Validation of automatic wheeze detection in patients with obstructed airways and in healthy subjects

Breath characteristics and adventitious lung sounds in healthy and asthmatic horses

BACKGROUND

Standard thoracic auscultation suffers from limitations, and no systematic analysis of breath sounds in asthmatic horses exists.

OBJECTIVES

First, characterize breath sounds in horses recorded using a novel digital auscultation device (DAD). Second, use DAD to compare breath variables and occurrence of adventitious sounds in healthy and asthmatic horses.

ANIMALS

Twelve healthy control horses (ctl), 12 horses with mild to moderate asthma (mEA), 10 horses with severe asthma (sEA) (5 in remission [sEA-], and 5 in exacerbation [sEA+]).

METHODS

Prospective multicenter case-control study. Horses were categorized based on the horse owner-assessed respiratory signs index. Each horse was digitally auscultated in 11 locations simultaneously for 1 hour. One-hundred breaths per recording were randomly selected, blindly categorized, and statistically analyzed.

RESULTS

Digital auscultation allowed breath sound characterization and scoring in horses. Wheezes, crackles, rattles, and breath intensity were significantly more frequent, higher (P < .001, P < .01, P = .01, P < .01, respectively) in sEA+ (68.6%, 66.1%, 17.7%, 97.9%, respectively), but not in sEA- (0%, 0.7%, 1.3%, 5.6%) or mEA (0%, 1.0%, 2.4%, 1.7%) horses, compared to ctl (0%, 0.6%, 1.8%, -9.4%, respectively). Regression analysis suggested breath duration and intensity as explanatory variables for groups, wheezes for tracheal mucus score, and breath intensity and wheezes for the 23-point weighted clinical score (WCS23).

CONCLUSIONS AND CLINICAL IMPORTANCE

The DAD permitted characterization and quantification of breath variables, which demonstrated increased adventitious sounds in sEA+. Analysis of a larger sample is needed to determine differences among ctl, mEA, and sEA- horses.

Reference free auscultation quality metric and its trends

Stethoscopes are used ubiquitously in clinical settings to 'listen' to lung sounds. The use of these systems in a variety of healthcare environments (hospitals, urgent care rooms, private offices, community sites, mobile clinics, etc.) presents a range of challenges in terms of ambient noise and distortions that mask lung signals from being heard clearly or processed accurately using auscultation devices. With advances in technology, computerized techniques have been developed to automate analysis or access a digital rendering of lung sounds. However, most approaches are developed and tested in controlled environments and do not reflect real-world conditions where auscultation signals are typically acquired. Without a priori access to a recording of the ambient noise (for signal-to-noise estimation) or a reference signal that reflects the true undistorted lung sound, it is difficult to evaluate the quality of the lung signal and its potential clinical interpretability. The current study proposes an objective reference-free Auscultation Quality Metric (AQM) which incorporates low-level signal attributes with high-level representational embeddings mapped to a nonlinear quality space to provide an independent evaluation of the auscultation quality. This metric is carefully designed to solely judge the signal based on its integrity relative to external distortions and masking effects and not confuse an adventitious breathing pattern as low-quality auscultation. The current study explores the robustness of the proposed AQM method across multiple clinical categorizations and different distortion types. It also evaluates the temporal sensitivity of this approach and its translational impact for deployment in digital auscultation devices.

SPRSound: Open-Source SJTU Paediatric Respiratory Sound Database

It has proved that the auscultation of respiratory sound has advantage in early respiratory diagnosis. Various methods have been raised to perform automatic respiratory sound analysis to reduce subjective diagnosis and physicians' workload. However, these methods highly rely on the quality of respiratory sound database. In this work, we have developed the first open-access paediatric respiratory sound database, SPRSound. The database consists of 2,683 records and 9,089 respiratory sound events from 292 participants. Accurate label is important to achieve a good prediction for adventitious respiratory sound classification problem. A custom-made sound label annotation software (SoundAnn) has been developed to perform sound editing, sound annotation, and quality assurance evaluation. A team of 11 experienced paediatric physicians is involved in the entire process to establish golden standard reference for the dataset. To verify the robustness and accuracy of the classification model, we have investigated the effects of different feature extraction methods and machine learning classifiers on the classification performance of our dataset. As such, we have achieved a score of 75.22%, 61.57%, 56.71%, and 37.84% for the four different classification challenges at the event level and record level.

