Affiliations: [a]
Department of Electrical and Electronics Engineering, Sona College of Technology, Salem, TamilNadu, India
| [b]
Department of Electrical and Electronics Engineering, Paavai Engineering College, Namakkal, TamilNadu, India
| [c]
Department of Mechanical Engineering, New Horizon College of Engineering, Bengaluru, Karnataka, India
| [d]
Department of Electrical and Electronics Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, TamilNadu, India
Correspondence:
[*]
Corresponding author. K.S. Yamuna, Department of Electrical and Electronics Engineering, Sona College of Technology, Salem, TamilNadu, India. Email: [email protected].
Abstract: Lung sound (LS) signals are a vital source of information for the identification of pulmonary disorders. Heart sound (HS) is the most common contaminant of lung sounds during auscultation from the chest walls. This directly affects the efficiency of lung sound processing in diagnosing lung diseases. In this work, Adaptive Variational Mode Decomposition (AVMD) technique is proposed to remove heart sound contaminants from lung sounds. The proposed AVMD method initially breakdown the noisy lung sound signal into a collective of bandlimited modes called variational mode functions (VMF). Then, based on the frequency spectrum, the HS is filtered out from the LS. The real time lung sound data is collected from 95 participants and the performance of VMD technique is evaluated using the statistical metrics measures. Thus, the proposed topology exhibits Higher SNR (29.6587dB, lowest Root Mean Square (RMSE) of 0.0102, lowest normalized Mean Absolute Error (nMAE) of 0.0336, and highest percentage in correlation coefficient Factor (CCF) of 99.79% respectively. These experimental results are found to be superior and outperform all other recently proposed techniques.
