Breath sounds

Tunable Q-factor wavelet transform based lung signal decomposition and statistical feature extraction for effective lung disease classification

The auscultation is a non-invasive and cost-effective method used for the diagnosis of lung diseases, which are one of the leading causes of death worldwide. However, the efficacy of the auscultation suffers from the limitations of the analog stethoscopes and the subjective nature of human interpretation. To overcome these limitations, the accurate diagnosis of these diseases by employing the computer based automated algorithms applied to the digitized lung sounds has been studied for the last decades. This study proposes a novel approach that uses a Tunable Q-factor Wavelet Transform (TQWT) based statistical feature extraction followed by individual and ensemble learning model training with the aim of lung disease classification. During the learning stage various machine learning algorithms are utilized as the individual learners as well as the hard and soft voting fusion approaches are employed for performance enhancement with the aid of the predictions of individual models. For an objective evaluation of the proposed approach, the study was structured into two main tasks that were investigated in detail by using several sub-tasks to comparison with state-of-the-art studies. Among the sub-tasks which investigates patient-based classification, the highest accuracy obtained for the binary classification was achieved as 97.63% (healthy vs. non-healthy), while accuracy values up to 66.32% for three-class classification (obstructive-related, restrictive-related, and healthy), and 53.42% for five-class classification (asthma, chronic obstructive pulmonary disease, interstitial lung disease, pulmonary infection, and healthy) were obtained. Regarding the other sub-task, which investigates sample-based classification, the proposed approach was superior to almost all previous findings. The proposed method underscores the potential of TQWT based signal decomposition that leverages the power of its adaptive time-frequency resolution property satisfied by Q-factor adjustability. The obtained results are very promising and the proposed approach paves the way for more accurate and automated digital auscultation techniques in clinical settings.

Deep learning-based lung sound analysis for intelligent stethoscope

Auscultation is crucial for the diagnosis of respiratory system diseases. However, traditional stethoscopes have inherent limitations, such as inter-listener variability and subjectivity, and they cannot record respiratory sounds for offline/retrospective diagnosis or remote prescriptions in telemedicine. The emergence of digital stethoscopes has overcome these limitations by allowing physicians to store and share respiratory sounds for consultation and education. On this basis, machine learning, particularly deep learning, enables the fully-automatic analysis of lung sounds that may pave the way for intelligent stethoscopes. This review thus aims to provide a comprehensive overview of deep learning algorithms used for lung sound analysis to emphasize the significance of artificial intelligence (AI) in this field. We focus on each component of deep learning-based lung sound analysis systems, including the task categories, public datasets, denoising methods, and, most importantly, existing deep learning methods, i.e., the state-of-the-art approaches to convert lung sounds into two-dimensional (2D) spectrograms and use convolutional neural networks for the end-to-end recognition of respiratory diseases or abnormal lung sounds. Additionally, this review highlights current challenges in this field, including the variety of devices, noise sensitivity, and poor interpretability of deep models. To address the poor reproducibility and variety of deep learning in this field, this review also provides a scalable and flexible open-source framework that aims to standardize the algorithmic workflow and provide a solid basis for replication and future extension: https://github.com/contactless-healthcare/Deep-Learning-for-Lung-Sound-Analysis .

Multifractal test for nonlinearity of interactions across scales in time series

The creativity and emergence of biological and psychological behavior tend to be nonlinear, and correspondingly, biological and psychological measures contain degrees of irregularity. The linear model might fail to reduce these measurements to a sum of independent random factors (yielding a stable mean for the measurement), implying nonlinear changes over time. The present work reviews some of the concepts implicated in nonlinear changes over time and details the mathematical steps involved in their identification. It introduces multifractality as a mathematical framework helpful in determining whether and to what degree the measured series exhibits nonlinear changes over time. These mathematical steps include multifractal analysis and surrogate data production for resolving when multifractality entails nonlinear changes over time. Ultimately, when measurements fail to fit the structures of the traditional linear model, multifractal modeling allows for making those nonlinear excursions explicit, that is, to come up with a quantitative estimate of how strongly events may interact across timescales. This estimate may serve some interests as merely a potentially statistically significant indicator of independence failing to hold, but we suspect that this estimate might serve more generally as a predictor of perceptuomotor or cognitive performance.

