Page 45 - 《中国医疗器械杂志》2026年第2期
P. 45
Chinese Journal of Medical Instrumentation 2026年 第50卷 第2期
研 究 与 论 著
[2] TOBALDINI E, COSTANTINO G, SOLBIATI M, et al. (ISAPE). Zhuhai, China: IEEE, 2021: 1-3.
Sleep, sleep deprivation, autonomic nervous system and [17] LI Z S, CHEN K, XIE Y Q. A deep learning method for
cardiovascular diseases[J]. Neurosci Biobehav Rev, 2017, human sleeping pose estimation with millimeter wave
74(Pt B): 321-329. radar[J]. Sensors(Basel), 2024, 24(18): 5900.
[3] CHEN J J, CHEN X, MAO R X, et al. Hypertension, [18] ZHUANG Z X, WANG F X, YANG X, et al. Accurate
sleep quality, depression, and cognitive function in contactless sleep apnea detection framework with signal
elderly: a cross-sectional study[J]. Front Aging Neurosci, processing and machine learning methods[J]. Methods,
2023, 15: 1051298. 2022, 205: 167-178.
[4] KONG J T, ZHOU L, LI X L, et al. Sleep disorders affect [19] ZHANG H, BO S N, ZHANG X, et al. Event-level
cognitive function in adults: an overview of systematic identification of sleep apnea using FMCW radar[J].
reviews and meta-analyses[J]. Sleep Biol Rhythms, 2023, Bioengineering (Basel), 2025, 12(4): 399.
21(2): 133-142. [20] ZHOU T, XIA Z, WANG X, et al. Human sleep posture
[5] GUAN Q, HU X H, MA N, et al. Sleep quality, recognition based on millimeter-wave radar[C]//2021
depression, and cognitive function in non-demented older Signal Processing Symposium (SPSympo). Piscataway:
adults[J]. J Alzheimers Dis, 2020, 76(4): 1637-1650. IEEE, 2021: 316-321.
[6] 王俊秀, 张衍, 李延泽, 等. 中国睡眠研究报告2025[M]. [21] IBER C, ANCOLI-ISREAL S, CHESSON A, et al. The
北京: 社会科学文献出版社, 2025. AASM manual for the scoring of sleep and associated
[7] KUSHIDA C A, LITTNER M R, MORGENTHALER T,
events: rules, terminology, and techinical specifications
et al. Practice parameters for the indications for
[J/OL]. 2007[2025-10-28]. https://api.semanticscholar.
polysomnography and related procedures: an update for
org/CorpusID:78939623.
2005[J]. Sleep, 2005, 28(4): 499-521.
[22] SATAPATHY S K, BRAHMA B, PANDA B, et al.
[8] LIANG S F, KUO C E, LEE Y C, et al. Development of
Machine learning-empowered sleep staging classification
an EOG-based automatic sleep-monitoring eye mask[J].
using multi-modality signals[J]. BMC Med Inform Decis
IEEE Trans Instrum Meas, 2015, 64(11): 2977-2985.
Mak, 2024, 24(1): 119.
[9] JIAO X, WANG X S, WANG X H, et al. Noncontact
[23] SIAM M S I, SIDDIQUI M S B, ABEDIN M, et al.
sleep monitoring system under a mattress[J]. IEEE
Bioradiolocation-based multi-class sleep stage
Access, 2021, 9: 111203-111213.
classification using time and frequency features with
[10] LI Y X, HUANG J L, YAO X Y, et al. A
random forest classifier[C]//2022 12th International
ballistocardiogram dataset with reference sensor signals
Conference on Electrical and Computer Engineering
in long-term natural sleep environments[J]. Sci Data,
(ICECE), Piscataway:IEEE, 2022: 208-211.
2024, 11(1): 1091.
[11] 梁梦麟. 检测睡眠的手表手环, 正在让失眠变得更严重 [24] ANISHCHENKO L N, BUGAEV A S, IVASHOV S I, et
al. Determination of the sleep structure via radar
[J]. 现代商业银行, 2019(14): 91-93.
monitoring of respiratory movements and motor
[12] DAFNA E, TARASIUK A, ZIGEL Y. Sleep-wake
activity[J]. J Commun Technol Electron, 2017, 62(8):
evaluation from whole-night non-contact audio recordings
886-893.
of breathing sounds[J]. PLoS One, 2015, 10(2):
e0117382. [25] ZAFFARONI A, DOHENY E P, GAHAN L, et al. Non-
[13] CHEN Z J, WANG Y. Sleep monitoring using an infrared contact estimation of sleep staging[C]//ESKOLA H,
VÄISÄNEN O, VIIK J, et al. EMBEC & NBC 2017.
thermal array sensor[C]//WANG K W, SOHN H,
Singapore: Springer, 2018: 77-80.
HUANG H, et al. Sensors and Smart Structures
Technologies for Civil, Mechanical, and Aerospace [26] HONG H, ZHANG L, GU C, et al. Noncontact sleep
Systems 2019. Denver, Colorado, United States: SPIE, stage estimation using a CW Doppler radar[J]. IEEE
2019, 10970: 109701D. Emerg Sel Top Circuits Syst, 2018, 8(2): 260-270.
[14] YOO Y K, JUNG C W, SHIN H C. Unsupervised [27] LEINO A, KORKALAINEN H, KALEVO L, et al. Deep
detection of multiple sleep stages using a single FMCW learning enables accurate automatic sleep staging based
radar[J]. Appl Sci, 2023, 13(7): 4468. on ambulatory forehead EEG[J]. IEEE Access, 2022, 10:
[15] KWON H B, CHOI S H, LEE D, et al. Attention-based 26554-26566.
LSTM for non-contact sleep stage classification using IR- [28] RAHMAN M M, BHUIYAN M I H, HASSAN A R.
UWB radar[J]. IEEE J Biomed Health Inform, 2021, Sleep stage classification using single-channel EOG[J].
25(10): 3844-3853. Comput Biol Med, 2018, 102: 211-220.
[16] WANG P, LUO Y L, SHI G, et al. Research progress in [29] ZHOU Y X, ZHAO S, WANG J Q, et al. Simplifying
millimeter wave radar-based non-contact sleep multimodal with single EOG modality for automatic sleep
monitoring-a review[C]//2021 13th International staging[J]. IEEE Trans Neural Syst Rehabil Eng, 2024,
Symposium on Antennas, Propagation and EM Theory 32: 1668-1678.
下转第211页
159

