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

Comparative study of respiratory sounds classification methods based on cepstral analysis and artificial neural networks

In this paper, we investigated and evaluated various machine learning-based approaches for automatically detecting wheezing sounds. We conducted a comprehensive comparison of these proposed systems, assessing their classification performance through metrics such as Sensitivity, Specificity, and Accuracy. The main approach to developing a machine learning-based system for classifying respiratory sounds involved the combination of a technique for extracting features from an unknown input sound with a classification method to determine its belonging class. The characterization techniques used in this study are based on the cepstral analysis, which was extensively employed in the automatic speech recognition field. While MFCC (Mel-Frequency Cepstral Coefficients) feature extraction methods are commonly used in respiratory sounds classification, our study introduces a novelty by employing GFCC (Gammatone-Frequency Cepstral Coefficients) and BFCC (Bark-Frequency Cepstral Coefficients) for this purpose. For the classification task, we employed two types of neural networks: the MLP (Multilayer Perceptron), a feedforward neural network, and a variant of the LSTM (Long Short-Term Memory) recurrent neural network called BiLSTM (Bidirectional LSTM). The proposed classification systems are evaluated using a database consisting of 497 wheezing segments and 915 normal respiratory segments, which are recorded from individuals diagnosticated with asthma and individuals without any respiratory issues, respectively. The highest classification performance was achieved by the BFCC-BiLSTM model, which demonstrated an exceptional accuracy rate of 99.8%.

Enabling Continuous Breathing-Phase Contextualization via Wearable-Based Impedance Pneumography and Lung Sounds: A Feasibility Study

Chronic respiratory diseases affect millions and are leading causes of death in the US and worldwide. Pulmonary auscultation provides clinicians with critical respiratory health information through the study of Lung Sounds (LS) and the context of the breathing-phase and chest location in which they are measured. Existing auscultation technologies, however, do not enable the simultaneous measurement of this context, thereby potentially limiting computerized LS analysis. In this work, LS and Impedance Pneumography (IP) measurements were obtained from 10 healthy volunteers while performing normal and forced-expiratory (FE) breathing maneuvers using our wearable IP and respiratory sounds (WIRS) system. Simultaneous auscultation was performed with the Eko CORE stethoscope (EKO). The breathing-phase context was extracted from the IP signals and used to compute phase-by-phase (Inspiratory (I), expiratory (E), and their ratio (I:E)) and breath-by-breath acoustic features. Their individual and added value was then elucidated through machine learning analysis. We found that the phase-contextualized features effectively captured the underlying acoustic differences between deep and FE breaths, yielding a maximum F1 Score of 84.1 ±11.4% with the phase-by-phase features as the strongest contributors to this performance. Further, the individual phase-contextualized models outperformed the traditional breath-by-breath models in all cases. The validity of the results was demonstrated for the LS obtained with WIRS, EKO, and their combination. These results suggest that incorporating breathing-phase context may enhance computerized LS analysis. Hence, multimodal sensing systems that enable this, such as WIRS, have the potential to advance LS clinical utility beyond traditional manual auscultation and improve patient care.

Reliability and Validity of Computerized Adventitious Respiratory Sounds in People with Bronchiectasis

Background: Computerized adventitious respiratory sounds (ARS), such as crackles and wheezes, have been poorly explored in bronchiectasis, especially their measurement properties. This study aimed to test the reliability and validity of ARS in bronchiectasis. Methods: Respiratory sounds were recorded twice at 4 chest locations on 2 assessment sessions (7 days apart) in people with bronchiectasis and daily sputum expectoration. The total number of crackles, number of wheezes and wheeze occupation rate (%) were the parameters extracted. Results: 28 participants (9 men; 62 ± 12 y) were included. Total number of crackles and wheezes showed moderate within-day (ICC 0.87, 95% CI 0.74−0.94; ICC 0.86, 95% CI 0.71−0.93) and between-day reliability (ICC 0.70, 95% CI 0.43−0.86; ICC 0.78, 95% CI 0.56−0.90) considering all chest locations and both respiratory phases; wheeze occupation rate showed moderate within-day reliability (ICC 0.86, 95% CI 0.71−0.93), but poor between-day reliability (ICC 0.71, 95% CI 0.33−0.87). Bland−Altman plots revealed no systematic bias, but wide limits of agreement, particularly in the between-days analysis. All ARS parameters correlated moderately with the amount of daily sputum expectoration (r > 0.4; p < 0.05). No other significant correlations were observed. Conclusion: ARS presented moderate reliability and were correlated with the daily sputum expectoration in bronchiectasis. The use of sequential measurements may be an option to achieve greater accuracy when ARS are used to monitor or assess the effects of physiotherapy interventions in this population.

