas, shopping malls, andother places.

ST-LSTM-SA:A New Ocean Sound Velocity Field Prediction Model Based on Deep Learning

The scarcity of in-situ ocean observations poses a challenge for real-time information acquisition in the ocean.Among the crucial hydroacoustic environmental parameters,ocean sound velocity exhibits significant spatial and temporal variability and it is highly relevant to oceanic research.In this study,we propose a new data-driven approach,leveraging deep learning techniques,for the prediction of sound velocity fields(SVFs).Our novel spatiotemporal prediction model,STLSTM-SA,combines Spatiotemporal Long Short-Term Memory(ST-LSTM) with a self-attention mechanism to enable accurate and real-time prediction of SVFs.To circumvent the limited amount of observational data,we employ transfer learning by first training the model using reanalysis datasets,followed by fine-tuning it using in-situ analysis data to obtain the final prediction model.By utilizing the historical 12-month SVFs as input,our model predicts the SVFs for the subsequent three months.We compare the performance of five models:Artificial Neural Networks(ANN),Long ShortTerm Memory(LSTM),Convolutional LSTM(ConvLSTM),ST-LSTM,and our proposed ST-LSTM-SA model in a test experiment spanning 2019 to 2022.Our results demonstrate that the ST-LSTM-SA model significantly improves the prediction accuracy and stability of sound velocity in both temporal and spatial dimensions.The ST-LSTM-SA model not only accurately predicts the ocean sound velocity field(SVF),but also provides valuable insights for spatiotemporal prediction of other oceanic environmental variables.

The Apple and the Ear, Grasping Sounds in Space—A Theory of Sound Localization

Sensing the direction of origin of a sound in space has long been attributed to the delay between arrival times between the two ears. This, now discredited two dimensional theory, was put to rest by the observation that a person deaf in one ear can locate sounds in three dimensional space. We present here a new theory of sound localization that has the re-quired three dimensional measurement. It is a theory that interprets the well researched biological structure of the mammalian cochlea in a new and logical way, which leads to a deeper understanding of how sound localization functions.

OMP-BASED MULTI-BAND SIGNAL RECONSTRUCTION FOR ECOLOGICAL SOUNDS RECOGNITION

The paper proposes a new method of multi-band signal reconstruction based on Orthogonal Matching Pursuit(OMP),which aims to develop a robust Ecological Sounds Recognition(ESR)system.Firstly,the OMP is employed to sparsely decompose the original signal,thus the high correlation components are retained to reconstruct in the first stage.Then,according to the frequency distribution of both foreground sound and background noise,the signal can be compensated by the residual components in the second stage.Via the two-stage reconstruction,high non-stationary noises are effectively reduced,and the reconstruction precision of foreground sound is improved.At recognition stage,we employ deep belief networks to model the composite feature sets extracted from reconstructed signal.The experimental results show that the proposed approach achieved superior recognition performance on 60 classes of ecological sounds in different environments under different Signal-to-Noise Ratio(SNR),compared with the existing method.

Introduction to the Special Issue on Perspectives on Soundscape and Challenges of Noise Pollution: A Multidisciplinary Approach to Sustainable Environmental Solutions

Over the past two decades,research on the subject of noise pollution and urban soundscapes has seen significant growth[1,2].The goal of these studies was to gain a better understanding of the urban acoustic environment by employing various methodologies and techniques to delve into the complexity of this topic.These research efforts have primarily revolved around two fundamental axes[3].On one hand,the first axis focused on combating noise pollution[4–6],emphasizing the reduction of unwanted sounds and compliance with sound levels set by environmental and health protection organizations[7,8].

Snoring sounds’ statistical characteristics depend on anthropometric parameters

Snoring is common in people with obstructive sleep apnea (OSA). Although not every snorer has OSA or vice-versa, many studies attempt to use snoring sounds for classification of people into two groups of OSA and simple snorers. This paper discusses the relationship between snorers’ anthropometric parameters and statistical characteristics of snoring sound (SS) and also reports on classification accuracies of methods using SS features for screening OSA from simple snorers when anthropometric parameters are either matched or unmatched. Tracheal respiratory sounds were collected from 60 snorers simultaneously with full-night Polysomnography (PSG). Energy, formant frequency, Skewness and Kurtosis were calculated from the SS segments. We also defined and calculated two features: Median Bifrequency (MBF), and projected MBF (PMBF). The statistical relationship between the extracted features and anthropometric parameters such as height, Body Mass Index (BMI), age, gender, and Apnea-Hypopnea Index (AHI) were investigated. The results showed that the SS features were not only sensitive to AHI but also to height, BMI and gender. Next, we performed two experiments to classify patients with Obstructive Sleep Apnea (OSA) and simple snorers: Experiment A: a small group of participants (22 OSA and 6 simple snorers) with matched height, BMI, and gender were selected and classified using Na?ve Bayes classifier, and Experiment B: the same number of participants with unmatched height, BMI, and gender were chosen for classification. A sensitivity of 93.2% (87.5%) and specificity of 88.4% (86.3%) was achieved for the matched (unmatched) groups.

