Measurement of respiratory acoustic signals. Effect of microphone air cavity width, shape, and venting

Optimized Acoustic Phantom Design for Characterizing Body Sound Sensors

Many commercial and prototype devices are available for capturing body sounds that provide important information on the health of the lungs and heart; however, a standardized method to characterize and compare these devices is not agreed upon. Acoustic phantoms are commonly used because they generate repeatable sounds that couple to devices using a material layer that mimics the characteristics of skin. While multiple acoustic phantoms have been presented in literature, it is unclear how design elements, such as the driver type and coupling layer, impact the acoustical characteristics of the phantom and, therefore, the device being measured. Here, a design of experiments approach is used to compare the frequency responses of various phantom constructions. An acoustic phantom that uses a loudspeaker to generate sound and excite a gelatin layer supported by a grid is determined to have a flatter and more uniform frequency response than other possible designs with a sound exciter and plate support. When measured on an optimal acoustic phantom, three devices are shown to have more consistent measurements with added weight and differing positions compared to a non-optimal phantom. Overall, the statistical models developed here provide greater insight into acoustic phantom design for improved device characterization.

Methods for Adventitious Respiratory Sound Analyzing Applications Based on Smartphones: A Survey

Detection and classification of adventitious acoustic lung sounds plays an important role in diagnosing, monitoring, controlling and, caring the patients with lung diseases. Such systems can be presented as different platforms like medical devices, standalone software or smartphone application. Ubiquity of smartphones and widespread use of the corresponding applications make such a device an attractive platform for hosting the detection and classification systems for adventitious lung sounds. In this paper, the smartphone-based systems for automatic detection and classification of the adventitious lung sounds are surveyed. Such adventitious sounds include cough, wheeze, crackle and, snore. Relevant sounds related to abnormal respiratory activities are considered as well. The methods are shortly described and the analyzing algorithms are explained. The analysis includes detection and/or classification of the sound events. A summary of the main surveyed methods together with the classification parameters and used features for the sake of comparison is given. Existing challenges, open issues and future trends will be discussed as well.

A Wearable Stethoscope for Long-Term Ambulatory Respiratory Health Monitoring

Lung sounds acquired by stethoscopes are extensively used in diagnosing and differentiating respiratory diseases. Although an extensive know-how has been built to interpret these sounds and identify diseases associated with certain patterns, its effective use is limited to individual experience of practitioners. This user-dependency manifests itself as a factor impeding the digital transformation of this valuable diagnostic tool, which can improve patient outcomes by continuous long-term respiratory monitoring under real-life conditions. Particularly patients suffering from respiratory diseases with progressive nature, such as chronic obstructive pulmonary diseases, are expected to benefit from long-term monitoring. Recently, the COVID-19 pandemic has also shown the lack of respiratory monitoring systems which are ready to deploy in operational conditions while requiring minimal patient education. To address particularly the latter subject, in this article, we present a sound acquisition module which can be integrated into a dedicated garment; thus, minimizing the role of the patient for positioning the stethoscope and applying the appropriate pressure. We have implemented a diaphragm-less acousto-electric transducer by stacking a silicone rubber and a piezoelectric film to capture thoracic sounds with minimum attenuation. Furthermore, we benchmarked our device with an electronic stethoscope widely used in clinical practice to quantify its performance.

Multi-channel lung sound classification with convolutional recurrent neural networks

In this paper, we present an approach for multi-channel lung sound classification, exploiting spectral, temporal and spatial information. In particular, we propose a frame-wise classification framework to process full breathing cycles of multi-channel lung sound recordings with a convolutional recurrent neural network. With our recently developed 16-channel lung sound recording device, we collect lung sound recordings from lung-healthy subjects and patients with idiopathic pulmonary fibrosis (IPF), within a clinical trial. From the lung sound recordings, we extract spectrogram features and compare different deep neural network architectures for binary classification, i.e. healthy vs. pathological. Our proposed classification framework with the convolutional recurrent neural network outperforms the other networks by achieving an F-score of F₁≈92%. Together with our multi-channel lung sound recording device, we present a holistic approach to multi-channel lung sound analysis.

