The functional basis of pulmonary sounds

Tunable Q-factor wavelet transform based lung signal decomposition and statistical feature extraction for effective lung disease classification

The auscultation is a non-invasive and cost-effective method used for the diagnosis of lung diseases, which are one of the leading causes of death worldwide. However, the efficacy of the auscultation suffers from the limitations of the analog stethoscopes and the subjective nature of human interpretation. To overcome these limitations, the accurate diagnosis of these diseases by employing the computer based automated algorithms applied to the digitized lung sounds has been studied for the last decades. This study proposes a novel approach that uses a Tunable Q-factor Wavelet Transform (TQWT) based statistical feature extraction followed by individual and ensemble learning model training with the aim of lung disease classification. During the learning stage various machine learning algorithms are utilized as the individual learners as well as the hard and soft voting fusion approaches are employed for performance enhancement with the aid of the predictions of individual models. For an objective evaluation of the proposed approach, the study was structured into two main tasks that were investigated in detail by using several sub-tasks to comparison with state-of-the-art studies. Among the sub-tasks which investigates patient-based classification, the highest accuracy obtained for the binary classification was achieved as 97.63% (healthy vs. non-healthy), while accuracy values up to 66.32% for three-class classification (obstructive-related, restrictive-related, and healthy), and 53.42% for five-class classification (asthma, chronic obstructive pulmonary disease, interstitial lung disease, pulmonary infection, and healthy) were obtained. Regarding the other sub-task, which investigates sample-based classification, the proposed approach was superior to almost all previous findings. The proposed method underscores the potential of TQWT based signal decomposition that leverages the power of its adaptive time-frequency resolution property satisfied by Q-factor adjustability. The obtained results are very promising and the proposed approach paves the way for more accurate and automated digital auscultation techniques in clinical settings.

Deep learning-based lung sound analysis for intelligent stethoscope

Auscultation is crucial for the diagnosis of respiratory system diseases. However, traditional stethoscopes have inherent limitations, such as inter-listener variability and subjectivity, and they cannot record respiratory sounds for offline/retrospective diagnosis or remote prescriptions in telemedicine. The emergence of digital stethoscopes has overcome these limitations by allowing physicians to store and share respiratory sounds for consultation and education. On this basis, machine learning, particularly deep learning, enables the fully-automatic analysis of lung sounds that may pave the way for intelligent stethoscopes. This review thus aims to provide a comprehensive overview of deep learning algorithms used for lung sound analysis to emphasize the significance of artificial intelligence (AI) in this field. We focus on each component of deep learning-based lung sound analysis systems, including the task categories, public datasets, denoising methods, and, most importantly, existing deep learning methods, i.e., the state-of-the-art approaches to convert lung sounds into two-dimensional (2D) spectrograms and use convolutional neural networks for the end-to-end recognition of respiratory diseases or abnormal lung sounds. Additionally, this review highlights current challenges in this field, including the variety of devices, noise sensitivity, and poor interpretability of deep models. To address the poor reproducibility and variety of deep learning in this field, this review also provides a scalable and flexible open-source framework that aims to standardize the algorithmic workflow and provide a solid basis for replication and future extension: https://github.com/contactless-healthcare/Deep-Learning-for-Lung-Sound-Analysis .

Skin disorders and interstitial lung disease: Part I-Screening, diagnosis, and therapeutic principles

Numerous inflammatory, neoplastic, and genetic skin disorders are associated with interstitial lung disease (ILD), the fibrosing inflammation of lung parenchyma that has significant morbidity and mortality. Therefore, the dermatologist plays a major role in the early detection and appropriate referral of patients at risk for ILD. Part 1 of this 2-part CME outlines the pathophysiology of ILD and focuses on clinical screening and therapeutic principles applicable to dermatological patients who are at risk for ILD. Patients with clinical symptoms of ILD should be screened with pulmonary function tests and high-resolution chest computed tomography. Screening for pulmonary hypertension should be considered in high-risk patients. Early identification and elimination of pulmonary risk factors, including smoking and gastroesophageal reflux disease, are essential in improving respiratory outcomes. First-line treatment interventions for ILD in a dermatological setting include mycophenolate mofetil, but the choice of therapeutic agents depends on the nature of the primary disease, the severity of ILD, and comorbidities and should be the result of a multidisciplinary assessment. Better awareness of ILD among medical dermatologists and close interdisciplinary collaborations are likely to prevent treatment delays improving long-term outcomes.

Regularity and mechanism of fake crackle noise in an electronic stethoscope

Background: Electronic stethoscopes are widely used for cardiopulmonary auscultation; their audio recordings are used for the intelligent recognition of cardiopulmonary sounds. However, they generate noise similar to a crackle during use, significantly interfering with clinical diagnosis. This paper will discuss the causes, characteristics, and occurrence rules of the fake crackle and establish a reference for improving the reliability of the electronic stethoscope in lung auscultation. Methods: A total of 56 participants with healthy lungs (no underlying pulmonary disease, no recent respiratory symptoms, and no adventitious lung sound, as confirmed by an acoustic stethoscope) were enrolled in this study. A 30-s audio recording was recorded from each of the nine locations of the larynx and lungs of each participant with a 3M Littmann 3200 electronic stethoscope, and the audio was output in diaphragm mode and auscultated by the clinician. The doctor identified the fake crackles and analyzed their frequency spectrum. High-pass and low-pass filters were used to detect the frequency distribution of the fake crackles. Finally, the fake crackle was artificially regenerated to explore its causes. Results: A total of 500 audio recordings were included in the study, with 61 fake crackle audio recordings. Fake crackles were found predominantly in the lower lung. There were significant differences between lower lung and larynx (p < 0.001), lower lung and upper lung (p = 0.005), lower lung and middle lung (p = 0.005), and lower lung and infrascapular region (p = 0.027). Furthermore, more than 90% of fake crackles appeared in the inspiratory phase, similar to fine crackles, significantly interfering with clinical diagnosis. The spectral analysis revealed that the frequency range of fake crackles was approximately 250-1950 Hz. The fake crackle was generated when the diaphragm of the electronic stethoscope left the skin slightly but not completely. Conclusion: Fake crackles are most likely to be heard when using an electronic stethoscope to auscultate bilateral lower lungs, and the frequency of a fake crackle is close to that of a crackle, likely affecting the clinician's diagnosis.