Human forced expiratory noise. Origin, apparatus and possible diagnostic applications

Forced expiratory (FE) noise is a powerful bioacoustic signal containing information on human lung biomechanics. FE noise is attributed to a broadband part and narrowband components-forced expiratory wheezes (FEWs). FE respiratory noise is composed by acoustic and hydrodynamic mechanisms. An origin of the most powerful mid-frequency FEWs (400-600 Hz) is associated with the 0th-3rd levels of bronchial tree in terms of Weibel [(2009). Swiss Med. Wkly. 139(27-28), 375-386], whereas high-frequency FEWs (above 600 Hz) are attributed to the 2nd-6th levels of bronchial tree. The laboratory prototype of the apparatus is developed, which includes the electret microphone sensor with stethoscope head, a laptop with external sound card, and specially developed software. An analysis of signals by the new method, including FE time in the range from 200 to 2000 Hz and band-pass durations and energies in the 200-Hz bands evaluation, is applied instead of FEWs direct measures. It is demonstrated experimentally that developed FE acoustic parameters correspond to basic indices of lung function evaluated by spirometry and body plethysmography and may be even more sensitive to some respiratory deviations. According to preliminary experimental results, the developed technique may be considered as a promising instrument for acoustic monitoring human lung function in extreme conditions, including diving and space flights. The developed technique eliminates the contact of the sensor with the human oral cavity, which is characteristic for spirometry and body plethysmography. It reduces the risk of respiratory cross-contamination, especially during outpatient and field examinations, and may be especially relevant in the context of the COVID-19 pandemic.

Analysis of Tracheal and Pulmonary Continuous Adventitious Respiratory Sounds in Asthma

Continuous adventitious sounds (CAS) are commonly observed in obstructive pulmonary diseases and are of great clinical interest. However, their evaluation is generally subjective. We have previously developed an automatic CAS segmentation and classification algorithm for CAS recorded on the chest surface. The aim of this study is to establish whether these pulmonary CAS can be identified in a similar way using a tracheal microphone. Respiratory sounds were originally recorded from 25 participants using five contact microphones, four on the chest and one on the trachea, during three progressive respiratory maneuvers. In this work CAS component detection was performed on the tracheal channel using our automatic algorithm based on the Hilbert spectrum. The tracheal CAS detected were then compared to the previously analyzed pulmonary CAS. The sensitivity of CAS identification was lower at the tracheal microphone, with CAS that appeared simultaneously in all four pulmonary recordings more likely to be identified in the tracheal recordings. These observations could be due to the CAS being obscured by the lower SNR present in the tracheal recordings or not being transmitted through the airways to the trachea. Further work to optimize the algorithm for the tracheal recordings will be conducted in the future.

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.

Feasibility of computerized adventitious respiratory sounds to assess the effects of airway clearance techniques in patients with bronchiectasis

Objective: To examine the feasibility of adventitious respiratory sound (ARS) as an outcome measure to assess the effects of airway clearance techniques (ACTs) in outpatients with bronchiectasis. Methods: ARS were registered pre/post four ACTs sessions. Clinical outcomes included: number of crackles (coarse and fine), number of wheezes (monophonic and polyphonic), wheezes occupation rate (%) and sputum quantity. Feasibility outcomes of ARS included: reasons for exclusion, suitability, safety, equipment and time required, magnitude of change after intervention and sample size estimation. Results: Seven patients (49.7 ± 20.5 years; FEV₁ 69.3 ± 15.8% predicted) were included. Recordings from four patients were excluded due to excessive environment noise. All ARS measurements were completed without any adverse events. An electronic stethoscope was acquired and the time spent to complete each assessment was 6 ± 3.5 min. The largest changes were observed for number of expiratory coarse crackles [effect size (95%CI) ES = 0.40 (0.01-0.79)], which correlated moderately with sputum quantity (r = 0.56), and inspiratory monophonic wheezes [ES = 0.61 (0.22-1.00)]. The estimated sample size for a full crossover trial was 46. Conclusions: ARS is feasible to assess the effects of ACTs in patients with bronchiectasis. Expiratory coarse crackles seem to be the most appropriate ARS parameter, but this finding needs to be confirmed in an adequately powered trial.