A Neural Network-Based Method for Respiratory Sound Analysis and Lung Disease Detection

Background: Respiratory sound analysis represents a research topic of growing interest in recent times. In fact, in this area, there is the potential to automatically infer the abnormalities in the preliminary stages of a lung dysfunction. Methods: In this paper, we propose a method to analyse respiratory sounds in an automatic way. The aim is to show the effectiveness of machine learning techniques in respiratory sound analysis. A feature vector is gathered directly from breath audio and, thus, by exploiting supervised machine learning techniques, we detect if the feature vector is related to a patient affected by a lung disease. Moreover, the proposed method is able to characterise the lung disease in asthma, bronchiectasis, bronchiolitis, chronic obstructive pulmonary disease, pneumonia, and lower or upper respiratory tract infection. Results: A retrospective experimental analysis on 126 patients with 920 recording sessions showed the effectiveness of the proposed method. Conclusion: The experimental analysis demonstrated that it is possible to detect lung disease by exploiting machine learning techniques. We considered several supervised machine learning algorithms, obtaining the most interesting performance with the neural network model, with an F-Measure of 0.983 in lung disease detection and equal to 0.923 in lung disease characterisation, increasing the state-of-the-art performance.

Assessing the accuracy of artificial intelligence enabled acoustic analytic technology on breath sounds in children

Interpretation of breath sounds by auscultation has high inter-observer variability, even when performed by trained healthcare professionals. This can be mitigated by using Artificial Intelligence (AI) acoustic analysis. We aimed to develop and validate a novel breath sounds analysis system using AI-enabled algorithms to accurately interpret breath sounds in children. Subjects from the respiratory clinics and wards were auscultated by two independent respiratory paediatricians blinded to their clinical diagnosis. A novel device consisting of a stethoscope head connected to a smart phone recorded the breath sounds. The audio files were categorised into single label (normal, wheeze and crackles) or multi-label sounds. Together with commercially available breath sounds, an AI classifier was trained using machine learning. Unique features were identified to distinguish the breath sounds. Single label breath sound samples were used to validate the finalised Support Vector Machine classifier. Breath sound samples (73 single label, 20 multi-label) were collected from 93 children (mean age [SD] = 5.40 [4.07] years). Inter-rater concordance was observed in 81 (87.1%) samples. Performance of the classifier on the 73 single label breath sounds demonstrated 91% sensitivity and 95% specificity. The AI classifier developed could identify normal breath sounds, crackles and wheeze in children with high accuracy.

Design and Comparative Performance of a Robust Lung Auscultation System for Noisy Clinical Settings

Chest auscultation is a widely used clinical tool for respiratory disease detection. The stethoscope has undergone a number of transformative enhancements since its invention, including the introduction of electronic systems in the last two decades. Nevertheless, stethoscopes remain riddled with a number of issues that limit their signal quality and diagnostic capability, rendering both traditional and electronic stethoscopes unusable in noisy or non-traditional environments (e.g., emergency rooms, rural clinics, ambulatory vehicles). This work outlines the design and validation of an advanced electronic stethoscope that dramatically reduces external noise contamination through hardware redesign and real-time, dynamic signal processing. The proposed system takes advantage of an acoustic sensor array, an external facing microphone, and on-board processing to perform adaptive noise suppression. The proposed system is objectively compared to six commercially-available acoustic and electronic devices in varying levels of simulated noisy clinical settings and quantified using two metrics that reflect perceptual audibility and statistical similarity, normalized covariance measure (NCM) and magnitude squared coherence (MSC). The analyses highlight the major limitations of current stethoscopes and the significant improvements the proposed system makes in challenging settings by minimizing both distortion of lung sounds and contamination by ambient noise.