Improvise approach for respiratory pathologies classification with multilayer convolutional neural networks

Every respiratory-related checkup includes audio samples collected from the individual, collected through different tools (sonograph, stethoscope). This audio is analyzed to identify pathology, which requires time and effort. The research work proposed in this paper aims at easing the task with deep learning by the diagnosis of lung-related pathologies using Convolutional Neural Network (CNN) with the help of transformed features from the audio samples. International Conference on Biomedical and Health Informatics (ICBHI) corpus dataset was used for lung sound. Here a novel approach is proposed to pre-process the data and pass it through a newly proposed CNN architecture. The combination of pre-processing steps MFCC, Melspectrogram, and Chroma CENS with CNN improvise the performance of the proposed system, which helps to make an accurate diagnosis of lung sounds. The comparative analysis shows how the proposed approach performs better with previous state-of-the-art research approaches. It also shows that there is no need for a wheeze or a crackle to be present in the lung sound to carry out the classification of respiratory pathologies.

The Utility of Real-Time Remote Auscultation Using a Bluetooth-Connected Electronic Stethoscope: Open-Label Randomized Controlled Pilot Trial

Background

The urgent need for telemedicine has become clear in the COVID-19 pandemic. To facilitate telemedicine, the development and improvement of remote examination systems are required. A system combining an electronic stethoscope and Bluetooth connectivity is a promising option for remote auscultation in clinics and hospitals. However, the utility of such systems remains unknown.

Objective

This study was conducted to assess the utility of real-time auscultation using a Bluetooth-connected electronic stethoscope compared to that of classical auscultation, using lung and cardiology patient simulators.

Methods

This was an open-label, randomized controlled trial including senior residents and faculty in the department of general internal medicine of a university hospital. The only exclusion criterion was a refusal to participate. This study consisted of 2 parts: lung auscultation and cardiac auscultation. Each part contained a tutorial session and a test session. All participants attended a tutorial session, in which they listened to 15 sounds on the simulator using a classic stethoscope and were told the correct classification. Thereafter, participants were randomly assigned to either the real-time remote auscultation group (intervention group) or the classical auscultation group (control group) for test sessions. In the test sessions, participants had to classify a series of 10 lung sounds and 10 cardiac sounds, depending on the study part. The intervention group listened to the sounds remotely using the electronic stethoscope, a Bluetooth transmitter, and a wireless, noise-canceling, stereo headset. The control group listened to the sounds directly using a traditional stethoscope. The primary outcome was the test score, and the secondary outcomes were the rates of correct answers for each sound.

Results

In total, 20 participants were included. There were no differences in age, sex, and years from graduation between the 2 groups in each part. The overall test score of lung auscultation in the intervention group (80/110, 72.7%) was not different from that in the control group (71/90, 78.9%; P=.32). The only lung sound for which the correct answer rate differed between groups was that of pleural friction rubs (P=.03); it was lower in the intervention group (3/11, 27%) than in the control group (7/9, 78%). The overall test score for cardiac auscultation in the intervention group (50/60, 83.3%) was not different from that in the control group (119/140, 85.0%; P=.77). There was no cardiac sound for which the correct answer rate differed between groups.

Conclusions

The utility of a real-time remote auscultation system using a Bluetooth-connected electronic stethoscope was comparable to that of direct auscultation using a classic stethoscope, except for classification of pleural friction rubs. This means that most of the real world’s essential cardiopulmonary sounds could be classified by a real-time remote auscultation system using a Bluetooth-connected electronic stethoscope.

Trial Registration

UMIN-CTR UMIN000040828; https://tinyurl.com/r24j2p6s and UMIN-CTR UMIN000041601; https://tinyurl.com/bsax3j5f

Wheeze Recognition Algorithm for Remote Medical Care Device in Children: Validation Study

BACKGROUND

Since 2020, peoples' lifestyles have been largely changed due to the COVID-19 pandemic worldwide. In the medical field, although many patients prefer remote medical care, this prevents the physician from examining the patient directly; thus, it is important for patients to accurately convey their condition to the physician. Accordingly, remote medical care should be implemented and adaptable home medical devices are required. However, only a few highly accurate home medical devices are available for automatic wheeze detection as an exacerbation sign.

OBJECTIVE

We developed a new handy home medical device with an automatic wheeze recognition algorithm, which is available for clinical use in noisy environments such as a pediatric consultation room or at home. Moreover, the examination time is only 30 seconds, since young children cannot endure a long examination time without crying or moving. The aim of this study was to validate the developed automatic wheeze recognition algorithm as a clinical medical device in children at different institutions.

METHODS

A total of 374 children aged 4-107 months in pediatric consultation rooms of 10 institutions were enrolled in this study. All participants aged ≥6 years were diagnosed with bronchial asthma and patients ≤5 years had reported at least three episodes of wheezes. Wheezes were detected by auscultation with a stethoscope and recorded for 30 seconds using the wheeze recognition algorithm device (HWZ-1000T) developed based on wheeze characteristics following the Computerized Respiratory Sound Analysis guideline, where the dominant frequency and duration of a wheeze were >100 Hz and >100 ms, respectively. Files containing recorded lung sounds were assessed by each specialist physician and divided into two groups: 177 designated as "wheeze" files and 197 as "no-wheeze" files. Wheeze recognitions were compared between specialist physicians who recorded lung sounds and those recorded using the wheeze recognition algorithm. We calculated the sensitivity, specificity, positive predictive value, and negative predictive value for all recorded sound files, and evaluated the influence of age and sex on the wheeze detection sensitivity.

RESULTS

Detection of wheezes was not influenced by age and sex. In all files, wheezes were differentiated from noise using the wheeze recognition algorithm. The sensitivity, specificity, positive predictive value, and negative predictive value of the wheeze recognition algorithm were 96.6%, 98.5%, 98.3%, and 97.0%, respectively. Wheezes were automatically detected, and heartbeat sounds, voices, and crying were automatically identified as no-wheeze sounds by the wheeze recognition algorithm.

CONCLUSIONS

The wheeze recognition algorithm was verified to identify wheezing with high accuracy; therefore, it might be useful in the practical implementation of asthma management at home. Only a few home medical devices are available for automatic wheeze detection. The wheeze recognition algorithm was verified to identify wheezing with high accuracy and will be useful for wheezing management at home and in remote medical care.

Influence of observer preferences and auscultatory skill on the choice of terms to describe lung sounds: a survey of staff physicians, residents and medical students

Background

In contrast with the technical progress of the stethoscope, lung sound terminology has remained confused, weakening the usefulness of auscultation. We examined how observer preferences regarding terminology and auscultatory skill influenced the choice of terms used to describe lung sounds.

