Deep Neural Network-Based Respiratory Pathology Classification Using Cough Sounds

Intelligent systems are transforming the world, as well our healthcare system. We propose a deep learning-based cough sound classification model that can distinguish between children with healthy versus pathological coughs such asthma, upper respiratory tract infection (URTI), and lower (LRTI). To train neural network model, we collected new dataset of sounds, labelled clinician’s diagnosis. The chosen is bidirectional long–short-term memory (BiLSTM) based on Mel-Frequency Cepstral Coefficients (MFCCs) features. resulting trained when for classifying two classes coughs—healthy or pathology (in general belonging to specific pathology)—reaches accuracy exceeding 84% label provided by physicians’ classify subject’s condition, results multiple epochs per subject were combined. prediction exceeds 91% all three pathologies. However, discriminate among four coughs, overall dropped: one class often misclassified other. if considers classified have some kind pathology, then four-class above 84%. A longitudinal study MFCC feature space comparing recovered from same subjects revealed fact irrespective underlying conditions, occupy making it harder differentiate only using