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BackgroundIn contrast with the technical progress of the stethoscope, lung sound terminology has remained confused, weakening the usefulness of auscultation. We examined how observer preferences regarding terminology and auscultatory skill influenced the choice of terms used to describe lung sounds.MethodsThirty-one staff physicians (SP), 65 residents (R) and 47 medical students (MS) spontaneously described the audio recordings of 5 lung sounds classified acoustically as: (1) normal breath sound; (2) wheezes; (3) crackles; (4) stridor and (5) pleural friction rub. A rating was considered correct if a correct term or synonym was used to describe it (term use ascribed to preference). The use of any incorrect terms was ascribed to deficient auscultatory skill.ResultsRates of correct sound identification were: (i) normal breath sound: SP=21.4%; R=11.6%; MS=17.1%; (ii) wheezes: SP=82.8%; R=85.2%; MS=86.4%; (iii) crackles: SP=63%; R=68.5%; MS=70.7%; (iv) stridor: SP=92.8%; R=90%; MS=72.1% and (v) pleural friction rub: SP=35.7%; R=6.2%; MS=3.2%. The 3 groups used 66 descriptive terms: 17 were ascribed to preferences regarding terminology, and 49 to deficient auscultatory skill. Three-group agreement on use of a term occurred on 107 occasions: 70 involved correct terms (65.4%) and 37 (34.6%) incorrect ones. Rate of use of recommended terms, rather than accepted synonyms, was 100% for the wheezes and the stridor, 55% for the normal breath sound, 22% for the crackles and 14% for the pleural friction rub.ConclusionsThe observers’ ability to describe lung sounds was high for the wheezes and the stridor, fair for the crackles and poor for the normal breath sound and the pleural friction rub. Lack of auscultatory skill largely surpassed observer preference as a factor determining the choice of terminology. Wide dissemination of educational programs on lung auscultation (eg, self-learning via computer-assisted learning tools) is urgently needed to promote use of standardised lung sound terminology.

Background: “Velcro-type” crackles on chest auscultation are considered a typical acoustic finding of Fibrotic Interstitial Lung Disease (FILD), however whether they may have a role in the early detection of these disorders has been unknown. This study investigated how “Velcro-type” crackles correlate with the presence of distinct patterns of FILD and individual radiologic features of pulmonary fibrosis on High Resolution Computed Tomography (HRCT).Methods: lung sounds were digitally recorded from subjects immediately prior to undergoing clinically indicated chest HRCT. Audio files were independently assessed by two chest physicians and both full volume and single HRCT sections corresponding to the recording sites were extracted. The relationships between audible “Velcro-type” crackles and radiologic HRCT patterns and individual features of pulmonary fibrosis were investigated using multivariate regression models.Results: 148 subjects were enrolled: bilateral “Velcro-type” crackles predicted the presence of FILD at HRCT (OR 13.46, 95% CI 5.85–30.96, p < 0.001) and most strongly the Usual Interstitial Pneumonia (UIP) pattern (OR 19.8, 95% CI 5.28–74. 25, p < 0.001). Extent of isolated reticulation (OR 2.04, 95% CI 1.62–2.57, p < 0.001), honeycombing (OR 1.88, 95% CI 1. 24–2.83, < 0.01), ground glass opacities (OR 1.74, 95% CI 1.29–2.32, p < 0.001) and traction bronchiectasis (OR 1.55, 95% CI 1.03–2.32, p < 0.05) were all independently associated with the presence of “Velcro-type” crackles.Conclusions: “Velcro-type” crackles predict the presence of FILD and directly correlate with the extent of distinct radiologic features of pulmonary fibrosis. Such evidence provides grounds for further investigation of lung sounds as an early identification tool in FILD.

Auscultation of the lung dates back more than 2000 years, but it has become a diagnostic tool since Laennec introduced the first form of the stethoscope [1]. The stethoscope is a widely used instrument to the extent that it has become the symbol of medical profession. Nevertheless, auscultation with a traditional stethoscope, which amplifies frequencies lower than 112 Hz and attenuates those higher than 112 Hz [2], is regarded to have low diagnostic value due to the attenuation of higher frequencies, which contain valuable diagnostic information regarding respiratory sounds, and due to the subjectivity involved in the evaluation of these sounds by medical doctors. With the advances in computer technology and signal processing algorithms, computerized analysis of respiratory sounds has provided new understanding in correlating lung sounds with diseases and disease states [3] and, also, in relating pulmonary acoustics with lung mechanics. Computational methods for the analysis of breath sounds offer additional advantages, such as digital storage, monitoring in critical settings, computer-supported analysis, comparison among different recordings, and provision of objective parameters in their evaluation. Despite this aroused research activity in breath sound processing and analysis, the main concern is in the standardization of the recording of breath sounds. Different approaches to record breath sounds have been reported in various publications, such as in [4–7]. Moreover, there have been efforts to offer guidelines for data acquisition equipment, such as the Computerized Respiratory Sound Analysis (CORSA) project financed by the European Community [8]; such efforts will eventually culminate in the commercial development of respiratory sound acquisition equipment, which will be accepted with general consensus by the medical community.