Regularity and mechanism of fake crackle noise in an electronic stethoscope

Background: Electronic stethoscopes are widely used for cardiopulmonary auscultation; their audio recordings are used for the intelligent recognition of cardiopulmonary sounds. However, they generate noise similar to a crackle during use, significantly interfering with clinical diagnosis. This paper will discuss the causes, characteristics, and occurrence rules of the fake crackle and establish a reference for improving the reliability of the electronic stethoscope in lung auscultation. Methods: A total of 56 participants with healthy lungs (no underlying pulmonary disease, no recent respiratory symptoms, and no adventitious lung sound, as confirmed by an acoustic stethoscope) were enrolled in this study. A 30-s audio recording was recorded from each of the nine locations of the larynx and lungs of each participant with a 3M Littmann 3200 electronic stethoscope, and the audio was output in diaphragm mode and auscultated by the clinician. The doctor identified the fake crackles and analyzed their frequency spectrum. High-pass and low-pass filters were used to detect the frequency distribution of the fake crackles. Finally, the fake crackle was artificially regenerated to explore its causes. Results: A total of 500 audio recordings were included in the study, with 61 fake crackle audio recordings. Fake crackles were found predominantly in the lower lung. There were significant differences between lower lung and larynx (p < 0.001), lower lung and upper lung (p = 0.005), lower lung and middle lung (p = 0.005), and lower lung and infrascapular region (p = 0.027). Furthermore, more than 90% of fake crackles appeared in the inspiratory phase, similar to fine crackles, significantly interfering with clinical diagnosis. The spectral analysis revealed that the frequency range of fake crackles was approximately 250-1950 Hz. The fake crackle was generated when the diaphragm of the electronic stethoscope left the skin slightly but not completely. Conclusion: Fake crackles are most likely to be heard when using an electronic stethoscope to auscultate bilateral lower lungs, and the frequency of a fake crackle is close to that of a crackle, likely affecting the clinician's diagnosis.

Multi-task learning neural networks for breath sound detection and classification in pervasive healthcare

With the emergence of many grave Chronic obstructive pulmonary diseases (COPDs) and the COVID-19 pandemic, there is a need for timely detection of abnormal respiratory sounds, such as deep and heavy breaths. Although numerous efficient pervasive healthcare systems have been proposed for tracking patients, few studies have focused on these breaths. This paper presents a method that supports physicians in monitoring in-hospital and at-home patients by monitoring their breath. The proposed method is based on three deep neural networks in audio analysis: RNNoise for noise suppression, SincNet - Convolutional Neural Network, and Residual Bidirectional Long Short-Term Memory for breath sound analysis at edge devices and centralized servers, respectively. We also developed a pervasive system with two configurations: (i) an edge architecture for in-hospital patients; and (ii) a central architecture for at-home ones. Furthermore, a dataset, named BreathSet, was collected from 27 COPD patients being treated at three hospitals in Vietnam to verify our proposed method. The experimental results demonstrated that our system efficiently detected and classified breath sounds with F1-scores of 90% and 91% for the tiny model version on low-cost edge devices, and 90% and 95% for the full model version on central servers, respectively. The proposed system was successfully implemented at hospitals to help physicians in monitoring respiratory patients in real time.

