Automated identification of the predominant site of upper airway collapse in obstructive sleep apnoea patients using snore signal. (1st October 2020)

• Record Type: Journal Article
• Authors: Sebastian, Arun; Cistulli, Peter A.; Cohen, Gary; de Chazal, Philip
• Is Part Of: Physiological measurement. Volume 41:Number 9(2020)
• Publication Date: 2020-10-01
• Subjects: obstructive sleep apnoea -- snore recording -- predominant site-of-collapse -- hypopnoea -- airflow signal -- nested cross validation -- linear discriminant analysis classifier; Physiology -- Measurement -- Periodicals; Patient monitoring -- Periodicals; 612
• DOI: 10.1088/1361-6579/abaa33
• Languages: English
• ISSNs: 0967-3334

Abstract

Abstract: Objective : This study provides a novel approach for an automated system using a machine learning algorithm to predict the predominant site of upper airway collapse into four classes ('lateral wall', 'palate', 'tongue-base' related collapse or 'multi-level' site-of-collapse) in obstructive sleep apnoea (OSA) patients from the audio signal recorded during normal sleep. Approach : Snore sounds from 58 patients were recorded simultaneously with full-night polysomnography during sleep with a ceiling mounted microphone. The probable site-of-airway collapse was determined by manual analysis of the shape of the airflow signal during hypopnoea. Time and frequency features of the audio signal were extracted from each hypopnoea event to classify the audio signal into 'lateral wall', 'palate' and 'tongue-base' related collapse according to prior research. The data was divided into two sets. The Learning Set contained the data of the first 45 patients and was used for building the model. The Hidden Set contained the data from the remaining 13 patients and was used for testing the performance of the model. Feature selection was employed to boost the classification performance. The classification was carried out with a multi-class linear discriminant analysis classifier to classify the predominant site-of-collapse for a patient into the four classes. Performance was evaluated by comparing the automatic and manually labelled data based on the predominant site-of-collapse and calculating the accuracy. Main results : The model achieved an overall accuracy on the Hidden Set of 77% for discriminating tongue/non-tongue collapse and an accuracy of 62% accuracy for all site-of-collapse classes. Significance : Our results demonstrate that the audio signal recorded during sleep can successfully identify the site-of-collapse in the upper airway. The additional information regarding the obstruction site may assist clinicians in deciding the most appropriate treatment for OSA.