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Automatic detection of obstructive sleep apnea based on speech or snoring sounds: a narrative review.
Cao, Shuang; Rosenzweig, Ivana; Bilotta, Federico; Jiang, Hong; Xia, Ming.
Afiliación
  • Cao S; Department of Anesthesiology, The Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Rosenzweig I; Sleep and Brain Plasticity Centre, CNS, IoPPN, King's College London, London, UK.
  • Bilotta F; Sleep Disorders Centre, Guy's and St Thomas' Hospital, GSTT NHS, London, UK.
  • Jiang H; Department of Anaesthesia and Critical Care Medicine, Policlinico Umberto 1 Hospital, Sapienza University of Rome, Rome, Italy.
  • Xia M; Department of Anesthesiology, The Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
J Thorac Dis ; 16(4): 2654-2667, 2024 Apr 30.
Article en En | MEDLINE | ID: mdl-38738242
ABSTRACT
Background and

Objective:

Obstructive sleep apnea (OSA) is a common chronic disorder characterized by repeated breathing pauses during sleep caused by upper airway narrowing or collapse. The gold standard for OSA diagnosis is the polysomnography test, which is time consuming, expensive, and invasive. In recent years, more cost-effective approaches for OSA detection based in predictive value of speech and snoring has emerged. In this paper, we offer a comprehensive summary of current research progress on the applications of speech or snoring sounds for the automatic detection of OSA and discuss the key challenges that need to be overcome for future research into this novel approach.

Methods:

PubMed, IEEE Xplore, and Web of Science databases were searched with related keywords. Literature published between 1989 and 2022 examining the potential of using speech or snoring sounds for automated OSA detection was reviewed. Key Content and

Findings:

Speech and snoring sounds contain a large amount of information about OSA, and they have been extensively studied in the automatic screening of OSA. By importing features extracted from speech and snoring sounds into artificial intelligence models, clinicians can automatically screen for OSA. Features such as formant, linear prediction cepstral coefficients, mel-frequency cepstral coefficients, and artificial intelligence algorithms including support vector machines, Gaussian mixture model, and hidden Markov models have been extensively studied for the detection of OSA.

Conclusions:

Due to the significant advantages of noninvasive, low-cost, and contactless data collection, an automatic approach based on speech or snoring sounds seems to be a promising tool for the detection of OSA.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Thorac Dis Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: China
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Thorac Dis Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: China