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.

Reliability, validity and minimal detectable change of computerized respiratory sounds in patients with chronic obstructive pulmonary disease

INTRODUCTION

Computerized respiratory sounds (CRS) are closely related to the movement of air within the tracheobronchial tree and are promising outcome measures in patients with chronic obstructive pulmonary disease (COPD). However, CRS measurement properties have been poorly tested.

OBJECTIVE

The aim of this study was to assess the reliability, validity and the minimal detectable changes (MDC) of CRS in patients with stable COPD.

METHODS

Fifty patients (36♂, 67.26 ± 9.31y, FEV₁ 49.52 ± 19.67%predicted) were enrolled. CRS were recorded simultaneously at seven anatomic locations (trachea; right and left anterior, lateral and posterior chest). The number of crackles, wheeze occupation rate, median frequency (F50) and maximum intensity (Imax) were processed using validated algorithms. Within-day and between-days reliability, criterion and construct validity, validity to predict exacerbations and MDC were established.

RESULTS

CRS presented moderate-to-excellent within-day reliability (ICC_1,3  ≥ 0.51; P < .05) and moderate-to-good between-days reliability (ICC_1,2  ≥ 0.47; P < .05) for most locations. Negligible-to-moderate correlations with FEV₁ %predicted were found (-0.53 < r_s  < -0.28; P < .05), and the inspiratory number of crackles were the best discriminator between mild-to-moderate and severe-to-very severe airflow limitations (area under the curve >0.78). CRS correlated poorly with patient-reported outcomes (r_s  < 0.48; P < .05) and did not predict exacerbations. Inspiratory number of crackles at posterior right chest, inspiratory F50 at trachea and anterior left chest and expiratory Imax at anterior right chest were simultaneously reliable and valid, and their MDC were 2.41, 55.27, 29.55 and 3.98, respectively.

CONCLUSION

CRS are reliable and valid. Their use, integrated with other clinical and patient-reported measures, may fill the gap of assessing small airways and contribute toward a patient's comprehensive evaluation.

Influences of gender and anthropometric features on inspiratory inhaler acoustics and peak inspiratory flow rate

Inhalers are hand-held devices used to treat chronic respiratory diseases such as asthma and chronic obstructive pulmonary disease (COPD). Medication is delivered from an inhaler to the user through an inhalation maneuver. It is unclear whether gender and anthropometric features such as age, height, weight and body mass index (BMI) influence the acoustic properties of inspiratory inhaler sounds and peak inspiratory flow rate (PIFR) in inhalers. In this study, healthy male (n=9) and female (n=7) participants were asked to inhale at an inspiratory flow rate (IFR) of 60 L/min in four commonly used inhalers (Turbuhaler(™), Diskus(™), Ellipta(™) and Evohaler(™)). Ambient inspiratory sounds were recorded from the mouthpiece of each inhaler and over the trachea of each participant. Each participant's PIFR was also recorded for each of the four inhalers. Results showed that gender and anthropometric features have the potential to influence the spectral properties of ambient and tracheal inspiratory inhaler sounds. It was also observed that males achieved statistically significantly higher PIFRs in each inhaler in comparison to females (p<;0.05). Acoustic features were found to be significantly different across inhalers suggesting that acoustic features are modulated by the inhaler design and its internal resistance to airflow.

Multiclass classification of subjects with sleep apnoea-hypopnoea syndrome through snoring analysis

The gold standard for diagnosing sleep apnoea-hypopnoea syndrome (SAHS) is polysomnography (PSG), an expensive, labour-intensive and time-consuming procedure. Accordingly, it would be very useful to have a screening method to allow early assessment of the severity of a subject, prior to his/her referral for PSG. Several differences have been reported between simple snorers and SAHS patients in the acoustic characteristics of snoring and its variability. In this paper, snores are fully characterised in the time domain, by their sound intensity and pitch, and in the frequency domain, by their formant frequencies and several shape and energy ratio measurements. We show that accurate multiclass classification of snoring subjects, with three levels of SAHS, can be achieved on the basis of acoustic analysis of snoring alone, without any requiring information on the duration or the number of apnoeas. Several classification methods are examined. The best of the approaches assessed is a Bayes model using a kernel density estimation method, although good results can also be obtained by a suitable combination of two binary logistic regression models. Multiclass snore-based classification allows early stratification of subjects according to their severity. This could be the basis of a single channel, snore-based screening procedure for SAHS.

