Analysis of tracheal sounds during forced exhalation in asthma patients and normal subjects: bronchodilator response effect

Body Acoustics for the Non-Invasive Diagnosis of Medical Conditions

In the past few decades, many non-invasive monitoring methods have been developed based on body acoustics to investigate a wide range of medical conditions, including cardiovascular diseases, respiratory problems, nervous system disorders, and gastrointestinal tract diseases. Recent advances in sensing technologies and computational resources have given a further boost to the interest in the development of acoustic-based diagnostic solutions. In these methods, the acoustic signals are usually recorded by acoustic sensors, such as microphones and accelerometers, and are analyzed using various signal processing, machine learning, and computational methods. This paper reviews the advances in these areas to shed light on the state-of-the-art, evaluate the major challenges, and discuss future directions. This review suggests that rigorous data analysis and physiological understandings can eventually convert these acoustic-based research investigations into novel health monitoring and point-of-care solutions.

Wheeze sound analysis using computer-based techniques: a systematic review

Wheezes are high pitched continuous respiratory acoustic sounds which are produced as a result of airway obstruction. Computer-based analyses of wheeze signals have been extensively used for parametric analysis, spectral analysis, identification of airway obstruction, feature extraction and diseases or pathology classification. While this area is currently an active field of research, the available literature has not yet been reviewed. This systematic review identified articles describing wheeze analyses using computer-based techniques on the SCOPUS, IEEE Xplore, ACM, PubMed and Springer and Elsevier electronic databases. After a set of selection criteria was applied, 41 articles were selected for detailed analysis. The findings reveal that 1) computerized wheeze analysis can be used for the identification of disease severity level or pathology, 2) further research is required to achieve acceptable rates of identification on the degree of airway obstruction with normal breathing, 3) analysis using combinations of features and on subgroups of the respiratory cycle has provided a pathway to classify various diseases or pathology that stem from airway obstruction.

Analysis of wheeze sounds during tidal breathing according to severity levels in asthma patients

Objective: This study aimed to statistically analyze the behavior of time-frequency features in digital recordings of wheeze sounds obtained from patients with various levels of asthma severity (mild, moderate, and severe), and this analysis was based on the auscultation location and/or breath phase. Method: Segmented and validated wheeze sounds were collected from the trachea and lower lung base (LLB) of 55 asthmatic patients during tidal breathing maneuvers and grouped into nine different datasets. The quartile frequencies F₂₅, F₅₀, F₇₅, F₉₀ and F₉₉, mean frequency (MF) and average power (AP) were computed as features, and a univariate statistical analysis was then performed to analyze the behavior of the time-frequency features. Results: All features generally showed statistical significance in most of the datasets for all severity levels [χ² = 6.021-71.65, p < 0.05, η² = 0.01-0.52]. Of the seven investigated features, only AP showed statistical significance in all the datasets. F₂₅, F₇₅, F₉₀ and F₉₉ exhibited statistical significance in at least six datasets [χ² = 4.852-65.63, p < 0.05, η² = 0.01-0.52], and F₂₅, F₅₀ and MF showed statistical significance with a large η² in all trachea-related datasets [χ² = 13.54-55.32, p < 0.05, η² = 0.13-0.33]. Conclusion: The results obtained for the time-frequency features revealed that (1) the asthma severity levels of patients can be identified through a set of selected features with tidal breathing, (2) tracheal wheeze sounds are more sensitive and specific predictors of severity levels and (3) inspiratory and expiratory wheeze sounds are almost equally informative.

Characterization and classification of asthmatic wheeze sounds according to severity level using spectral integrated features

OBJECTIVE

This study aimed to investigate and classify wheeze sounds of asthmatic patients according to their severity level (mild, moderate and severe) using spectral integrated (SI) features.

