Analysis of forced wheezes in asthma patients

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.

A randomized controlled trial of respiratory physiotherapy in lower respiratory tract infections

INTRODUCTION

Physiotherapy may play a role in the recovery of signs, symptoms and function of patients with lower respiratory tract infections (LRTI) but its effectiveness is still controversial.

OBJECTIVES

To assess the effects of respiratory physiotherapy compared with standard pharmacological care on symptoms and function in outpatients with LRTI.

DESIGN

Single-blind, randomised controlled trial.

SETTING

Outpatients were recruited from the casualties of a central hospital.

PARTICIPANTS

Outpatients with LRTI were recruited and randomly allocated to the control (pharmacological) or experimental (pharmacological and respiratory physiotherapy) group.

INTERVENTION

The intervention consisted of conventional pharmacological treatment and conventional pharmacological treatment plus respiratory physiotherapy. Respiratory physiotherapy included breathing and airway clearance techniques, exercise training and education during 3-weeks, 3 times per week.

MAIN OUTCOME MEASURE

Primary outcome measures - occupation rate of wheezes Wh%; Secondary outcome measures - number of crackles, peripheral oxygen saturation (SpO2) modified Borg scale (mBorg), modified Medical Research Council scale (mMRC), 6-min walk test (6MWT), forced expiratory volume in 1 s and forced vital capacity, and volume and density of the lung and bronchial tree volume.

RESULTS

Ninety-seven patients (53 controls and 44 experimental) completed the intervention. After the intervention, both groups improved significantly in all variables (0.0001 < p < 0.04; 0.001<ƞ²<0.092), with the exception of the mBorg. The magnitude of improvement of the experimental group exceeded the control group in the number of crackles, SpO2 levels, mMRC and 6MWT (0.002 < p < 0.032; 0.002<ƞ²<0.092).

CONCLUSION

Adding respiratory physiotherapy to the pharmacological treatment of outpatients with LRTI results in greater recovery of symptoms and function parameters.

TRIAL REGISTRATION

NCT02053870.

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.

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.

Computerized respiratory sounds: a comparison between patients with stable and exacerbated COPD

INTRODUCTION

Diagnosis of acute exacerbations of chronic obstructive pulmonary disease (AECOPD) is often challenging as it relies on patients' clinical presentation. Computerized respiratory sounds (CRS), namely crackles and wheezes, may have the potential to contribute for the objective diagnosis/monitoring of an AECOPD.

OBJECTIVES

This study explored if CRS differ during stable and exacerbation periods in patients with COPD.

METHODS

13 patients with stable COPD and 14 with AECOPD were enrolled. CRS were recorded simultaneously at trachea, anterior, lateral and posterior chest locations using seven stethoscopes. Airflow (0.4-0.6l/s) was recorded with a pneumotachograph. Breathing phases were detected using airflow signals; crackles and wheezes with validated algorithms.

RESULTS

At trachea, anterior and lateral chest, no significant differences were found between the two groups in the number of inspiratory/expiratory crackles or inspiratory wheeze occupation rate. At posterior chest, the number of crackles (median 2.97-3.17 vs. 0.83-1.2, P < 0.001) and wheeze occupation rate (median 3.28%-3.8% vs. 1.12%-1.77%, P = 0.014-0.016) during both inspiration and expiration were significantly higher in patients with AECOPD than in stable patients. During expiration, wheeze occupation rate was also significantly higher in patients with AECOPD at trachea (median 3.12% vs. 0.79%, P < 0.001) and anterior chest (median 3.55% vs. 1.28%, P < 0.001).

CONCLUSION

Crackles and wheezes are more frequent in patients with AECOPD than in stable patients, particularly at posterior chest. These findings suggest that these CRS can contribute to the objective diagnosis/monitoring of AECOPD, which is especially valuable considering that they can be obtained by integrating computerized techniques with pulmonary auscultation, a noninvasive method that is a component of patients' physical examination.

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.