Investigating a compact phantom and setup for testing body sound transducers

Contact transducers are a key element in experiments involving body sounds. The characteristics of these devices are often not known with accuracy. There are no standardized calibration setups or procedures for testing these sensors. This study investigated the characteristics of a new computer-controlled sound source phantom for testing sensors. Results suggested that sensors with different sizes require special phantom requirements. The effectiveness of certain approaches on increasing the spatial and spectral uniformity of the phantom surface signal was studied. Non-uniformities > 20 dB were removable, which can be particularly helpful in comparing the characteristics of different size sensors more accurately.

Continuous analysis and monitoring of snores and their relationship to the apnea-hypopnea index

OBJECTIVES/HYPOTHESIS

We used a new automatic snoring detection and analysis system to monitor snoring during full-night polysomnography to assess whether the acoustic characteristics of snores differ in relation to the apnea-hypopnea index (AHI) and to classify subjects according to their AHI.

STUDY DESIGN

Individual Case-Control Study.

METHODS

Thirty-seven snorers (12 females and 25 males; ages 40-65 years; body mass index (BMI), 29.65 +/- 4.7 kg/m(2)) participated. Subjects were divided into three groups: G1 (AHI <5), G2 (AHI >or=5, <15) and G3 (AHI >or=15). Snore and breathing sounds were recorded with a tracheal microphone throughout 6 hours of nighttime polysomnography. The snoring episodes identified were automatically and continuously analyzed with a previously trained 2-layer feed-forward neural network. Snore number, average intensity, and power spectral density parameters were computed for every subject and compared among AHI groups. Subjects were classified using different AHI thresholds by means of a logistic regression model.

RESULTS

There were significant differences in supine position between G1 and G3 in sound intensity; number of snores; standard deviation of the spectrum; power ratio in bands 0-500, 100-500, and 0-800 Hz; and the symmetry coefficient (P < .03). Patients were classified with thresholds AHI = 5 and AHI = 15 with a sensitivity (specificity) of 87% (71%) and 80% (90%), respectively.

CONCLUSIONS

A new system for automatic monitoring and analysis of snores during the night is presented. Sound intensity and several snore frequency parameters allow differentiation of snorers according to obstructive sleep apnea syndrome severity (OSAS). Automatic snore intensity and frequency monitoring and analysis could be a promising tool for screening OSAS patients, significantly improving the managing of this pathology.

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.

Design of a DSP-based instrument for real-time classification of pulmonary sounds

Auscultation of pulmonary sounds provides valuable clinical information but has been regarded as a tool of low diagnostic value due to the inherent subjectivity in the evaluation of these sounds. In this work, a Digital Signal Processor is used to design an instrument capable of acquiring, parameterizing and subsequently classifying lung sounds into two classes with an aim to evaluate them objectively in real time. The instrument operates on sound signal from a chest microphone and flow signal from a pneumotachograph. The classification is carried out separately on the 12 reference libraries (pathological and healthy) of six sub-phases of a full respiration cycle and the results are combined to arrive at a final decision. The k-nearest neighbour and minimum distance classifiers with different distance metrics have been implemented in the instrument. The instrument was tested in the clinical environment, attaining 96% accuracy in real-time classification.

A multi-channel device for respiratory sound data acquisition and transient detection

In this study, a multi-channel analog data acquisition and processing device with the additional feature of detecting adventitious sounds has been designed and implemented. The overall system consists of fourteen microphones attached on the backside, an airflow measuring unit, a fifteen-channel amplifier and filter unit connected to a personal computer (PC) via a data acquisition (DAQ) card, and an interface and adventitious sound detection program prepared using LabVIEW (6.0, National Instruments) and MATLAB (7.0.1, MathWorks). The system records the fourteen-channel respiratory sound data at the posterior chest wall and in addition measures the air flow to synchronize the pulmonary signal on the respiration cycle. Respiratory data are amplified and band-pass filtered, whereas flow signal is only low-pass filtered since it is a low-frequency signal with sufficiently high amplitude. All data are sent to a PC to be digitized by DAQ card, then to be processed and stored. An algorithm based on wavelet decomposition is developed which detects the adventitious pulmonary sounds, mainly the crackles and wheezes. This system is intended to be used for mapping the pulmonary sounds and detecting and locating the adventitious pulmonary sounds.