Methods

Thirty-one staff physicians (SP), 65 residents (R) and 47 medical students (MS) spontaneously described the audio recordings of 5 lung sounds classified acoustically as: (1) normal breath sound; (2) wheezes; (3) crackles; (4) stridor and (5) pleural friction rub. A rating was considered correct if a correct term or synonym was used to describe it (term use ascribed to preference). The use of any incorrect terms was ascribed to deficient auscultatory skill.

Results

Rates of correct sound identification were: (i) normal breath sound: SP=21.4%; R=11.6%; MS=17.1%; (ii) wheezes: SP=82.8%; R=85.2%; MS=86.4%; (iii) crackles: SP=63%; R=68.5%; MS=70.7%; (iv) stridor: SP=92.8%; R=90%; MS=72.1% and (v) pleural friction rub: SP=35.7%; R=6.2%; MS=3.2%. The 3 groups used 66 descriptive terms: 17 were ascribed to preferences regarding terminology, and 49 to deficient auscultatory skill. Three-group agreement on use of a term occurred on 107 occasions: 70 involved correct terms (65.4%) and 37 (34.6%) incorrect ones. Rate of use of recommended terms, rather than accepted synonyms, was 100% for the wheezes and the stridor, 55% for the normal breath sound, 22% for the crackles and 14% for the pleural friction rub.

Conclusions

The observers’ ability to describe lung sounds was high for the wheezes and the stridor, fair for the crackles and poor for the normal breath sound and the pleural friction rub. Lack of auscultatory skill largely surpassed observer preference as a factor determining the choice of terminology. Wide dissemination of educational programs on lung auscultation (eg, self-learning via computer-assisted learning tools) is urgently needed to promote use of standardised lung sound terminology.

Influence of language skills on the choice of terms used to describe lung sounds in a language other than English: a cross-sectional survey of staff physicians, residents and medical students

INTRODUCTION

The value of chest auscultation would be enhanced by the use of a standardised terminology. To that end, the recommended English terminology must be transferred to a language other than English (LOTE) without distortion.

OBJECTIVE

To examine the transfer to Hebrew-taken as a model of LOTE-of the recommended terminology in English.

DESIGN/SETTING

Cross-sectional study; university-based hospital.

PARTICIPANTS

143 caregivers, including 31 staff physicians, 65 residents and 47 medical students.

METHODS

Observers provided uninstructed descriptions in Hebrew and English of audio recordings of five common sounds, namely, normal breath sound (NBS), wheezes, crackles, stridor and pleural friction rub (PFR).

OUTCOMES

(a) Rates of correct/incorrect classification; (b) correspondence between Hebrew and recommended English terms; c) language and auscultation skills, assessed by crossing the responses in the two languages with each other and with the classification of the audio recordings validated by computer analysis.

RESULTS

Range (%) of correct rating was as follows: NBS=11.3-20, wheezes=79.7-87.2, crackles=58.6-69.8, stridor=67.4-96.3 and PFR=2.7-28.6. Of 60 Hebrew terms, 11 were correct, and 5 matched the recommended English terms. Many Hebrew terms were adaptations or transliterations of inadequate English terms. Of 687 evaluations, good dual-language and single-language skills were found in 586 (85.3%) and 41 (6%), respectively. However, in 325 (47.3%) evaluations, good language skills were associated with poor auscultation skills.

CONCLUSION

Poor auscultation skills surpassed poor language skills as a factor hampering the transfer to Hebrew (LOTE) of the recommended English terminology. Improved education in auscultation emerged as the main factor to promote the use of standardised lung sound terminology. Using our data, a strategy was devised to encourage the use of standardised terminology in non-native English-speaking countries.

Monophonic and Polyphonic Wheezing Classification Based on Constrained Low-Rank Non-Negative Matrix Factorization

The appearance of wheezing sounds is widely considered by physicians as a key indicator to detect early pulmonary disorders or even the severity associated with respiratory diseases, as occurs in the case of asthma and chronic obstructive pulmonary disease. From a physician's point of view, monophonic and polyphonic wheezing classification is still a challenging topic in biomedical signal processing since both types of wheezes are sinusoidal in nature. Unlike most of the classification algorithms in which interference caused by normal respiratory sounds is not addressed in depth, our first contribution proposes a novel Constrained Low-Rank Non-negative Matrix Factorization (CL-RNMF) approach, never applied to classification of wheezing as far as the authors' knowledge, which incorporates several constraints (sparseness and smoothness) and a low-rank configuration to extract the wheezing spectral content, minimizing the acoustic interference from normal respiratory sounds. The second contribution automatically analyzes the harmonic structure of the energy distribution associated with the estimated wheezing spectrogram to classify the type of wheezing. Experimental results report that: (i) the proposed method outperforms the most recent and relevant state-of-the-art wheezing classification method by approximately 8% in accuracy; (ii) unlike state-of-the-art methods based on classifiers, the proposed method uses an unsupervised approach that does not require any training.

Wheezing Characteristics and Predicting Reactivity to Inhaled β2-Agonist in Children for Home Medical Care

Background: Given that wheezing is treated with inhaled β2-agonists, their effect should be reviewed before the condition becomes severe; however, few methods can currently predict reactivity to inhaled β2-agonists. We investigated whether preinhalation wheezing characteristics identified by lung sound analysis can predict reactivity to inhaled β2-agonists. Methods: In 202 children aged 10-153 months, wheezing was identified by auscultation. Lung sounds were recorded for 30 s in the chest region on the chest wall during tidal breathing. We analyzed the wheezing before and after β2-agonist inhalation. Wheezing was displayed as horizontal bars of intensity defined as a wheeze power band, and the wheezing characteristics (number, frequency, and maximum intensity frequency) were evaluated by lung sound analysis. The participants were divided into two groups: non-disappears (wheezing did not disappear after inhalation) and disappears (wheezing disappeared after inhalation). Wheezing characteristics before β2-agonist inhalation were compared between the two groups. The characteristics of wheezing were not affected by body size. The number of wheeze power bands of the non-responder group was significantly higher than those of the responder group (P < 0.001). The number of wheeze power bands was a predictor of reactivity to inhaled β2-agonists, with a cutoff of 11.1. The 95% confidence intervals of sensitivity, specificity, and positive and negative predictive values were 88.8, 42, 44, and 81.1% (P < 0.001), respectively. Conclusions: The number of preinhalation wheeze power bands shown by lung sound analysis was a useful indicator before treatment. This indicator could be a beneficial index for managing wheezing in young children.

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 wheeze recognition algorithm for practical implementation in children

BACKGROUND

The detection of wheezes as an exacerbation sign is important in certain respiratory diseases. However, few highly accurate clinical methods are available for automatic detection of wheezes in children. This study aimed to develop a wheeze detection algorithm for practical implementation in children.