Snoring is one of the earliest symptoms of Obstructive Sleep Apnea (OSA). However, the unavailability of an objective snore definition is a major obstacle in developing automated snore analysis system for OSA screening. The objectives of this paper is to develop a method to identify and extract snore sounds from a continuous sound recording following an objective definition of snore that is independent of snore loudness. Nocturnal sounds from 34 subjects were recorded using a non-contact microphone and computerized data-acquisition system. Sound data were divided into non-overlapping training (n = 21) and testing (n = 13) datasets. Using training dataset an Artificial Neural Network (ANN) classifier were trained for snore and non-snore classification. Snore sounds were defined based on the key observation that sounds perceived as ‘snores’ by human are characterized by repetitive packets of energy that are responsible for creating the vibratory sound peculiar to snorers. On the testing dataset, the accuracy of ANN classifier ranged between 86 – 89%. Our results indicate that it is possible to define snoring using loudness independent, objective criteria, and develop automated snore identification and extraction algorithms.

Background: Breath sound analysis is one of the methods for examining the characteristics of recorded respiratory sounds. This approach may allow physicians to evaluate an abnormal breath sound precisely. To determine the usefulness of this approach to predict the length of symptomatic days of wheezy children, we evaluate the frequency spectrum of recorded breath sounds. Methods: We enrolled the subjects who were admitted with wheezy bronchitis or bronchial asthma. Breath sounds were recorded by electro stethoscope (Litmann Electronic Stethoscope Model 4100). We examined the relationship between wheezy sound frequency spectrum and the length of symptomatic days. Recorded sounds were analyzed using the sound analysis software (Litmann, version 1.0). Results: Eighteen subjects were enrolled in this study (median age, 2.9 y). The median length of symptomatic days were 6.0 days (IQR:1.0 to 10.0). Polyphonic wheezy subjects in an inspiration phase had more long symptomatic periods than monophonic wheezy subjects (r = 0.47, p = 0.04). A maximum value frequency detected at expiration phase were negatively correlated with the length of symptomatic days (r = -0.47, p = 0.049). Conclusions: Breath sound frequency analysis may be a useful objective method for determining the severity of wheezy symptom. Breath sound recording was easy to perform for children without harmful event, and this method could perform precise analysis of wheezy sounds.

Since breath sound (BS) contains important indicators of respiratory health and disease, analysis and detection of BS has become an important topic, with diagnostic and assessment of treatment capabilities. In this paper, the identification and classification of respiratory disorders based on the enhanced perceptual and cepstral feature set (PerCepD) is proposed. The hybrid PerCepD feature can capture the time-frequency characteristics of BS very well. Thus, it is very effective for the exploration and classification of normal and pathological BS related data. The classification models based on support vector machine (SVM) and artificial neural network (ANN) have been adopted to achieve automatic detection from BS data. The high detection accuracy results validate the performance of the proposed feature sets and classification model. The experimental results also demonstrate that the high accuracy of the pathological BS data can provide reliable diagnostic suggestions for breath disorders, such as flu, pneumonia and bronchitis.

To evaluate the effect of acute lung injury on the frequency spectra of breath sounds, we made serial acoustic recordings from nondependent, midlung and dependent regions of both lungs in ten 35- to 45-kg anesthetized, intubated, and mechanically ventilated pigs during development of acute lung injury induced with intravenous oleic acid in prone or supine position. Oleic acid injections rapidly produced severe derangements in the gas exchange and mechanical properties of the lung, with an average increase in venous admixture from 16 ± 12 to 62 ± 16% ( P < 0.01), and a reduction in dynamic respiratory system compliance from 25 ± 4 to 14 ± 4 ml/cmH2O ( P < 0.01). A concomitant increase in sound power was seen in all lung regions ( P < 0.05), predominantly in frequencies 150–800 Hz. The deterioration in gas exchange and lung mechanics correlated best with concurrent spectral changes in the nondependent lung regions. Acute lung injury increases the power of breath sounds likely secondary to redistribution of ventilation from collapsed to aerated parts of the lung and improved sound transmission in dependent, consolidated areas.

