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

Wireless broadband acousto-mechanical sensing system for continuous physiological monitoring

The human body generates various forms of subtle, broadband acousto-mechanical signals that contain information on cardiorespiratory and gastrointestinal health with potential application for continuous physiological monitoring. Existing device options, ranging from digital stethoscopes to inertial measurement units, offer useful capabilities but have disadvantages such as restricted measurement locations that prevent continuous, longitudinal tracking and that constrain their use to controlled environments. Here we present a wireless, broadband acousto-mechanical sensing network that circumvents these limitations and provides information on processes including slow movements within the body, digestive activity, respiratory sounds and cardiac cycles, all with clinical grade accuracy and independent of artifacts from ambient sounds. This system can also perform spatiotemporal mapping of the dynamics of gastrointestinal processes and airflow into and out of the lungs. To demonstrate the capabilities of this system we used it to monitor constrained respiratory airflow and intestinal motility in neonates in the neonatal intensive care unit (n = 15), and to assess regional lung function in patients undergoing thoracic surgery (n = 55). This broadband acousto-mechanical sensing system holds the potential to help mitigate cardiorespiratory instability and manage disease progression in patients through continuous monitoring of physiological signals, in both the clinical and nonclinical setting.

Real-time counting of wheezing events from lung sounds using deep learning algorithms: Implications for disease prediction and early intervention

This pioneering study aims to revolutionize self-symptom management and telemedicine-based remote monitoring through the development of a real-time wheeze counting algorithm. Leveraging a novel approach that includes the detailed labeling of one breathing cycle into three types: break, normal, and wheeze, this study not only identifies abnormal sounds within each breath but also captures comprehensive data on their location, duration, and relationships within entire respiratory cycles, including atypical patterns. This innovative strategy is based on a combination of a one-dimensional convolutional neural network (1D-CNN) and a long short-term memory (LSTM) network model, enabling real-time analysis of respiratory sounds. Notably, it stands out for its capacity to handle continuous data, distinguishing it from conventional lung sound classification algorithms. The study utilizes a substantial dataset consisting of 535 respiration cycles from diverse sources, including the Child Sim Lung Sound Simulator, the EMTprep Open-Source Database, Clinical Patient Records, and the ICBHI 2017 Challenge Database. Achieving a classification accuracy of 90%, the exceptional result metrics encompass the identification of each breath cycle and simultaneous detection of the abnormal sound, enabling the real-time wheeze counting of all respirations. This innovative wheeze counter holds the promise of revolutionizing research on predicting lung diseases based on long-term breathing patterns and offers applicability in clinical and non-clinical settings for on-the-go detection and remote intervention of exacerbated respiratory symptoms.

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.

Albiflorin alleviates ovalbumin (OVA)-induced pulmonary inflammation in asthmatic mice

In the present study, the effects of albiflorin (ALB) on the pulmonary inflammation induced by ovalbumin (OVA) in an asthmatic mouse model were investigated. Airway hyperreactivity (AHR) in asthmatic mice was detected using the acetylcholine stimulation test. Eosinophilia cells in the serum of asthmatic mice were counted. Hematoxylin and eosin (H&E) staining was used to observe pathological changes in lung tissue. Inflammatory cytokines, including interleukin (IL)-1β, IL-6, and tumor necrosis factor (TNF)-α were detected in bronchoalveolar lavage fluid (BALF) and lung tissue using enzyme-linked immunosorbent assay (ELISA). Western blotting was used to detect the mitogen-activated protein kinase/nuclear factor kappa B (MAPK/NF-κB) signaling pathway in the lungs of asthmatic mice. The results from the present study indicated that ALB dramatically suppressed the expression of inflammatory cytokines including IL-1β, IL-6, and TNF-α, and inflammatory cells. In addition, ALB significantly decreased malondialdehyde (MDA) content as well as increased superoxide dismutase (SOD) activity. ALB also alleviated AHR in asthmatic mice and improved pathological changes in the lungs. In addition, ALB inhibited the MAPK/NF-κB signaling pathway in the lungs of the asthmatic mice. Thus, ALB appears to inhibit lung inflammation in asthmatic mice via regulation of the MAPK/NF-κB signaling pathway.

