A simple lung sound enhancement for automatic identification of lung pathologies

• Main Authors: Chua, Cadwallader C., Cocuaco, Kevin Lloyd D., Lao, Alexis Jamie R., Tan, Eldridge Sherwin S., Cordel, Macario O., II, Ilao, Joel P., Rabe, Adrian Paul J.
• Format: text
• Published: Animo Repository 2022
• Subjects: Lungs—Sounds; Auscultation; Noise control—Equipment and supplies; Computer Sciences
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/5439; https://www.researchgate.net/publication/283086754_A_Simple_Lung_Sound_Enhancement_for_Automatic_Identification_of_Lung_Pathologies
• Institution: De La Salle University

Auscultation or lung sound analysis depends on the familiarity of the physician on detecting sound patterns. However, typical environment for auscultation are performed in rooms susceptible to different sounds such as vocal sound, ventilation machines and ambient noise, which may impede the subjective evaluation of the lung sounds. This paper presents a simple signal enhancement scheme for normal lung sounds in order to successfully extract the features which include the bandwidth, peak frequency and center frequency. The extracted features could be used in automatic detection and classifications of lung sound abnormalities of different. Results show that the enhanced signal has features in the 300 to 700 Hz range while the raw and denoised signals have features below 300 Hz. Listening test shows improved score in enhanced signals over scores on the raw and denoised signals with an average score of 1.3 over 1.03 in raw and 0.82 in denoised signals.