Objective: The main objective of this survey was to assess the efficiency of Comprehensive simulation program in Mock Code training among neonatal intensive care unit (NICU) nurses.Method: A survey consisting of five questions on the topic Mock Code training was conducted in Sim central at Texas Tech Health Sciences Center, Amarillo, TX. To gather feedback from Sim Central trainees we requested 47 NICU nurses from North West Texas Healthcare system to participate in the study and asked them to grade the satisfaction from 1 (being strongly disagree) to 5 (being strongly agree). No low ratings of 1 or 2 were submitted.Result: The summary of the response was: A. Compared with previous training, training received at Sim Central for Mock Codes provided a better learning experience? Evaluation grade/ responses: 3 (10.6%); 4(21.27%); 5 (63.8%). B. Does having a “patient” with “vital signs” such as “breath sound” and, “heart beat” enhanced the learning experience? Evaluation grade/ responses: 3(6.3%), 4 (25.5%); 5 (63.8%). C. Was there enough time to practice skills? Evaluation grade/ responses: 3 (2.1%), 4 (40.42%); 5 (57.44%). D. Was all the equipment needed for Mock Code training available? Evaluation grade/ responses: 3 (8.5%); 4 (40.42%); 5 (51%). E. Was orientation/training at Sim Central adequate for you to function in the NICU? Evaluation grade/ responses: 3(2.1%), 4 (40.42%); 5 (57.44%).Conclusion: From this survey, Comprehensive simulation program found to be effective and well received by NICU nurses for Mock Code training.

Background: Reliable symptom assessment is essential in asthma management. We developed new technology for analyzing breath sounds and assessed its clinical usefulness for monitoring asthmatic children. Methods: Eighty asthmatic children and 59 non-asthmatic children underwent breath sound analysis in an asymptomatic state. Their asthma control was assessed by the Asthma Control TestTM or Childhood ACTTM scores and divided into two groups, namely, well-controlled (perfect) (n = 19) and not well-controlled (not perfect) (n = 61). Breath sounds were recorded using two sensors, located on the right anterior chest and trachea. We calculated the acoustic transfer characteristics between the two points, which indicated the relationship between frequencies and attenuation during breath sound propagation. Two indices of sound parameters, the chest wall sound index (CWI) and the tracheal sound index (TRI), were calculated from the transfer characteristics and tracheal sounds. We also developed a new parameter, the breath sound index (BSI), on a 2-dimensional diagram of CWI and TRI and tried to determine whether BSI may clarify asthma control better than CWI or TRI alone. Results: There was a significant difference in TRI and BSI between asthmatic and non-asthmatic children (p = 0.007, p < 0.001). There was a significant difference in CWI and TRI between the well-controlled and not-wellcontrolled groups (p < 0.001). BSI discriminated between the two groups accurately (p < 0.001). The sensitivity and specificity of BSI for asthma control were 83.6% and 84.2%, respectively. Conclusions: Asthma control could be evaluated using a new index calculated from breath sound analysis.

Background: Auscultation is a medical procedure used for the initial diagnosis and assessment of lung and heart diseases. From this perspective, we propose assessing the performance of the extreme learning machine (ELM) classifiers for the diagnosis of pulmonary pathology using breath sounds. Methods: Energy and entropy features were extracted from the breath sound using the wavelet packet transform. The statistical significance of the extracted features was evaluated by one-way analysis of variance (ANOVA). The extracted features were inputted into the ELM classifier. Results: The maximum classification accuracies obtained for the conventional validation (CV) of the energy and entropy features were 97.36% and 98.37%, respectively, whereas the accuracies obtained for the cross validation (CRV) of the energy and entropy features were 96.80% and 97.91%, respectively. In addition, maximum classification accuracies of 98.25% and 99.25% were obtained for the CV and CRV of the ensemble features, respectively. Conclusion: The results indicate that the classification accuracy obtained with the ensemble features was higher than those obtained with the energy and entropy features.

Sleep apnea syndrome is hard to be found and it may cause a serious problem if the state is gradually worsened. Since, sleep apnea syndrome applies big load on the heart, brain and blood vessel, the merging the risk of cardiovascular disease is higher compared to healthy individuals. According to the study over 18 years in the United States, the mild SAS patients, whose apnea-hypopnea index (AHI) is higher than 5, has the 5.2 times higher risk of death from cardiovascular disease than the normal persons. Nocturnal polysomnography (PSG) is the only equipment used in hospital to monitor and quantify the number of respiratory events (i.e., obstructive, central, or complex) and the resultant hypoxemia and arousals related to the respiratory events or even independent of the respiratory events. However, night-to-night variability may exist in patients who have a high probability but a low apnea index. Further, it costs a lot and influences the normal sleeping state The author presents a novel method and simple system to monitor the SAS events by measuring the breathing sound. The monitoring system consists of a wireless microphone and smartphone for breathing sound measurement and data transmission to the data analysis server. The breathing sound will be recorded during the whole night sleeping and the result will be reported when he/she waked up in the morning. The proposed system and method are validated with comparisons of the commercial devices such as the SleepScan made by Tanita and Sleep Meter by Omuron. Furthermore, the results obtained by our system has good agreement with the results obtained by the nocturnal polysomnography in the hospital. The proposed system shows the high potential to be a convenient device to monitor sleep apnea events and sleep state at home.

