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Speech Signal and Facial Image Processing for Obstructive Sleep Apnea Assessment.

Espinoza-Cuadros F, Fernández-Pozo R, Toledano DT, Alcázar-Ramírez JD, López-Gonzalo E, Hernández-Gómez LA - Comput Math Methods Med (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: GAPS Signal Processing Applications Group, Universidad Politécnica de Madrid, 28040 Madrid, Spain.

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.

No MeSH data available.


Related in: MedlinePlus

Landmarks on frontal and profile view.
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fig2: Landmarks on frontal and profile view.

Mentions: A first critical step for extracting local facial features is to identify a set of relevant landmarks on images of subjects under study. The database of facial images contains frontal and profile digital photographs of 285 male subjects, that is, 570 digital photographs needed to be processed to obtain landmarks. Manual annotation of all images, as done in [4, 5], can be tedious and, even if done by skilled personal, it is prone to errors due to subjectivity. Consequently, we decided to use a widely used automatic landmarking method, first introduced by Cootes et al. in 2001 [35], based on Active Appearance Model (AAM) [36]. Based on a priori knowledge of landmark positions, AAM combines a statistical model, which represents the variation of shape and texture of the face (object), with a gradient-descent fitting algorithm. As Figure 2 shows, in AAMs for frontal and profile photographs we use a grid of 52 landmarks taken from a general face identification system and a set of 24 landmarks including specific marks for the neck area, respectively.


Speech Signal and Facial Image Processing for Obstructive Sleep Apnea Assessment.

Espinoza-Cuadros F, Fernández-Pozo R, Toledano DT, Alcázar-Ramírez JD, López-Gonzalo E, Hernández-Gómez LA - Comput Math Methods Med (2015)

Landmarks on frontal and profile view.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4664800&req=5

fig2: Landmarks on frontal and profile view.
Mentions: A first critical step for extracting local facial features is to identify a set of relevant landmarks on images of subjects under study. The database of facial images contains frontal and profile digital photographs of 285 male subjects, that is, 570 digital photographs needed to be processed to obtain landmarks. Manual annotation of all images, as done in [4, 5], can be tedious and, even if done by skilled personal, it is prone to errors due to subjectivity. Consequently, we decided to use a widely used automatic landmarking method, first introduced by Cootes et al. in 2001 [35], based on Active Appearance Model (AAM) [36]. Based on a priori knowledge of landmark positions, AAM combines a statistical model, which represents the variation of shape and texture of the face (object), with a gradient-descent fitting algorithm. As Figure 2 shows, in AAMs for frontal and profile photographs we use a grid of 52 landmarks taken from a general face identification system and a set of 24 landmarks including specific marks for the neck area, respectively.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: GAPS Signal Processing Applications Group, Universidad Politécnica de Madrid, 28040 Madrid, Spain.

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.

No MeSH data available.


Related in: MedlinePlus