METHOD

Segmented and validated wheeze sounds were obtained from auscultation recordings of the trachea and lower lung base of 55 asthmatic patients during tidal breathing manoeuvres. The segments were multi-labelled into 9 groups based on the auscultation location and/or breath phases. Bandwidths were selected based on the physiology, and a corresponding SI feature was computed for each segment. Univariate and multivariate statistical analyses were then performed to investigate the discriminatory behaviour of the features with respect to the severity levels in the various groups. The asthmatic severity levels in the groups were then classified using the ensemble (ENS), support vector machine (SVM) and k-nearest neighbour (KNN) methods.

RESULTS AND CONCLUSION

All statistical comparisons exhibited a significant difference (p < 0.05) among the severity levels with few exceptions. In the classification experiments, the ensemble classifier exhibited better performance in terms of sensitivity, specificity and positive predictive value (PPV). The trachea inspiratory group showed the highest classification performance compared with all the other groups. Overall, the best PPV for the mild, moderate and severe samples were 95% (ENS), 88% (ENS) and 90% (SVM), respectively. With respect to location, the tracheal related wheeze sounds were most sensitive and specific predictors of asthma severity levels. In addition, the classification performances of the inspiratory and expiratory related groups were comparable, suggesting that the samples from these locations are equally informative.

Acoustic Methods for Pulmonary Diagnosis

Recent developments in sensor technology and computational analysis methods enable new strategies to measure and interpret lung acoustic signals that originate internally, such as breathing or vocal sounds, or are externally introduced, such as in chest percussion or airway insonification. A better understanding of these sounds has resulted in a new instrumentation that allows for highly accurate as well as portable options for measurement in the hospital, in the clinic, and even at home. This review outlines the instrumentation for acoustic stimulation and measurement of the lungs. We first review the fundamentals of acoustic lung signals and the pathophysiology of the diseases that these signals are used to detect. Then, we focus on different methods of measuring and creating signals that have been used in recent research for pulmonary disease diagnosis. These new methods, combined with signal processing and modeling techniques, lead to a reduction in noise and allow improved feature extraction and signal classification. We conclude by presenting the results of human subject studies taking advantage of both the instrumentation and signal processing tools to accurately diagnose common lung diseases. This paper emphasizes the active areas of research within modern lung acoustics and encourages the standardization of future work in this field.

Multichannel lung sound analysis for asthma detection

BACKGROUND AND OBJECTIVE

Lung sound signals convey valuable information of the lung status. Auscultation is an effective technique to appreciate the condition of the respiratory system using lung sound signals. The prior works on asthma detection from lung sound signals rely on the presence of wheeze. In this paper, we have classified normal and asthmatic subjects using advanced signal processing of posterior lung sound signals, even in the absence of wheeze.

METHODS

We collected lung sounds of 60 subjects (30 normal and 30 asthma) using a novel 4-channel data acquisition system from four different positions over the posterior chest, as suggested by the pulmonologist. A spectral subband based feature extraction scheme is proposed that works with artificial neural network (ANN) and support vector machine (SVM) classifiers for the multichannel signal. The power spectral density (PSD) is estimated from extracted lung sound cycle using Welch's method, which then decomposed into uniform subbands. A set of statistical features is computed from each subband and applied to ANN and SVM classifiers to classify normal and asthmatic subjects.

RESULTS

In the first part of this study, the performances of each individual channel and four channels together are evaluated where the combined channel performance is found superior to that of individual channels. Next, the performances of all possible combinations of the channels are investigated and the best classification accuracies of 89.2( ± 3.87)% and 93.3( ± 3.10)% are achieved for 2-channel and 3-channel combinations in ANN and SVM classifiers, respectively.

CONCLUSIONS

The proposed multichannel asthma detection method where the presence of wheeze in lung sound is not a necessary requirement, outperforms commonly used lung sound classification methods in this field and provides significant relative improvement. The channel combination study gives insight into the contribution of respective lung sound collection areas and their combinations in asthma detection.