Lung Sound Analysis Provides A Useful Index For Both Airway Narrowing And Airway Inflammation In Patients With Bronchial Asthma

Background

The expiration-to-inspiration sound power ratio in a midfrequency range (E/I MF), a parameter of lung sound analysis (LSA), has been reported to be useful as an index of airway inflammation in patients with bronchial asthma. However, the E/I MF reflects airway narrowing caused by airway inflammation, and there is thus concern that it may not be an index of airway eosinophilic inflammation itself.

Methods

A total of 131 patients with bronchial asthma were classified into four groups according to the presence or absence of airway narrowing and airway inflammation to examine whether the E/I MF could serve as an index of airway inflammation.

Results

The E/I MF was significantly higher in patients with a normal forced expiratory volume in one second (FEV₁) and high fractional exhaled nitric oxide (FeNO), those with a low FEV₁ and normal FeNO, and those with a low FEV₁ and high FeNO than in those with a normal FEV₁ and normal FeNO (p < 0.05–0.01). In particular, the E/I MF was high even in the patients who had no airway narrowing but had airway inflammation (p < 0.01). The results of multivariate analysis of factors involved in FeNO in patients with a normal FEV₁ revealed that the E/I MF was an independent factor (p = 0.0281).

Conclusion

The E/I MF is a useful index of airway inflammation in the treatment of asthma, regardless of the presence or absence of airway narrowing.

A Lung Sound Category Recognition Method Based on Wavelet Decomposition and BP Neural Network

In this paper, a method of characteristic extraction and recognition on lung sounds is given. Wavelet de-noised method is adopted to reduce noise of collected lung sounds and extract wavelet characteristic coefficients of the de-noised lung sounds by wavelet decomposition. Considering the problem that lung sounds characteristic vectors are of high dimensions after wavelet decomposition and reconstruction, a new method is proposed to transform the characteristic vectors from reconstructed signals into reconstructed signal energy. In addition, we use linear discriminant analysis (LDA) to reduce the dimension of characteristic vectors for comparison in order to obtain a more efficient way for recognition. Finally, we use BP neural network to carry out lung sounds recognition where comparatively high-dimensional characteristic vectors and low- dimensional vectors are set as input and lung sounds categories as output with a recognition accuracy of 82.5% and 92.5%.

Phenotype classification using the combination of lung sound analysis and fractional exhaled nitric oxide for evaluating asthma treatment

BACKGROUND

We report the utility of combining lung sound analysis and fractional exhaled nitric oxide (FeNO) for phenotype classification of airway inflammation in patients with bronchial asthma. We investigated the usefulness of the combination of the expiration-to-inspiration sound power ratio in the mid-frequency range (E/I MF) of 200-400 Hz and FeNO for comprehensively classifying disease type and evaluating asthma treatment.

METHODS

A total of 233 patients with bronchial asthma were included. The cutoff values of FeNO and E/I MF were set to 38 ppb and 0.36, respectively, according to a previous study. The patients were divided into 4 subgroups based on the FeNO and E/I MF cutoff values. Respiratory function, the percentages of sputum eosinophils and neutrophils, and patient background characteristics were compared among groups.

RESULTS

Respiratory function was well controlled in the FeNO low/E/I MF low group (good control). Sputum neutrophil was higher and FEV₁,%pred was lower in the FeNO low/E/I MF high group (poor control). History of childhood asthma and atopic asthma were associated with the FeNO high/E/I MF low group (insufficient control). The FeNO high/E/I MF high group corresponded to a longer disease duration, increased blood or sputum eosinophils, and lower FEV₁/FVC (poor control).

CONCLUSIONS

The combination of FeNO and E/I MF assessed by lung sound analysis allows the condition of airway narrowing and the degree of airway inflammation to be assessed in patients with asthma and is useful for evaluating bronchial asthma treatments.