Academic Journal
Auditory Property-Based Features and Artificial Neural Network Classifiers for the Automatic Detection of Low-Intensity Snoring/Breathing Episodes
| العنوان: | Auditory Property-Based Features and Artificial Neural Network Classifiers for the Automatic Detection of Low-Intensity Snoring/Breathing Episodes |
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| المؤلفون: | Kenji Hamabe, Takahiro Emoto, Osamu Jinnouchi, Naoki Toda, Ikuji Kawata |
| المصدر: | Applied Sciences, Vol 12, Iss 4, p 2242 (2022) |
| بيانات النشر: | MDPI AG, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Technology LCC:Engineering (General). Civil engineering (General) LCC:Biology (General) LCC:Physics LCC:Chemistry |
| مصطلحات موضوعية: | obstructive sleep apnea syndrome, auditory property, polysomnography, artificial neural network, snoring/breathing episode, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999 |
| الوصف: | 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%). |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 12042242 2076-3417 |
| Relation: | https://www.mdpi.com/2076-3417/12/4/2242; https://doaj.org/toc/2076-3417 |
| DOI: | 10.3390/app12042242 |
| URL الوصول: | https://doaj.org/article/36d556b15a0c453cb7a3aae68aba3f94 |
| رقم الانضمام: | edsdoj.36d556b15a0c453cb7a3aae68aba3f94 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 12042242 20763417 |
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| DOI: | 10.3390/app12042242 |