METHODS

A wheeze recognition algorithm was developed based on wheezes features following the Computerized Respiratory Sound Analysis guidelines. Wheezes can be detected by auscultation with a stethoscope and using an automatic computerized lung sound analysis. Lung sounds were recorded for 30 s in 214 children aged 2 months to 12 years and 11 months in a pediatric consultation room. Files containing recorded lung sounds were assessed by two specialist physicians and divided into two groups: 65 were designated as "wheeze" files, and 149 were designated as "no-wheeze" files. All lung sound judgments were agreed between two specialist physicians. We compared wheeze recognition between the specialist physicians and using the wheeze recognition algorithm and calculated the sensitivity, specificity, positive predictive value, and negative predictive value for all recorded sound files to evaluate the influence of age on the wheeze detection sensitivity.

RESULTS

The detection of wheezes was not influenced by age. In all files, wheezes were differentiated from noise using the wheeze recognition algorithm. The sensitivity, specificity, positive predictive value, and negative predictive value of the wheeze recognition algorithm were 100%, 95.7%, 90.3%, and 100%, respectively.

CONCLUSIONS

The wheeze recognition algorithm could identify wheezes in sound files and therefore may be useful in the practical implementation of respiratory illness management at home using properly developed devices.

Wheeze sound characteristics are associated with nighttime sleep disturbances in younger children

Background

Wheezing is a typical symptom of respiratory conditions. Few objective methods are available for predicting sleep disturbance in young children with wheezing.

Objective

We investigated whether wheezing characteristics, detected by lung-sound analysis, were associated with risk of sleep disturbance.

Methods

We recorded the lung sounds of 66 young children (4–59 months) every morning, for the entire duration of a wheezing episode. On lung-sound analysis, wheezing was displayed as horizontal bars of intensity with corresponding sharp peaks of power. The sharp peak of power was defined as a wheeze band. Wheezing characteristics (e.g., number, frequency, duration, and frequency of maximum intensity of wheeze bands) were analyzed using lung-sound analysis. Patients were divided into 3 groups based on sleep disturbance on the first night after wheezing was recorded: mild group (no sleep disturbance and disappearance of wheezing within 2 days), moderate group (no sleep disturbance but disappearance of wheezing after 3 or more days), and severe group (sleep disturbance and disappearance of wheezing after 3 or more days). Wheezing characteristics on the first morning were compared among the 3 groups based on sleep disturbance on the first night.

Results

The highest frequency, the frequency of maximum intensity, and the number of wheeze bands per 30 seconds were significantly higher in the severe group than in the mild group (p < 0.005, p < 0.005, p < 0.001, respectively). The number of wheeze bands per 30 seconds was a predictor of nighttime sleep disturbance, with a cutoff value of 11.1. The sensitivity, specificity, and positive- and negative-predictive values were 100%, 65%, 32%, and 100% (p < 0.001), respectively, with an area under the curve of 0.86 ± 0.05.

Conclusions

The number of wheeze bands per 30 seconds on lung-sound analysis was a useful indicator of risk of prolonged exacerbation.

Time to Say Goodbye to Bronchiolitis, Viral Wheeze, Reactive Airways Disease, Wheeze Bronchitis and All That

The diagnosis and management of infants and children with a significant viral lower respiratory tract illness remains the subject of much debate and little progress. Over the decades various terms for such illnesses have been in and fallen out of fashion or have evolved to mean different things to different clinicians. Terms such as "bronchiolitis," "reactive airways disease," "viral wheeze," and many more are used to describe the same condition and the same term is frequently used to describe illnesses caused by completely different dominant pathologies. This lack of clarity is due, in large part, to a failure to understand the basic underlying inflammatory and associated processes and, in part, due to the lack of a simple test to identify a condition such as asthma. Moreover, there is a lack of insight into the fact that the same pathology can produce different clinical signs at different ages. The consequence is that terminology and fashions in treatment have tended to go around in circles. As was noted almost 60 years ago, amongst pre-school children with a viral LRTI and airways obstruction there are those with a "viral bronchitis" and those with asthma. In the former group, a neutrophil dominated inflammation response is responsible for the airways' obstruction whilst amongst asthmatics much of the obstruction is attributable to bronchoconstriction. The airways obstruction in the former group is predominantly caused by airways secretions and to some extent mucosal oedema (a "snotty lung"). These patients benefit from good supportive care including supplemental oxygen if required (though those with a pre-existing bacterial bronchitis will also benefit from antibiotics). For those with a viral exacerbation of asthma, characterized by bronchoconstriction combined with impaired b-agonist responsiveness, standard management of an exacerbation of asthma (including the use of steroids to re-establish bronchodilator responsiveness) represents optimal treatment. The difficulty is identifying which group a particular patient falls into. A proposed simplified approach to the nomenclature used to categorize virus associated LRTIs is presented based on an understanding of the underlying pathological processes and how these contribute to the physical signs.

Recurrent Wheezing in Pre-school Age: Not Only Airway Reactivity!

Background: About a fifth of all mediastinal masses are primary cysts arising in the absence of other underlying pathology. Bronchogenic cysts, although rare, are the most frequent type responsible for lower airways compression as they often develop in the peripheral branches of the tracheobronchial tree. Case presentation: We report the case of a 6-months-old child admitted for acute respiratory distress and wheezing not responsive to asthma treatment. Digestive and airway endoscopy proved a mild and a marked reduction of the esophageal and tracheal lumen, respectively. The nocturnal polygraphy showed an underlying obstructive disorder and the chest CT scan confirmed the presence of a wide mediastinal cyst compressing the trachea. The mass, later identified as a bronchogenic cyst, was surgically removed with complete resolution of the patient's respiratory symptoms. Discussion: Our case shows that differential diagnosis of wheezing in pre-school aged children should encompass causes others than airway reactivity, thus prompting further evaluation and management.

