Citation: Hamabe, K.; Emoto, T.;
Jinnouchi, O.; Toda, N.; Kawata, I.
Auditory Property-Based Features
and Artificial Neural Network
Classifiers for the Automatic
Detection of Low-Intensity
Snoring/Breathing Episodes. Appl.
Sci. 2022, 12, 2242. https://doi.org/
10.3390/app12042242
Academic Editors: Keun Ho Ryu and
Nipon Theera-Umpon
Received: 13 January 2022
Accepted: 11 February 2022
Published: 21 February 2022
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Article
Auditory Property-Based Features and Artificial Neural
Network Classifiers for the Automatic Detection of
Low-Intensity Snoring/Breathing Episodes
Kenji Hamabe
1
, Takahiro Emoto
2,
* , Osamu Jinnouchi
3
, Naoki Toda
4
and Ikuji Kawata
5
1
Graduate School of Advanced Technology and Science, Tokushima University, Tokushima 770-8506, Japan;
c612034034@tokushima-u.ac.jp
2
Graduate School of Technology, Industrial and Social Sciences, Tokushima University,
Tokushima 770-8506, Japan
3
Research Laboratory, Imai Otorhinolaryngology Clinic, Tokushima 770-0026, Japan; jichy036@gmail.com
4
Department of Otolaryngology, Anan Medical Center, Tokushima 774-0045, Japan; naoki527@outlook.jp
5
Department of Otolaryngology, Yoshinogawa Medical Center, Tokushima 776-8511, Japan;
ymc.jibika@ja-ymc.jp
* Correspondence: emoto@tokushima-u.ac.jp
Abstract:
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%).
Keywords:
obstructive sleep apnea syndrome; auditory property; polysomnography; artificial neural
network; snoring/breathing episode
1. Introduction
Obstructive sleep apnea syndrome (OSAS) is characterized by complete or incomplete
obstruction of the upper airway during sleep. The main symptoms of OSAS are light
sleep, excessive daytime sleepiness, and snoring; these are said to increase the risk of
developing serious illnesses, such as ischemic heart disease, hypertension, stroke, and
cognitive dysfunction [
1
]. Furthermore, it is said that 6–19% of females and 13–33% of
males have OSAS, with the prevalence rate increasing with age [
2
,
3
]. A definitive diagnosis
of OSAS is currently made using polysomnography (PSG) tests. However, this test requires
multiple measurement sensors (e.g., oral thermistor, nasal pressure cannula, chest belt)
to be worn directly on the body all night, which imposes a heavy burden on the patient.
Previous studies suggested that the discomfort of wearing multiple sensors during PSG
and restricted movements affect sleep efficiency, electrocardiographic (EEG) spectral power,
and rapid-eye movements [4–7].
Appl. Sci. 2022, 12, 2242. https://doi.org/10.3390/app12042242 https://www.mdpi.com/journal/applsci
