| Item type |
文献 / Documents(1) |
| 公開日 |
2024-01-29 |
| アクセス権 |
|
|
アクセス権 |
open access |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| 出版社版DOI |
|
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.3390/app12042242 |
|
|
言語 |
ja |
|
|
関連名称 |
10.3390/app12042242 |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| タイトル |
|
|
タイトル |
Auditory Property-Based Features and Artificial Neural Network Classifiers for the Automatic Detection of Low-Intensity Snoring/Breathing Episodes |
|
言語 |
en |
| 著者 |
ハマベ, ケンジ
榎本, 崇宏
陣内, 自治
戸田, 直紀
カワタ, イクジ
|
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
The definitive diagnosis of obstructive sleep apnea syndrome (OSAS) is made using an overnight polysomnography (PSG) test. This test requires that a patient wears multiple measurement sensors during an overnight hospitalization. However, this setup imposes physical constraints and a heavy burden on the patient. Recent studies have reported on another technique for conducting OSAS screening based on snoring/breathing episodes (SBEs) extracted from recorded data acquired by a noncontact microphone. However, SBEs have a high dynamic range and are barely audible at intensities >90 dB. A method is needed to detect SBEs even in low-signal-to-noise-ratio (SNR) environments. Therefore, we developed a method for the automatic detection of low-intensity SBEs using an artificial neural network (ANN). However, when considering its practical use, this method required further improvement in terms of detection accuracy and speed. To accomplish this, we propose in this study a new method to detect low SBEs based on neural activity pattern (NAP)-based cepstral coefficients (NAPCC) and ANN classifiers. Comparison results of the leave-one-out cross-validation demonstrated that our proposed method is superior to previous methods for the classification of SBEs and non-SBEs, even in low-SNR conditions (accuracy: 85.99 ± 5.69% vs. 75.64 ± 18.8%). |
|
言語 |
en |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
obstructive sleep apnea syndrome |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
auditory property |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
polysomnography |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
artificial neural network |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
snoring/breathing episode |
| 書誌情報 |
en : Applied Sciences
巻 12,
号 4,
p. 2242,
発行日 2022-02-21
|
| 収録物ID |
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|
収録物識別子タイプ |
ISSN |
|
収録物識別子 |
20763417 |
| 出版者 |
|
|
出版者 |
MDPI |
|
言語 |
en |
| 権利情報 |
|
|
言語 |
en |
|
権利情報 |
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
| EID |
|
|
識別子 |
402181 |
|
識別子タイプ |
URI |
| 言語 |
|
|
言語 |
eng |
app_12_4_2242.pdf (1.33 MB)
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