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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

Face width.
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fig4: Face width.

Mentions: In Lee et al. [11], magnetic resonance imaging (MRI) was used to study the relationship between surface facial dimensions and upper airway structure in subjects with OSA. Significant positive correlations were detected between surface facial dimensions and upper airway structures, in particular midface width and interocular width. Based on Lee's work we used these two facial dimensions to define a face width uncalibrated measurement as the midface width to interocular width ratio. The corresponding landmarks and measures are depicted in Figure 4.


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)

Face width.
© Copyright Policy
Related In: Results  -  Collection

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

fig4: Face width.
Mentions: In Lee et al. [11], magnetic resonance imaging (MRI) was used to study the relationship between surface facial dimensions and upper airway structure in subjects with OSA. Significant positive correlations were detected between surface facial dimensions and upper airway structures, in particular midface width and interocular width. Based on Lee's work we used these two facial dimensions to define a face width uncalibrated measurement as the midface width to interocular width ratio. The corresponding landmarks and measures are depicted in Figure 4.

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