Abnormal Respiratory Sounds Classification Using Deep CNN Through Artificial Noise Addition

Rizwana Zulfiqar; Fiaz Majeed; Rizwana Irfan; Hafiz Tayyab Rauf; Elhadj Benkhelifa; Abdelkader Nasreddine Belkacem

doi:10.3389/fmed.2021.714811

Abnormal Respiratory Sounds Classification Using Deep CNN Through Artificial Noise Addition

Front Med (Lausanne). 2021 Nov 17:8:714811. doi: 10.3389/fmed.2021.714811. eCollection 2021.

Authors

Rizwana Zulfiqar¹, Fiaz Majeed¹, Rizwana Irfan², Hafiz Tayyab Rauf³, Elhadj Benkhelifa⁴, Abdelkader Nasreddine Belkacem⁵

Affiliations

¹ Faculty of Computing and Information Technology, University of Gujrat, Gujrat, Pakistan.
² Department of Information Technology, College of Computing and Information Technology at Khulais, University of Jeddah, Jeddah, Saudi Arabia.
³ Centre for Smart Systems, AI and Cybersecurity, Staffordshire University, Stoke-on-Trent, United Kingdom.
⁴ Cloud Computing and Applications Reseach Lab, Staffordshire University, Stoke-on-Trent, United Kingdom.
⁵ Department of Computer and Network Engineering, College of Information Technology, UAE University, Al Ain, United Arab Emirates.

Abstract

Respiratory sound (RS) attributes and their analyses structure a fundamental piece of pneumonic pathology, and it gives symptomatic data regarding a patient's lung. A couple of decades back, doctors depended on their hearing to distinguish symptomatic signs in lung audios by utilizing the typical stethoscope, which is usually considered a cheap and secure method for examining the patients. Lung disease is the third most ordinary cause of death worldwide, so; it is essential to classify the RS abnormality accurately to overcome the death rate. In this research, we have applied Fourier analysis for the visual inspection of abnormal respiratory sounds. Spectrum analysis was done through Artificial Noise Addition (ANA) in conjunction with different deep convolutional neural networks (CNN) to classify the seven abnormal respiratory sounds-both continuous (CAS) and discontinuous (DAS). The proposed framework contains an adaptive mechanism of adding a similar type of noise to unhealthy respiratory sounds. ANA makes sound features enough reach to be identified more accurately than the respiratory sounds without ANA. The obtained results using the proposed framework are superior to previous techniques since we simultaneously considered the seven different abnormal respiratory sound classes.

Keywords: abnormal respiratory sounds; continuous adventitious sounds (CAS); deep CNN; discontinuous adventitious sounds (DAS); respiratory sounds.