Novel approach to continuous adventitious respiratory sound analysis for the assessment of bronchodilator response

Background

A thorough analysis of continuous adventitious sounds (CAS) can provide distinct and complementary information about bronchodilator response (BDR), beyond that provided by spirometry. Nevertheless, previous approaches to CAS analysis were limited by certain methodology issues. The aim of this study is to propose a new integrated approach to CAS analysis that contributes to improving the assessment of BDR in clinical practice for asthma patients.

Methods

Respiratory sounds and flow were recorded in 25 subjects, including 7 asthma patients with positive BDR (BDR+), assessed by spirometry, 13 asthma patients with negative BDR (BDR-), and 5 controls. A total of 5149 acoustic components were characterized using the Hilbert spectrum, and used to train and validate a support vector machine classifier, which distinguished acoustic components corresponding to CAS from those corresponding to other sounds. Once the method was validated, BDR was assessed in all participants by CAS analysis, and compared to BDR assessed by spirometry.

Results

BDR+ patients had a homogenous high change in the number of CAS after bronchodilation, which agreed with the positive BDR by spirometry, indicating high reversibility of airway obstruction. Nevertheless, we also found an appreciable change in the number of CAS in many BDR- patients, revealing alterations in airway obstruction that were not detected by spirometry. We propose a categorization for the change in the number of CAS, which allowed us to stratify BDR- patients into three consistent groups. From the 13 BDR- patients, 6 had a high response, similar to BDR+ patients, 4 had a noteworthy medium response, and 1 had a low response.

Conclusions

In this study, a new non-invasive and integrated approach to CAS analysis is proposed as a high-sensitive tool for assessing BDR in terms of acoustic parameters which, together with spirometry parameters, contribute to improving the stratification of BDR levels in patients with obstructive pulmonary diseases.

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.

Lung sound analysis can be an index of the control of bronchial asthma

BACKGROUND

We assessed whether lung sound analysis (LSA) is a valid measure of airway obstruction and inflammation in patients with bronchial asthma during treatment with inhaled corticosteroids (ICSs).

METHODS

63 good adherence patients with bronchial asthma and 18 poor adherence patients were examined by LSA, spirometry, fractional exhaled nitric oxide (FeNO), and induced sputum. The expiration-to-inspiration lung sound power ratio at low frequencies between 100 and 200 Hz (E/I LF) obtained by LSA was compared between healthy volunteers and bronchial asthma patients. Next, post-ICS treatment changes were compared in bronchial asthma patients between the good adherence patients and the poor adherence patients.

RESULTS

E/I LF was significantly higher in bronchial asthma patients (0.62 ± 0.21) than in healthy volunteers (0.44 ± 0.12, p < 0.001). The good adherence patients demonstrated a significant reduction in E/I LF from pre-treatment to post-treatment (0.55 ± 0.21 to 0.46 ± 0.16, p = 0.002), whereas the poor adherence patients did not show a significant change. The decrease of E/I LF correlated with the improvement of FEV₁/FVC ratio during the ICS treatment (r = -0.26, p = 0.04). The subjects with higher pre-treatment E/I LF values had significantly lower FEV₁/FVC and V_50,%pred (p < 0.001), and significantly higher FeNO and sputum eosinophil percentages (p = 0.008 and p < 0.001, respectively).

CONCLUSIONS

The E/I LF measurement obtained by LSA is useful as an indicator of changes in airway obstruction and inflammation and can be used for monitoring the therapeutic course of bronchial asthma patients.