Background: Respiratory tract infections are the major cause of morbidity and mortality among children and are responsible for around 12 million under-five deaths in a year. Objective: The aim of this study is to evaluate the effectiveness of breathing exercises as play method on cardiopulmonary parameters among children with acute respiratory tract infections. Materials and Methods: True experimental pretest and posttest design was used. A total of 67 children aged 3–12 years having acute respiratory tract infections were randomly divided into experimental (34) and control group (33). The data collection tool included baseline information of participants, cardiopulmonary parameters assessment, and pediatric dyspnea scale. Pretest data were collected in both the groups. Balloon blowing breathing exercises were administered to the experimental group. Post interventional cardiopulmonary parameters were assessed after 1 week in both the groups. Result: The mean posttest heart rate (96.47), respiration rate (25.47), and Spo2 (95.2) of the experimental group was significantly near to normal as compared to control group. The mean posttest cough score (1.62), breath sound score (2.18), chest expansion score (2.16), and dyspnea score (3.74) of experimental group was significantly low than the control group. There was significant association between cardiopulmonary parameters and selected demographic variables. Conclusion: Breathing exercises have advantageous effects on cardiopulmonary parameters among children with acute respiratory infections.

Background: Vibration response imaging (VRI) is a new technology that provides a radiation-free dynamic lung image from the vibrations produced by airflow. The vibration energy from the respiratory cycle can be quantified for any lung region. This is obtained by integrating the energy profiles from 42 acoustic sensors placed on the patient’s back. Objective: The aim of this study was to evaluate whether a physician trained in interpreting acoustic images can accurately distinguish between normal and abnormal breath sound distribution in patients with pneumothoraces compared to healthy controls. Methods: In total, 14 patients with spontaneous or iatrogenic pneumothoraces and 15 healthy volunteers were enrolled in the study. VRI recordings and physical examination were obtained in all cases. Chest radiographs (CXRs) were reviewed in patients with pneumothoraces. Dynamic images recorded during one complete respiratory cycle were analyzed for each subject. Results: The VRI images of patients with pneumothoraces demonstrated a reduction in the vibration response on the affected side which correlated with the CXR. Sensitivity was 100%, specificity was 87% and positive predictive value and negative predictive value were 86 and 100%, respectively. Conclusions: This study demonstrates that VRI can be safely used to assess patients with pneumothoraces. Moreover, the analysis of breath sound distribution images obtained noninvasively can distinguish between patients with a normal chest examination from those with pneumothoraces.

Background: Computerized analysis of the regional distribution of breath sound intensity during respiration has generated interest as a possible diagnostic modality. Objectives: We hypothesized that pleural effusions would create a dependent region of absent breath sounds and thus vibration response imaging (VRI) could be used in the detection of such pleural effusions. Methods: A prospective, single-blinded and open-labeled trial was carried out, and VRI recordings were compared to upright chest X-rays, bedside ultrasound examinations and volume of fluid drained via thoracentesis. VRI images were interpreted by a physician who was blinded to the patients’ clinical history, physical examination and diagnostic tests. Quantitative assessment of pleural effusion size in the VRI images was performed by ImageJ software and an automatic pixel count analysis. Results: VRI recordings were performed on 57 consecutive patients and correctly predicted the diagnosis in 45 cases (45/56, 80%) as compared to chest X-rays. The calculated sensitivity, specificity, positive predictive value and negative predictive value for diagnosis of pleural effusion were 86% (62/72), 93% (37/40), 95% (62/65) and 79% (37/47), respectively, in a per-hemithorax analysis. In the quantification of effusion size, there were high correlations between VRI images and chest X-ray area as assessed by ImageJ (r = 0.67) and pixel count (r = –0.77). The level of agreement between VRI readings and ultrasonography was 75% (41/55), and correlation with the volume of fluid drained in therapeutic thoracentesis was moderate (r = –0.49). No side effects from the VRI recordings were documented. Conclusions: VRI can be used to detect and quantify pleural effusions.

Foreign body aspiration is a common problem in toddlers and school-aged children. Bronchoscopy is required to make a definitive diagnosis of foreign body aspiration. We examined dynamic images of breath sound distribution in 6 children with suspected foreign body aspiration (4 boys, 2 girls; age range, 9 to 14 y) and 3 healthy controls (1 boy, 2 girls; age range, 11 to 13 y). Three of the children with suspected foreign body aspiration had an abnormal distribution of breath sounds between their right and left lungs in both the images and quantitative assessments; bronchoscopy confirmed the presence of a foreign body. Two plastic pen caps and an irregularly shaped plastic piece were removed from the obstructed bronchi. The other 3 children showed a normal distribution, and bronchoscopy was negative for the presence of a foreign body. Healthy controls also demonstrated normal (relatively symmetrical) distribution of breath sounds in both lungs. Visualization of breath sounds provides an objective qualitative and quantitative method that may identify abnormalities in breath sound distribution that occur in cases of foreign body aspiration. Further research is warranted to establish the sensitivity and specificity of this technique.