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.

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.

Automatic Differentiation of Normal and Continuous Adventitious Respiratory Sounds Using Ensemble Empirical Mode Decomposition and Instantaneous Frequency

Differentiating normal from adventitious respiratory sounds (RS) is a major challenge in the diagnosis of pulmonary diseases. Particularly, continuous adventitious sounds (CAS) are of clinical interest because they reflect the severity of certain diseases. This study presents a new classifier that automatically distinguishes normal sounds from CAS. It is based on the multiscale analysis of instantaneous frequency (IF) and envelope (IE) calculated after ensemble empirical mode decomposition (EEMD). These techniques have two major advantages over previous techniques: high temporal resolution is achieved by calculating IF-IE and a priori knowledge of signal characteristics is not required for EEMD. The classifier is based on the fact that the IF dispersion of RS signals markedly decreases when CAS appear in respiratory cycles. Therefore, CAS were detected by using a moving window to calculate the dispersion of IF sequences. The study dataset contained 1494 RS segments extracted from 870 inspiratory cycles recorded from 30 patients with asthma. All cycles and their RS segments were previously classified as containing normal sounds or CAS by a highly experienced physician to obtain a gold standard classification. A support vector machine classifier was trained and tested using an iterative procedure in which the dataset was randomly divided into training (65%) and testing (35%) sets inside a loop. The SVM classifier was also tested on 4592 simulated CAS cycles. High total accuracy was obtained with both recorded (94.6% ± 0.3%) and simulated (92.8% ± 3.6%) signals. We conclude that the proposed method is promising for RS analysis and classification.

Prediction of airway inflammation in patients with asymptomatic asthma by using lung sound analysis

BACKGROUND

The intensity and frequency of sounds in a lung sound analysis (LSA) may be related to airway constriction; however, whether any factors of an LSA can predict airway eosinophilic inflammation in patients with asthma is unknown.

OBJECTIVE

To determine whether an LSA can predict airway eosinophilic inflammation in patients with asymptomatic asthma.

METHODS

The expiratory-inspiratory ratios of sound power in the low-frequency range (E-I LF) from 36 patients with asymptomatic asthma were compared with those of 14 healthy controls. The relations of E-I LF with airway eosinophilic inflammation were analyzed. The E-I LF cutoff value for predicting airway eosinophilic inflammation also was analyzed.

RESULTS

The mean ± SD E-I LF was higher in the patients with asthma and with increased sputum eosinophils than in those patients without increased sputum eosinophils (0.45 ± 0.24 vs 0.20 ± 0.12; P < .001) or in the healthy controls (0.25 ± 0.10; P = .003). A multiple regression analysis showed that the sputum eosinophil ratio and exhaled nitric oxide were independently correlated with E-I LF, P = .0003 and P = .032, respectively. For the prediction of increased sputum eosinophils and increased fractional exhaled nitric oxide levels, the E-I LF thresholds of 0.29 and 0.30 showed sensitivities of 0.80 and 0.74 and specificities of 0.83 and 0.77, respectively.

CONCLUSIONS

We showed that LSAs can safely predict airway inflammation of patients with asymptomatic asthma.

Physiological acoustic sensing based on accelerometers: a survey for mobile healthcare

This paper reviews the applications of accelerometers on the detection of physiological acoustic signals such as heart sounds, respiratory sounds, and gastrointestinal sounds. These acoustic signals contain a rich reservoir of vital physiological and pathological information. Accelerometer-based systems enable continuous, mobile, low-cost, and unobtrusive monitoring of physiological acoustic signals and thus can play significant roles in the emerging mobile healthcare. In this review, we first briefly explain the operation principle of accelerometers and specifications that are important for mobile healthcare. Applications of accelerometer-based monitoring systems are then presented. Next, we review a variety of accelerometers which have been reported in literatures for physiological acoustic sensing, including both commercial products and research prototypes. Finally, we discuss some challenges and our vision for future development.

