Reviewing the connection between speech and obstructive sleep apnea

Biomedical Engineering Online
Fernando Espinoza-CuadrosLuis A Hernández-Gómez

Abstract

Sleep 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. A 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 p...Continue Reading

References

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Mar 22, 2015·Journal of Voice : Official Journal of the Voice Foundation·Ana Montero BenavidesLuis A Hernández Gómez

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Citations

Jul 6, 2016·Journal of Voice : Official Journal of the Voice Foundation·Ana Montero Benavides
Oct 29, 2020·Sleep & Breathing = Schlaf & Atmung·Yiming DingDemin Han
Dec 7, 2021·Journal of Clinical Sleep Medicine : JCSM : Official Publication of the American Academy of Sleep Medicine·Yiming DingDemin Han

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BETA
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HTK
LIBSVM
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MSR Identity ToolBox for [UNK]
MapAdapt
HCopy

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