The objective assessment of cough frequency is essential for evaluation of cough and antitussive therapies. Nonetheless, available algorithms for automatic detection of cough sound have limited sensitivity and the analysis of cough sound often requires input from human observers. Therefore, an algorithm for the cough sound detection with high sensitivity would be very useful for development of automatic cough monitors. Here we present a novel algorithm for cough sounds classification based on 8-dimensional numbers octonions and compare it with the algorithm based on standard neural network. The performance was evaluated on a dataset of 5200 cough sounds and 90000 of non-cough sounds generated from the sound recordings in 18 patients with frequent cough caused by various respiratory diseases. Standard classification algorithm had sensitivity 82.2% and specificity 96.4%. In contrast, octonionic classification algorithm had significantly higher sensitivity 96.8% and specificity 98.4%. The use of octonions for classification of cough sounds improved sensitivity and specificity of cough sound detection.
- Artificial neural network
- Cough monitoring
ASJC Scopus subject areas
- Pulmonary and Respiratory Medicine