Enhancing Lung Acoustic Signals Classification With Eigenvectors-Based and Traditional Augmentation Methods

Identifying lung sound signal patterns is essential for detecting and monitoring respiratory diseases. Existing approaches for analyzing respiratory sounds need domain specialists. Therefore, an accurate and automated lung sound classification tool is required. In this paper, we have developed an automatic diagnostic system to classify these signals. It can support healthcare systems in low-resource environments with limited resources and a shortage of qualified medical professionals. This paper presents an eigenvectors-based data augmentation method to enhance the detection rate of automatic diagnostic systems. This proposed method provides noise-free data samples with the principal components that capture the most significant variations in the data. In the classification process, various machine learning-based classifiers are employed along with spectrogram-based features.