Auditory Receptive Field Net Based Automatic Snore Detection for Wearable Devices
文献类型：期刊论文
| 作者 | Hu, Xiyuan4 ; Sun, Jingpeng2; Dong, Jinping1; Zhang, Xuyun3
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| 刊名 | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
 |
| 出版日期 | 2023-05-01 |
| 卷号 | 27期号:5页码:2255-2263 |
| ISSN号 | 2168-2194 |
| 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 |
| 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. |
| WOS关键词 | NEURAL-NETWORK ; CLASSIFICATION ; SEGMENTATION |
| 资助项目 | ARC DECRA[DE210101458] |
| 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 |
| 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.Weifang Univ Sci & Technol, Weifang Key Lab Blockchain Agr Vegetables, Weifang 262799, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100045, Peoples R China 3.Macquarie Univ, Sch Comp, Macquarie Pk, NSW 2109, Australia 4.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Jiangsu, 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. |
入库方式： OAI收割
来源：自动化研究所
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