Seeking Inspiration: Examining the Validity and Reliability of a New Smartphone Respiratory Therapy Exergame App

Background: Clinically valid and reliable simulated inspiratory sounds were required for the development and evaluation of a new therapeutic respiratory exergame application (i.e., QUT Inspire). This smartphone application virtualises incentive spirometry, a longstanding respiratory therapy technique. Objectives: Inspiratory flows were simulated using a 3 litre calibration syringe and validated using clinical reference devices. Syringe flow nozzles of decreasing diameter were applied to model the influence of mouth shape on audible sound levels generated. Methods: A library of calibrated audio inspiratory sounds was created to determine the reliability and range of inspiratory sound detection at increasing distances separating the sound source and smartphones running the app. Results: Simulated inspiratory sounds were reliably detected by the new application at higher air inflows (high, medium), using smaller mouth diameters (<25 mm) and where smartphones were held proximal (≤5 cm) to the mouth (or at distances up to 50 cm for higher airflows). Performance was comparable for popular smartphone types and using different phone orientations (i.e., held horizontally, at 45° or 90°). Conclusions: These observations inform future application refinements, including prompts to reduce mouth diameter, increase inspiratory flow and maintain proximity to the phone to optimise sound detection. This library of calibrated inspiratory sounds offers reproducible non-human reference data suitable for development, evaluation and regression testing of a therapeutic respiratory exergame application for smartphones.

Unwrapping the phase portrait features of adventitious crackle for auscultation and classification: a machine learning approach

The paper delves into the plausibility of applying fractal, spectral, and nonlinear time series analyses for lung auscultation. The thirty-five sound signals of bronchial (BB) and pulmonary crackle (PC) analysed by fast Fourier transform and wavelet not only give the details of number, nature, and time of occurrence of the frequency components but also throw light onto the embedded air flow during breathing. Fractal dimension, phase portrait, and sample entropy help in divulging the greater randomness, antipersistent nature, and complexity of airflow dynamics in BB than PC. The potential of principal component analysis through the spectral feature extraction categorises BB, fine crackles, and coarse crackles. The phase portrait feature-based supervised classification proves to be better compared to the unsupervised machine learning technique. The present work elucidates phase portrait features as a better choice of classification, as it takes into consideration the temporal correlation between the data points of the time series signal, and thereby suggesting a novel surrogate method for the diagnosis in pulmonology. The study suggests the possible application of the techniques in the auscultation of coronavirus disease 2019 seriously affecting the respiratory system.

Investigating into segmentation methods for diagnosis of respiratory diseases using adventitious respiratory sounds

Respiratory condition has received a great amount of attention nowadays since respiratory diseases recently become the globally leading causes of death. Traditionally, stethoscope is applied in early diagnosis but it requires clinician with extensive training experience to provide accurate diagnosis. Accordingly, a subjective and fast diagnosing solution of respiratory diseases is highly demanded. Adventitious respiratory sounds (ARSs), such as crackle, are mainly concerned during diagnosis since they are indication of various respiratory diseases. Therefore, the characteristics of crackle are informative and valuable regarding to develop a computerised approach for pathology-based diagnosis. In this work, we propose a framework combining random forest classifier and Empirical Mode Decomposition (EMD) method focusing on a multi-classification task of identifying subjects in 6 respiratory conditions (healthy, bronchiectasis, bronchiolitis, COPD, pneumonia and URTI). Specifically, 14 combinations of respiratory sound segments were compared and we found segmentation plays an important role in classifying different respiratory conditions. The classifier with best performance (accuracy = 0.88, precision = 0.91, recall = 0.87, specificity = 0.91, F1-score = 0.81) was trained with features extracted from the combination of early inspiratory phase and entire inspiratory phase. To our best knowledge, we are the first to address the challenging multi-classification problem.

Lung Sound Analysis Provides A Useful Index For Both Airway Narrowing And Airway Inflammation In Patients With Bronchial Asthma

Background

The expiration-to-inspiration sound power ratio in a midfrequency range (E/I MF), a parameter of lung sound analysis (LSA), has been reported to be useful as an index of airway inflammation in patients with bronchial asthma. However, the E/I MF reflects airway narrowing caused by airway inflammation, and there is thus concern that it may not be an index of airway eosinophilic inflammation itself.