Computerized respiratory sounds in patients with COPD: a systematic review

Computerized respiratory sound analysis provides objective information about the respiratory system and may be useful to monitor patients with chronic obstructive pulmonary disease (COPD) and detect exacerbations early. For these purposes, a thorough understanding of the typical computerized respiratory sounds in patients with COPD during stable periods is essential. This review aimed to systematize the existing evidence on computerized respiratory sounds in stable COPD. A literature search in the Medline, EBSCO, Web of Knowledge and Scopus databases was performed. Seven original articles were included. The maximum frequencies of normal inspiratory sounds at the posterior chest were between 113 and 130Hz, lower than the frequency found at trachea (228 Hz). During inspiration, the frequency of normal respiratory sounds was found to be higher than expiration (130 vs. 100Hz). Crackles were predominantly inspiratory (2.9-5 vs. expiratory 0.73-2) and characterized by long durations of the variables initial deflection width (1.88-2.1 ms) and two cycle duration (7.7-11.6 ms). Expiratory wheeze rate was higher than inspiratory rate. In patients with COPD normal respiratory sounds seem to follow the pattern observed in healthy people and adventitious respiratory sounds are mainly characterized by inspiratory and coarse crackles and expiratory wheezes. Further research with larger samples and following the Computerized Respiratory Sound Analysis (CORSA) guidelines are needed.

Acoustical flow estimation in patients with obstructive sleep apnea during sleep

Tracheal respiratory sound analysis is a simple and non-invasive way to study the pathophysiology of the upper airways; it has recently been used for acoustical flow estimation and sleep apnea diagnosis. However in none of the previous studies, the accuracy of acoustical flow estimation was investigated neither during sleep nor in people with obstructive sleep apnea (OSA). In this study, we recorded tracheal sound, flow rate and head position from 11 individuals with OSA during sleep and wakefulness. We investigated two approaches for calibrating the parameters of acoustical flow estimation model based on the known data recorded during wakefulness and sleep. The results show that the acoustical flow estimation parameters change from wakefulness to sleep. Therefore, if the model is calibrated based on the data recorded during wakefulness, although the estimated flow follows the relative variations of the recorded flow, the quantitative flow estimation error would be high during sleep. On the other hand, when the calibration parameters are extracted from tracheal sound and flow recordings during sleep, the flow estimation error is less than 5%. These results confirm the reliability of acoustical methods for estimating breathing flow during sleep and detecting the partial or complete obstructions of the upper airways during sleep.

Respiratory flow-sound relationship during both wakefulness and sleep and its variation in relation to sleep apnea

Tracheal respiratory sound analysis is a simple and non-invasive way to study the pathophysiology of the upper airway and has recently been used for acoustic estimation of respiratory flow and sleep apnea diagnosis. However in none of the previous studies was the respiratory flow-sound relationship studied in people with obstructive sleep apnea (OSA), nor during sleep. In this study, we recorded tracheal sound, respiratory flow, and head position from eight non-OSA and 10 OSA individuals during sleep and wakefulness. We compared the flow-sound relationship and variations in model parameters from wakefulness to sleep within and between the two groups. The results show that during both wakefulness and sleep, flow-sound relationship follows a power law but with different parameters. Furthermore, the variations in model parameters may be representative of the OSA pathology. The other objective of this study was to examine the accuracy of respiratory flow estimation algorithms during sleep: we investigated two approaches for calibrating the model parameters using the known data recorded during either wakefulness or sleep. The results show that the acoustical respiratory flow estimation parameters change from wakefulness to sleep. Therefore, if the model is calibrated using wakefulness data, although the estimated respiratory flow follows the relative variations of the real flow, the quantitative flow estimation error would be high during sleep. On the other hand, when the calibration parameters are extracted from tracheal sound and respiratory flow recordings during sleep, the respiratory flow estimation error is less than 10%.

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.

The effect of anthropometric variations on acoustical flow estimation: proposing a novel approach for flow estimation without the need for individual calibration

Tracheal sound average power is directly related to the breathing flow rate and recently it has attracted considerable attention for acoustical flow estimation. However, the flow-sound relationship is highly variable among people and it also changes for the same person at different flow rates. Hence, a robust model capable of estimating flow from tracheal sounds at different flow rates in a large group of individuals does not exist. In this paper, a model is proposed to estimate respiratory flow from tracheal sounds. The proposed model eliminates the dependence of the previous methods on calibrating the model for every individual and at different flow rates. To validate the model, it was applied to the respiratory sound and flow data of 93 healthy individuals. We investigated the statistical correlation between the model parameters and anthropometric features of the subjects. The results have shown that gender, height, and smoking are the most significant factors that affect the model parameters. Hence, we grouped nonsmoker subjects into four groups based on their gender and height. The average of model parameters in each group was defined as the group-calibrated model parameters. These models were applied to estimate flow from data of subjects within the same group and in the other groups. The results show that flow estimation error based on the group-calibrated model is less than 10%. The low estimation errors confirm the possibility of defining a general flow estimation model for subjects with similar anthropometric features with no need for calibrating the model parameters for every individual. This technique simplifies the acoustical flow estimation in general applications including sleep studies and patients' screening in health care facilities.