An objective measure of signal quality for pediatric lung auscultations

A stethoscope is a ubiquitous tool used to 'listen' to sounds from the chest in order to assess lung and heart conditions. With advances in health technologies including digital devices and new wearable sensors, access to these sounds is becoming easier and abundant; yet proper measures of signal quality do not exist. In this work, we develop an objective quality metric of lung sounds based on low-level and high-level features in order to independently assess the integrity of the signal in presence of interference from ambient sounds and other distortions. The proposed metric outlines a mapping of auscultation signals onto rich low-level features extracted directly from the signal which capture spectral and temporal characteristics of the signal. Complementing these signal-derived attributes, we propose high-level learnt embedding features extracted from a generative auto-encoder trained to map auscultation signals onto a representative space that best captures the inherent statistics of lung sounds. Integrating both low-level (signal-derived) and high-level (embedding) features yields a robust correlation of 0.85 to infer the signal-to-noise ratio of recordings with varying quality levels. The method is validated on a large dataset of lung auscultation recorded in various clinical settings with controlled varying degrees of noise interference. The proposed metric is also validated against opinions of expert physicians in a blind listening test to further corroborate the efficacy of this method for quality assessment.

An open access database for the evaluation of respiratory sound classification algorithms

OBJECTIVE

Over the last few decades, there has been significant interest in the automatic analysis of respiratory sounds. However, currently there are no publicly available large databases with which new algorithms can be evaluated and compared. Further developments in the field are dependent on the creation of such databases.

APPROACH

This paper describes a public respiratory sound database, which was compiled for an international competition, the first scientific challenge of the IFMBE's International Conference on Biomedical and Health Informatics. The database includes 920 recordings acquired from 126 participants and two sets of annotations. One set contains 6898 annotated respiratory cycles, some including crackles, wheezes, or a combination of both, and some with no adventitious respiratory sounds. In the other set, precise locations of 10 775 events of crackles and wheezes were annotated.

MAIN RESULTS

The best system that participated in the challenge achieved an average score of 52.5% with the respiratory cycle annotations and an average score of 91.2% with the event annotations.

SIGNIFICANCE

The creation and public release of this database will be useful to the research community and could bring attention to the respiratory sound classification problem.

The highs and lows of wheezing: A review of the most popular adventitious lung sound

Wheezing is the most widely reported adventitious lung sound in the English language. It is recognized by health professionals as well as by lay people, although often with a different meaning. Wheezing is an indicator of airway obstruction and therefore of interest particularly for the assessment of young children and in other situations where objective documentation of lung function is not generally available. This review summarizes our current understanding of mechanisms producing wheeze, its subjective perception and description, its objective measurement, and visualization, and its relevance in clinical practice.

Computerized Lung Sound Screening for Pediatric Auscultation in Noisy Field Environments

GOAL

Chest auscultations offer a non-invasive and low-cost tool for monitoring lung disease. However, they present many shortcomings, including inter-listener variability, subjectivity, and vulnerability to noise and distortions. This work proposes a computer-aided approach to process lung signals acquired in the field under adverse noisy conditions, by improving the signal quality and offering automated identification of abnormal auscultations indicative of respiratory pathologies.

METHODS

The developed noise-suppression scheme eliminates ambient sounds, heart sounds, sensor artifacts, and crying contamination. The improved high-quality signal is then mapped onto a rich spectrotemporal feature space before being classified using a trained support-vector machine classifier. Individual signal frame decisions are then combined using an evaluation scheme, providing an overall patient-level decision for unseen patient records.