Methods

A total of 131 patients with bronchial asthma were classified into four groups according to the presence or absence of airway narrowing and airway inflammation to examine whether the E/I MF could serve as an index of airway inflammation.

Results

The E/I MF was significantly higher in patients with a normal forced expiratory volume in one second (FEV₁) and high fractional exhaled nitric oxide (FeNO), those with a low FEV₁ and normal FeNO, and those with a low FEV₁ and high FeNO than in those with a normal FEV₁ and normal FeNO (p < 0.05–0.01). In particular, the E/I MF was high even in the patients who had no airway narrowing but had airway inflammation (p < 0.01). The results of multivariate analysis of factors involved in FeNO in patients with a normal FEV₁ revealed that the E/I MF was an independent factor (p = 0.0281).

Conclusion

The E/I MF is a useful index of airway inflammation in the treatment of asthma, regardless of the presence or absence of airway narrowing.

Physical signs in patients with chronic obstructive pulmonary disease

We reviewed the various physical signs of chronic obstructive pulmonary disease, their pathogenesis, and clinical importance. We searched PubMed, EMBASE, and the CINAHL from inception to March 2018. We used the following search terms: chronic obstructive pulmonary disease, physical examination, purse-lip breathing, breath sound intensity, forced expiratory time, abdominal paradox, Hoover's sign, barrel-shaped chest, accessory muscle use, etc. All types of studies were chosen. Globally, history taking and clinical examination of the patients is on the wane. One reason can be a significant development in the field of medical technology, resulting in overreliance on sophisticated diagnostic machines, investigative procedures, and medical tests as first-line modalities of patient's management. In resource-constrained countries, detailed history taking and physical examination should be emphasized as one of the important modalities in patient's diagnosis and management. Declining bedside skills and clinical aptitude among the physician is indeed a concern nowadays. Physical diagnosis of chronic obstructive pulmonary disease (COPD) is the quickest and reliable modalities that can lead to early diagnosis and management of COPD patients. Bedside elicitation of physical signs should always be the starting point for any diagnosis and therapeutic approach.

Automatic adventitious respiratory sound analysis: A systematic review

Background

Automatic detection or classification of adventitious sounds is useful to assist physicians in diagnosing or monitoring diseases such as asthma, Chronic Obstructive Pulmonary Disease (COPD), and pneumonia. While computerised respiratory sound analysis, specifically for the detection or classification of adventitious sounds, has recently been the focus of an increasing number of studies, a standardised approach and comparison has not been well established.

Objective

To provide a review of existing algorithms for the detection or classification of adventitious respiratory sounds. This systematic review provides a complete summary of methods used in the literature to give a baseline for future works.

Data sources

A systematic review of English articles published between 1938 and 2016, searched using the Scopus (1938-2016) and IEEExplore (1984-2016) databases. Additional articles were further obtained by references listed in the articles found. Search terms included adventitious sound detection, adventitious sound classification, abnormal respiratory sound detection, abnormal respiratory sound classification, wheeze detection, wheeze classification, crackle detection, crackle classification, rhonchi detection, rhonchi classification, stridor detection, stridor classification, pleural rub detection, pleural rub classification, squawk detection, and squawk classification.

Study selection

Only articles were included that focused on adventitious sound detection or classification, based on respiratory sounds, with performance reported and sufficient information provided to be approximately repeated.

Data extraction

Investigators extracted data about the adventitious sound type analysed, approach and level of analysis, instrumentation or data source, location of sensor, amount of data obtained, data management, features, methods, and performance achieved.

Data synthesis

A total of 77 reports from the literature were included in this review. 55 (71.43%) of the studies focused on wheeze, 40 (51.95%) on crackle, 9 (11.69%) on stridor, 9 (11.69%) on rhonchi, and 18 (23.38%) on other sounds such as pleural rub, squawk, as well as the pathology. Instrumentation used to collect data included microphones, stethoscopes, and accelerometers. Several references obtained data from online repositories or book audio CD companions. Detection or classification methods used varied from empirically determined thresholds to more complex machine learning techniques. Performance reported in the surveyed works were converted to accuracy measures for data synthesis.

