Speech Signal and Facial Image Processing for Obstructive Sleep Apnea Assessment

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.The activities in this paper were funded by the Spanish Ministry of Economy and Competitiveness and the European Union (FEDER) as part of the TEC2012-37585-C02 (CMC-V2) project. Authors also thank Sonia Martinez Diaz for her effort in collecting the OSA database that is used in this study

The effect of sleep deprivation on objective and subjective measures of facial appearance

This study was funded by the Swedish Research Council, FORTE (Swedish Research Council for Health, Working Life and Welfare), and The Swedish Foundation for Humanities and Social Sciences.The faces of people who are sleep deprived are perceived by others as looking paler, less healthy and less attractive compared to when well rested. However, there is little research using objective measures to investigate sleep‐loss‐related changes in facial appearance. We aimed to assess the effects of sleep deprivation on skin colour, eye openness, mouth curvature and periorbital darkness using objective measures, as well as to replicate previous findings for subjective ratings. We also investigated the extent to which these facial features predicted ratings of fatigue by others and could be used to classify the sleep condition of the person. Subjects (n = 181) were randomised to one night of total sleep deprivation or a night of normal sleep (8–9 hr in bed). The following day facial photographs were taken and, in a subset (n = 141), skin colour was measured using spectrophotometry. A separate set of participants (n = 63) later rated the photographs in terms of health, paleness and fatigue. The photographs were also digitally analysed with respect to eye openness, mouth curvature and periorbital darkness. The results showed that neither sleep deprivation nor the subjects’ sleepiness was related to differences in any facial variable. Similarly, there was no difference in subjective ratings between the groups. Decreased skin yellowness, less eye openness, downward mouth curvature and periorbital darkness all predicted increased fatigue ratings by others. However, the combination of appearance variables could not be accurately used to classify sleep condition. These findings have implications for both face‐to‐face and computerised visual assessment of sleep loss and fatigue.PostprintPeer reviewe

Multispectral Video Fusion for Non-contact Monitoring of Respiratory Rate and Apnea

Continuous monitoring of respiratory activity is desirable in many clinical applications to detect respiratory events. Non-contact monitoring of respiration can be achieved with near- and far-infrared spectrum cameras. However, current technologies are not sufficiently robust to be used in clinical applications. For example, they fail to estimate an accurate respiratory rate (RR) during apnea. We present a novel algorithm based on multispectral data fusion that aims at estimating RR also during apnea. The algorithm independently addresses the RR estimation and apnea detection tasks. Respiratory information is extracted from multiple sources and fed into an RR estimator and an apnea detector whose results are fused into a final respiratory activity estimation. We evaluated the system retrospectively using data from 30 healthy adults who performed diverse controlled breathing tasks while lying supine in a dark room and reproduced central and obstructive apneic events. Combining multiple respiratory information from multispectral cameras improved the root mean square error (RMSE) accuracy of the RR estimation from up to 4.64 monospectral data down to 1.60 breaths/min. The median F1 scores for classifying obstructive (0.75 to 0.86) and central apnea (0.75 to 0.93) also improved. Furthermore, the independent consideration of apnea detection led to a more robust system (RMSE of 4.44 vs. 7.96 breaths/min). Our findings may represent a step towards the use of cameras for vital sign monitoring in medical applications

Validação do protocolo casa score e sua correlação a dados morfológicos e funcionais orofaciais na apneia obstrutiva do sono

Objetivos: Criação e validação do protocolo Cheeks Appearance to Sleep Apnea score (CASA score) como triagem para apneia obstrutiva do sono (AOS), correlacionando aos dados morfológicos e funcionais da musculatura orofacial, como a espessura de língua, dos músculos masseter e bucinador, pressão de língua e bochechas. Hipótese: A aparência das bochechas prediz a AOS em sujeitos adultos, assim como indivíduos com AOS apresentam maior espessura de musculatura e declínio de pressão das estruturas orofaciais ao comparar com o grupo sem AOS. Métodos: Foi dividido em duas fases. Na primeira etapa foram incluídos 248 participantes com queixas de transtornos de sono e avaliados por polissonografia e CASA score. Na segunda etapa, 68 participantes foram avaliados por polissonografia, CASA score, ultrassonografia de língua, dos músculos masseter e bucinador e avaliação de pressão de língua e de bochechas. Resultados: CASA score apresentou desempenho adequado como preditor AOS na população avaliada – resultados de validação apresentam área sob a curva ROC de 0,89, sensibilidade 87% e especificidade 82%. Sujeitos com AOS apresentam maior espessura de músculos masseter e bucinador; entretanto, não houve diferença na espessura máxima de língua. Em relação à pressão de língua e de bochechas não houve significância estatística. CASA score se correlacionou à espessura do músculo masseter. Conclusão: CASA score é uma adequada nova ferramenta para predizer AOS em adultos. Sujeitos com AOS apresentam maior pontuação no CASA score, musculatura mais espessa dos músculos masseter e bucinador, porém nenhuma diferença em relação à espessura máxima de língua, assim como sem significância estatística em pressão de língua e bochechas ao comparado ao grupo controle.Objectives: Development and validation of Cheeks Appearance to Sleep Apnea score (CASA score) protocol to screen obstructive sleep apnea (OSA), associate to morphological and functional evaluation of orofacial musculature correlating to thickness of tongue, masseter and buccinator and tongue- and cheeks-generated pressure evaluations. Hypothesis: The cheeks appearance predicts OSA in adults’ subjects and individuals with OSA present greater thickness of musculature and decrease of the pressure of orofacial structures in comparison to non-OSA subjects. Methods: It was divided in two parts. The first part included 248 participants: polysomnography and CASA score evaluations. The second part, 68 participants were included evaluated by polysomnography, CASA score, and ultrasonography of tongue, masseter and buccinator, tongue- and cheeks-generated pressure evaluations. Results: CASA presented an adequate performance as a predictor to OSA in the assessed sample– validation outcomes presented area under the curve AUC 0.89, sensitivity 87% and specificity 82%. OSA participants presented greater thickness of masseter and buccinator; nevertheless, tongue- and cheeks-generated mean peak pressure did not differ significantly. The findings showed that CASA score correlated to masseter thickness. Conclusion: CASA score is an adequate new tool to predict OSA in adults. OSA participants’ present grater muscle thickness on the masseter and buccinator, tongue- and cheeks-generated mean peak pressure did not differ significantly