RESULTS

All methods are evaluated on a large dataset with 1000 children enrolled, 1-59 months old. The noise suppression scheme is shown to significantly improve signal quality, and the classification system achieves an accuracy of 86.7% in distinguishing normal from pathological sounds, far surpassing other state-of-the-art methods.

CONCLUSION

Computerized lung sound processing can benefit from the enforcement of advanced noise suppression. A fairly short processing window size (  s) combined with detailed spectrotemporal features is recommended, in order to capture transient adventitious events without highlighting sharp noise occurrences.

SIGNIFICANCE

Unlike existing methodologies in the literature, the proposed work is not limited in scope or confined to laboratory settings: This work validates a practical method for fully automated chest sound processing applicable to realistic and noisy auscultation settings.

Design of Wearable Breathing Sound Monitoring System for Real-Time Wheeze Detection

In the clinic, the wheezing sound is usually considered as an indicator symptom to reflect the degree of airway obstruction. The auscultation approach is the most common way to diagnose wheezing sounds, but it subjectively depends on the experience of the physician. Several previous studies attempted to extract the features of breathing sounds to detect wheezing sounds automatically. However, there is still a lack of suitable monitoring systems for real-time wheeze detection in daily life. In this study, a wearable and wireless breathing sound monitoring system for real-time wheeze detection was proposed. Moreover, a breathing sounds analysis algorithm was designed to continuously extract and analyze the features of breathing sounds to provide the objectively quantitative information of breathing sounds to professional physicians. Here, normalized spectral integration (NSI) was also designed and applied in wheeze detection. The proposed algorithm required only short-term data of breathing sounds and lower computational complexity to perform real-time wheeze detection, and is suitable to be implemented in a commercial portable device, which contains relatively low computing power and memory. From the experimental results, the proposed system could provide good performance on wheeze detection exactly and might be a useful assisting tool for analysis of breathing sounds in clinical diagnosis.

Computerized wheeze detection in young infants: comparison of signals from tracheal and chest wall sensors

Computerized wheeze detection is an established method for objective assessment of respiratory sounds. In infants, this method has been used to detect subclinical airway obstruction and to monitor treatment effects. The optimal location for the acoustic sensors, however, is unknown. The aim of this study was to evaluate the quality of respiratory sound recordings in young infants, and to determine whether the position of the sensor affected computerized wheeze detection. Respiratory sounds were recorded over the left lateral chest wall and the trachea in 112 sleeping infants (median postmenstrual age: 49 weeks) on 129 test occasions using an automatic wheeze detection device (PulmoTrack^®). Each recording lasted 10 min and the recordings were stored. A trained clinician retrospectively evaluated the recordings to determine sound quality and disturbances. The wheeze rates of all undisturbed tracheal and chest wall signals were compared using Bland-Altman plots. Comparison of wheeze rates measured over the trachea and the chest wall indicated strong correlation (r  ⩾  0.93, p  <  0.001), with a bias of 1% or less and limits of agreement of within 3% for the inspiratory wheeze rate and within 6% for the expiratory wheeze rate. However, sounds from the chest wall were more often affected by disturbances than sounds from the trachea (23% versus 6%, p  <  0.001). The study suggests that in young infants, a better quality of lung sound recordings can be obtained with the tracheal sensor.

Adaptive Noise Suppression of Pediatric Lung Auscultations With Real Applications to Noisy Clinical Settings in Developing Countries

GOAL

Chest auscultation constitutes a portable low-cost tool widely used for respiratory disease detection. Though it offers a powerful means of pulmonary examination, it remains riddled with a number of issues that limit its diagnostic capability. Particularly, patient agitation (especially in children), background chatter, and other environmental noises often contaminate the auscultation, hence affecting the clarity of the lung sound itself. This paper proposes an automated multiband denoising scheme for improving the quality of auscultation signals against heavy background contaminations.

METHODS

The algorithm works on a simple two-microphone setup, dynamically adapts to the background noise and suppresses contaminations while successfully preserving the lung sound content. The proposed scheme is refined to offset maximal noise suppression against maintaining the integrity of the lung signal, particularly its unknown adventitious components that provide the most informative diagnostic value during lung pathology.