Practical implementation of artificial intelligence algorithms in pulmonary auscultation examination

Lung auscultation is an important part of a physical examination. However, its biggest drawback is its subjectivity. The results depend on the experience and ability of the doctor to perceive and distinguish pathologies in sounds heard via a stethoscope. This paper investigates a new method of automatic sound analysis based on neural networks (NNs), which has been implemented in a system that uses an electronic stethoscope for capturing respiratory sounds. It allows the detection of auscultatory sounds in four classes: wheezes, rhonchi, and fine and coarse crackles. In the blind test, a group of 522 auscultatory sounds from 50 pediatric patients were presented, and the results provided by a group of doctors and an artificial intelligence (AI) algorithm developed by the authors were compared. The gathered data show that machine learning (ML)-based analysis is more efficient in detecting all four types of phenomena, which is reflected in high values of recall (also called as sensitivity) and F1-score.Conclusions: The obtained results suggest that the implementation of automatic sound analysis based on NNs can significantly improve the efficiency of this form of examination, leading to a minimization of the number of errors made in the interpretation of auscultation sounds. What is Known: • Auscultation performance of average physician is very low. AI solutions presented in scientific literature are based on small data bases with isolated pathological sounds (which are far from real recordings) and mainly on leave-one-out validation method thus they are not reliable. What is New: • AI learning process was based on thousands of signals from real patients and a reliable description of recordings was based on multiple validation by physicians and acoustician resulting in practical and statistical prove of AI high performance.

Wheeze type classification using non-dyadic wavelet transform based optimal energy ratio technique

BACKGROUND AND OBJECTIVE

Wheezes in pulmonary sounds are anomalies which are often associated with obstructive type of lung diseases. The previous works on wheeze-type classification focused mainly on using fixed time-frequency/scale resolution based on Fourier and wavelet transforms. The main contribution of the proposed method, in which the time-scale resolution can be tuned according to the signal of interest, is to discriminate monophonic and polyphonic wheezes with higher accuracy than previously suggested time and time-frequency/scale based methods.

METHODS

An optimal Rational Dilation Wavelet Transform (RADWT) based peak energy ratio (PER) parameter selection method is proposed to discriminate wheeze types. Previously suggested Quartile Frequency Ratios, Mean Crossing Irregularity, Multiple Signal Classification, Mel-frequency Cepstrum and Dyadic Discrete Wavelet Transform approaches are also applied and the superiority of the proposed method is demonstrated in leave-one-out (LOO) and leave-one-subject-out (LOSO) cross validation schemes with support vector machine (SVM), k nearest neighbor (k-NN) and extreme learning machine (ELM) classifiers.

RESULTS

The results show that the proposed RADWT based method outperforms the state-of-the-art time, frequency, time-frequency and time-scale domain approaches for all classifiers in both LOO and LOSO cross validation settings. The highest accuracy values are obtained as 86% and 82.9% in LOO and LOSO respectively when the proposed PER features are fed into SVM.

CONCLUSIONS

It is concluded that time and frequency domain characteristics of wheezes are not steady and hence, tunable time-scale representations are more successful in discriminating polyphonic and monophonic wheezes when compared with conventional fixed resolution representations.

Acoustic Methods for Pulmonary Diagnosis

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

Detection of inspiratory recruitment of atelectasis by automated lung sound analysis as compared to four-dimensional computed tomography in a porcine lung injury model

Background

Cyclic recruitment and de-recruitment of atelectasis (c-R/D) is a contributor to ventilator-induced lung injury (VILI). Bedside detection of this dynamic process could improve ventilator management. This study investigated the potential of automated lung sound analysis to detect c-R/D as compared to four-dimensional computed tomography (4DCT).

Methods

In ten piglets (25 ± 2 kg), acoustic measurements from 34 thoracic piezoelectric sensors (Meditron ASA, Norway) were performed, time synchronized to 4DCT scans, at positive end-expiratory pressures of 0, 5, 10, and 15 cmH₂O during mechanical ventilation, before and after induction of c-R/D by surfactant washout. 4DCT was post-processed for within-breath variation in atelectatic volume (Δ atelectasis) as a measure of c-R/D. Sound waveforms were evaluated for: 1) dynamic crackle energy (dCE): filtered crackle sounds (600–700 Hz); 2) fast Fourier transform area (FFT area): spectral content above 500 Hz in frequency and above −70 dB in amplitude in proportion to the total amount of sound above −70 dB amplitude; and 3) dynamic spectral coherence (dSC): variation in acoustical homogeneity over time. Parameters were analyzed for global, nondependent, central, and dependent lung areas.

Results

In healthy lungs, negligible values of Δ atelectasis, dCE, and FFT area occurred. In lavage lung injury, the novel dCE parameter showed the best correlation to Δ atelectasis in dependent lung areas (R² = 0.88) where c-R/D took place. dCE was superior to FFT area analysis for each lung region examined. The analysis of dSC could predict the lung regions where c-R/D originated.

Conclusions

c-R/D is associated with the occurrence of fine crackle sounds as demonstrated by dCE analysis. Standardized computer-assisted analysis of dCE and dSC seems to be a promising method for depicting c-R/D.

Electronic supplementary material

The online version of this article (10.1186/s13054-018-1964-6) contains supplementary material, which is available to authorized users.

Application of semi-supervised deep learning to lung sound analysis

The analysis of lung sounds, collected through auscultation, is a fundamental component of pulmonary disease diagnostics for primary care and general patient monitoring for telemedicine. Despite advances in computation and algorithms, the goal of automated lung sound identification and classification has remained elusive. Over the past 40 years, published work in this field has demonstrated only limited success in identifying lung sounds, with most published studies using only a small numbers of patients (typically N<;20) and usually limited to a single type of lung sound. Larger research studies have also been impeded by the challenge of labeling large volumes of data, which is extremely labor-intensive. In this paper, we present the development of a semi-supervised deep learning algorithm for automatically classify lung sounds from a relatively large number of patients (N=284). Focusing on the two most common lung sounds, wheeze and crackle, we present results from 11,627 sound files recorded from 11 different auscultation locations on these 284 patients with pulmonary disease. 890 of these sound files were labeled to evaluate the model, which is significantly larger than previously published studies. Data was collected with a custom mobile phone application and a low-cost (US$30) electronic stethoscope. On this data set, our algorithm achieves ROC curves with AUCs of 0.86 for wheeze and 0.74 for crackle. Most importantly, this study demonstrates how semi-supervised deep learning can be used with larger data sets without requiring extensive labeling of data.

Enhancement of lung sounds based on empirical mode decomposition and Fourier transform algorithm

BACKGROUND AND OBJECTIVE

There is always heart sound (HS) signal interfering during the recording of lung sound (LS) signals. This obscures the features of LS signals and creates confusion on pathological states, if any, of the lungs. In this work, a new method is proposed for reduction of heart sound interference which is based on empirical mode decomposition (EMD) technique and prediction algorithm.

METHOD

In this approach, first the mixed signal is split into several components in terms of intrinsic mode functions (IMFs). Thereafter, HS-included segments are localized and removed from them. The missing values of the gap thus produced, is predicted by a new Fast Fourier Transform (FFT) based prediction algorithm and the time domain LS signal is reconstructed by taking an inverse FFT of the estimated missing values.

