Convolutional Neural Network-Based Obstructive Sleep Apnea Identification
Conference proceedings article
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Strategic Research Themes
Publication Details
Author list: Dong Q., Jiraraksopakun Y., Bhatranand A.
Publisher: Elsevier
Publication year: 2021
Start page: 424
End page: 428
Number of pages: 5
ISBN: 9780738126043
ISSN: 0928-4931
eISSN: 1873-0191
Languages: English-United States (EN-US)
Abstract
Obstructive Sleep Apnea (OSA) identification aims to recognize the sounds from the obstructive sleep apneahypopnea syndrome (OSANHS) patients. Despite remarkable advances have been made, the performance heavily relies on the sound representation. Feature selection is needed to improve the performance. Generally, the normal snoring and the snoring of OSANHS patients have a greater difference in acoustic characteristics. Ordinary snoring of human breathing is a regular, fluctuating and cyclical state, while OSANHS pathological snoring is often accompanied by a long pause. Based on the acoustic characteristics, this paper proposes an OSA recognition algorithm based on a convolutional neural network. First, the Mel-scale frequency cepstral coefficient (MFCC) of the sound are extracted. Then, convolutional neural network is deployed to predict the possibility of OSA. To empirically investigate the effectiveness and robustness of the proposed approach, extensive experiments were performed on a benchmark dataset. The obtained results showed that our method significantly outperforms related baselines and is also competitive or superior to the recently reported systems. © 2021 IEEE.
Keywords
Obstructive Sleep Apnea
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