RESULTS

The algorithm is applied to digital recordings obtained in the field in a busy clinic in West Africa and evaluated using objective signal fidelity measures and perceptual listening tests performed by a panel of licensed physicians. A strong preference of the enhanced sounds is revealed.

SIGNIFICANCE

The strengths and benefits of the proposed method lie in the simple automated setup and its adaptive nature, both fundamental conditions for everyday clinical applicability. It can be simply extended to a real-time implementation, and integrated with lung sound acquisition protocols.

Validation of computerized wheeze detection in young infants during the first months of life

BACKGROUND

Several respiratory diseases are associated with specific respiratory sounds. In contrast to auscultation, computerized lung sound analysis is objective and can be performed continuously over an extended period. Moreover, audio recordings can be stored. Computerized lung sounds have rarely been assessed in neonates during the first year of life. This study was designed to determine and validate optimal cut-off values for computerized wheeze detection, based on the assessment by trained clinicians of stored records of lung sounds, in infants aged <1 year.

METHODS

Lung sounds in 120 sleeping infants, of median (interquartile range) postmenstrual age of 51 (44.5-67.5) weeks, were recorded on 144 test occasions by an automatic wheeze detection device (PulmoTrack®). The records were retrospectively evaluated by three trained clinicians blinded to the results. Optimal cut-off values for the automatically determined relative durations of inspiratory and expiratory wheezing were determined by receiver operating curve analysis, and sensitivity and specificity were calculated.

RESULTS

The optimal cut-off values for the automatically detected durations of inspiratory and expiratory wheezing were 2% and 3%, respectively. These cutoffs had a sensitivity and specificity of 85.7% and 80.7%, respectively, for inspiratory wheezing and 84.6% and 82.5%, respectively, for expiratory wheezing. Inter-observer reliability among the experts was moderate, with a Fleiss' Kappa (95% confidence interval) of 0.59 (0.57-0.62) for inspiratory and 0.54 (0.52 - 0.57) for expiratory wheezing.

CONCLUSION

Computerized wheeze detection is feasible during the first year of life. This method is more objective and can be more readily standardized than subjective auscultation, providing quantitative and noninvasive information about the extent of wheezing.

Developing a reference of normal lung sounds in healthy Peruvian children

PURPOSE

Lung auscultation has long been a standard of care for the diagnosis of respiratory diseases. Recent advances in electronic auscultation and signal processing have yet to find clinical acceptance; however, computerized lung sound analysis may be ideal for pediatric populations in settings, where skilled healthcare providers are commonly unavailable. We described features of normal lung sounds in young children using a novel signal processing approach to lay a foundation for identifying pathologic respiratory sounds.

METHODS

186 healthy children with normal pulmonary exams and without respiratory complaints were enrolled at a tertiary care hospital in Lima, Peru. Lung sounds were recorded at eight thoracic sites using a digital stethoscope. 151 (81%) of the recordings were eligible for further analysis. Heavy-crying segments were automatically rejected and features extracted from spectral and temporal signal representations contributed to profiling of lung sounds.

RESULTS

Mean age, height, and weight among study participants were 2.2 years (SD 1.4), 84.7 cm (SD 13.2), and 12.0 kg (SD 3.6), respectively; and, 47% were boys. We identified ten distinct spectral and spectro-temporal signal parameters and most demonstrated linear relationships with age, height, and weight, while no differences with genders were noted. Older children had a faster decaying spectrum than younger ones. Features like spectral peak width, lower-frequency Mel-frequency cepstral coefficients, and spectro-temporal modulations also showed variations with recording site.

CONCLUSIONS

Lung sound extracted features varied significantly with child characteristics and lung site. A comparison with adult studies revealed differences in the extracted features for children. While sound-reduction techniques will improve analysis, we offer a novel, reproducible tool for sound analysis in real-world environments.