Limitations

Direct comparison of the performance of surveyed works cannot be performed as the input data used by each was different. A standard validation method has not been established, resulting in different works using different methods and performance measure definitions.

Conclusion

A review of the literature was performed to summarise different analysis approaches, features, and methods used for the analysis. The performance of recent studies showed a high agreement with conventional non-automatic identification. This suggests that automated adventitious sound detection or classification is a promising solution to overcome the limitations of conventional auscultation and to assist in the monitoring of relevant diseases.

Lung sound analysis helps localize airway inflammation in patients with bronchial asthma

Purpose

Airway inflammation can be detected by lung sound analysis (LSA) at a single point in the posterior lower lung field. We performed LSA at 7 points to examine whether the technique could identify the location of airway inflammation in patients with asthma.

Patients and methods

Breath sounds were recorded at 7 points on the body surface of 22 asthmatic subjects. Inspiration sound pressure level (I_SPL), expiration sound pressure level (E_SPL), and the expiration-to-inspiration sound pressure ratio (E/I) were calculated in 6 frequency bands. The data were analyzed for potential correlation with spirometry, airway hyperresponsiveness (PC₂₀), and fractional exhaled nitric oxide (FeNO).

Results

The E/I data in the frequency range of 100–400 Hz (E/I low frequency [LF], E/I mid frequency [MF]) were better correlated with the spirometry, PC₂₀, and FeNO values than were the I_SPL or E_SPL data. The left anterior chest and left posterior lower recording positions were associated with the best correlations (forced expiratory volume in 1 second/forced vital capacity: r=−0.55 and r=−0.58; logPC₂₀: r=−0.46 and r=−0.45; and FeNO: r=0.42 and r=0.46, respectively). The majority of asthmatic subjects with FeNO ≥70 ppb exhibited high E/I MF levels in all lung fields (excluding the trachea) and V₅₀%pred <80%, suggesting inflammation throughout the airway. Asthmatic subjects with FeNO <70 ppb showed high or low E/I MF levels depending on the recording position, indicating uneven airway inflammation.

Conclusion

E/I LF and E/I MF are more useful LSA parameters for evaluating airway inflammation in bronchial asthma; 7-point lung sound recordings could be used to identify sites of local airway inflammation.

Auscultation of the respiratory system

Auscultation of the lung is an important part of the respiratory examination and is helpful in diagnosing various respiratory disorders. Auscultation assesses airflow through the trachea-bronchial tree. It is important to distinguish normal respiratory sounds from abnormal ones for example crackles, wheezes, and pleural rub in order to make correct diagnosis. It is necessary to understand the underlying pathophysiology of various lung sounds generation for better understanding of disease processes. Bedside teaching should be strengthened in order to avoid erosion in this age old procedure in the era of technological explosion.

Detection of lungs status using morphological complexities of respiratory sounds

Traditionally, the clinical diagnosis of a respiratory disease is made from a careful clinical examination including chest auscultation. Objective analysis and automatic interpretation of the lung sound based on its physical characters are strongly warranted to assist clinical practice. In this paper, a new method is proposed to distinguish between the normal and the abnormal subjects using the morphological complexities of the lung sound signals. The morphological embedded complexities used in these experiments have been calculated in terms of texture information (lacunarity), irregularity index (sample entropy), third order moment (skewness), and fourth order moment (Kurtosis). These features are extracted from a mixed data set of 10 normal and 20 abnormal subjects and are analyzed using two different classifiers: extreme learning machine (ELM) and support vector machine (SVM) network. The results are obtained using 5-fold cross-validation. The performance of the proposed method is compared with a wavelet analysis based method. The developed algorithm gives a better accuracy of 92.86% and sensitivity of 86.30% and specificity of 86.90% for a composite feature vector of four morphological indices.

