Formant Frequencies and Bandwidths in Relation to Clinical Variables in an Obstructive Sleep Apnea Population

Benavides, Ana Montero; Murillo, José Luis Blanco; Pozo, Rubén Fernández; Espinoza-Cuadros, Fernando; Toledano, Doroteo T.; Alcázar-Ramírez, José D.; Gómez, Luis A. Hernández

doi:10.1016/j.jvoice.2015.01.006

Cited by 24 publications

(11 citation statements)

References 34 publications

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Section: Discussioncontrasting

confidence: 71%

“…A first discrepancy, though not related with machine learning issues, was addressed in our research [ 36 ] were we found notable differences with the seminal work by Robb et al [ 8 ]. In [ 8 ] statistical significant differences between OSA and non-OSA speakers were found for several formants frequencies and bandwidths extracted from sustained vowels, while our study in [ 36 ] only revealed very weak correlations with two formant bandwidths. In this case, the discrepancy can be mainly attributed to the small and biased sample in Robb’s exploratory analysis (10 OSA and 10 no-OSA subjects, including extreme AHI differences between individuals); while in our study [ 36 ] we explored a larger sample of 241 male subjects representing a wide range of AHI values.…”

Section: Discussioncontrasting

confidence: 71%

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Reviewing the connection between speech and obstructive sleep apnea

et al. 2016

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BackgroundSleep apnea (OSA) is a common sleep disorder characterized by recurring breathing pauses during sleep caused by a blockage of the upper airway (UA). The altered UA structure or function in OSA speakers has led to hypothesize the automatic analysis of speech for OSA assessment. In this paper we critically review several approaches using speech analysis and machine learning techniques for OSA detection, and discuss the limitations that can arise when using machine learning techniques for diagnostic applications.MethodsA large speech database including 426 male Spanish speakers suspected to suffer OSA and derived to a sleep disorders unit was used to study the clinical validity of several proposals using machine learning techniques to predict the apnea–hypopnea index (AHI) or classify individuals according to their OSA severity. AHI describes the severity of patients’ condition. We first evaluate AHI prediction using state-of-the-art speaker recognition technologies: speech spectral information is modelled using supervectors or i-vectors techniques, and AHI is predicted through support vector regression (SVR). Using the same database we then critically review several OSA classification approaches previously proposed. The influence and possible interference of other clinical variables or characteristics available for our OSA population: age, height, weight, body mass index, and cervical perimeter, are also studied.ResultsThe poor results obtained when estimating AHI using supervectors or i-vectors followed by SVR contrast with the positive results reported by previous research. This fact prompted us to a careful review of these approaches, also testing some reported results over our database. Several methodological limitations and deficiencies were detected that may have led to overoptimistic results.ConclusionThe methodological deficiencies observed after critically reviewing previous research can be relevant examples of potential pitfalls when using machine learning techniques for diagnostic applications. We have found two common limitations that can explain the likelihood of false discovery in previous research: (1) the use of prediction models derived from sources, such as speech, which are also correlated with other patient characteristics (age, height, sex,…) that act as confounding factors; and (2) overfitting of feature selection and validation methods when working with a high number of variables compared to the number of cases. We hope this study could not only be a useful example of relevant issues when using machine learning for medical diagnosis, but it will also help in guiding further research on the connection between speech and OSA.

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Section: Discussioncontrasting

confidence: 71%

Section: Discussioncontrasting

confidence: 71%

Reviewing the connection between speech and obstructive sleep apnea

et al. 2016

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“…Because of the association between the blockage of the UA and OSA, abnormal or particular speech may be expected in subjects with OSA due to the altered structure of their UA. Likewise, the association between clinical variables (ie, height and weight) and speech [ 12 ] is known; thus, indirect association might be expected between speech and OSA. Accordingly, correlations between formant frequencies, bandwidths, and clinical variables are presented in Table 4 .…”

Section: Resultsmentioning

confidence: 99%

“…To analyze the gender influence, correlation results in Table 4 were compared with those of a male population, published in our previous study [ 12 ] ( Table 5 ). Those results include most of male subjects of the population used in this paper.…”

Section: Resultsmentioning

confidence: 99%

“…For an easy interpretation of our results, similar to [ 12 ], acoustic analysis is performed by evaluating formant frequencies and bandwidths on sustained phonations of vowel sounds. Facial features are extracted by identifying a set of relevant landmarks on subjects’ images, following also a rather simple procedure similar to the one we presented in [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

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Obstructive Sleep Apnea in Women: Study of Speech and Craniofacial Characteristics

Tyan¹,

Espinoza-Cuadros²,

Pozo³

et al. 2017

JMIR Mhealth Uhealth

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BackgroundObstructive sleep apnea (OSA) is a common sleep disorder characterized by frequent cessation of breathing lasting 10 seconds or longer. The diagnosis of OSA is performed through an expensive procedure, which requires an overnight stay at the hospital. This has led to several proposals based on the analysis of patients’ facial images and speech recordings as an attempt to develop simpler and cheaper methods to diagnose OSA.ObjectiveThe objective of this study was to analyze possible relationships between OSA and speech and facial features on a female population and whether these possible connections may be affected by the specific clinical characteristics in OSA population and, more specifically, to explore how the connection between OSA and speech and facial features can be affected by gender.MethodsAll the subjects are Spanish subjects suspected to suffer from OSA and referred to a sleep disorders unit. Voice recordings and photographs were collected in a supervised but not highly controlled way, trying to test a scenario close to a realistic clinical practice scenario where OSA is assessed using an app running on a mobile device. Furthermore, clinical variables such as weight, height, age, and cervical perimeter, which are usually reported as predictors of OSA, were also gathered.Acoustic analysis is centered in sustained vowels. Facial analysis consists of a set of local craniofacial features related to OSA, which were extracted from images after detecting facial landmarks by using the active appearance models. To study the probable OSA connection with speech and craniofacial features, correlations among apnea-hypopnea index (AHI), clinical variables, and acoustic and facial measurements were analyzed.ResultsThe results obtained for female population indicate mainly weak correlations (r values between .20 and .39). Correlations between AHI, clinical variables, and speech features show the prevalence of formant frequencies over bandwidths, with F2/i/ being the most appropriate formant frequency for OSA prediction in women. Results obtained for male population indicate mainly very weak correlations (r values between .01 and .19). In this case, bandwidths prevail over formant frequencies. Correlations between AHI, clinical variables, and craniofacial measurements are very weak.ConclusionsIn accordance with previous studies, some clinical variables are found to be good predictors of OSA. Besides, strong correlations are found between AHI and some clinical variables with speech and facial features. Regarding speech feature, the results show the prevalence of formant frequency F2/i/ over the rest of features for the female population as OSA predictive feature. Although the correlation reported is weak, this study aims to find some traces that could explain the possible connection between OSA and speech in women. In the case of craniofacial measurements, results evidence that some features that can be used for predicting OSA in male patients are not suitable for testing female population.

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