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Real-Time Detection of Sleep Apnea Based on Breathing Sounds and Prediction Reinforcement Using Home Noises: Algorithm Development and Validation.
Le, Vu Linh; Kim, Daewoo; Cho, Eunsung; Jang, Hyeryung; Reyes, Roben Delos; Kim, Hyunggug; Lee, Dongheon; Yoon, In-Young; Hong, Joonki; Kim, Jeong-Whun.
Afiliação
  • Le VL; ASLEEP Inc, Seoul, Republic of Korea.
  • Kim D; ASLEEP Inc, Seoul, Republic of Korea.
  • Cho E; ASLEEP Inc, Seoul, Republic of Korea.
  • Jang H; Department of Artificial Intelligence, Dongguk University, Seoul, Republic of Korea.
  • Reyes RD; ASLEEP Inc, Seoul, Republic of Korea.
  • Kim H; ASLEEP Inc, Seoul, Republic of Korea.
  • Lee D; ASLEEP Inc, Seoul, Republic of Korea.
  • Yoon IY; Department of Psychiatry, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
  • Hong J; Seoul National University College of Medicine, Seoul, Republic of Korea.
  • Kim JW; ASLEEP Inc, Seoul, Republic of Korea.
J Med Internet Res ; 25: e44818, 2023 02 22.
Article em En | MEDLINE | ID: mdl-36811943
ABSTRACT

BACKGROUND:

Multinight monitoring can be helpful for the diagnosis and management of obstructive sleep apnea (OSA). For this purpose, it is necessary to be able to detect OSA in real time in a noisy home environment. Sound-based OSA assessment holds great potential since it can be integrated with smartphones to provide full noncontact monitoring of OSA at home.

OBJECTIVE:

The purpose of this study is to develop a predictive model that can detect OSA in real time, even in a home environment where various noises exist.

METHODS:

This study included 1018 polysomnography (PSG) audio data sets, 297 smartphone audio data sets synced with PSG, and a home noise data set containing 22,500 noises to train the model to predict breathing events, such as apneas and hypopneas, based on breathing sounds that occur during sleep. The whole breathing sound of each night was divided into 30-second epochs and labeled as "apnea," "hypopnea," or "no-event," and the home noises were used to make the model robust to a noisy home environment. The performance of the prediction model was assessed using epoch-by-epoch prediction accuracy and OSA severity classification based on the apnea-hypopnea index (AHI).

RESULTS:

Epoch-by-epoch OSA event detection showed an accuracy of 86% and a macro F1-score of 0.75 for the 3-class OSA event detection task. The model had an accuracy of 92% for "no-event," 84% for "apnea," and 51% for "hypopnea." Most misclassifications were made for "hypopnea," with 15% and 34% of "hypopnea" being wrongly predicted as "apnea" and "no-event," respectively. The sensitivity and specificity of the OSA severity classification (AHI≥15) were 0.85 and 0.84, respectively.

CONCLUSIONS:

Our study presents a real-time epoch-by-epoch OSA detector that works in a variety of noisy home environments. Based on this, additional research is needed to verify the usefulness of various multinight monitoring and real-time diagnostic technologies in the home environment.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Síndromes da Apneia do Sono / Apneia Obstrutiva do Sono Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: J Med Internet Res Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Síndromes da Apneia do Sono / Apneia Obstrutiva do Sono Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: J Med Internet Res Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2023 Tipo de documento: Article