A multiresolution analysis for detection of abnormal lung sounds

Automated analysis and detection of abnormal lung sound patterns has great potential for improving access to standardized diagnosis of pulmonary diseases, especially in low-resource settings. In the current study, we develop signal processing tools for analysis of paediatric auscultations recorded under non-ideal noisy conditions. The proposed model is based on a biomimetic multi-resolution analysis of the spectro-temporal modulation details in lung sounds. The methodology provides a detailed description of joint spectral and temporal variations in the signal and proves to be more robust than frequency-based techniques in distinguishing crackles and wheezes from normal breathing sounds.

Soft stethoscope for detecting asthma wheeze in young children

Asthma is a chronic disease that is commonly suffered by children. Asthmatic children have a lower quality of life than other children. Physicians and pediatricians recommend that parents record the frequency of attacks and their symptoms to help manage their children's asthma. However, the lack of a convenient device for monitoring the asthmatic condition leads to the difficulties in managing it, especially when it is suffered by young children. This work develops a wheeze detection system for use at home. A small and soft stethoscope was used to collect the respiratory sound. The wheeze detection algorithm was the Adaptive Respiratory Spectrum Correlation Coefficient (RSACC) algorithm, which has the advantages of high sensitivity/specificity and a low computational requirement. Fifty-nine sound files from eight young children (one to seven years old) were collected in the emergency room and analyzed. The results revealed that the system provided 88% sensitivity and 94% specificity in wheeze detection. In conclusion, this small soft stethoscope can be easily used on young children. A noisy environment does not affect the effectiveness of the system in detecting wheeze. Hence, the system can be used at home by parents who wish to evaluate and manage the asthmatic condition of their children.

Computerized analysis of respiratory sounds during COPD exacerbations

Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is a major event in the natural course of the disease, and is associated with significant mortality and socioeconomic impact. Abnormal respiratory sounds are commonly present in patients with AECOPD. Computerized analysis of these sounds can assist in diagnosis and in evaluation during follow-up. Exploratory data analysis methods were applied to respiratory sounds in these patients when they were hospitalized because of exacerbation. Two different patterns of presentation and evolution of respiratory sounds in AECOPD were found and described from the method of computerized respiratory sound analysis and unsupervised clustering that was devised. Based on the findings of the study, remote monitoring of respiratory sounds may be useful for the detection and/or follow-up of COPD exacerbation.

Computer-aided diagnosis of pneumonia in patients with chronic obstructive pulmonary disease

BACKGROUND

Early diagnosis of pneumonia and discrimination between this disease and chronic obstructive pulmonary disease (COPD) exacerbations in patients with COPD are crucial for optimal clinical management and treatment.

OBJECTIVES

To examine the use of computerized analysis of respiratory sounds, a hybrid system based on principal component analysis (PCA) and probabilistic neural networks (PNNs), to aid the detection of coexisting pneumonia in patients with COPD.

METHODS AND MATERIALS

A convenience sample of 58 patients with COPD (25 patients hospitalized for community-acquired pneumonia and 33 owing to acute exacerbation of COPD) was studied. Auscultations were performed by the patients themselves on their suprasternal notch. Short-time Fourier transform analysis was used to extract features from the recorded respiratory sounds, PCA was selected for dimensionality reduction and a PNN was trained as classifier. 10-Fold cross-validation and receiver operating characteristic curve analysis were used to estimate the system performance.

RESULTS

Based on the cross-validation results, a sensitivity and a specificity of 72% and 81.8%, respectively, were achieved in validation data. The operating point was selected to maximize the specificity and sensitivity pair in the training set.

DISCUSSION

The results strongly suggest that electronic self-auscultation at a single location (suprasternal notch) can support diagnosis of pneumonia in patients with COPD.

