DKPNet41: Directed knight pattern network-based cough sound classification model for automatic disease diagnosis. (December 2022)

• Record Type: Journal Article
• Authors: Kuluozturk, Mutlu; Kobat, Mehmet Ali; Barua, Prabal Datta; Dogan, Sengul; Tuncer, Turker; Tan, Ru-San; Ciaccio, Edward J.; Acharya, U Rajendra
• Is Part Of: Medical engineering & physics. Volume 110(2022)
• Publication Date: 2022-12
• Subjects: Directed knight pattern -- cough sound -- multiple pooling -- DKPNet41 -- acute asthma -- Covid-19 -- heart failure; Biomedical engineering -- Periodicals; Biomedical Engineering -- Periodicals; Physics -- Periodicals; Génie biomédical -- Périodiques; Biomedical engineering; Electronic journals; Periodicals; 610.28
• DOI: 10.1016/j.medengphy.2022.103870
• Languages: English
• ISSNs: 1350-4533

Abstract

HIGHLIGHTS: New cough sound dataset is collected. New local feature generator is proposed. An effective machine learning model is presented (DKPNet41) This model is aims to classify Asthma, Covid-19, Heart failure and healthy categories. DKPNet41 achieved 99.39% accuracy on the cough sound dataset. Abstract: Problem: Cough-based disease detection is a hot research topic for machine learning, and much research has been published on the automatic detection of Covid-19. However, these studies are useful for the diagnosis of different diseases. Aim: In this work, we collected a new and large (n=642 subjects) cough sound dataset comprising four diagnostic categories: 'Covid-19', 'heart failure', 'acute asthma', and 'healthy', and used it to train, validate, and test a novel model designed for automatic detection. Method: The model consists of four main components: novel feature generation based on a specifically directed knight pattern (DKP), signal decomposition using four pooling methods, feature selection using iterative neighborhood analysis (INCA), and classification using the k-nearest neighbor (kNN) classifier with ten-fold cross-validation. Multilevel multiple pooling decomposition combined with DKP yielded 41 feature vectors (40 extracted plus one original cough sound). From these, the ten best feature vectors were selected. Based on each vector's misclassification rate, redundant feature vectors were eliminated and then merged. The merged vector's most informative features automatically selected using INCA were input to a standard kNN classifier. Results: The model, called DKPNet41, attained a high accuracy of 99.39% for cough sound-based multiclass classification of the four categories. Conclusions: The results obtained in the study showed that the DKPNet41 model automatically and efficiently classifies cough sounds for disease diagnosis.