Self-generated sounds of locomotion and ventilation and the evolution of human rhythmic abilities

It has been suggested that the basic building blocks of music mimic sounds of moving humans, and because the brain was primed to exploit such sounds, they eventually became incorporated in human culture. However, that raises further questions. Why do genetically close, culturally well-developed apes lack musical abilities? Did our switch to bipedalism influence the origins of music? Four hypotheses are raised: (1) Human locomotion and ventilation can mask critical sounds in the environment. (2) Synchronization of locomotion reduces that problem. (3) Predictable sounds of locomotion may stimulate the evolution of synchronized behavior. (4) Bipedal gait and the associated sounds of locomotion influenced the evolution of human rhythmic abilities. Theoretical models and research data suggest that noise of locomotion and ventilation may mask critical auditory information. People often synchronize steps subconsciously. Human locomotion is likely to produce more predictable sounds than those of non-human primates. Predictable locomotion sounds may have improved our capacity of entrainment to external rhythms and to feel the beat in music. A sense of rhythm could aid the brain in distinguishing among sounds arising from discrete sources and also help individuals to synchronize their movements with one another. Synchronization of group movement may improve perception by providing periods of relative silence and by facilitating auditory processing. The adaptive value of such skills to early ancestors may have been keener detection of prey or stalkers and enhanced communication. Bipedal walking may have influenced the development of entrainment in humans and thereby the evolution of rhythmic abilities.

Incidental sounds of locomotion in animal cognition

The highly synchronized formations that characterize schooling in fish and the flight of certain bird groups have frequently been explained as reducing energy expenditure. I present an alternative, or complimentary, hypothesis that synchronization of group movements may improve hearing perception. Although incidental sounds produced as a by-product of locomotion (ISOL) will be an almost constant presence to most animals, the impact on perception and cognition has been little discussed. A consequence of ISOL may be masking of critical sound signals in the surroundings. Birds in flight may generate significant noise; some produce wing beats that are readily heard on the ground at some distance from the source. Synchronization of group movements might reduce auditory masking through periods of relative silence and facilitate auditory grouping processes. Respiratory locomotor coupling and intermittent flight may be other means of reducing masking and improving hearing perception. A distinct border between ISOL and communicative signals is difficult to delineate. ISOL seems to be used by schooling fish as an aid to staying in formation and avoiding collisions. Bird and bat flocks may use ISOL in an analogous way. ISOL and interaction with animal perception, cognition, and synchronized behavior provide an interesting area for future study.

Discountinuous lung sounds and hysteresis in control and Tween 20-rinsed excised rat lungs

In the past, the relationship between pulmonary hysteresis and a model of the recruitment-derecruitment of lung units has been explored (Cheng, W., DeLong, D.S., Franz, G.N., Petsonk, E.L., Frazer, D.G., 1995, Resp. Physiol. 102, 205-215). The recruitment process is characterized by a sequence of events which represents discrete configurational changes in lung structure. It is assumed that energy released during the opening of lung units is associated with the formation of discontinuous lung sounds. The goal of this study was to record tracheal sounds for lungs inflated from different end-expiratory pressures and to relate the sound power to the normalized hysteresis of individual pressure-volume (PL-VL) loops. PL-VL curves and lung sounds were recorded for control lungs and lungs rinsed with Tween 20 in order to estimate the role of alveolar surfactant on the recruitment-derecruitment process. Results indicate that there may be two populations of lung units, one which is altered by Tween 20 and another which is not. The population not affected by Tween 20 appears to be responsible for producing discrete lung sounds and may represent the opening of larger conducting airways. The second population, possibly within the respiratory zone, is affected by alterations in surface tension and contributes to pulmonary hysteresis, but, apparently, does not contribute significantly to lung sound power measured at the trachea.

Significant differences in flow standardised breath sound spectra in patients with chronic obstructive pulmonary disease, stable asthma, and healthy lungs

BACKGROUND

Spectral characteristics of breath sounds in asthma and chronic obstructive pulmonary disease (COPD) have not previously been compared, although the structural differences in these disorders might be reflected in breath sounds.

