Abnormal Respiratory Sounds Classification Using Deep CNN Through Artificial Noise Addition

Abstract

Respiratory sound (RS) attributes and their analyses structure a fundamental piece of pneumonic pathology, and it gives symptomatic data regarding a patient's lung. A couple of decades back, doctors depended on their hearing to distinguish symptomatic signs in lung audios by utilizing the typical stethoscope, which is usually considered a cheap and secure method for examining the patients. Lung disease is the third most ordinary cause of death worldwide, so; it is essential to classify the RS abnormality accurately to overcome the death rate. In this research, we have applied Fourier analysis for the visual inspection of abnormal respiratory sounds. Spectrum analysis was done through Artificial Noise Addition (ANA) in conjunction with different deep convolutional neural networks (CNN) to classify the seven abnormal respiratory sounds—both continuous (CAS) and discontinuous (DAS). The proposed framework contains an adaptive mechanism of adding a similar type of noise to unhealthy respiratory sounds. ANA makes sound features enough reach to be identified more accurately than the respiratory sounds without ANA. The obtained results using the proposed framework are superior to previous techniques since we simultaneously considered the seven different abnormal respiratory sound classes.

An accurate deep learning model for wheezing in children using real world data

“…The use of CNN, RNN, and other methods has been proposed as deep-learning architectures. Among them, several studies have evaluated CNN as most optimal for the respiratory sound classification model 27 , 28 . CNN operates the neural network by applying convolutional operations and is used in various fields such as image, video, and natural language interpretation.…”

“…However, overfitting may occur as the layer becomes deeper, which increases the complexity of the model and reduces performance 30 . Based on CNN, several hybrid models have been proposed to compensate for such problems and achieve optimal performance 15 , 28 , 31 . As for the most recent research, a model with performance higher than that of the existing breathing sound classification models by adding artificial noise addition technique to the general CNN structure has been proposed 28 .…”

“…Based on CNN, several hybrid models have been proposed to compensate for such problems and achieve optimal performance 15 , 28 , 31 . As for the most recent research, a model with performance higher than that of the existing breathing sound classification models by adding artificial noise addition technique to the general CNN structure has been proposed 28 . Moreover, a study proposed a model that achieved good performance using the combination of the co-tuning and stochastic normalization techniques of the CNN-based pre-trained ResNet as backbone 15 .…”

Design of Wheezing Sound Detection Wearable Device Based on Internet of Things

“…According to Computerized Respiratory Sound Analysis (CORSA), wheezing is a sound that has a frequency between 100-1000Hz with a duration of more than 100ms by displaying a narrow band trajectory and peaks spectrum over time [4] . Some other studies say that the minimum duration of wheezing is usually between 80-100ms [5] .…”

Interpretation of lung disease classification with light attention connected module

“…It is characterized by the sound of bubbles bursting discontinuously and is distributed in the frequency band of 60–2000 Hz. The representative diseases include interstitial lung disease (ILD), bronchiectasis, and pneumonia [8] , [9] .…”