Wheezing is a whistling sound which is made during the inspiratory or expiratory phase. By definition, wheezes are continuous sounds longer than 250 msec in duration and are higher pitched and of more musical quality than rhonchi. These are commonly found in patients with asthma and although the mechanisms are not entirely clear, consensus on it originating from obstruction is evident. Most patients, and even a few physicians, believe that wheezing is synonymous with asthma. However, there are multiple conditions that produce this specific breath sound. We report a case of a patient who was misdiagnosed with asthma.

Obstructive sleep apnea (OSA) is a widespread disorder that is cumbersome to diagnose using the goldstandard, overnight polysomnography (PSG). This paper highlights further development of our Awake-OSA method for predicting whether someone has severe sleep apnea using breath sounds recorded during wakefulness. We propose the use of an expert classification approach that consists of individual majority-voting classifiers. Each classifier is trained to distinguish one class of subject from all other classes. The outcomes of these classifiers are, in turn, combined using a truth matrix to determine the final outcome. Using the breath sound features of 249 subjects, the classifiers attempted to classify 180 subjects as either non-OSA (AHI less than 5) or severe-OSA (AHI greater than 30). 79% and 75% of OSA and non-OSA subjects, respectively, could be classified. Of those classified, the resultant testing sensitivity and specificity were found to be 78% and 86%, respectively. The consistency of the testing to training accuracies indicates the robustness and generalizability of using multiple expert classifiers on the dataset. This technique has the potential to be used in a doctor's office to rapidly and cheaply pre-screen for OSA, so that physicians may be better able to determine which patients are in need of overnight PSG.

Obstructive Sleep Apnea (OSA) is currently diagnosed by a full nocturnal polysomnography (PSG), a very expensive and time-consuming method. In previous studies we were able to distinguish patients with OSA through formant frequencies of breath sound during sleep. In this study we aimed at identifying OSA patients from breath sound analysis during wakefulness. The respiratory sound was acquired by a tracheal microphone simultaneously to PSG recordings. We selected several cycles of consecutive inspiration and exhalation episodes in 10 mild-moderate (AHI=30) OSA patients during their wake state before getting asleep. Each episode's formant frequencies were estimated by linear predictive coding. We studied several formant features, as well as their variability, in consecutive inspiration and exhalation episodes. In most subjects formant frequencies were similar during inspiration and exhalation. Formant features in some specific frequency band were significantly different in mild OSA as compared to severe OSA patients, and showed a decreasing correlation with OSA severity. These formant characteristics, in combination with some anthropometric measures, allowed the classification of OSA subjects between mild-moderate and severe groups with sensitivity (specificity) up to 88.9% (84.6%) and accuracy up to 86.4%. In conclusion, the information provided by formant frequencies of tracheal breath sound recorded during wakefulness may allow identifying subjects with severe OSA.

The transpulmonary speed of sound input at the mouth has been shown to vary with lung volume. To avoid the disadvantages that exist in certain clinical situations in inputting sound at the mouth, we input sound in the supraclavicular space of 21 healthy volunteers to determine whether similar information on the relationship of sound speed to lung volume could be obtained. We measured the transit time at multiple microphones placed over the chest wall using a 16-channel lung sound analyzer (Stethographics). There was a tight distribution of transit times in this population of subjects. At functional residual capacity, it was 9 ± 1 (SD) ms at the apical sites and 13 ± 1 ms at the lung bases. The sound speed at total lung capacity was 24 ± 2 m/s and was 22 ± 2 m/s at residual volume ( P < 0.001). In all subjects, the speed of sound was faster at higher lung volume. This improved method of studying the mechanism of sound transmission in the lung may help in the development of noninvasive tools for diagnosis and monitoring of lung diseases.

An algorithm based on the two-channel processing and the confluence analysis techniques is proposed for obtaining the averaged spectra of additive components of a nonstationary random signal. A numerical experiment demonstrating the efficiency of the proposed algorithm is conducted. The algorithm is tested using a real signal recorded at a human thorax and including breath and cardiac sounds and a stationary background noise.

