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Portable Sleep Apnea Syndrome Screening and Event Detection Using Long Short-Term Memory Recurrent Neural Network.

Publication ,  Journal Article
Chang, H-C; Wu, H-T; Huang, P-C; Ma, H-P; Lo, Y-L; Huang, Y-H
Published in: Sensors (Basel, Switzerland)
October 2020

Obstructive sleep apnea/hypopnea syndrome (OSAHS) is characterized by repeated airflow partial reduction or complete cessation due to upper airway collapse during sleep. OSAHS can induce frequent awake and intermittent hypoxia that is associated with hypertension and cardiovascular events. Full-channel Polysomnography (PSG) is the gold standard for diagnosing OSAHS; however, this PSG evaluation process is unsuitable for home screening. To solve this problem, a measuring module integrating abdominal and thoracic triaxial accelerometers, a pulsed oximeter (SpO2) and an electrocardiogram sensor was devised in this study. Moreover, a long short-term memory recurrent neural network model is proposed to classify four types of sleep breathing patterns, namely obstructive sleep apnea (OSA), central sleep apnea (CSA), hypopnea (HYP) events and normal breathing (NOR). The proposed algorithm not only reports the apnea-hypopnea index (AHI) through the acquired overnight signals but also identifies the occurrences of OSA, CSA, HYP and NOR, which assists in OSAHS diagnosis. In the clinical experiment with 115 participants, the performances of the proposed system and algorithm were compared with those of traditional expert interpretation based on PSG signals. The accuracy of AHI severity group classification was 89.3%, and the AHI difference for PSG expert interpretation was 5.0±4.5. The overall accuracy of detecting abnormal OSA, CSA and HYP events was 92.3%.

Duke Scholars

Published In

Sensors (Basel, Switzerland)

DOI

EISSN

1424-8220

ISSN

1424-8220

Publication Date

October 2020

Volume

20

Issue

21

Start / End Page

E6067

Related Subject Headings

  • Sleep Apnea, Obstructive
  • Polysomnography
  • Oximetry
  • Neural Networks, Computer
  • Memory, Short-Term
  • Male
  • Humans
  • Female
  • Analytical Chemistry
  • 4606 Distributed computing and systems software
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Chang, H.-C., Wu, H.-T., Huang, P.-C., Ma, H.-P., Lo, Y.-L., & Huang, Y.-H. (2020). Portable Sleep Apnea Syndrome Screening and Event Detection Using Long Short-Term Memory Recurrent Neural Network. Sensors (Basel, Switzerland), 20(21), E6067. https://doi.org/10.3390/s20216067
Chang, Hung-Chi, Hau-Tieng Wu, Po-Chiun Huang, Hsi-Pin Ma, Yu-Lun Lo, and Yuan-Hao Huang. “Portable Sleep Apnea Syndrome Screening and Event Detection Using Long Short-Term Memory Recurrent Neural Network.Sensors (Basel, Switzerland) 20, no. 21 (October 2020): E6067. https://doi.org/10.3390/s20216067.
Chang H-C, Wu H-T, Huang P-C, Ma H-P, Lo Y-L, Huang Y-H. Portable Sleep Apnea Syndrome Screening and Event Detection Using Long Short-Term Memory Recurrent Neural Network. Sensors (Basel, Switzerland). 2020 Oct;20(21):E6067.
Chang, Hung-Chi, et al. “Portable Sleep Apnea Syndrome Screening and Event Detection Using Long Short-Term Memory Recurrent Neural Network.Sensors (Basel, Switzerland), vol. 20, no. 21, Oct. 2020, p. E6067. Epmc, doi:10.3390/s20216067.
Chang H-C, Wu H-T, Huang P-C, Ma H-P, Lo Y-L, Huang Y-H. Portable Sleep Apnea Syndrome Screening and Event Detection Using Long Short-Term Memory Recurrent Neural Network. Sensors (Basel, Switzerland). 2020 Oct;20(21):E6067.

Published In

Sensors (Basel, Switzerland)

DOI

EISSN

1424-8220

ISSN

1424-8220

Publication Date

October 2020

Volume

20

Issue

21

Start / End Page

E6067

Related Subject Headings

  • Sleep Apnea, Obstructive
  • Polysomnography
  • Oximetry
  • Neural Networks, Computer
  • Memory, Short-Term
  • Male
  • Humans
  • Female
  • Analytical Chemistry
  • 4606 Distributed computing and systems software