RESULTS

The experiments have been conducted on simulated and recorded HS corrupted LS signals at three different flow rates and various SNR levels. The performance of the proposed method is evaluated by qualitative and quantitative analysis of the results.

CONCLUSIONS

It is found that the proposed method is superior to the baseline method in terms of quantitative and qualitative measurement. The developed method gives better results compared to baseline method for different SNR levels. Our method gives cross correlation index (CCI) of 0.9488, signal to deviation ratio (SDR) of 9.8262, and normalized maximum amplitude error (NMAE) of 26.94 for 0 dB SNR value.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Peripheral bronchial obstruction evaluation in patients with asthma by lung sound analysis and impulse oscillometry

BACKGROUND

Computer-aided lung sound analysis (LSA) has been reported to be useful for evaluating airway inflammation and obstruction in asthma patients. We investigated the relation between LSA and impulse oscillometry with the evaluation of peripheral airway obstruction.

METHODS

A total of 49 inhaled corticosteroid-naive bronchial asthma patients underwent LSA, spirometry, impulse oscillometry, and airway hyperresponsiveness testing. The data were analyzed to assess correlations between the expiration: inspiration lung sound power ratio (dB) at low frequencies between 100 and 195 Hz (E/I LF) and various parameters.

RESULTS

E/I LF and X5 were identified as independent factors that affect V˙_{50,%predicted}. E/I LF showed a positive correlation with R5 (r = 0.34, p = 0.017), R20 (r = 0.34, p = 0.018), reactance area (AX, r = 0.40, p = 0.005), and resonant frequency of reactance (Fres, r = 0.32, p = 0.024). A negative correlation was found between E/I LF and X5 (r = -0.47, p = 0.0006). E/I LF showed a negative correlation with FEV₁/FVC(%), FEV_1,%predicted, V˙_{50,%predicted}, and V˙_{25,%predicted} (r = -0.41, p = 0.003; r = -0.44, p = 0.002; r = -0.49, p = 0.0004; and r = -0.30, p = 0.024, respectively). E/I LF was negatively correlated with log PC₂₀ (r = -0.30, p = 0.024). Log PC₂₀, X5, and past smoking were identified as independent factors that affected E/I LF level.

CONCLUSIONS

E/I LF as with X5 can be an indicator of central and peripheral airway obstruction in bronchial asthma patients.

Changes in lung sounds during asthma progression in a guinea pig model

BACKGROUND

Lung sound analysis is useful for objectively evaluating airways even in children with asymptomatic asthma. However, the relationship between lung sounds and morphological changes in the airways has not been elucidated. We examined the relationship between lung sounds and chronic morphological changes in the airways during the progression of asthma from onset in guinea pigs.

METHODS

Eleven male guinea pigs were examined; of these, seven were used as asthma models and four as controls. The asthma models were sensitized and repeatedly challenged by inhaling albumin chicken egg. We measured lung sounds and lung function twice a week for 21 weeks. After the final antigen challenge, the lungs were excised for histological examination. We measured the ratio of airway wall thickness to the total airway area and the ratio of the internal area to the total airway area in the trachea, third bronchi, and terminal bronchioles.

RESULTS

Among the lungs sounds, the difference between the two groups was greatest with respect to inspiratory sound intensity. The ratio of airway wall thickness to the total airway area of the terminal bronchioles was greater in the asthma models than in the controls, and it correlated best with the changes in inspiratory sound intensity in the 501-1000-Hz range (r = 0.76, p < 0.003).

CONCLUSIONS

Lung sound intensity in the middle frequency range from 501 to 1000 Hz correlated with peripheral airway wall thickness. Inspiratory sound intensity appeared to be an indicator of morphological changes in small airways in asthma.

Imaging of Hypersensitivity Pneumonitis

The management of hypersensitivity pneumonitis (HP) depends on early identification of the disease process, which is complicated by its nonspecific clinical presentation in addition to variable and diverse laboratory and radiologic findings. HP is the result of exposure and sensitization to myriad aerosolized antigens. HP develops in the minority of antigenic exposures, and conversely has been documented in patients with no identifiable exposure, complicating the diagnostic algorithm significantly. Prompt diagnosis and early intervention are critical in slowing the progression of irreversible parenchymal damage, and additionally in preserving the quality of life of affected patients.

Wheezing recognition algorithm using recordings of respiratory sounds at the mouth in a pediatric population

BACKGROUND

Respiratory diseases in children are a common reason for physician visits. A diagnostic difficulty arises when parents hear wheezing that is no longer present during the medical consultation. Thus, an outpatient objective tool for recognition of wheezing is of clinical value.

METHOD

We developed a wheezing recognition algorithm from recorded respiratory sounds with a Smartphone placed near the mouth. A total of 186 recordings were obtained in a pediatric emergency department, mostly in toddlers (mean age 20 months). After exclusion of recordings with artefacts and those with a single clinical operator auscultation, 95 recordings with the agreement of two operators on auscultation diagnosis (27 with wheezing and 68 without) were subjected to a two phase algorithm (signal analysis and pattern classifier using machine learning algorithms) to classify records.

RESULTS

The best performance (71.4% sensitivity and 88.9% specificity) was observed with a Support Vector Machine-based algorithm. We further tested the algorithm over a set of 39 recordings having a single operator and found a fair agreement (kappa=0.28, CI95% [0.12, 0.45]) between the algorithm and the operator.

CONCLUSIONS

The main advantage of such an algorithm is its use in contact-free sound recording, thus valuable in the pediatric population.

Auscultation of the respiratory system

Auscultation of the lung is an important part of the respiratory examination and is helpful in diagnosing various respiratory disorders. Auscultation assesses airflow through the trachea-bronchial tree. It is important to distinguish normal respiratory sounds from abnormal ones for example crackles, wheezes, and pleural rub in order to make correct diagnosis. It is necessary to understand the underlying pathophysiology of various lung sounds generation for better understanding of disease processes. Bedside teaching should be strengthened in order to avoid erosion in this age old procedure in the era of technological explosion.

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.

