Table of Contents Author Guidelines Submit a Manuscript
Computational and Mathematical Methods in Medicine
Volume 2015 (2015), Article ID 489761, 13 pages
Research Article

Speech Signal and Facial Image Processing for Obstructive Sleep Apnea Assessment

1GAPS Signal Processing Applications Group, Universidad Politécnica de Madrid, 28040 Madrid, Spain
2ATVS Biometric Recognition Group, Universidad Autónoma de Madrid, Madrid, Spain
3Respiratory Department, Sleep Unit, Hospital Quirón, Málaga, Spain

Received 13 August 2015; Revised 15 October 2015; Accepted 20 October 2015

Academic Editor: Edite Figueiras

Copyright © 2015 Fernando Espinoza-Cuadros et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.