Design, construction, and evaluation of a bioacoustic transducer testing (BATT) system for respiratory sounds

Many different transducers are employed for recording respiratory sounds including accelerometers and microphones in couplers. However, there is no standard lung sound transducer or any device to compare transducers so that measurements from different laboratories can be determined to be of physiologic origin rather than technical artifacts of the transducers. To address this problem, we designed and constructed a prototype of a device that can be used to compare accelerometers, microphones enclosed in couplers, and stethoscopes. The prototype device consists of a rigid chamber containing a loudspeaker that opens to an antechamber covered by a viscoelastic material with mechanical properties similar to human skin and subcutaneous tissue. When driven by a white noise source, we found the sound output at the surface to be useful to comparatively evaluate sensors between 100 and 1200 Hz where lung sounds have most of their spectral energy. We compared the viscoelastic layer to similar thicknesses of fresh meat and fat and found them to produce similar acoustic spectra. This device allows air-coupled transducers, accelerometers, and stethoscopes used in respiratory sounds measurements to be compared under physical conditions similar to their intended use.

Comparison of lung sound transducers using a bioacoustic transducer testing system

Sensors used for lung sound research are generally designed by the investigators or adapted from devices used in related fields. Their relative characteristics have never been defined. We employed an artificial chest wall with a viscoelastic surface and a white noise signal generator as a stable source of sound to compare the frequency response and pulse waveform reproduction of a selection of devices used for lung sound research. We used spectral estimation techniques to determine frequency response and cross-correlation of pulses to determine pulse shape fidelity. The sensors evaluated were the Siemens EMT 25 C accelerometer (Siemens); PPG 201 accelerometer (PPG); Sony ECM-T150 electret condenser microphone with air coupler (air coupler; with cylindrical air chambers of 5-, 10-, and 15-mm diameter and conical air chamber of 10-mm diameter); Littman classic stethoscope head (Littman) connected to an electret condenser microphone; and the Andries Tek (Andries) electronic stethoscope. We found that the size and shape of the air coupler chamber to have no important effect on the detected sound. The Siemens, air coupler, and Littman performed similarly with relatively flat frequency responses from 200 to 1,200 Hz. The PPG had the broadest frequency response, with useful sensitivity extending to 4,000 Hz. The Andries' frequency response was the poorest above 1,000 Hz. Accuracy in reproducing pulses roughly corresponded with the high-frequency sensitivity of the sensors. We conclude that there are important differences among commonly used lung sound sensors that have to be defined to allow the comparison of data from different laboratories.

Respiratory acoustic thoracic imaging (RATHI): Assessing deterministic interpolation techniques

As respiratory sounds contain mechanical and clinical pulmonary information, technical efforts have been devoted during the past decades to analysing, processing and visualising them. The aim of this work was to evaluate deterministic interpolating functions to generate surface respiratory acoustic thoracic images (RATHIs), based on multiple acoustic sensors. Lung sounds were acquired from healthy subjects through a 5 x 5 microphone array on the anterior and posterior thoracic surfaces. The performance of five interpolating functions, including the linear, cubic spline, Hermite, Lagrange and nearest neighbour method, were evaluated to produce images of lung sound intensity during both breathing phases, at low (approximately 0.5ls(-1)) and high (approximately 1.0ls(-1)) airflows. Performance indexes included the normalised residual variance nrv (i.e. inaccuracy), the prediction covariance cv (i.e. precision), the residual covariance rcv (i.e. bias) and the maximum squared residual error semax (i.e. tolerance). Among the tested interpolating functions and in all experimental conditions, the Hermite function (nrv=0.146 +/- 0.059, cv= 0.925 +/- 0.030, rcv = -0.073 +/- 0.068, semax = 0.005 +/- 0.004) globally provided the indexes closest to the optimum, whereas the nearest neighbour (nrv=0.339 +/- 0.023, cv = 0.870 +/- 0.033, rcv= 0.298 +/- 0.032, semax = 0.007 +/- 0.005) and the Lagrange methods (nrv = 0.287 +/- 0.148, cv = 0.880 +/- 0.039, rcv = -0.524 +/- 0.135, semax = 0.007 +/- 0.0001) presented the poorest statistical measurements. It is concluded that, although deterministic interpolation functions indicate different performances among tested techniques, the Hermite interpolation function presents a more confident deterministic interpolation for depicting surface-type RATHI.

