Journal of Xinjiang University (Natural Science Edition in Chinese and English)

References

[1]缪冬玉．基于连续波雷达的呼吸模式分类技术研究[D]．南京：南京理工大学，2017.Miao D Y. Research on respiratory pattern classification techniques based on continuous wave radar[D]. Nanjing:Nanjing University of Science and Technology,2017.(in Chinese)

[2]姜潇，丛舒，杨淼，等．中国居民慢性呼吸道症状流行情况及其影响因素分析[J]．中华流行病学杂志，2022,43(3):315-323.Jiang X,Cong S,Yang M,et al. Prevalence of chronic respiratory symptoms and dyspnea and related factors in residents in China[J].Chinese Journal of Epidemiology,2022,43(3):315-323.(in Chinese)

[3]Bubu M O.Assessing the impact of adequate OSA treatment on markers of sleepiness related to cognition and AD pathology among Black and Hispanic subjects[J].Alzheimer’s&Dementia,2024,20(S8):e095587.

[4]中国医师协会睡眠医学专业委员会，中国医师协会神经内科医师分会睡眠学组．中国成人失眠共病阻塞性睡眠呼吸暂停诊治指南（2024版）[J/OL]．中国全科医学，2024-11-28.https：//link.cnki.net/urlid/13.1222.R.20241128.0955.002.Sleep Medicine Group,China Neurologist Association,Chinese Academy Society of Sleep Medicine,Chinese Medical Doctor Association.Chinese Guideline for Diagnosis and Treatment of Co-morbid Insomnia and Sleep Apnea(2024)[J/OL].Chinese General Practice,2024-11-28.https：//link.cnki.net/urlid/13.1222.R.20241128.0955.002.(in Chinese)

[5]祁富贵，张华，李盛，等．一种基于小波信息熵的非接触呼吸暂停检测技术研究[J]．医疗卫生装备，2015,36(4):1-4.Qi F G,Zhang H,Li S,et al.Study on technique for non-contact detection of apnea based on wavelet information entropy[J].Chinese Medical Equipment Journal,2015,36(4):1-4.(in Chinese)

[6]John A,Cardiff B,John D.A 1D-CNN based deep learning technique for sleep apnea detection in IoT sensors New York:[C]//2021 IEEE International Symposium on Circuits and Systems(ISCAS),Daegu,Korea.New York:IEEE,2021:1-5.

[7]张大可，马隽，王立英，等．基于粒子群优化-支持向量机的睡眠呼吸暂停检测[J]．科学技术与工程，2022,22(33):14644-14651.Zhang D K,Ma J,Wang L Y,et al. Sleep apnea detection based on particle swarm optimization-support vector machine[J].Science Technology and Engineering,2022,22(33):14644-14651.(in Chinese)

[8]Hou L X,Zhuang Y,Zhang H,et al.Time-hybrid OSAformer(THO):A hybrid temporal sequence transformer for accurate detection of obstructive sleep apnea via single-lead ECG signals[J].Computer Methods and Programs in Biomedicine,2025,206:108558.

[9]Bhongade A,Gandhi K T,Prathosh A P.Automatic identification of obstructive sleep apnea using multimodal features[J].Biomedical Signal Processing and Control,2025,105:107609.

[10]Wu S,Yao S,Liu W,et al.Study on a novel UWB linea array human respiration model and detection method[J]. IEEE Journal of Selected Topics in Applied Earth Observations&Remote Sensing,2016,9(1):125-140.

[11]沈建飞，陈益强，谷洋．基于时频信息融合网络的非干扰呼吸检测方法[J]．高技术通讯，2020,30(10):998-1009.Shen J F,Chen Y Q,Gu Y.Non-contact respiratory detection based on time and frequency fusion network[J].High Technology Letters,2020,30(10):998-1009.(in Chinese)

[12]Goldberger A,Amaral L,Glass L,et al.PhysioBank,PhysioToolkit and PhysioNet:Components of a new research resource for complex physiologic signals[J/OL].PhysioNet,2022,101(23):e215-e220.

[13]胡博文，殳国华，常浩．基于毫米波雷达的呼吸检测与分类算法研究[J]．电气自动化，2023,45(6):86-88.Hu B W,Shu G H,Chang H.Research on breath test&classification algorithm based on millimetre-wave radar[J].Electrical Automation,2023,45(6):86-88.(in Chinese)

[14]田哲嘉．基于毫米波雷达的高可靠呼吸与心率提取算法的设计与实现[D]．西安：西安理工大学，2021.Tian Z J.Design and implementation of highly reliable respiration and heart rate extraction algorithm based on millimeter wave radar[D].Xi’an:Xi’an University of Technology,2021.(in Chinese)

[15]荆东星，陈杨晖，全哲．基于1D-CNN和SWLSTM的风电轴承故障诊断方法[J]．机械强度，2023,45(6):1309-1317.Jing D X,Chen Y H,Quan Z.Wind turbine rolling bearing fault diagnosis method based on 1D-CNN and SWLSTM[J].Journal of Mechanical Strength,2023,45(6):1309-1317.(in Chinese)

[16]蒲姗姗，郑恩让，陈蓓．基于1D-CNN的近红外光谱分类算法研究[J]．光谱学与光谱分析，2023,43(8):2446-2451.Pu S S,Zheng E R,Chen B.Research on a classification algorithm of near-infrared spectroscopy based on 1D-CNN[J].Spectroscopy and Spectral Analysis,2023,43(8):2446-2451.(in Chinese)

[17]Hu J,Shen L,Sun G,et al.Squeeze-and-excitation networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Daegu,Korea.New York:IEEE, 2018:7132-7141.

[18]He K,Zhang X,Ren S,et al.Deep residual learning for image recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas,USA.New York:IEEE,2016:770-778.

[19]Glorot X,Bengio Y.Understanding the difficulty of training deep feedforward neural networks[C]//Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics.Sardinia:JMLR Workshop and Conference Proceedings,2010:249-256.

[20]李晨洋，王威，黄炜峻，等．新型雷达设备诊断阻塞性睡眠呼吸暂停：一项评价与多导睡眠监测等效性的平行对照研究[J]．中华耳鼻咽喉头颈外科杂志，2024,59(8):857-863.Li C Y,Wang W,Huang W J,et al. Diagnosis of obstructive sleep apnea by a new radar device:A parallel controlled study evaluating agreement with polysomnographic monitoring[J].Chinese Journal of Otorhinolaryngology and Head and Neck Surgery,2024,59(8):857-863.(in Chinese)

[21]郭梦琦．基于生命体征雷达的呼吸异常辨识技术研究[D]．南京：南京理工大学，2021.Guo M Q.Research on respiratory abnormality detection technology based on vital signs radar[D].Nanjing:Nanjing University of Science and Technology,2021.(in Chinese)

[22]Yang Q A,Zou L,Wei K M,et al.Obstructive sleep apnea detection from single-lead electrocardiogram signals using onedimensional squeeze-and-excitation residual group network[J].Computers in Biology and Medicine,2022,140:105124.

[23]Erdenebayar U,Kim J Y,Park J,et al.Deep learning approaches for automatic detection of sleep apnea events from an electrocardiogram[J].Computer Methods and Programs in Biomedicine,2019,180:105001.

[24]Zarei A,Beheshti H,Asl B M.Detection of sleep apnea using deep neural networks and single-lead ECG signals[J].Biomedical Signal Processing and Control,2022,71:103125.

[25]Tanvir M,Ahmed I K,Ibn T M,et al.Sleep apnea detection from variational mode decomposed EEG signal using a hybrid CNN-BiLSTM[J].IEEE ACCESS,2021,9:102355-102367.