Auditory Receptive Field Net Based Automatic Snore Detection for Wearable Devices

doi:10.1109/JBHI.2022.3164517

CORC > 自动化研究所 > 中国科学院自动化研究所 > 智能制造技术与系统研究中心 > 多维数据分析团队

	Auditory Receptive Field Net Based Automatic Snore Detection for Wearable Devices
	Hu, Xiyuan 1; Sun, Jingpeng 4; Dong, Jinping 2; Zhang, Xuyun 3
刊名	IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
	2023-05-01
卷号	27 期号:5 页码:2255-2263
关键词	Feature extraction Sleep apnea Hidden Markov models Mel frequency cepstral coefficient Convolution Computational modeling Brain modeling Artificial intelligence (AI) convolutional neural networks (CNNs) auditory receptive field (ARF) module snore detection
ISSN号	2168-2194
DOI	10.1109/JBHI.2022.3164517
通讯作者	Sun, Jingpeng(jingpeng.sun@ia.ac.cn)
英文摘要	Although obstructive sleep apnea and hypopnea syndrome (OSAHS) is a common sleep disease, it is sometimes difficult to be detected in time because of the inconvenience of polysomnography (PSG) examination. Since snoring is one of the earliest symptoms of OSAHS, it can be used for early OSAHS prediction. With the recent development of wearable and IoT sensors, we proposed a deep learning-based accurate snore detection model for long-term home monitoring of snoring during sleep. To enhance the discriminability of features between snoring and non-snoring events, an auditory receptive field (ARF) net was proposed and integrated into the feature extraction network. Based on the feature maps derived by the feature extraction network, the detection model predicted a series of candidate boxes and corresponding confidence scores for each candidate box, which denoted whether the candidate box contained a snore event from the input sound waveforms. A snore detection dataset with a total duration of more than 4600 min was developed to evaluate the proposed model. The experimental results on this dataset revealed that the proposed model outperformed other traditional approaches and deep learning models.
资助项目	ARC DECRA[DE210101458]
WOS关键词	NEURAL-NETWORK ; CLASSIFICATION ; SEGMENTATION
WOS研究方向	Computer Science ; Mathematical & Computational Biology ; Medical Informatics
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000982840900011
资助机构	ARC DECRA
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/53302]
专题	自动化研究所_智能制造技术与系统研究中心_多维数据分析团队
通讯作者	Sun, Jingpeng
作者单位	1.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Jiangsu, Peoples R China 2.Weifang Univ Sci & Technol, Weifang Key Lab Blockchain Agr Vegetables, Weifang 262799, Peoples R China 3.Macquarie Univ, Sch Comp, Macquarie Pk, NSW 2109, Australia 4.Chinese Acad Sci, Inst Automat, Beijing 100045, Peoples R China
推荐引用方式 GB/T 7714	Hu, Xiyuan,Sun, Jingpeng,Dong, Jinping,et al. Auditory Receptive Field Net Based Automatic Snore Detection for Wearable Devices[J]. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,2023,27(5):2255-2263.
APA	Hu, Xiyuan,Sun, Jingpeng,Dong, Jinping,&Zhang, Xuyun.(2023).Auditory Receptive Field Net Based Automatic Snore Detection for Wearable Devices.IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,27(5),2255-2263.
MLA	Hu, Xiyuan,et al."Auditory Receptive Field Net Based Automatic Snore Detection for Wearable Devices".IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 27.5(2023):2255-2263.