The development of an automated device for asthma monitoring for adolescents: methodologic approach and user acceptability

BACKGROUND

Many adolescents suffer serious asthma related morbidity that can be prevented by adequate self-management of the disease. The accurate symptom monitoring by patients is the most fundamental antecedent to effective asthma management. Nonetheless, the adequacy and effectiveness of current methods of symptom self-monitoring have been challenged due to the individuals' fallible symptom perception, poor adherence, and inadequate technique. Recognition of these limitations led to the development of an innovative device that can facilitate continuous and accurate monitoring of asthma symptoms with minimal disruption of daily routines, thus increasing acceptability to adolescents.

OBJECTIVE

The objectives of this study were to: (1) describe the development of a novel symptom monitoring device for teenagers (teens), and (2) assess their perspectives on the usability and acceptability of the device.

METHODS

Adolescents (13-17 years old) with and without asthma participated in the evolution of an automated device for asthma monitoring (ADAM), which comprised three phases, including development (Phase 1, n=37), validation/user acceptability (Phase 2, n=84), and post hoc validation (Phase 3, n=10). In Phase 1, symptom algorithms were identified based on the acoustic analysis of raw symptom sounds and programmed into a popular mobile system, the iPod. Phase 2 involved a 7 day trial of ADAM in vivo, and the evaluation of user acceptance using an acceptance survey and individual interviews. ADAM was further modified and enhanced in Phase 3.

RESULTS

Through ADAM, incoming audio data were digitized and processed in two steps involving the extraction of a sequence of descriptive feature vectors, and the processing of these sequences by a hidden Markov model-based Viterbi decoder to differentiate symptom sounds from background noise. The number and times of detected symptoms were stored and displayed in the device. The sensitivity (true positive) of the updated cough algorithm was 70% (21/30), and, on average, 2 coughs per hour were identified as false positive. ADAM also kept track of the their activity level throughout the day using the mobile system's built in accelerometer function. Overall, the device was well received by participants who perceived it as attractive, convenient, and helpful. The participants recognized the potential benefits of the device in asthma care, and were eager to use it for their asthma management.

CONCLUSIONS

ADAM can potentially automate daily symptom monitoring with minimal intrusiveness and maximal objectivity. The users' acceptance of the device based on its recognized convenience, user-friendliness, and usefulness in increasing symptom awareness underscores ADAM's potential to overcome the issues of symptom monitoring including poor adherence, inadequate technique, and poor symptom perception in adolescents. Further refinement of the algorithm is warranted to improve the accuracy of the device. Future study is also needed to assess the efficacy of the device in promoting self-management and asthma outcomes.

Diagnosis of hidden bronchial obstruction using computer-assessed tracheal forced expiratory noise time

BACKGROUND AND OBJECTIVE

Increased forced expiratory time was first recognized as a marker of obstruction half a century ago. However, the reported diagnostic capabilities of both auscultated forced expiratory time (FET(as)) and spirometric forced expiratory time are contradictory. Computer analysis of respiratory noises provides a precise estimation of acoustic forced expiratory noise time (FET(a)) being the object-measured analogue of FET(as). The aim of this study was to analyse FET(a) diagnostic capabilities in patients with asthma based on the hypothesis that FET(a) could reveal hidden bronchial obstruction.

METHODS

A group of asthma patients involved 149 males aged 16-25 years. In this group, 71 subjects had spirometry features of bronchial obstruction, meanwhile, the remaining 78 had normal spirometry. A control group involved 77 healthy subjects. Spirometry and forced expiratory tracheal noise recording were sequentially measured for each participant. FET(a) values were estimated by means of a developed computer procedure, including bandpass filtration (200-2000 Hz), waveform envelope calculation with accumulation period of 0.01 s, automated measurement of FET(a) at 0.5% level from the peak amplitude.

RESULTS

Specificity, sensitivity and area under Receiver Operating Characteristic curve of FET(a) and its ratios to squared chest circumference, height, weight were indistinguishable with baseline spirometry index FEV1 /forced vital capacity. Meanwhile, acoustic features of obstruction were revealed in 41%-49% of subgroup of patients with asthma but normal spirometry.