Tracheal sounds acquisition using smartphones

Tracheal sounds have received a lot of attention for estimating ventilation parameters in a non-invasive way. The aim of this work was to examine the feasibility of extracting accurate airflow, and automating the detection of breath-phase onset and respiratory rates all directly from tracheal sounds acquired from an acoustic microphone connected to a smartphone. We employed the Samsung Galaxy S4 and iPhone 4s smartphones to acquire tracheal sounds from N = 9 healthy volunteers at airflows ranging from 0.5 to 2.5 L/s. We found that the amplitude of the smartphone-acquired sounds was highly correlated with the airflow from a spirometer, and similar to previously-published studies, we found that the increasing tracheal sounds' amplitude as flow increases follows a power law relationship. Acquired tracheal sounds were used for breath-phase onset detection and their onsets differed by only 52 ± 51 ms (mean ± SD) for Galaxy S4, and 51 ± 48 ms for iPhone 4s, when compared to those detected from the reference signal via the spirometer. Moreover, it was found that accurate respiratory rates (RR) can be obtained from tracheal sounds. The correlation index, bias and limits of agreement were r² = 0.9693, 0.11 (-1.41 to 1.63) breaths-per-minute (bpm) for Galaxy S4, and r² = 0.9672, 0.097 (-1.38 to 1.57) bpm for iPhone 4s, when compared to RR estimated from spirometry. Both smartphone devices performed similarly, as no statistically-significant differences were found.

Rare causes of persistent wheeze that mimic poorly controlled asthma

Upper airway obstruction can present with stridor or expiratory or inspiratory wheeze and is commonly misdiagnosed as asthma. As asthma is common, such cases can remain hidden among patients with lower airway obstruction who attend primary care or respiratory clinics. We describe four causes of upper airway obstruction (paradoxical vocal cord movement, subglottic stenosis, retrosternal goitre and double aortic arch) which were misdiagnosed as 'poorly controlled asthma'.

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.

Wheezing after respiratory tract infection in athletes

Wheezing is a commonly encountered complaint by patients seen in sports medicine practice. Wheezes are a continuous musical sound heard best on expiration and can originate from one or more of several defined anatomical locations in the human airway. While common causes of wheezing include exercise-induced bronchoconstriction, postnasal drip, and asthma, wheezing also follows specific respiratory infections and can persist for months after the onset of symptoms. Abnormal lung physiology following pneumonia can persist for decades. These postinfectious pulmonary changes affect the ability of athletes to return to sports. In addition to history and physical examination, diagnosis may require pulmonary function testing and exercise challenge testing. The cornerstone to management is an accurate diagnosis and using lifestyle and pharmacologic intervention. Return to play should be gradual and allowed only after individuals demonstrate adequate pulmonary capacity to meet the demands of their sport. Providers also should be aware of governing body regulations regarding treatments and required therapeutic use exemptions.

Signal feature extraction by multi-scale PCA and its application to respiratory sound classification

Respiratory sound (RS) signals carry significant information about the underlying functioning of the pulmonary system by the presence of adventitious sounds. Although many studies have addressed the problem of pathological RS classification, only a limited number of scientific works have focused in multi-scale analysis. This paper proposes a new signal classification scheme for various types of RS based on multi-scale principal component analysis as a signal enhancement and feature extraction method to capture major variability of Fourier power spectra of the signal. Since we classify RS signals in a high dimensional feature subspace, a new classification method, called empirical classification, is developed for further signal dimension reduction in the classification step and has been shown to be more robust and outperform other simple classifiers. An overall accuracy of 98.34% for the classification of 689 real RS recording segments shows the promising performance of the presented method.

Adventitious sounds identification and extraction using temporal-spectral dominance-based features

Respiratory sound (RS) signals carry significant information about the underlying functioning of the pulmonary system by the presence of adventitious sounds (ASs). Although many studies have addressed the problem of pathological RS classification, only a limited number of scientific works have focused on the analysis of the evolution of symptom-related signal components in joint time-frequency (TF) plane. This paper proposes a new signal identification and extraction method for various ASs based on instantaneous frequency (IF) analysis. The presented TF decomposition method produces a noise-resistant high definition TF representation of RS signals as compared to the conventional linear TF analysis methods, yet preserving the low computational complexity as compared to those quadratic TF analysis methods. The discarded phase information in conventional spectrogram has been adopted for the estimation of IF and group delay, and a temporal-spectral dominance spectrogram has subsequently been constructed by investigating the TF spreads of the computed time-corrected IF components. The proposed dominance measure enables the extraction of signal components correspond to ASs from noisy RS signal at high noise level. A new set of TF features has also been proposed to quantify the shapes of the obtained TF contours, and therefore strongly, enhances the identification of multicomponents signals such as polyphonic wheezes. An overall accuracy of 92.4±2.9% for the classification of real RS recordings shows the promising performance of the presented method.

Gurgling breath sounds may predict hospital-acquired pneumonia

OBJECTIVES

To determine whether gurgling sounds heard during speech or quiet breathing, with or without a stethoscope over the glottis, predict hospital-acquired pneumonia (HAP).

METHODS

All patients admitted to the respiratory or general medicine ward of a 350-bed community teaching hospital were eligible. Patients were examined each day, and those who had upper airway gurgling, heard with or without the stethoscope, during breathing or speech at any point during admission were noted. Assuming an overall incidence of HAP (>48 h after admission) of 5% to 10% and estimated incidence of 30% to 50% in patients with gurgle, 20 patients with gurgle and 60 patients without gurgle, matched on the same day and ward of admission, were included in the study. Demographic, physiologic, and outcome variables were compared using univariate and multivariate techniques to ascertain whether gurgling is independently associated with HAP, rate of transfer to ICU, and inhospital mortality.

RESULTS

Twenty patients with gurgle were compared with 60 patients without gurgle. Patients with gurgle were older (78.5 vs 65.2 y; P < .001), more likely to reside in nursing homes (75% vs 6%; P < .001), and were more likely to have dementia (70% vs 13%; P < .001). In multivariate analysis, dementia (odds ratio [OR] = 23.4; 95% CI, 4.2-131.9) and recent (within 24 h) treatment with opiates (OR = 14.7; 95% CI, 2.2-97.5) emerged as the only statistically significant independent predictors of gurgling. HAP occurred in 55% of patients with gurgle compared with 1.7% of patients without gurgle (P < .001), and 50% of patients with vs 3.3% of patients without gurgle required transfer to ICU (P < .001). After adjustment for age, Charlson score, dementia, opiate administration, and stroke, gurgling emerged as the sole independent predictor of HAP (OR = 140.1; 95% CI, 5.6-3,529.4) and ICU transfer (OR = 35.1; 95% CI, 4.1-303.7). Gurgling did not predict mortality; the Charlson comorbidity index was the only significant predictor of inhospital death.

CONCLUSIONS

Gurgling sounds heard during quiet breathing or speech are independently associated with HAP.

