Crackles are discontinuous, non-musical lung sounds heard mostly during inspiration. Of these adventitious lung sounds, crackles are seen in those affected by COPD, pneumonia, or heart diseases. Therefore, an investigation for identifying the signature characteristics of normal and abnormal sounds is essential for the proper diagnosis of lung diseases. Adventitious lung sounds like crackles, wheeze, rhonchi, and pleural rubs can be an implication of its improper pulmonary functioning. The adventitious abnormal breath sounds indicate acquired lung pathologies like obstruction in large airways, collection of fluid in or around lungs, airway narrowing causing inflation of a part of lungs, inflammation of the pleural layer, and others. Also, in vesicular sounds, the ratio of the duration of the inspiration to expiration is 3:1, while it is 1:1 in BB. The pitch and the ratio of inspiration time to expiration time vary with the origin of the breath sound. While bronchial sounds are high pitched, loud, and heard over the trachea, vesicular sounds are low pitched, soft, and heard at the posterior lung bases. Vesicular sound arises from lung parenchyma and bronchial sound from the trachea-bronchial tree. Based on the acoustic properties and source of origin, a typical lung sound is again categorised as vesicular and bronchial (BB). īreath sounds can be classified as normal and abnormal. Lung sounds are produced when air passes from narrow to broader space of the respiratory tract such as glottis and the opening of bronchioles to alveoli during inspiration or due to turbulent and vorticose airflow through the trachea-bronchial tree. It provides primary information on pulmonary functioning from lung sound. Auscultation using a stethoscope is an older technique used to understand the pathology and physiology of lungs and airways. Although COPD is not a completely curable disease, proper diagnosis and treatment would help to reduce its burden. While inflammation and mucus obstruct the airways in chronic bronchitis and asthma, emphysema is a condition in which the lung tissues get damaged, leading to shortness of breath. COPD represents a group of respiratory diseases like chronic bronchitis, asthma, and emphysema and is characterised by breathing difficulties. Hence, research in this field is essential to devise novel methods of diagnosis and thereby to improve the public health system. Studies reveal that globally, 65 million people suffer from moderate to acute, chronic obstructive (CO) pulmonary disease (PD), out of which thirty lakh die a year, making it a significant cause of mortality. The study suggests the possible application of the techniques in the auscultation of coronavirus disease 2019 seriously affecting the respiratory system.Įxcessive exposure to polluted air and unhealthy lifestyle has contributed to several lung and respiratory disorders. The present work elucidates phase portrait features as a better choice of classification, as it takes into consideration the temporal correlation between the data points of the time series signal, and thereby suggesting a novel surrogate method for the diagnosis in pulmonology. The phase portrait feature-based supervised classification proves to be better compared to the unsupervised machine learning technique. The potential of principal component analysis through the spectral feature extraction categorises BB, fine crackles, and coarse crackles. Fractal dimension, phase portrait, and sample entropy help in divulging the greater randomness, antipersistent nature, and complexity of airflow dynamics in BB than PC. The thirty-five sound signals of bronchial (BB) and pulmonary crackle (PC) analysed by fast Fourier transform and wavelet not only give the details of number, nature, and time of occurrence of the frequency components but also throw light onto the embedded air flow during breathing. The paper delves into the plausibility of applying fractal, spectral, and nonlinear time series analyses for lung auscultation.
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