Using facial images for the diagnosis of genetic syndromes: A survey

El, Marwa Chendeb; Werghi, Naoufel; Muhairi, Hassan Al; Alsafar, Habiba

doi:10.1109/iccspa.2015.7081271

Cited by 7 publications

(1 citation statement)

References 29 publications

(61 reference statements)

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“…As a simple alternative, based on the correlation between craniofacial anthropometry and photogrammetry, it can be reasonable to explore the use of image processing as noninvasive, faster, and more readily accessible techniques. Face characterization technologies have already tested on the diagnosis of genetic syndromes [ 10 ]. In the reference work Lee et al [ 4 ] compare the craniofacial morphological phenotype of sleep apnea and control populations applying photogrammetry on frontal and profile digital photographs of the head of subjects under study.…”

Section: Introductionmentioning

confidence: 99%

Speech Signal and Facial Image Processing for Obstructive Sleep Apnea Assessment

Espinoza-Cuadros

Fernández-Pozo

Toledano

et al. 2015

Computational and Mathematical Methods in Medicine

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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.

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Section: Introductionmentioning

confidence: 99%