Computerized analysis of respiratory sounds during COPD exacerbations

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

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

Lung Auscultation Using the Smartphone-Feasibility Study in Real-World Clinical Practice

Conventional lung auscultation is essential in the management of respiratory diseases. However, detecting adventitious sounds outside medical facilities remains challenging. We assessed the feasibility of lung auscultation using the smartphone built-in microphone in real-world clinical practice. We recruited 134 patients (median[interquartile range] 16[11-22.25]y; 54% male; 31% cystic fibrosis, 29% other respiratory diseases, 28% asthma; 12% no respiratory diseases) at the Pediatrics and Pulmonology departments of a tertiary hospital. First, clinicians performed conventional auscultation with analog stethoscopes at 4 locations (trachea, right anterior chest, right and left lung bases), and documented any adventitious sounds. Then, smartphone auscultation was recorded twice in the same four locations. The recordings (n = 1060) were classified by two annotators. Seventy-three percent of recordings had quality (obtained in 92% of the participants), with the quality proportion being higher at the trachea (82%) and in the children's group (75%). Adventitious sounds were present in only 35% of the participants and 14% of the recordings, which may have contributed to the fair agreement between conventional and smartphone auscultation (85%; k = 0.35(95% CI 0.26-0.44)). Our results show that smartphone auscultation was feasible, but further investigation is required to improve its agreement with conventional auscultation.

Long-Term Auscultation in Chronic Obstructive Pulmonary Disease: Renaissance of an Ideograph of Medical Care

BACKGROUND

Electronic auscultation technology has advanced dramatically in the last few years. Therefore, long-term pulmonary auscultation could provide additional information about respiratory system by monitoring acute chronic obstructive pulmonary disease (AECOPD) exacerbations or by identifying wheezing phenotypes amongst stable COPD patients.

OBJECTIVES

Comparison of respiratory sounds in stable versus AECOPD patients recorded with a portable respiratory sound monitor over a period of 24 h.

METHODS

This prospective trial evaluated cough and wheezing events using an auscultation monitor specially developed for this purpose with 4 integrated highly sensitive microphones, in stable and severely AECOPD patients for a period of 24 h in an inpatient setting.

RESULTS

Twenty stable COPD patients (12 male, 60%) and 20 severely exacerbated COPD patients (14 male, 70%) were analyzed. In AECOPD patients, long-term auscultation revealed a significantly higher number of wheezing epochs than stable COPD patients (591 [IQR: 145-1,645] vs. 152 [IQR: 90-400]; p = 0.021). Conversely, cough epochs did not differ between AECOPD and stable COPD patients (213 [IQR: 140-327] vs. 162 [IQR: 123-243]; p = 0.256). The Borg-dyspnea scale, CAT score, and total CCQ score each showed no correlation with wheezing frequency, while CAT and CCQ scores did correlate with coughing frequency.

CONCLUSION

Wheezing, but not coughing, occurs more frequently in AECOPD patients than in stable COPD patients, indicating that severe wheezing is an important clinical sign of exacerbation, while coughing is not. Therefore, the patterns of wheezing and coughing, as assessed by long-term auscultation, differ in stable versus exacerbated COPD patients.

Nonlinear time series and principal component analyses: Potential diagnostic tools for COVID-19 auscultation

The development of novel digital auscultation techniques has become highly significant in the context of the outburst of the pandemic COVID 19. The present work reports the spectral, nonlinear time series, fractal, and complexity analysis of vesicular (VB) and bronchial (BB) breath signals. The analysis is carried out with 37 breath sound signals. The spectral analysis brings out the signatures of VB and BB through the power spectral density plot and wavelet scalogram. The dynamics of airflow through the respiratory tract during VB and BB are investigated using the nonlinear time series and complexity analyses in terms of the phase portrait, fractal dimension, Hurst exponent, and sample entropy. The higher degree of chaoticity in BB relative to VB is unwrapped through the maximal Lyapunov exponent. The principal component analysis helps in classifying VB and BB sound signals through the feature extraction from the power spectral density data. The method proposed in the present work is simple, cost-effective, and sensitive, with a far-reaching potential of addressing and diagnosing the current issue of COVID 19 through lung auscultation.

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

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

Changes in inhaler inhalation acoustic features during induced bronchoconstriction: a pilot study

Asthma is a chronic respiratory disease affecting millions of people worldwide, and is consequently a major issue for global health. Exacerbations are acute events involving the worsening of asthma's primary respiratory symptoms and are a major cause of morbidity in asthma patients, largely due to the unpredictability of their onset. This study aimed to investigate the relationship between changes in acoustic features of inhaler inhalations and changes in forced expiratory volume in one second (FEV1) that occur during a simulated exacerbation, a bronchial challenge test (BCT). This is a clinical test that simulates an asthma exacerbation through the administration of a bronchoconstrictor agent. Eight patients indicated for a BCT were recruited for this study. Non-contact and tracheal microphones were employed to record Diskus™ inhaler inhalations throughout the course of a BCT. A spirometer was employed to measure inhaler peak inspiratory flow rate (PIFR). In patients responsive to the BCT (n=4), significant correlations between changes in FEV1 and acoustic features on both microphones existed, with fractal increment of Katz fractal dimension yielding the strongest correlation (R=0.58), and between FEV1 and PIFR (R=0.62). These findings suggest that inhaler inhalation acoustic features may assist in the early detection of exacerbations. Future research will determine whether this is the case in a larger cohort of patients with non-simulated exacerbations.