METHODS

Flow standardised inspiratory breath sounds in patients with COPD (n = 17) and stable asthma (n = 10) with significant airways obstruction and in control patients without any respiratory disorders (n = 11) were compared in terms of estimates of the power spectrum. Breath sounds were recorded simultaneously at the chest and at the trachea.

RESULTS

The median frequency (F50) of the mean (SD) breath sound spectra recorded at the chest was higher in asthmatics (239 (19) Hz) than in both the control patients (206 (14) Hz) and the patients with COPD (201 (21) Hz). The total spectral power of breath sounds recorded at the chest in terms of root mean square (RMS) was higher in asthmatics than in patients with COPD. In patients with COPD the spectral parameters were not statistically different from those of control patients. The F50 recorded at the trachea in the asthmatics was significantly related to forced expiratory volume in one second (FEV1) (r = -0.77), but this was not seen in the other groups.

CONCLUSIONS

The observed differences in frequency content of breath sounds in patients with asthma and COPD may reflect altered sound generation or transmission due to structural changes of the bronchi and the surrounding lung tissue in these diseases. Spectral analysis of breath sounds may provide a new non-invasive method for differential diagnosis of obstructive pulmonary diseases.

Changes in frequency spectra of breath sounds during histamine challenge test in adult asthmatics and healthy control subjects

Air-flow standardized breath sounds were recorded at the chest and at the trachea during histamine challenge test and after subsequent bronchodilation in 12 asthmatics and 6 healthy controls for spectral analysis, to be compared with simultaneous changes in spirometric variables. Of all the lung sound variables measured, the changes in median frequency of the power spectrum (F50) of tracheal expiratory sounds were found to correlate best (r = 0.853, p < 0.0001) with changes in FEV1. The increase of F50 during histamine challenge was significantly larger in asthmatics than in healthy control subjects (p < 0.005). The provocative dose of histamine inducing a decrease of 15 percent in FEV1 (PD15FEV1) and the provocative dose causing an increase of 30 percent in tracheal expiratory F50 (PD30F50) were significantly related (r = 0.754, p = 0.012). In asthmatics, the breath sound frequency distribution in terms of median frequency reflected acute changes in airways obstruction with high sensitivity and specificity. The present method for breath sound analysis can be applied for patients with limited cooperation during bronchial challenge tests.

Analysis of breath sounds in normal and asthmatic children and adults using computer digitized airway phonopneumography (CDAP)

Analysis of breath sounds using the stethoscope is a major part of physicians evaluation of their patients. However, the use of a stethoscope is often inadequate to give quantitative measurements of the clinical state of the individual. In this study a modification of a previously described computer analysis of breath sounds was used to measure sound intensity levels in both normal and asthmatic children who, in most cases, were unable to perform pulmonary function. The intensity levels were derived using a microcomputer-based program that digitizes audio signals and calculates energy values at 25-ms intervals throughout each signal. There were statistical differences between mean intensity levels for normal breath sounds in children between 2 and 6 years and the mean intensity levels for wheezing sounds in the same age group, as well as wheezing sounds in asthmatic patients over the age of 8 years (P less than 0.002). Also, the mean intensity levels for normal breath sounds could be clearly differentiated from intensity levels for other sounds from the chest, including heart sounds and voice sounds. Thus, computer digitized airway phonopneumography (CDAP) proved to be a reproducible, quantifiable method for demonstrating airway obstruction in those children and patients unable to perform pulmonary function testing.

Airflow and normal lung sounds

The relationship between total air flow and normal breath sounds recorded at 2 sites on the chest was investigated. Sounds were measured during rhythmic breathing, during flow rate tracking, and during flow rate tracking against an external resistance by subjects seated and in the left lateral decubitus position. The sound amplitude during inspiration varied directly with the square of the air flow at the mouth. Changes in subject position and breathing pattern altered the gain between the square of the flow and the sound amplitude but not the functional relationship.

