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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

Cervicomental contour area.
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fig3: Cervicomental contour area.

Mentions: One of the anatomical risk factors for OSA is the fat deposition on the anterior neck [38]. In [4, 5] this risk factor is captured by cervicomental angle (neck-cervical point-mentum), where an increase of neck fat deposition causes an increase of this angle. However, considering our limited photography capture process, it is extremely difficult to detect points such as: cervical point, thyroid, cricoid, neck plane, or sternal notch involved in the cervicomental region. Consequently, we defined an alternative measurement, more robust to both our image capture and automatic landmarking processes. This measure was defined using a contour in the cervicomental region traced by six points, placed equidistantly, which were annotated with high reliability following our semiautomatic AAM method (see Figure 3). In this cervicomental measure, the area of a rectangle defined by bottom left point 23 and upper right point 11 is used to normalize the area defined by points 11 12 20 to 23 and the right and low sides of the 23–11 rectangle. This results in an uncalibrated measurement with a value that decreases as the fat deposition on the anterior neck increases.


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)

Cervicomental contour area.
© Copyright Policy
Related In: Results  -  Collection

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

fig3: Cervicomental contour area.
Mentions: One of the anatomical risk factors for OSA is the fat deposition on the anterior neck [38]. In [4, 5] this risk factor is captured by cervicomental angle (neck-cervical point-mentum), where an increase of neck fat deposition causes an increase of this angle. However, considering our limited photography capture process, it is extremely difficult to detect points such as: cervical point, thyroid, cricoid, neck plane, or sternal notch involved in the cervicomental region. Consequently, we defined an alternative measurement, more robust to both our image capture and automatic landmarking processes. This measure was defined using a contour in the cervicomental region traced by six points, placed equidistantly, which were annotated with high reliability following our semiautomatic AAM method (see Figure 3). In this cervicomental measure, the area of a rectangle defined by bottom left point 23 and upper right point 11 is used to normalize the area defined by points 11 12 20 to 23 and the right and low sides of the 23–11 rectangle. This results in an uncalibrated measurement with a value that decreases as the fat deposition on the anterior neck increases.

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