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

Computerized lung-sound analysis is a sensitive and quantitative method to identify wheezing by its typical pattern on spectral analysis. We evaluated the accuracy of the VRI, a multi-sensor, computer-based device with an automated technique of wheeze detection. The method was validated in 100 sound files from seven subjects with asthma or chronic obstructive pulmonary disease and seven healthy subjects by comparison of auscultation findings, examination of audio files, and computer detection of wheezes. Three blinded physicians identified 40 sound files with wheezes and 60 sound files without wheezes. Sensitivity and specificity were 83% and 85%, respectively. Negative predictive value and positive predictive value were 89% and 79%, respectively. Overall inter-rater agreement was 84%. False positive cases were found to contain sounds that simulate wheezes, such as background noises with high frequencies or strong noises from the throat that could be heard and identified without a stethoscope. The present findings demonstrate that the wheeze detection algorithm has good accuracy, sensitivity, specificity, negative predictive value and positive predictive value for wheeze detection in regional analyses with a single sensor and multiple sensors. Results are similar to those reported in the literature. The device is user-friendly, requires minimal patient effort, and, distinct from other devices, it provides a dynamic image of breath sound distribution with wheeze detection output in less than 1 minute.

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 respiratory sounds: state of the art

OBJECTIVE

This paper describes state of the art, scientific publications and ongoing research related to the methods of analysis of respiratory sounds.

METHODS AND MATERIAL

Review of the current medical and technological literature using Pubmed and personal experience.

RESULTS

The study includes a description of the various techniques that are being used to collect auscultation sounds, a physical description of known pathologic sounds for which automatic detection tools were developed. Modern tools are based on artificial intelligence and on technics such as artificial neural networks, fuzzy systems, and genetic algorithms…

CONCLUSION

The next step will consist in finding new markers so as to increase the efficiency of decision aid algorithms and tools.

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.

Nasal expiratory sound analysis in healthy people

OBJECTIVE

To evaluate the practicability of Odiosoft-Rhino (OR), a new experimental method for assessing the nasal airflow and resistance, in normal subjects and to compare the results with acoustic rhinometry (AR) findings.

STUDY DESIGN AND SETTING

OR and AR were carried out in 72 healthy subjects. Their visual analogue scales of nasal obstruction, minimal cross sectional areas (MCA(1) and MCA(2)), and nasal expiration sounds were analyzed and noted for both nasal cavities.

RESULTS

Statistically significant correlations (P < 0.05) were found between OR and AR in 2,000 to 4,000 Hz and 4,000 to 6,000 Hz with MCA(1) and MCA(2).

CONCLUSIONS

OR is a noninvasive and rapid test. It is easy to carry out and requires little patient cooperation. It seems that it may give compatible results with other reliable methods that assess nasal airflow.

SIGNIFICANCE

We assume that OR is a sensitive method for evaluating nasal airflow in normal subjects in an easy way.

EBM RATING

A-1b.

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.

Automatic wheeze detection based on auditory modelling

Automatic wheeze detection has several potential benefits compared with reliance on human auscultation: it is experience independent, an automated historical record can easily be kept, and it allows quantification of wheeze severity. Previous attempts to detect wheezes automatically have had partial success but have not been reliable enough to become widely accepted as a useful tool. In this paper an improved algorithm for automatic wheeze detection based on auditory modelling is developed, called the frequency- and duration-dependent threshold algorithm. The mean frequency and duration of each wheeze component are obtained automatically. The detected wheezes are marked on a spectrogram. In the new algorithm, the concept of a frequency- and duration-dependent threshold for wheeze detection is introduced. Another departure from previous work is that the threshold is based not on global power but on power corresponding to a particular frequency range. The algorithm has been tested on 36 subjects, 11 of whom exhibited characteristics of wheeze. The results show a marked improvement in the accuracy of wheeze detection when compared with previous algorithms.