Design of Wearable Breathing Sound Monitoring System for Real-Time Wheeze Detection

In the clinic, the wheezing sound is usually considered as an indicator symptom to reflect the degree of airway obstruction. The auscultation approach is the most common way to diagnose wheezing sounds, but it subjectively depends on the experience of the physician. Several previous studies attempted to extract the features of breathing sounds to detect wheezing sounds automatically. However, there is still a lack of suitable monitoring systems for real-time wheeze detection in daily life. In this study, a wearable and wireless breathing sound monitoring system for real-time wheeze detection was proposed. Moreover, a breathing sounds analysis algorithm was designed to continuously extract and analyze the features of breathing sounds to provide the objectively quantitative information of breathing sounds to professional physicians. Here, normalized spectral integration (NSI) was also designed and applied in wheeze detection. The proposed algorithm required only short-term data of breathing sounds and lower computational complexity to perform real-time wheeze detection, and is suitable to be implemented in a commercial portable device, which contains relatively low computing power and memory. From the experimental results, the proposed system could provide good performance on wheeze detection exactly and might be a useful assisting tool for analysis of breathing sounds in clinical diagnosis.

Computerised Analysis of Telemonitored Respiratory Sounds for Predicting Acute Exacerbations of COPD

Chronic obstructive pulmonary disease (COPD) is one of the commonest causes of death in the world and poses a substantial burden on healthcare systems and patients' quality of life. The largest component of the related healthcare costs is attributable to admissions due to acute exacerbation (AECOPD). The evidence that might support the effectiveness of the telemonitoring interventions in COPD is limited partially due to the lack of useful predictors for the early detection of AECOPD. Electronic stethoscopes and computerised analyses of respiratory sounds (CARS) techniques provide an opportunity for substantial improvement in the management of respiratory diseases. This exploratory study aimed to evaluate the feasibility of using: (a) a respiratory sensor embedded in a self-tailored housing for ageing users; (b) a telehealth framework; (c) CARS and (d) machine learning techniques for the remote early detection of the AECOPD. In a 6-month pilot study, 16 patients with COPD were equipped with a home base-station and a sensor to daily record their respiratory sounds. Principal component analysis (PCA) and a support vector machine (SVM) classifier was designed to predict AECOPD. 75.8% exacerbations were early detected with an average of 5 ± 1.9 days in advance at medical attention. The proposed method could provide support to patients, physicians and healthcare systems.

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

INTRODUCTION

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSION

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

Wheezing, a significant clinical phenotype of COPD: experience from the Taiwan Obstructive Lung Disease Study

BACKGROUND

COPD is an important public health challenge with significant heterogeneity of clinical presentation and disease progression. Clinicians have been trying to find phenotypes that may be linked to distinct prognoses and different therapeutic choices. Not all patients with COPD present with wheezing, a possible clinical phenotype that can help differentiate patient subgroups.

METHODS

The Taiwan Obstructive Lung Disease study was a retrospective, multicenter research study to investigate the treatment patterns of COPD after the implementation of the Global Initiative for Chronic Obstructive Lung Disease 2011 guidelines. Between November 2012 and August 2013, medical records were retrieved from patients with COPD aged ≥40 years; patients diagnosed with asthma were excluded. Demographic data, lung function, symptom scores, and acute exacerbation were recorded and analyzed, and the differences between patients with and without wheezing were evaluated.

RESULTS

Of the 1,096 patients with COPD, 424 (38.7%) had the wheezing phenotype. The wheezing group had significantly higher COPD Assessment Test scores (12.4±7.8 versus 10.5±6.7, P<0.001), higher modified Medical Research Council grade (2.0±1.0 versus 1.7±0.9, P<0.001), and more acute exacerbations within the past year (0.9±1.3 versus 0.4±0.9, P<0.001) than the nonwheezing group. The postbronchodilator forced expiratory volume in 1 second was lower in wheezing patients (1.2±0.5 L versus 1.5±0.6 L, P<0.001). Even in patients with maintenance treatment fitting the Global Initiative for Chronic Obstructive Lung Disease 2011 guidelines, the wheezing group still had worse symptom scores and more exacerbations.

CONCLUSION

Wheezing is an important phenotype in patients with COPD. Patients with COPD having the wheezing phenotype are associated with worse symptoms, more exacerbations, and worse lung function.

Chaos to randomness: distinguishing between healthy and non-healthy lung sound behaviour

Lung abnormalities and respiratory diseases increase as side effects of urban life and development. Therefore, understanding lung dynamics and its changes during the presence of abnormalities are critical in order to design more reliable tools for the early diagnosis and screening of lung pathology. The goal of this paper is to indicate the chaotic nature of normal lung sound and its transition to randomness in the presence of lung disease. The latter characteristic could serve as an indicator for evaluating the recovery process for patients suffering from lung disease. To verify this idea, we compared group of healthy and non-healthy subjects and also group of non-healthy subjects before and after treatments. Chaotic and randomness indices applied to lung sound signals which captured by multichannel data acquisition system. Results show that the normal lung displays chaotic dynamics. However, with the increase in lung abnormality, moves toward more random behaviour and away from its original chaotic state. Also, chaotic and randomness indices indicate their abilities to classify healthy and non-healthy lung sounds.

Computerized respiratory sounds in patients with COPD: a systematic review

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