Respiratory sound classification utilizing human auditory-based feature extraction
Rishabh, Dhirendra Kumar, Yogendra Meena, Kuldeep Singh · Physica Scripta · 2025
Abstract A major worldwide health concern is chronic respiratory diseases (CRDs), which include disorders including asthma, pulmonary hypertension, occupational lung diseases, and chronic obstructive pulmonary disease (COPD). Improving clinical results and treatment efficacy requires an early and precise diagnosis. In order to classify respiratory sounds, this study presents a novel framework that incorporates auditory-inspired characteristics, such as Mel-Frequency Cepstral Coefficients (MFCCs), Mel Spectrograms, and Cochleograms, into a CNN-LSTM architecture. The framework uses sophisticated feature extraction techniques in conjunction with strong data augmentation approaches to address the issue of class imbalance and guarantee a thorough representation of a variety of respiratory sound patterns. Using the Respiratory Sound Database, the suggested model was assessed and showed remarkable performance, obtaining an F1 score of 98.94%, accuracy of 98.90%, specificity of 99.80%, sensitivity of 98.90%, and an ICBHI score of 99.40%. These findings demonstrate the model’s potential as a reliable and efficient tool for the early identification and evaluation of CRDs, which would significantly improve patient care and the management of respiratory illnesses. The outstanding performance further emphasizes the importance in clinical settings, enabling improved management and early identification of chronic respiratory conditions.