CONCLUSIONS

A simple, cost-effective method has been proposed to aid decision-making in areas with no radiological facilities available and in resource-constrained settings, and could have a great diagnostic impact on telemedicine applications.

Vibration response imaging: a novel noninvasive tool for evaluating the initial therapeutic effect of noninvasive positive pressure ventilation in patients with acute exacerbation of chronic obstructive pulmonary disease

BACKGROUND

The popular methods for evaluating the initial therapeutic effect (ITE) of noninvasive positive pressure ventilation (NPPV) can only roughly reflect the therapeutic outcome of a patient's ventilation because they are subjective, invasive and time-delayed. In contrast, vibration response imaging (VRI) can monitor the function of a patient's ventilation over the NPPV therapy in a non-invasive manner. This study aimed to investigate the value of VRI in evaluating the ITE of NPPV for patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD).

METHODS

Thirty-six AECOPD patients received VRI at three time points: before NPPV treatment (T1), at 15 min of NPPV treatment (T2), and at 15 min after the end of NPPV treatment (T4). Blood gas analysis was also performed at T1 and at 2 hours of NPPV treatment (T3). Thirty-nine healthy volunteers also received VRI at T1 and T2. VRI examination at the time point T2 in either the patients or volunteers did not require any interruption of the on-going NPPV. The clinical indices at each time point were compared between the two groups. Moreover, correlations between the PaCO2 changes (T3 vs T1) and abnormal VRI scores (AVRIS) changes (T2 vs T1) were analyzed.

RESULTS

No significant AVRIS differences were found between T1 and T2 in the healthy controls (8.51 ± 3.36 vs. 8.53 ± 3.57, P > 0.05). The AVRIS, dynamic score, MEF score and EVP score showed a significant decrease in AECOPD patients at T2 compared with T1 (P < 0.05), but a significant increase at T4 compared with T2 (P < 0.05). We also found a positive correlation (R2 = 0.6399) between the PaCO2 changes (T3 vs T1) and AVRIS changes (T2 vs T1).

CONCLUSIONS

VRI is a promising noninvasive tool for evaluating the initial therapeutic effects of NPPV in AECOPD patients and predicting the success of NPPV in the early stage.

Asthma outcomes: pulmonary physiology

BACKGROUND

Outcomes of pulmonary physiology have a central place in asthma clinical research.

OBJECTIVE

At the request of National Institutes of Health (NIH) institutes and other federal agencies, an expert group was convened to provide recommendations on the use of pulmonary function measures as asthma outcomes that should be assessed in a standardized fashion in future asthma clinical trials and studies to allow for cross-study comparisons.

METHODS

Our subcommittee conducted a comprehensive search of PubMed to identify studies that focused on the validation of various airway response tests used in asthma clinical research. The subcommittee classified the instruments as core (to be required in future studies), supplemental (to be used according to study aims and in a standardized fashion), or emerging (requiring validation and standardization). This work was discussed at an NIH-organized workshop in March 2010 and finalized in September 2011.

RESULTS

A list of pulmonary physiology outcomes that applies to both adults and children older than 6 years was created. These outcomes were then categorized into core, supplemental, and emerging. Spirometric outcomes (FEV(1), forced vital capacity, and FEV(1)/forced vital capacity ratio) are proposed as core outcomes for study population characterization, for observational studies, and for prospective clinical trials. Bronchodilator reversibility and prebronchodilator and postbronchodilator FEV(1) also are core outcomes for study population characterization and observational studies.

CONCLUSIONS

The subcommittee considers pulmonary physiology outcomes of central importance in asthma and proposes spirometric outcomes as core outcomes for all future NIH-initiated asthma clinical research.

Computerized lung sound analysis as diagnostic aid for the detection of abnormal lung sounds: a systematic review and meta-analysis

RATIONALE

The standardized use of a stethoscope for chest auscultation in clinical research is limited by its inherent inter-listener variability. Electronic auscultation and automated classification of recorded lung sounds may help prevent some of these shortcomings.

