Robust respiratory disease classification using breathing sounds (RRDCBS) multiple features and models

• Published in: Neural computing & applications 2022-05, Vol.34 (10), p.8155-8172
• Main Authors: Revathi, A., Sasikaladevi, N., Arunprasanth, D., Amirtharajan, Rengarajan
• Format: Article
• Language: English
• Subjects: Acoustics; Artificial Intelligence; Artificial neural networks; Breathing; Classification; Computational Biology/Bioinformatics; Computational Science and Engineering; Computed tomography; Computer Science; Data Mining and Knowledge Discovery; Feature extraction; Image classification; Image Processing and Computer Vision; Modelling; Original Article; Probability and Statistics in Computer Science; Respiratory diseases; Robustness; Support vector machines; Vector quantization
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-022-06915-0

Classification of respiratory diseases using X-ray and CT scan images of lungs is currently practised and used by many medical practitioners for clinical diagnosis. Respiratory disease classification, using breathing and wheezing sounds, remains scarce in the research field and is slowly upcoming. In this work, robust respiratory disease classification using breathing sounds ( RRDCBS) is implemented by extracting multiple features from sounds, creating multiple modelling techniques, and experimental identification of diseases using appropriate testing procedures for multi-class and binary classification of respiratory diseases. Decision level fusion of features for Vector quantisation (VQ) modelling technique has provided 100% accuracy for classifying five respiratory diseases and healthy subjects. Decision level fusion of indices on the features has provided 100% accuracy for VQ, support vector machine (SVM), and K-nearest neighbour (KNN) modelling techniques to perform binary classification of the respiratory disease against healthy data sound. Deep recurrent and convolutional neural networks are also evaluated for multiple/binary classification of respiratory diseases.