The relationship between normal lung sounds, age, and gender

Auscultation is one of the most important noninvasive and feasible methods for the detection of lung diseases. Systematic changes in breathing sounds with increasing age are of diagnostic importance. To investigate these changes, we recorded lung sounds taken from four locations in the posterior thorax of 162 subjects, together with airflow. The data were analyzed according to age, sex, and smoking habit. In order to describe the power spectrum of the lung sounds, we calculated mean and median frequency, frequency with the highest power, and a ratio (Q) of relative power of the two frequency bands of 330 to 600 Hz and 60 to 330 Hz. Linear regression analysis was used as a measurement of age-dependence of these variables. Significant differences in Q were found in men versus women (p < 0.05), but not in smokers versus nonsmokers. Within the groups, a small but significant correlation existed between Q and age (r(2) </= 0.1, p < 0.05). For both men and women, a slight increase of the relative power in the frequency band of 330 to 600 Hz was recorded with increasing age. However, on the basis of large individual variations, these small changes (DeltaQ approximately 5%, SD(Q) >/= +/- 5%) have no clinical significance and need not to be considered in the automatic detection of lung diseases by analyzing lung sounds.

Effect of ambient respiratory noise on the measurement of lung sounds

The effect of ambient sounds, generated during breathing, that may reach a sensor at the chest surface by transmission from mouth and nose through air in the room, rather than through the airways, lungs and chest wall, is studied. Five healthy male non-smokers, aged from 11 to 51 years, are seated in a sound-proof acoustic chamber. Ambient respiratory noise levels are modified by directing expiratory flow outside the recording chamber. Low-density gas (HeO2 = 80% helium, 20% oxygen) is used to modify airway resonances. Spectral analysis is applied to ambient noise and to respiratory sounds measured on the chest and neck. Flow-gated average sound spectra are compared statistically. A prominent spectral peak around 960 Hz appears in ambient noise and over the chest and neck during expiration in all subjects. Ambient noise reduction decreases the amplitude of this peak by 20 +/- 4 dB in the room and by 6 +/- 3.6 dB over the chest. Another prominent spectral peak, around 700 Hz in adults and 880 Hz in children, shows insignificant change, i.e. a maximum reduction of 3 dB, during modifications of ambient respiratory noise. HeO2 causes an upward shift in tracheal resonances that is also seen in the anterior chest recordings. Ambient respiratory noise explains some, but not all, peaks in the spectra of expiratory lung sounds. Resonance peaks in the spectra of expiratory tracheal sounds are also apparent in the spectra of expiratory lung sounds at the anterior chest.

Asymmetry of respiratory sounds and thoracic transmission

Breath sounds heard with a stethoscope over homologous sites of both lungs in healthy subjects are presumed to have similar characteristics. Passively transmitted sounds introduced at the mouth, however, are known to lateralise, with right-over-left dominance in power at the anterior upper chest. Both spontaneous breath sounds and passively transmitted sounds are studied in four healthy adults, using contact sensors at homologous sites on the anterior upper and posterior lower chest. At standardised air flow, breath sound intensity shows a right-over-left dominance at the anterior upper chest, similar to passively transmitted sounds. At the posterior lung base, breath sounds are louder on the left, with a trend to similar lateralisation in transmitted sounds. It is likely that the observed asymmetries are related to the effects of cardiovascular structures and airway geometry on sound generation and transmission.