Breath Sound Intensity during Tidal Breathing in COPD Patients

Validation of a Body-Conducted Sound Sensor for Respiratory Sound Monitoring and a Comparison with Several Sensors

The ideal respiratory sound sensor exhibits high sensitivity, wide-band frequency characteristics, and excellent anti-noise properties. We investigated the body-conducted sound sensor (BCS) and verified its usefulness in respiratory sound monitoring through comparison with an air-coupled microphone (ACM) and acceleration sensor (B & K: 8001). We conducted four experiments for comparison: (1) estimation by equivalent circuit model of sensors and measurement by a sensitivity evaluation system; (2) measurement of tissue-borne sensitivity-to-air-noise sensitivity ratio (SRTA); (3) respiratory sound measurement through a simulator; and (4) actual respiratory sound measurement using human subjects. For (1), the simulation and measured values of all the sensors showed good agreement; BCS demonstrated sensitivity ~10 dB higher than ACM and higher sensitivity in the high-frequency segments compared with 8001. In (2), BCS showed high SRTA in the 600–1000 and 1200–2000-Hz frequency segments. In (3), BCS detected wheezes in the high-frequency segments of the respiratory sound. Finally, in (4), the sensors showed similar characteristics and features in the high-frequency segments as the simulators, where typical breathing sound detection was possible. BCS displayed a higher sensitivity and anti-noise property in high-frequency segments compared with the other sensors and is a useful respiratory sound sensor.

Respiratory Sound Based Classification of Chronic Obstructive Pulmonary Disease: a Risk Stratification Approach in Machine Learning Paradigm

This article investigates the classification of normal and COPD subjects on the basis of respiratory sound analysis using machine learning techniques. Thirty COPD and 25 healthy subject data are recorded. Total of 39 lung sound features and 3 spirometry features are extracted and evaluated. Various parametric and nonparametric tests are conducted to evaluate the relevance of extracted features. Classifiers such as support vector machine (SVM), k-nearest neighbor (KNN), logistic regression (LR), decision tree and discriminant analysis (DA) are used to categorize normal and COPD breath sounds. Classification based on spirometry parameters as well as respiratory sound parameters are assessed. Maximum classification accuracy of 83.6% is achieved by the SVM classifier while using the most relevant lung sound parameters i.e. median frequency and linear predictive coefficients. Further, SVM classifier and LR classifier achieved classification accuracy of 100% when relevant lung sound parameters, i.e. median frequency and linear predictive coefficient are combined with the spirometry parameters, i.e. forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV₁). It is concluded that combining lung sound based features with spirometry data can improve the accuracy of COPD diagnosis and hence the clinician's performance in routine clinical practice. The proposed approach is of great significance in a clinical scenario wherein it can be used to assist clinicians for automated COPD diagnosis. A complete handheld medical system can be developed in the future incorporating lung sounds for COPD diagnosis using machine learning techniques.

Airway inflammation phenotype prediction in asthma patients using lung sound analysis with fractional exhaled nitric oxide

BACKGROUND

We previously reported the results of lung sound analysis in patients with bronchial asthma and demonstrated that the exhalation-to-inhalation sound pressure ratio in the low frequency range between 100 and 200 Hz (E/I LF) was correlated with the presence of airway inflammation and airway obstruction. We classified asthma patients by airway inflammation phenotype using the induced sputum eosinophil and neutrophil ratio and determined whether this phenotype could be predicted using E/I LF and fractional exhaled nitric oxide values.

METHODS

Steroid-naive bronchial asthma patients were classified into four phenotypes, including "Low inflammation" (35 patients), "Eosinophilic type" (58 patients), "Neutrophilic type" (15 patients), and "Mixed type" (15 patients) based on the results of induced sputum examinations. The E/I LF data and FeNO levels were then evaluated for the four phenotype groups; the prediction powers of these two indices were then analyzed for each phenotype.

RESULTS

The median E/I LF value was highest in the "Mixed type" and lowest in the "Low inflammation" group. FeNO differentiated between the "Low inflammation" and "Eosinophilic type" groups, "Low inflammation" and "Neutrophilic type" groups, and "Neutrophilic type" and "Mixed type" (p < 0.0001, p = 0.007, and p = 0.04, respectively). E/I LF differentiated between the "Low inflammation" and "Eosinophilic type" groups (p = 0.006). E/I LF could distinguish the "Mixed type" group from the "Low inflammation" and "Eosinophilic type" groups (p = 0.002).

CONCLUSIONS

A combination of the E/I LF value and FeNO may be useful for the classification of the airway inflammation phenotype in patients with bronchial asthma.

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

Purpose

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

Patients and methods

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

Results

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

Conclusion

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

Regional Lung Sound Asynchrony in Chronic Obstructive Pulmonary Disease Patients

<b><i>Background:</i></b> Regional lung sound distribution in chronic obstructive pulmonary disease (COPD) is reported to be asynchronous. Mathematical analyses using vibration response imaging (VRI), such as left and right lung asynchrony (gap index; GI) and regional lung asynchrony (asynchrony score; AS), are useful measures to evaluate lung sound asynchrony. <b><i>Objectives:</i></b> The aim of this study was to investigate the association of lung sound asynchrony with pulmonary functions and emphysematous lesions in COPD patients. <b><i>Methods:</i></b> VRI recordings and pulmonary function tests were performed in 46 stable male COPD patients and in 40 healthy male smokers. Lung sound asynchrony was evaluated using GI, AS of the left and right lung (AS L-R), and AS of the upper and lower lung (AS U-L). In 38 patients, computed tomography taken within 6 months was available and analyzed. <b><i>Results:</i></b> AS L-R and AS U-L were significantly higher in COPD patients than in healthy smokers, with no significant difference in GI. There were no significant correlations with either AS and pulmonary functions, excluding a negative correlation between AS U-L and diffusion capacity. Although there were no significant correlations between both AS and severity of emphysema, significant positive correlations were observed between heterogeneity of emphysematous lesions and AS L-R (&#x03C1; = 0.38, p < 0.05) or AS U-L (&#x03C1; = 0.51, p < 0.005). <b><i>Conclusions:</i></b> Regional lung sounds are distributed more asynchronously in COPD patients than in healthy smokers, which correlates with the heterogeneous distribution of emphysematous lesions.