Archive | 2021
Comparison of Various Time-Frequency Analysis Methods and Classification of Respiratory Sounds Using Pre-trained GoogLeNet Classifier
Abstract
Medical practitioners regularly listen to person’s lungs using a stethoscope for the prognosis of chronic obstructive pulmonary diseases (COPDs) and other lower respiratory infections including asthma. As majority of the biomedical signals are non-stationary and time-varying in nature, time-frequency analysis can provide exhaustive description of signals in different time-frequency planes. The non-stationary signal considered for this study are respiratory signals, and four time-frequency analysis methods, namely short-time fourier transform (STFT), continuous wavelet transform (CWT), Wigner–Ville distribution (WVD), and constant Q-Gabor transform (CQT) were considered to analyze the lung sounds. For this purpose, lung sounds are decomposed into several oscillatory modes called intrinsic mode functions (IMF) using empirical mode decomposition (EMD). The vesicular and adventitious sounds viz crackles, wheezes, and rhonchi are classified using a pre-trained GoogLeNet classifier. The classification accuracy is compared for all the four methods studied in this work, and it is demonstrated that an improved accuracy of 96.3% is achieved through the scalogram representation of the third IMF.