Recognition System for Diagnosing Pneumonia and Bronchitis Using Children’s Breathing Sounds Based on Transfer Learning

Respiratory infections in children increase the risk of fatal lung disease,making effective identification and analysis of breath sounds essential.However,most studies have focused on adults ignoring pediatric patients whose lungs are more vulnerable due to an imperfect immune system,and the scarcity of medical data has limited the development of deep learning methods toward reliability and high classification accuracy.In this work,we collected three types of breath sounds from children with normal(120 recordings),bronchitis(120 recordings),and pneumonia(120 recordings)at the posterior chest position using an off-the-shelf 3M electronic stethoscope.Three features were extracted from the wavelet denoised signal:spectrogram,mel-frequency cepstral coefficients(MFCCs),and Delta MFCCs.The recog-nition model is based on transfer learning techniques and combines fine-tuned MobileNetV2 and modified ResNet50 to classify breath sounds,along with software for displaying analysis results.Extensive experiments on a real dataset demonstrate the effectiveness and superior performance of the proposed model,with average accuracy,precision,recall,specificity and F1 scores of 97.96%,97.83%,97.89%,98.89%and 0.98,respectively,achieving superior performance with a small dataset.The proposed detection system,with a high-performance model and software,can help parents perform lung screening at home and also has the potential for a vast screening of children for lung disease.

Variation in the emission rate of sounds in a captive group of false killer whales Pseudorca crassidens during feedings: possible food anticipatory vocal activity?

This study examines whether a group of captive false killer whales(P seudorca crassidens) showed variations in the vocal rate around feeding times. The high level of motivation to express appetitive behaviors in captive animals may lead them to respond with changes of the behavioral activities during the time prior to food deliveries which are referred to as food anticipatory activity. False killer whales at Qingdao Polar Ocean World(Qingdao, China) showed signifi cant variations of the rates of both the total sounds and sound classes(whistles, clicks, and burst pulses) around feedings. Precisely, from the Transition interval that recorded the lowest vocalization rate(3.40 s/m/d), the whales increased their acoustic emissions upon trainers' arrival(13.08 s/m/d). The high rate was maintained or intensifi ed throughout the food delivery(25.12 s/m/d), and then reduced immediately after the animals were fed(9.91 s/m/d). These changes in the false killer whales sound production rates around feeding times supports the hypothesis of the presence of a food anticipatory vocal activity. Although sound rates may not give detailed information regarding referential aspects of the animal communication it might still shed light about the arousal levels of the individuals during different social or environmental conditions. Further experiments should be performed to assess if variations of the time of feeding routines may affect the vocal activity of cetaceans in captivity as well as their welfare.

Lung sounds auscultation technology based on ANC - ICA algorithm in high bat- tlefield noise environment

AIM:To explore the more accurate lung sounds auscultation technology in high battlefield noise environment.METHODS: In this study, we restrain high background noise using a new method-adaptive noise canceling based on independent component analysis (ANC-ICA), the method, by incorporating both second-order and higher-order statistics can remove noise components of the primary input signal based on statistical independence.RESULTS:The algorithm retained the local feature of lung sounds while eliminating high background noise, and performed more effectively than the conventional LMS algorithm.CONCLUSION:This method can cancel high battlefield noise of lung sounds effectively thus can help diagnose lung disease more accurately.

Sounds of Home

The weather had been unusually warm for May in Brandon, Miss. My wife Pat and I were nursing aSunday-morning cup of coffee on our deck and watch-ing thunderheads build rapidly into mountainous cloudson the southern horizon. There was barely any breeze,and the humidity was so thick you could almost rollit in your palms. By the time we finished our second cup, the

Sound Quality of Turn Indicator Sounds-Use of aMultidimensional Approach in the Automotive ProductDevelopment

Vehicle sounds are important factors of customer satisfaction and have a decisive influence on the product automobile andits quality impression. It becomes more and more important to connect customer requirements and technical specifications to developa vehicle sound with high quality. The turn indicator sound can be described as one sound, which gives the customer an importantfeedback of correct function performance and can be seen as one of the sounds, which play a role in the customer＇s perception ofvehicle quality. In a laboratory experimental study, the question was investigated, how a turn indicator sound must be designed to beperceived as pleasant and high-quality. A multidimensional approach was chosen to combine subjective customer assessments,objective psychophysiological responses of the study participants and physical parameters of the sounds. In total, 15 different tumindicator sounds were assessed by 48 subjects. The study shows how the connection of subjective and objective parameters cansupport product development. The multi-dimensional approach helps to derive recommendations for action to improve the soundquality of the product automobile. Also, the study shows a possibility to involve the human factor in a highly technical environment.