一种基于ECG信号的无线睡眠分期系统

冯晓星; 唐飞; 李琰; 俞航; 葛彬杰

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一种基于ECG信号的无线睡眠分期系统

更新时间：2026-09-04

- 一种基于ECG信号的无线睡眠分期系统
- A Wireless Sleep-Staging System Based on Single Channel ECG
- 微电子学与计算机 2019年36卷第4期页码：23-28
- 作者机构：
  
  1. 深圳大学机构计算机与软件学院,广东,深圳,518060
  2. 深圳信息职业技术学院机构,广东,深圳,518029
- 作者简介：
- 基金信息：
  
  国家自然基金 61471245国家自然基金 U1201256广东省协同创新基金 2014B090901031深圳产业发展基金 JCYJ20160308095019383深圳产业发展基金 JSGG20150529160945187
- DOI：
  中图分类号：
- 纸质出版：2019
- 稿件说明：
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冯晓星, 唐飞, 李琰, 俞航, 葛彬杰. 一种基于ECG信号的无线睡眠分期系统[J]. 微电子学与计算机, 2019, 36(4): 23-28.

FENG Xiao-xing, TANG Fei, LI Yan, et al. A Wireless Sleep-Staging System Based on Single Channel ECG[J]. 2019, 36(4): 23-28.
冯晓星, 唐飞, 李琰, 俞航, 葛彬杰. 一种基于ECG信号的无线睡眠分期系统[J]. 微电子学与计算机, 2019, 36(4): 23-28. DOI：

FENG Xiao-xing, TANG Fei, LI Yan, et al. A Wireless Sleep-Staging System Based on Single Channel ECG[J]. 2019, 36(4): 23-28. DOI：

摘要

为了提升睡眠分期系统的准确率和易用性

采用了无线心电贴和桌上型心电处理设备的系统架构

并设计了一款低功耗心电芯片.睡眠分期算法基于单通道ECG信号的RRI和HRV的六个衍生特征训练HMM并作为分类器

采用了EEMD算法对RRI序列标准化.为了提升心电贴的续航时间

采用系统级和电路级的低功耗手段.测试表明

系统平均分类准确率达到93.2%.心电芯片在SMIC 0.18 μm工艺下面积仅为1.6 mm

平均功耗1.3 mW

100 mAh的电池供电心电贴能够工作70小时以上.

Abstract

To improve accuracy and convenience of sleep-staging system

a structure with desktop ECG harvest center and wireless ECG paste which integrates special ASIC is presented in this paper. The sleep-staging algorithm is based on six features which are derived from RRI and HRV of single channel ECG

and these features are fed to HMM as a classifier. In which

EEMD algorithm is used to standardize RRI sequence. To improve battery life of the ECG paste

low power design method on system level and circuit level are used. The test results show that the classification accuracy of sleep-staging system is 93.2% on an average. The ECG chip occupies only 1.6 mm on SMIC 0.18 μm process and average power consumption is only 1.3 mW

which means 70 h continuously work under a 100 mAh battery for the ECG pastes.

关键词

Keywords

references

Denver TEOFILO L. Sleep: A Comprehensive Handbook. [M], Denver: John Wiley % Sons, 2006.

TERZANO M, PARRINO L, Smerieri A, et al. A Walters. Atlas, rules, and recording techniques for the scoring of cyclic alternating pattern (CAP) in human sleep [J]. Sleep Med, 2001, 2(6):537-553. DOI: 537

BUYSSE D, REYNOLDSs C, MONK T, et al.The Pittsburgh sleep quality index: a new instrument for psychiatric practice and research [J]. Psychiatry Res, 1989, 28 (2):193-213. DOI: 193

Denver BENBADIS S. Sleep: A Comprehensive Handbook.[M], Denver:John Wiley % Sons, 2005.

HOBSON J. Sleep is of the brain, by the brain and for the brain [J]. Nature, 2005, 437(10):1254-1256. DOI: 1254

DWORAK M, WIATER A, AIFER D, et al. Increased slow wave sleep and reduced stage 2 sleep in children depending on exercise intensity [J]. Sleep Med., 2008, 9(3):266-272. DOI: 266

YOON H, HWANG SH, LEE Y J, et al. Slow-wave sleep estimation for healthy subjects and OSA patients using R-R intervals [J]. Biomedical and health informatics, 2018, 22(1):119-128. DOI: 119

IMTIAZ S, JIANG Z, RODRIGUEZ-VILLEGAS E. An ultralow power system on chip for automatic sleep staging [J]. IEEE J Sol StaCirc, 2017, 52(3): 822-833. DOI: 822

PARBHA A, TRIVEDI A, KUMAR A, et al. Automated system for obstructive sleep apnea detection using heart rate variability and respiratory rate variability [C]. International Conference on Advances in Computing, 2017: 1303-1307.

CHEN Y, ZHU X, CHEN W, et al. Automatic sleep staging based on ECG signals using hidden Markov models [C]. IEEE International Conference of EMBC, 2015: 530-533.

BANDS S, DINGES D. Behavioral and physiological consequences of sleep restriction [J] Sleep Med, 2007, 3(5): 519-528. DOI: 519

MENDEZ M, MATTEUCCI M, CERUTTI S, et al. Sleep staging classification based on HRV: time-variant analysis [C]. IEEE 31st Annual International conference on EMBS, 2009: 9-12.

CHEN Z, LIN M, LANE ND, et al. Unobtrusive sleep monitoring using smartphones [C]. Pervasive Computing Technologies for Healthcare, 2013: 145-152.

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