Neural network-based design and evaluation of performance metrics using adaptive line enhancer with adaptive algorithms for auscultation analysis

Abstract

Auscultation is an important key for the physical (respiratory and circulatory) examination and is helpful in diagnosing various disorders. Auscultation is performed for the purposes of examining the circulatory and respiratory sounds and gastrointestinal system (bowel sounds). Besides inconsistencies in the propagation of the normal sounds, there are also several types of specific irregularities that can be heard in respiratory sounds: commonly known abnormal sounds in lung sound (wheezes, crackles, stridor, squawks, rhonchi and crackles) and heart sounds (heart murmurs). However, detection of abnormal sounds during auscultation needs extensive training and experience. Real-time separation of these heart sound signals from the lung sound signals is of great research interest and difficult to achieve. In this work, the authors proposed a novel adaptive line enhancer using nonlinear ANN design used for auscultation analysis. Our proposed designs are trained using different networks training algorithms which resulted in better mean square error reduction within compact time.