Accumulated bispectral image-based respiratory sound signal classification using deep learning

• Published in: Signal, image and video processing Vol. 17; no. 7; pp. 3629 - 3636
• Main Authors: Sangle, Sandeep B., Gaikwad, Chandrakant J.
• Format: Journal Article
• Language: English
• Published: London Springer London 01-10-2023; Springer Nature B.V
• Subjects: Algorithms; Artificial neural networks; Computer Imaging; Computer Science; Image Processing and Computer Vision; Machine learning; Multimedia Information Systems; Original Paper; Pattern Recognition and Graphics; Signal classification; Signal,Image and Speech Processing; Vision; COVID-19; Bispectral image; CNN; Accumulated bispectrum
• ISSN: 1863-1703; 1863-1711
• DOI: 10.1007/s11760-023-02589-w

The COVID-19 virus is increasingly crucial to human health since new variants appear frequently. Detection of COVID-19 through respiratory sound has been an important area of research. This study analyzes respiratory sounds using novel accumulated bi-spectral features. The principal domain bispectrum is used for computing accumulated bispectrum. The resulting magnitude bispectrum is used in forming the bispectral image. In this work, a convolutional neural network (CNN) and ResNet-50 algorithms are designed to classify respiratory sounds as either COVID-19 or healthy. The performance of the proposed method is compared with the state-of-the-art methods. The proposed CNN-based method achieves the highest accuracy of 87.68% for shallow breath sounds, and ResNet-50 achieves the highest accuracy of 87.62% for deep breath sounds. Similarly, proposed methods gives the improved performance for other respiratory sounds.