OBJECTIVE

We sought to perform a systematic review and meta-analysis of studies implementing computerized lung sound analysis (CLSA) to aid in the detection of abnormal lung sounds for specific respiratory disorders.

METHODS

We searched for articles on CLSA in MEDLINE, EMBASE, Cochrane Library and ISI Web of Knowledge through July 31, 2010. Following qualitative review, we conducted a meta-analysis to estimate the sensitivity and specificity of CLSA for the detection of abnormal lung sounds.

MEASUREMENTS AND MAIN RESULTS

Of 208 articles identified, we selected eight studies for review. Most studies employed either electret microphones or piezoelectric sensors for auscultation, and Fourier Transform and Neural Network algorithms for analysis and automated classification of lung sounds. Overall sensitivity for the detection of wheezes or crackles using CLSA was 80% (95% CI 72-86%) and specificity was 85% (95% CI 78-91%).

CONCLUSIONS

While quality data on CLSA are relatively limited, analysis of existing information suggests that CLSA can provide a relatively high specificity for detecting abnormal lung sounds such as crackles and wheezes. Further research and product development could promote the value of CLSA in research studies or its diagnostic utility in clinical settings.

Accuracy of gray-scale coding in lung sound mapping

Stethoscope evaluation of the lungs is widely accepted and practiced; however, there are some widely recognized, major limitations with its use. A safe device that helped solve these limitations by translating sound into objective, quantifiable images would have clinical utility. Translating lung sounds into quantifiable images in which regional differences or asymmetry in intensities of breath sounds are presented as gradients in gray-scale is not a trivial process. Healthy lungs and lung pathology are characterized by different patterns of regional breath sound distribution and, therefore, the accuracy of mapping gray-scale images must be ensured in a controlled systematic fashion prior to clinical use of such a technique. Vibration response imaging (VRI) maps lung sounds from 40 sensors to a two-dimensional gray-scale image. To assess mapping accuracy, a simulated lung sound map with uniform signals was compared to modified maps where sound signals were reduced (1-3db) at one sensor. Also, 8 readers evaluated the gray-scale images. The computer algorithm accurately displayed gray-scale coding changes in correct locations in 97% of images. There was 95+/-4% accuracy rate by readers to correctly identify gray-scale changes. In addition, quantitative data at different stages of signal processing were investigated in a LSM of a subject with asthma. Signal processing was 97% accurate overall in that the gray-scale values from which the image was derived corresponded with intensity values from recorded signals. These results suggest VRI accurately maps acoustic signals to a gray-scale image and that trained readers can detect small changes.

Effect of airflow rate on vibration response imaging in normal lungs

Background:

Evaluating the effect of airflow rate on amplitude of lung sound energy and regional distribution of lung sounds may assist in the interpretation of computerized acoustic measurements.

Objectives:

The aim of this study was to assess the effect of airflow rate on Vibration Response Imaging (VRI) measurement in healthy lungs.

Methods:

Lung sounds were recorded from 20 healthy adults in the frequency range of 150-250 Hz using 40 piezoelectric sensors positioned on the posterior chest wall. During the recordings, subjects were breathing at airflow rates ranging between 0.3 and 1.7L/s. Online visual feedback was provided using a pneumotach mouthpiece.

Results:

Amplitude of lung sound energy significantly increased with increasing airflow rate (p<0.00001, Friedman test). A strong relationship (R2=0.95) was obtained between amplitude of lung sound energy at peak inspiration and airflow rate raised to the third power. This correlation did not significantly affect normalized lung sound distribution maps at peak inspiration, especially when airflow was higher than 1.0L/s. Acoustic maps obtained at airflow rates below 0.7L/s differed from those recorded above 1.0L/s (p<0.05, Wilcoxon matched-paired signed-ranks test).

Conclusion:

These findings may be of importance when comparing healthy and diseased lungs or when monitoring changes in lung sounds during treatment follow-up.