Cough sound analysis for diagnosing croup in pediatric patients using biologically inspired features

This paper aims to diagnose croup in children using cough sound signal classification. It proposes the use of a time-frequency image-based feature, referred as the cochleagram image feature (CIF). Unlike the conventional spectrogram image, the cochleagram utilizes a gammatone filter which models the frequency selectivity property of the human cochlea. This helps reveal more spectral information in the time-frequency image making it more useful for feature extraction. The cochleagram image is then divided into blocks and central moments are extracted as features. Classification is performed using logistic regression model (LRM) and support vector machine (SVM) on a comprehensive real-world cough sound signal database containing 364 patients with various clinically diagnosed respiratory tract infections divided into croup and non-croup. The best results, sensitivity of 88.37% and specificity of 91.59%, are achieved using SVM classification on a combined feature set of CIF and the conventional mel-frequency cepstral coefficients (MFCCs).