Lung sounds in bronchial asthma

Modern understanding of lung sounds started with a historical article by Forgacs. Since then, many studies have clarified the changes of lung sounds due to airway narrowing as well as the mechanism of genesis for these sounds. Studies using bronchoprovocation have shown that an increase of the frequency and/or intensity of lung sounds was a common finding of airway narrowing and correlated well with lung function. Bronchoprovocation studies have also disclosed that wheezing may not be as sensitive as changes in basic lung sounds in acute airway narrowing. A forced expiratory wheeze (FEW) may be an early sign of airway obstruction in patients with bronchial asthma. Studies of FEW showed that airway wall oscillation and vortex shedding in central airways are the most likely mechanisms of the generation of expiratory wheezes. Studies on the genesis of wheezes have disclosed that inspiratory and expiratory wheezes may have the same mechanism of generation as a flutter/flow limitation mechanism, either localized or generalized. In lung sound analysis, the narrower the airways are, the higher the frequency of breathing sounds is, and, if a patient has higher than normal breathing sounds, i.e., bronchial sounds, he or she may have airway narrowing or airway inflammation. It is sometimes difficult to detect subtle changes in lung sounds; therefore, we anticipate that automated analysis of lung sounds will be used to overcome these difficulties in the near future.

Monitoring of wheeze duration during spontaneous respiration in asthmatic patients

Respiratory sound analysis can offer important information related to pulmonary diseases. Wheezes have been reported as adventitious respiratory sounds in asthmatic or obstructive patients, during forced exhalation maneuvers. In this work, we propose a method for monitoring and analysis of respiratory sounds in frequency domain, during spontaneous ventilation. The database analyzed was acquired during spontaneous ventilation for 120 seconds (DBsv), of 26 asthmatics patients. Using an autoregressive model (AR, order 16), the Power Spectral Density (PSD) was calculated for every phase of expiration and inspiration and the maximum frequency (fp) was estimated. From this parameter we study the time duration of the wheezes. The effect of bronchodilator inhalation in asthmatic patients was studied analyzing the duration of the wheezes in the bandwidth 600-2000 Hz (HFband). The wheeze duration is evaluated as the number of consecutive segments, with fp is inside of HFband, (for 3 or more segments in a cycle). The difference of the wheeze duration inside the respiratory cycles (Dwheez), before and after bronchodilator inhalation (POST) was evaluated. It was found a good correlation between Dwheez and FEV 1% improvement (FEV 1%_imp), for FEV1%_imp greater than 8%, whereas values FEV1%_imp less than 8% did not show any change of Dwheez. This last result suggests no difference in the wheeze duration between the baseline and POST records. This method could predict the FEV1%_imp by means of estimation of Dwheez during spontaneous ventilation.

Analysis of wheezes in asthmatic patients during spontaneous respiration

Respiratory sound analysis can offer important information related to pulmonary diseases. Wheezes have been reported as adventitious respiratory sounds in asthmatic or obstructive patients, during forced exhalation maneuvers. In this work, we propose a method for analysis of respiratory sounds in frequency domain, during spontaneous ventilation. Two databases were analyzed: signals acquired during spirometry (DBspir), composed by 23 subjects (N=15 asthmatics, N=8 control); and signals acquired during spontaneous ventilation for 120 seconds (DBsv), composed by 26 asthmatics. Using an autoregressive model (AR, order 16), it was calculated the Power Spectral Density (PSD) for each expiration and the peak frequency (fp) was estimated. Higher values of fp were found in asthmatic patients with severe obstruction in relation to light obstruction or control subjects. The effect of bronchodilator inhalation in asthmatic patients was studied in the database DBsv, analyzing contribution of wheezes in the bandwidth 600-2000 Hz (HFband)., Differences of number of respiratory cycles with wheezes (Dwheez index), before and after bronchodilator inhalation were evaluated. It was found a good correlation between Dwheez and FEV1% improvement (FEV1>%_imp), for FEV1%_imp > 10%. This method could predict the FEV1%_imp by means of estimation of Dwheez index during spontaneous ventilation.

Analysis of forced wheezes in asthma patients

BACKGROUND

Spirometric parameters can be normal in many stable asthma patients, making a diagnosis difficult at certain times in the course of disease.

OBJECTIVES

The present study aims to find differences and similarities in the acoustic characteristics of forced wheezes among asthma patients with and without normal spirometric values.

