Abstract
Obstructive sleep apnea (OSA) is a common sleep disorder characterized by recurring breathing pauses during sleep caused by a blockage of the upper airway (UA). OSA is generally diagnosed through a costly procedure requiring an overnight stay of the patient at the hospital. This has led to proposing less costly procedures based on the analysis of patients' facial images and voice recordings to help in OSA detection and severity assessment. In this paper we investigate the use of both image and speech processing to estimate the apnea-hypopnea index, AHI (which describes the severity of the condition), over a population of 285 male Spanish subjects suspected to suffer from OSA and referred to a Sleep Disorders Unit. Photographs and voice recordings were collected in a supervised but not highly controlled way trying to test a scenario close to an OSA assessment application running on a mobile device (i.e., smartphones or tablets). Spectral information in speech utterances is modeled by a state-of-the-art low-dimensional acoustic representation, called i-vector. A set of local craniofacial features related to OSA are extracted from images after detecting facial landmarks using Active Appearance Models (AAMs). Support vector regression (SVR) is applied on facial features and i-vectors to estimate the AHI.
Highlights
Sleep disorders are receiving increased attention as a cause of daytime sleepiness, impaired work, and traffic accidents and are associated with hypertension, heart failure, arrhythmia, and diabetes
These procedures are studied for an obstructive sleep apnea (OSA)-symptomatic population; our ultimate goal will be to help in setting priorities to proceed to the PSG diagnosis based on the expected OSA severity
AHIpredicted critical review of previous research using speech processing for OSA assessment, presented in [41], where we report several limitations and methodological deficiencies that may have led to previous overoptimistic results
Summary
Sleep disorders are receiving increased attention as a cause of daytime sleepiness, impaired work, and traffic accidents and are associated with hypertension, heart failure, arrhythmia, and diabetes. In this work we explore alternative procedures for estimating the AHI using voice and facial data These procedures are studied for an OSA-symptomatic population (i.e., individuals that have been referred to a sleep unit for PSG); our ultimate goal will be to help in setting priorities to proceed to the PSG diagnosis based on the expected OSA severity (i.e., stratification). This will ensure a better treatment of patients according to their needs and Computational and Mathematical Methods in Medicine will be relevant in some countries as Spain where waiting lists for PSG may exceed one year [3]
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