CONCLUSIONS

FET(a) of tracheal noise and its ratio to anthropometric parameters seem to be sensitive and specific tests of hidden bronchial obstruction in young male asthma patients.

Validation of an automated algorithm for detecting apneas and hypopneas by acoustic analysis of breath sounds

BACKGROUND

Sleep-disordered breathing (SDB) is common and is associated with increased risk for cardiovascular disease. However, most patients remain undiagnosed due to lack of access to sleep laboratories. We therefore tested the validity of a single-channel monitoring setup that captures and analyzes breath sounds (BSs) to detect SDB.

METHODS

BS were recorded from 50 patients undergoing simultaneous polysomnography (PSG). Using custom-designed automatic software, BS were subjected to a set of pattern recognition rules to identify apneas and hypopneas from which the acoustic apnea-hypopnea index (AHI-a) was calculated. Apneas and hypopneas from PSG were scored blindly by three technicians according to two criteria; one relying solely on the drop of the respiratory signal by >90% for an apnea and by 50% to 90% for a hypopnea (TV50 criteria), and another that also required a desaturation or an arousal for a hypopnea (American Association of Sleep Medicine [AASM] criteria). PSG AHI (AHI-p) was calculated for each technician according to both criteria.

RESULTS

There was no significant difference between AHI-p scores according to TV50 and AASM criteria. AHI-a was strongly correlated with AHI-p according to both TV50 (R=94%) and AASM criteria (R=93%). Bland-Altman plot analysis revealed that 98% and 92% of AHI-a fell within the limits of agreement for AHI-p according to TV50 and AASM criteria, respectively. Based on a diagnostic cutoff of AHI-p≥10 for SDB, overall accuracy of AHI-a reached 88% and negative predictive value reached 100%.

CONCLUSION

Acoustic analysis of BS is a reliable method for quantifying AHI and diagnosing SDB compared to simultaneous PSG.

Asthma history and presentation

Asthma is suspected from a history of key symptoms, including cough, wheezing, dyspnea, chest tightness, and increased mucus production. A positive family or personal history of atopic diseases and diseases that are comorbid with asthma, such as allergic rhinitis and rhinosinusitis, is also important. The differential diagnosis of asthma is broad and includes potentially life-threatening diseases. Pediatric asthma and psychiatric mimics require special attention to prevent misdiagnosis. Differentiating asthma from these other disease states by history alone is not always possible. Because accurate diagnosis is critical to successful treatment, objective testing by spirometry and methacholine challenge should be employed.

Dynamic visualization of lung sounds with a vibration response device: a case series

BACKGROUND

The field of computer-assisted mapping of lung sounds is constantly evolving and several devices have been developed in this field.

OBJECTIVES

Our objective was to evaluate a new computer-assisted lung sound imaging system, 'vibration response imaging' (VRI), that records and creates a dynamic image of breath sounds. We postulated that the VRI display format would qualitatively and quantitatively reveal breath sound distribution throughout the breathing cycle.

METHODS

Lung sounds were recorded from 5 healthy adults and 14 patients with various respiratory illnesses using VRI. The lung sounds were processed by the VRI software, which incorporates an algorithm to convert breath sounds in the frequency range of 150-250 Hz to a dynamic image and quantitative assessment of breath sound distribution.

RESULTS

Images and quantifications from recordings of the healthy adults showed distinct patterns for inspiration and expiration. Images and quantifications from the subjects with respiratory illness differed substantially from the images of the healthy subjects. Both healthy and pathological subjects presented some expected characteristics of breath sound distribution.

CONCLUSIONS

The VRI device may provide a new perspective in acoustic imaging and quantification of breath sounds by adding aspects of time analysis and quantification of distribution to existing methods. Further studies will be required in order to establish reliability of repeated recordings and to validate the sensitivity of the system in detecting various lung pathologies.