METHODS

Eleven chronic asthma patients (8 men/3 women) with moderate-to-severe airway obstruction (FEV1 48.4%), 9 stable asthma patients (6 males/3 females) with normal spirometry (FEV1 84.0%) and a positive methacholine test and 14 healthy subjects (8/6) were enrolled in the study. A contact sensor was placed on the trachea, and wheezes were detected by a modified Shabtai-Musih algorithm in a time-frequency representation.

RESULTS

More wheezes were recorded in obstructive asthma patients than in stable asthma and control subjects: nonstable asthma 13.6 (13.3), stable asthma 3.5 (3.0) and control subjects 2.5 (2.1). The mean frequency of all wheezes detected was higher in control subjects than in either stable or non-stable asthma patients. The change in the total number of wheezes after terbutaline inhalation was more pronounced in nonstable asthma patients than in stable asthmatics and control subjects.

CONCLUSIONS

This study confirms that wheeze recording during forced expiratory maneuvers can be a complementary measure to spirometry to identify asthma patients.

Time-frequency detection and analysis of wheezes during forced exhalation

The objective of the present work was to detect and analyze wheezes by means of a highly sensitive time-frequency algorithm. Automatic measurements were compared with clinical auscultation for forced exhalation segments from 1.2 to 0 liters/second (l/s). Sensitivities between 100% and 71%, as a function of flow level related to wheezing segments detection, were achieved. Time-frequency wheeze parameters were measured for the flow range from 1.2 to 0.2 l/s. Wheezes were detected in both analyzed groups; asthmatics (N = 16) and control subjects (N = 15). Significant differences between groups were found for the mean number of wheezes detected at basal condition (p = 0.0003). Frequency parameter differences were also significant (0.0112 < p < 0.0307). All these parameters were also studied after applying a bronchodilator drug (Terbutaline). Significant differences between patient groups were found when studying the changes in the number of wheezes for each patient (p = 0.0195). Finally, limited bandwidth parameters, which measure the bronchodilator response, were also studied.

Detection of wheezing during maximal forced exhalation in patients with obstructed airways

STUDY OBJECTIVES

Wheezing is a common clinical finding in patients with asthma and COPD during episodes of severe airway obstruction, and can also be heard in normal subjects during forced expiratory maneuvers; however, the properties of wheezing are difficult to perceive and quantify during auscultation. We therefore developed and evaluated a new technique for recording and analyzing wheezing during forced expiratory maneuvers in a group of patients with obstructed airways (asthma, COPD) and a control group of healthy subjects.

MATERIAL AND METHODS

Sixteen patients with asthma (9 men and 7 women), 6 patients with COPD (6 men), and 15 healthy subjects (7 men and 8 women) were enrolled. The patients had moderate-to-severe obstruction (FEV(1) of 40 to 53% predicted). A contact sensor on the trachea was used to record sound during forced expiratory maneuvers. Wheeze detection was carried out by a modified algorithm in a frequency-time space after applying the fast Fourier transform.

RESULTS

More wheezes were recorded in patients with obstructed airways than in control subjects: asthma patients, 8.4 +/- 6.4 wheezes; COPD patients, 10.4 +/- 6.1 wheezes; and control subjects, 2.9 +/- 2.0 wheezes (mean +/- SD). The mean frequency of all detected wheezes was higher in control subjects than in patients with obstructed airways (asthma patients, 560.9 +/- 140.8 Hz; COPD patients, 669.4 +/- 250.1 Hz; and control subjects, 750.7 +/- 175.7 Hz). The total number of wheezes after terbutaline inhalation changed more in patients with obstructed airways than in control subjects.

CONCLUSIONS

The new method that we describe for studying airway behavior during forced expiratory maneuvers is able to identify and analyze wheeze segments generated in patients with obstructed airways, as evidenced by the greater number of wheezes detected in the patient group, the main finding of this study. This method clearly and objectively identifies the presence of obstructive disease.