ArticleComputational and mathematical methods in medicine2015
Speech Signal and Facial Image Processing for Obstructive Sleep Apnea Assessment.
Article in Computational and mathematical methods in medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 47 citations in OpenAlex.
- Digital Health and Sleep-Disordered Breathing: A Systematic Review and Meta-Analysis.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2018Pooled it
- Cheeks appearance as a novel predictor of obstructive sleep apnea: the CASA score study.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2024Observational
- Obstructive sleep apnea detection during wakefulness: a comprehensive methodological review.Medical & biological engineering & computing · 2024Review
- Automatic detection of obstructive sleep apnea based on speech or snoring sounds: a narrative review.Journal of thoracic disease · 2024Review
- Sleep apnea syndrome associated with gonadal hormone imbalance (Review).Biomedical reports · 2023Review
- [Digital technology and children's maxillofacial management].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2023Article
- Prediction of obstructive sleep apnea using deep learning in 3D craniofacial reconstruction.Journal of thoracic disease · 2023Article
- Detecting obstructive sleep apnea by craniofacial image-based deep learning.Sleep & breathing = Schlaf & Atmung · 2022Article
- Is There a Relationship between Voice Quality and Obstructive Sleep Apnea Severity and Cumulative Percentage of Time Spent at Saturations below Ninety Percent: Voice Analysis in Obstructive Sleep Apnea Patients.Medicina (Kaunas, Lithuania) · 2022Article
- Automated Scoring of Respiratory Events in Sleep With a Single Effort Belt and Deep Neural Networks.IEEE transactions on bio-medical engineering · 2022Article
- A Comprehensive Review: Computational Models for Obstructive Sleep Apnea Detection in Biomedical Applications.BioMed research international · 2022Review
- Voice disorders in severe obstructive sleep apnea patients and comparison of two acoustic analysis software programs: MDVP and Praat.Sleep & breathing = Schlaf & Atmung · 2021Article
- Predicting sleep apnea from three-dimensional face photography.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2020Article
- Obstructive Sleep Apnea in Women: Study of Speech and Craniofacial Characteristics.JMIR mHealth and uHealth · 2017Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.