The author studied the intensity and frequency content of breath sounds in sixteen patients with pulmonary emphysema as compared with those in normal controls. The patients were clinically diagnosed with pulmonary emphysema with moderate to severe obstructive ventilatory impairment (FEV 1/FVC 50.7±10.5%) on pulmonary function test and with low attenuation area suggestive of emphysema on CT. Control subjects were eight non-smoking healthy men with normal pulmonary function test. Recording of breath sounds was done with the subjects in a sitting position for thirty seconds with various flow rates. Flow rate was measured by pneumotachometer via mouth. ECG was recorded simultaneously to eliminate contamination by heart sounds. The signals of breath sounds at six sites on the chest wall were through an amplifier and a band-pass filter (0.1 to 2000 Hz), and digitized to be recorded on a personal computer with signal of flow rate and ECG. Power spectra of the breath sounds for inspiratory blocks of 102. 4 msec when the flow rate was 350ml/sec (low flow rate) or 700ml/sec (high flow rate) were obtained using fast Fourier transform. The segments which were contaminated by heart sounds and various noise were excluded by inspection of sound-spectrogram. Total power was calculated by the sum of power spectra within bandwidth of 100-2000 Hz. Parameters of F25, F50, F75, F95, and H/M power ratio (H : 400～800 Hz, M: 200～400 Hz) were calculated to quantify distribution of frequency spectra of breath sounds. Total power of breath sounds was increased･on upper lung fields at both flow rates and on lower lung fields at low flow rate as compared with those in normal controls. H/M ratio was increased on upper lung fields at both flow rates and on lower lung fields at high flow rate. F95 was increased on both lung fields at high flow rate. In conclusion, at a given flow rate the breath sounds in a patient with emphysema do not decrease in intensity but may increase in frequency. This fact suggests that decreased breath sounds in patients with pulmonary emphysema is due to decreased flow rate.

UNLABELLED Although the esophageal stethoscope has been used for many years, the effect of the depth of placement on the quality of the sounds obtained has never been investigated. The amplitude and frequency characteristics of the first and second heart sound and of inspiratory and expiratory breath sounds were determined at various stethoscope depths (from the distal tip) in 17 healthy anesthetized adults. The amplitude for each type of sound varied markedly with depth. Maximal amplitude for S1 was at 34 +/- 3 cm, for S2 at 27 +/- 2 cm, for inspiratory breath sound at 28 +/- 2 cm, and for expiratory breath sound at 26 +/- 2 cm. There was a positive linear correlation between the depth of maximal amplitude of these sounds and patient height. Peak frequency, in general, did not change with depth. We conclude that investigators should measure and document depth when performing studies involving the esophageal stethoscope. IMPLICATIONS Analysis of sound from the esophageal stethoscope at various depths reveals that placement depth greatly affects the sounds. A depth of 28-32 cm is recommended for clinical use; S1, S2, and inspiratory and expiratory sounds have a high amplitude in that range.

Breath sounds heard with a stethoscope over homologous sites of both lungs in healthy subjects are presumed to have similar characteristics. Passively transmitted sounds introduced at the mouth, however, are known to lateralise, with right-over-left dominance in power at the anterior upper chest. Both spontaneous breath sounds and passively transmitted sounds are studied in four healthy adults, using contact sensors at homologous sites on the anterior upper and posterior lower chest. At standardised air flow, breath sound intensity shows a right-over-left dominance at the anterior upper chest, similar to passively transmitted sounds. At the posterior lung base, breath sounds are louder on the left, with a trend to similar lateralisation in transmitted sounds. It is likely that the observed asymmetries are related to the effects of cardiovascular structures and airway geometry on sound generation and transmission.

Study objective: To determine whether the environment of a moving ambulance affects the ability of out-of-hospital care providers to auscultate breath sounds. Methods: Out-of-hospital care providers assessed breath sounds with a previously described breath-sounds model in a quiet environment (control) and in a moving ambulance. The setting was a nonurban emergency medical services system and an interhospital transport agency based at a 600-plus-bed tertiary care center. The participants were physicians, transport nurses, and advanced life support EMS providers routinely involved in the emergency out-of-hospital treatment and transportation of the ill and injured. The accuracy with which participants identified the presence or absence of breath sounds in the two environments was compared with the use of the χ 2 test, with the α-value set at .05. Results: The accuracy of breath-sounds assessment in the control environment was 96% (251 of 260); the sensitivity was 96% and the specificity 97%. The accuracy of breath-sounds assessment in the experimental environment was 54% (140 of 260); the sensitivity was .09% and the specificity 98%. Participants were significantly less likely to hear breath sounds in the moving ambulance than in the quiet room ( P Conclusion: Assessment of breath sounds is hampered by the environment of a moving ambulance. [Brown LH, Gough JE, Bryan-Berg DM, Hunt RC: Assessment of breath sounds during ambulance transport. Ann Emerg Med February 1997;29:228-231.]