Poor breath sounds with good voice sounds. A sign of bronchial stenosis

The auscultatory finding of disparity in breath and voice sounds, the former being absent or faint whereas the latter are easily heard when compared to the corresponding site over the opposite lung, predicts stenosis of a main, intermediate, or lobar bronchus. Stenosis limits airflow and consequently reduces turbulence, causing diminution or absence of breath sounds over the poorly ventilated region; however, flow-independent voice sounds are not significantly impaired. This sign was present in ten patients, in each of whom bronchial stenosis was confirmed by bronchoscopy.

Lung sound analysis for continuous evaluation of airflow obstruction in asthma

We developed a system for monitoring airflow obstruction noninvasively, based on the principle that the proportion of the breath cycle occupied by wheezing (Tw/Ttot) in any one subject corresponds to the severity of airways obstruction. Lung sounds were recorded continuously from the chest wall. Fifty 250 ms sound segments were randomly chosen from five-minute periods and analyzed for the presence or absence of wheezes. The proportion with wheezes was used as an estimate of Tw/Ttot (Est Tw/Ttot). For 12 wheezy patients, there was a good correlation between the Est Tw/Ttot and the forced expiratory volume in one second (r = 0.893, p less than 0.001). The system was used to evaluate nocturnal asthma. Five subjects were studied over eight nights. It was found that there was more wheezing from 4:00 to 4:30 AM than from midnight to 12:30 AM (p less than 0.05). This technique may prove useful in continuous, noninvasive monitoring of wheezy patients.

Frequency analysis of adventitious lung sounds

Adventitious lung sounds from twenty patients with respiratory diseases have been recorded and analysed by analogue methods. The results obtained are compared with those from five normal subjects. The difference in the frequency components of lung sounds from patients with pulmonary obstruction in the airways and those from normal subjects are pointed out. This technique may well prove to be an objective method of diagnosis in pulmonary medicine.

Wideband acoustic energy studies of pulmonary airways

The impulse response of the pulmonary system has been measured by exciting the system with wideband acoustic noise introduced through the mouth. The transmitted sound is detected using microphones placed on the patient's back at appropriate locations. A specially designed analog correlator is used to obtain the impulse response of the pulmonary system through cross-correlation techniques. Uniquely characteristic responses have been obtained from smoking and nonsmoking patient groups.

Auscultation of the lung: past lessons, future possibilities

Review of the history of auscultation of the lung reveals few scientific investigations. The majority of these have led to inconclusive results. The mechanism of production of normal breath sounds remains uncertain. Hypotheses for the generation of adventitious sounds are unproven. Advances in instrumentation for lung sound recording and analysis have provided little of clinical value. There has been a recent resurgence of interest in lung sounds. Space-age technology has improved methodology for sonic analysis significantly. Lung sounds are complex signals that probably reflect regional lung pathophysiology. If they were understood more clearly important non-invasive diagnostic tools could be devised and the value of clinical auscultation could be improved. A multidisciplinary effort will be required to achieve this.

Breath sounds in the clinical assessment of airflow obstruction

In a group of 34 inpatients showing varying degrees of airflow obstruction we studied the relationship between breath sound intensity (BSI) and abnormalities of lung function. The BSI was evaluated by chest auscultation to provide a score, in a manner similar to that described by Pardee et al. (1976), and was found to correlate closely with indices of airflow obstruction of their logarithms such as specific conductance (r = 0.759), maximal expiratory flow at 50% of vital capacity (r = 0.790), forced expiratory volume in one second (r = 0.768), and forced expiratory volume to vital capacity ratio (r = 0.860). Correlations with lung volumes, although statistically significant, were weaker. Multiple correlation studies showed that BSI score correlated independently with indices of both airflow obstruction and lung distension. In our experience, BSI score can be useful not only in the detection but also the quantification of airflow obstruction, although its predictive power is impaired in subjects with associated restrictive disorders. It can also fail to detect mild, pure airflow obstruction.