Changes in the highest frequency of breath sounds without wheezing during methacholine inhalation challenge in children

BACKGROUND AND OBJECTIVE

It is difficult for clinicians to identify changes in breath sounds caused by bronchoconstriction when wheezing is not audible. A breath sound analyser can identify changes in the frequency of breath sounds caused by bronchoconstriction. The present study aimed to identify the changes in the frequency of breath sounds during bronchoconstriction and bronchodilatation using a breath sound analyser.

METHODS

Thirty-six children (8.2 +/- 3.7 years; males : females, 22 : 14) underwent spirometry, methacholine inhalation challenge and breath sound analysis. Methacholine inhalation challenge was performed and baseline respiratory resistance, minimum dose of methacholine (bronchial sensitivity) and speed of bronchoconstriction in response to methacholine (Sm: bronchial reactivity) were calculated. The highest frequency of inspiratory breath sounds (HFI), the highest frequency of expiratory breath sounds (HFE) and the percentage change in HFI and HFE were determined. The HFI and HFE were compared before methacholine inhalation (pre-HFI and pre-HFE), when respiratory resistance reached double the baseline value (max HFI and max HFE), and after bronchodilator inhalation (post-HFI and post-HFE).

RESULTS

Breath sounds increased during methacholine-induced bronchoconstriction. Max HFI was significantly greater than pre-HFI (P < 0.001), and decreased to the basal level after bronchodilator inhalation. Post-HFI was significantly lower than max HFI (P < 0.001). HFI and HFE were also significantly changed (P < 0.001). The percentage change in HFI showed a significant correlation with the speed of bronchoconstriction in response to methacholine (P = 0.007).

CONCLUSIONS

Methacholine-induced bronchoconstriction significantly increased HFI, and the increase in HFI was correlated with bronchial reactivity.

Regression simulation of the dependence of forced expiratory tracheal noises duration on human respiratory system biomechanical parameters

BACKGROUND

Estimating the duration of forced exhalation tracheal noises shows promise for recognizing bronchial obstruction.

OBJECTIVE

Experimental simulation of an influence of biomechanical parameters on the duration of normal forced exhalation tracheal noises.

METHOD AND MATERIALS

Thirty-two healthy non-smoking men aged 16-22 years were examined. The duration of noises, the parameters of computer spirometry, and the maximum static expiratory pressure are recorded. These data were analyzed by means of multiple linear regression simulation for logarithms of the elements of the proportionality relation obtained with the use of a one-component biomechanical model of forced exhalation and a linearized approximation of flow-volume curve.

RESULTS

Dependence between duration of the forced expiratory noises recorded on human trachea and the product of forced volume capacity (in power of 1.05 +/- 0.27), maximum static expiratory pressure (in power of 0.46 +/- 0.23), equivalent expiratory resistance in the stage of functional expiratory stenosis (in power of 0.72 +/- 0.15 in healthy is an estimate of the equivalent expiratory resistance of human bronchial tree in the functional expiratory stenosis phase, whereas in patients with bronchial obstruction it is supposed to take into account an excess of noise generation time compared with the time predicted from normal individual value of this resistance.

Respiratory flow estimation from tracheal sound by adaptive filters

In this study, average power of tracheal sound (Pave) was used to estimate flow by parametric method as well as adaptive filters as a nonparametric method. Based on some preliminary studies, an exponential model was used for describing the relationship between flow and Pave for parametric method. It was assumed that flow signal of at least one breath from each target flow is available for calibration. The error for flow estimation with parametric method, was found to be 9 &#177; 3 % and 10 &#177; 4 % for inspiration and expiration, respectively. Considering nonparametric method, the estimation error was the least for the third order adaptive filter using the average power of the tracheal sound (dB), which was 10 &#177; 3 % and 11 &#177; 4 % for inspiration and expiration, respectively.

Physical diagnosis of chronic obstructive pulmonary disease

Among the various diagnostic strategies of chronic obstructive pulmonary disease (COPD), physical diagnosis is the quickest and requires no extra cost. Rapid physical diagnosis of COPD in primary care practice can lead to earlier actions of preventive measures and counseling for patients. Further, rapid physical diagnosis of COPD in an emergency department is also crucial for timely use of potentially lifesaving therapy specific for COPD patients. In this review, we will present a broad scope of physical findings for rapid physical diagnosis of COPD.

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.

Respiratory flow in a realistic tracheostenosis model

The possible mechanism of wheeze generation in tracheostenosis was identified by measuring inspiratory and expiratory flow in a "morphological and distensible" realistic tracheostenosis model. The shape of the model was based on CT (Computed Tomography) images of a patient that had tracheostenosis. A trachea consists of tracheal cartilage rings and smooth muscle. Spatial variation of wall distensibility was achieved in the model by varying the wall thickness based on the elastic modulus measured in pig airways. The spatial variation influenced the flow in the airway and the turbulence production rate decreased faster at smooth muscles. Using the model, we investigated the mechanism of wheeze generation by focusing on the turbulence intensity. The turbulence intensity in expiratory flow was about twice that in inspiratory flow, and larger vortices existed in post-stenosis in expiratory flow, and thus might contribute to wheeze generation.

Reoperative tracheal surgery

Tracheal resection and reconstruction for postintubation stenosis is successful in more than 95% of initial repair attempts. The most likely causes of anastomotic failure are anastomotic tension, local devascularization, and granulomatous foreign body reaction. Incomplete resection of areas of stenosis or malacia might also lead to postoperative airway compromise. A variety of systemic factors might contribute to poor anastomotic healing. Postoperative respiratory difficulty requires immediate evaluation. In a patient with recurrent tracheal stenosis, the airway can be managed with dilation, or a tracheostomy or T-tube can be inserted through the failed anastomosis. Patients who are candidates for reoperative tracheal resection and reconstruction can expect good or satisfactory results in 91.9% of cases. Preoperatively addressing the patient's risk factors for failing, and liberally employing release procedures to reduce tension on the anastomosis contribute to the success of a reoperative procedure.

Pulmonary impairment and disability

Respiratory disorders, including occupational and environmental lung diseases, are prevalent. Physicians are frequently called upon to determine impairment and aid in the assessment of disability caused by these conditions, either as the treating physician or as an independent medical examiner. In this article we reviewed the role of physicians in determining the presence and severity of pulmonary disorders. A comprehensive clinical assessment and appropriate standardized tests, to objectively characterize the severity of impairment, are the key elements of the evaluation. This assessment may also include the physician's opinion regarding causative factors. Finally, disability determination is made by nonclinicians, through administrative means, based on the degree of impairment and a review of circumstances specific to the individual. Knowledge of these components of disability evaluation will help physicians to better serve their patients and supply appropriate data to the adjudicating system.