Detecting aspiration after swallows (the entry of bolus into trachea) is often a difficult task particularly when the patient does not cough; those are called silent aspiration. In this study, the application of acoustical analysis in detecting silent aspiration is investigated. We recorded the swallowing and the breath sounds of 10 individuals with swallowing disorders, who demonstrated silent aspiration during the fiberoptic endoscopic evaluation of swallowing (FEES) assessment. We analyzed the power spectral density (PSD) of the breath sound signals following each swallow; the PSD showed higher magnitude at low frequencies for the breath sounds following an aspiration. Therefore, we divided the frequency range below 300 Hz into 3 sub-bands, over which we calculated the average power as the characteristic features for the classification purpose. Then, the fuzzy k-means unsupervised classification method was deployed to find the two clusters in the data set: the aspirated and non-aspirated groups. The results were evaluated using the FEES assessments provided by the speech language pathologists. The results show 82.3% accuracy in detecting swallows with silent aspiration. Although the proposed method should be verified on a larger dataset, the results are promising for the use of acoustical analysis as a clinical tool to detect silent aspiration.

Summary This article provides a brief review of various methodologies adopted for the analysis of lung sounds. The speculated source of lung sound production and the confusion surrounding current sound description terminology are discussed. The aim of this article is to arouse physiotherapists' interest in sound interpretation, as well as in research into this area.

This research paper describes the study of the breath sound analysis and application development for two fold purposes: (1) for diseases diagnosis tool, specifically the lung and respiratory diseases; and (2) for stress measurement. The Lung Diseases Diagnosis Application consists of software and hardware components which formed a first-hand diagnosis tool of the lung diseases, to support the paramedics/doctors in lung diseases diagnosis. One of the most common and proven technique for lung disease diagnosis is the Auscultation, the term refers to listening to the internal sounds of the respiratory system (breath sounds), usually by using a stethoscope. However, this diagnosis way depends heavily on the listening skills and judgment of the paramedics/doctors only. In this research, we develop a new diagnosis tool with a new front-end equipment for the lung diseases diagnosis application: hand-phone equipped with a highly sensitive microphone, to record the breath-sound. This recorded breath-sound is then processed to generate the lung sound, which is approximately similar with the lung-sound heard and recorded by a stethoscope and the lung-sound is analysed to diagnose whether the patient's lung is healthy or not. This application would hopefully support the paramedics/doctors mobility to rural areas as well as remote diagnosis. The requirement is to create a first-hand diagnostic tool which allows paramedic mobility to rural areas for lung diseases diagnostic purposes. The study first describes about the existing lung diseases diagnostic tools and researches, stress measurement, then analysis and preliminary design of lung diseases diagnosis application. To enable each Lung Health Public Council (Balai Besar Kesehatan Paru Masyarakat) and Hospitals as data owner to maintain its own data, and to provide the data confidentiality as well, the cloud computing environment is then applied. Hopefully this research could serve both purposes: the lung diseases diagnosis and stress measurement.

Summary Background We created a system that allows the visualization of breath sounds (visual stethoscope). Aim We compared the visual stethoscope technique with auscultation for the detection of bronchial intubation in pediatric patients. Methods In the auscultation group, an anesthesiologist advanced the tracheal tube, while another anesthesiologist auscultated bilateral breath sounds to detect the change and/or disappearance of unilateral breath sounds. In the visualization group, the stethoscope was used to detect changes in breath sounds and/or disappearance of unilateral breath sounds. The distance from the edge of the mouth to the carina was measured using a fiberoptic bronchoscope. Results Forty pediatric patients were enrolled in the study. At the point at which irregular breath sounds were auscultated, the tracheal tube was located at 0.5 ± 0.8 cm on the bronchial side from the carina. When a detectable change of shape of the visualized breath sound was observed, the tracheal tube was located 0.1 ± 1.2 cm on the bronchial side (not significant). At the point at which unilateral breath sounds were auscultated or a unilateral shape of the visualized breath sound was observed, the tracheal tube was 1.5 ± 0.8 or 1.2 ± 1.0 cm on the bronchial side, respectively (not significant). Conclusions The visual stethoscope allowed to display the left and the right lung sound simultaneously and detected changes of breath sounds and unilateral breath sound as a tracheal tube was advanced.

In this study, the feasibility of acoustical analysis for detection of swallowing silent aspiration is investigated. As a pilot study, we analyzed the breath sounds of 21 dysphagic individuals, 11 of which demonstrated aspiration during the fiberoptic endoscopic evaluation of swallowing (FEES) or videofluoroscopic swallowing study (VFSS). We found that the low frequency components of the power spectrum of the breath sounds after a swallow show higher magnitude when there is aspiration. Thus, we divided the frequency range below 300 Hz into three sub-bands and calculated the average power of the breath sound signal in each sub-band as the characteristic features for the stage 1 classification into two groups of aspirated and non-aspirated patients. Then, for the aspirated group, the unsupervised fuzzy k-means clustering algorithm was deployed to label the breath sounds immediately after a swallow as aspiration or non-aspiration. The results were compared with the FEES/VFSS assessments provided by the speech language pathologists. The results are encouraging: more than 86 % accuracy in detection of silent aspiration. While the proposed method should be verified on a larger dataset, the results are promising for the use of acoustical analysis as a clinical tool to detect silent aspiration.

