DKPNet41: Directed knight pattern network-based cough sound classification model for automatic disease diagnosis

DKPNet41: Directed knight pattern network-based cough sound classification model for automatic disease diagnosis

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.

Files associated with this item cannot be displayed due to copyright restrictions.