A Lung Sound Classification System Based on Data Augmenting Using ELM-Wavelet-AE

Abstract

The method is of great importance in systems that include machine learning and classification steps. As a result, academics are constantly working to improve the process. However, the data pertaining to the methodology's performance is equally as valuable as the methodology's creation. While the data is utilized to show the result of the modeling process, it is critical to consider the proper labeling of the data, the technique of acquisition, and the volume. Obtaining data in certain sectors, particularly medical fields, can be costly and time consuming. Thus, data augmenting via classical and synthetic methods has recently gained popularity. Our study uses synthetic data augmentation since it is newer, more efficient, and produces the desired effect. Our study's goal is to classify a data collection of lung sounds into four groups using data augmenting. Obtaining and standardizing the wavelet scatter transformation of each cycle of lung sounds, splitting the transformed data into test and training, augmenting and classifying the training data. In the augmenting stage, we utilized ELM-AE, then ELM-W-AE, with six wavelet functions (Gaussian, Morlet, Mexican, Shannon, Meyer, Ggw) added. The SVM and EBT classifiers improved performance by 4% and 3% in ELM-W-AE compared to the original structure.