Changes in lung water are known to change breath sound acoustics [1]. Using two pig models, we observed that continuous elevation of lung sound amplitude may indicate an increase in total lung water content [2]. Here we report three cases of ventilated patients in whom continuous acoustic monitoring was done during extravascular lung water (EVLW) measurements.

Speech has been previously investigated as means of gaining insight into certain psychiatric disorders. Correlation has been found between temporal characteristics of speech and negative symptoms associated with schizophrenia. However the presence of breath sounds in speech may lead to a decreased performance of classification between patient and control groups. This study presents an algorithmic approach for both breath sounds detection and removal, and also analyses its impact on the ability of a Linear Discriminant Analysis (LDA) classifier to discriminate between schizophrenic patients and control subject. Results demonstrate that more accurate feature extraction yielded to a 6.7% increase in discrimination ability from 67.5% to 74.2% to differentiate between schizophrenic patients and control subject based on speech alone.

Separation of heart and lung sounds from breath sound recordings is a challenging task due to the temporal and spectral overlap of the two signals. In this paper, the use of a spectro-temporal representation to improve signal separation is investigated. The representation is obtained by means of a frequency decomposition (termed modulation frequency) of temporal trajectories of short-term spectral components. Experiments described herein suggest that improved separability of heart (HS) and lung sounds (LS) is attained in the modulation frequency domain. Bandpass and bandstop modulation filters are designed to separate HS and LS signals from breath sound recordings, respectively. Visual and auditory inspection, quantitative analysis, as well as algorithm execution time are used to assess algorithm performance. Log-spectral distances below 1 dB corroborate our listening test which found no audible artifacts in separated heart and lung sound signals.

While lung sounds intensity is significantly different during inspiratory and expiratory phases, such difference is not audible between the two respiratory phases when listening to tracheal breath sounds. In this study we investigated whether any difference exists between the average power and log-variance of the band-pass filtered tracheal breath sound between the respiratory phases. We used data from 9 healthy subjects without any pulmonary diseases at 4 different flow rates (low, medium, high and very high) and compared the two features at six different frequency ranges from 70 to 1200Hz. The most pronounced differences between the two respiratory phases were found in the 300-450Hz and 800-1000Hz for the average power and log-variance, respectively.

Background Perfusion scintigraphy has been used to evaluate and monitor graft function in single-lung transplant recipients. In this study, our objective was to determine whether quantitatively measured regional breath sounds, using a computerized breath sound analysis device, correlate with the standard methods of monitoring graft function used at our facility for single-lung transplant patients. Methods Quantitative regional breath sound measurements (QLD VRI) were performed with a VRIxp device on 37 single-lung transplant patients, who underwent perfusion scans and lung function testing for routine follow-up. The measurements were conducted on the same day. Results Using a quantitative output based on objectively measured breath sounds, we established that there is good correlation (0.73) between QLD VRI and perfusion measurements for the grafted lung. Moreover, no significant differences were found between the two measurements ( p = 0.898, t -test for paired data). In addition, the fraction of forced expiratory volume in 1 second (FEV 1 ; liters) of the graft lung was measured twice: once as a function of FEV 1 and regional perfusion and then as a function of FEV 1 and QLD VRI. High correlation ( r = 0.88) and no significant differences ( p = 0.72) were found between FEV 1tx (perfusion) and FEV 1tx (QLD VRI). Absolute error was 0.13 liter and the root-mean-square error (RMSE) was 0.17 liter. Conclusions Objectively measured breath sound distribution in single-lung transplant patients can be readily quantified and correlated with graft function measurements. The method is quick and non-invasive and may provide useful information to aid clinicians in managing single-lung transplant patients.

We describe a method based on the acoustical analysis of breath sound for helping the diagnosis of lung diseases. The signal is recorded at the trachea simultaneously with the flow rate at several values of this one. The signal is digitized at the rate of 5120 Hz, FFTs are taken on 1024 sample blocks over a duration of about 30 seconds. We plot versus time the mean frequency and the mean amplitude of the spectrum, the flow rate and it slope. We compute also the mean frequency spectrum. We obtain two kinds of results. First the variations of the mean frequency in function of the flow rate give us informations on the breath sound formation. Second the differences in mean frequency spectra between asthmatic and normal subjects give us a good discrimination of the two cases.