In their study, the reported accuracy was 98.79%. used a S-transform-based approach coupled with deep residual networks to classify lung sounds: crackle, wheeze, and normal. On ICBHI17 dataset, the highest accuracy of 83.2% was reported. Using 20 healthy and non-healthy subjects, they reported an accuracy of 85%. used Gaussian mixture model and support vector machine-based classifier. In their experiments, with 1K+ volunteers (over 250 hours of data), an accuracy of 86.7% was reported. proposed a robust approach to identify lung sounds in the presence of noise. On ICBHI17 dataset, an accuracy of 52.26% was reported. developed a system to distinguish lung sounds using a resnet-based approach. They also reported 90.6% true positives for normal sounds with fourier transform-based features. They worked with 12 instances from each class and reported the highest true positive value of 76.6% for wheezing sounds. Bahoura and Pelletier used cepstral features to distinguish normal and wheezing sounds. Among the 20 cases, they found that in 17 cases, the observers concluded presence of atleast 1 adventitious sound. They worked with 1 clip each from 10 adults and children and obtained fleiss kappa values of 0.62 and 0.59 for crackles and wheezes, respectively. studied the classification of lung sounds by 12 observers. An accuracy of 92% was reported by using Multilayer Perceptron (MLP). In their experiments, nine different categories from 36 patients were used. Dokur used machine learning approaches to distinguish respiratory sounds. They reported an accuracy of 71.81% on the ICBHI17 dataset of size 6800+ clips. presented a deep learning-based approach for lung sound classification. They reported a AUC value of 0.8919 with MFCC-based features. Of 425 events, 223 were wheezes and the rest were normal. classified normal respiratory sounds and wheezes on a dataset of 38 recordings. On a dataset of 17930 sounds from 1630 subjects, an accuracy of 86% (for healthy-pathological classification) was reported. presented a convolutional network plus mel frequency cepstral coefficient-support vector machine-based approach for lung sound classification. It includes different type of sounds in the thick of internal and external sounds. They studied acoustic aspects for different lung diseases. discussed acoustic techniques for pulmonary analysis. The dataset contains respiratory cycles that were recorded and annotated by professionals as wheezes, crackles, both, or no abnormal sounds. They are common in patients with obstructive airway diseases and indicate obstructive airway conditions, such as asthma and COPD. Wheezes are high pitched sounds that last more than 100 ms. that are typically less than 20 ms that occur frequently in cardiorespiratory diseases associated with lung fibrosis (fine crackles) or chronic airway obstruction (coarse crackles). Crackles are discontinuous sounds, explosive, and non-musical. Adventitious sounds are RS superimposed on normal respiratory sounds, which can be crackles or wheezes. Respiratory sounds are generally classified as normal or adventitious. In 2017, the largest publicly available respiratory sound database was compiled and encouraged the development of algorithms that can identify common abnormal breath sounds (wheezes and crackles) from clinical and nonclinical settings. Automated analysis was made possible with the use of electronic stethoscope. Lung sounds are difficult to analyze and distinguish because they are non-stationary and non-linear signals. Therefore, it opens a door to develop computerized respiratory sound analysis tools/techniques, where automation is integral. Besides, for various reasons (e.g., faulty instrument), false positives can happen. Such procedure is limited to trained physicians. Over several years, it has been an effective tool to analyze lung disorders and/or abnormalities. Auscultation is a technique that involves listening to the internal human body sounds with the aid of a stethoscope. Even though, spirometry is one of the most commonly available lung function tests, it is limited to patient’s cooperation. Respiratory conditions are diagnosed through spirometry and lung auscultation. Audio analysis aids in timely diagnosis of respiratory ailments more effortlessly in the early stages of a respiratory dysfunction. Like in other application domains, audio signal analysis tools can potentially help in analyzing respiratory sounds to detect problems in the respiratory tract. As rapid growth of respiratory diseases is witnessed around the world, medical research field has gained interest in integrating potential audio signal analysis-based technique. Respiratory diseases are the third leading cause of death worldwide.
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