Design of Low-Power ECG Sampling and Compression Circuit

Author:

Zhao Zuoqin1,Nai Yufei1,Yu Zhiguo1,Xu Xin1,Cao Xiaoyang1,Gu Xiaofeng1ORCID

Affiliation:

1. Engineering Research Center of IoT Technology Applications (Ministry of Education), Department of Electronic Engineering, Jiangnan University, Wuxi 214122, China

Abstract

Compressed Sensing (CS) has been applied to electrocardiogram monitoring in wireless sensor networks, but existing sampling and compression circuits consume too much hardware. This paper proposes a low-power and small-area sampling and compression circuit with an Analog-to-Digital Converter (ADC) and a CS module. The ADC adopts split capacitors to reduce hardware consumption and uses a calibration technique to decrease offset voltage. The CS module uses an approximate addition calculation for compression and stores the compressed data in pulsed latches. The proposed addition completes the accurate calculation of the high part and the approximate calculation of the low part. In a 55 nm CMOS process, the ADC has an area of 0.011 mm2 and a power consumption of 0.214 μW at 10 kHz. Compared with traditional design, the area and power consumption of the proposed CS module are reduced by 19.5% and 31.7%, respectively. The sampling and compression circuit area is 0.325 mm2, and the power consumption is 2.951 μW at 1.2 V and 100 kHz. The compressed data are reconstructed with a percentage root mean square difference of less than 2%. The results indicate that the proposed circuit has performance advantages of hardware consumption and reconstruction quality.

Funder

Fundamental Research Funds for the Central Universities

Jiangsu Province Key Research and Development Program

Joint Project of Yangtze River Delta Community of Sci-Tech Innovation

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Taxonomy of Low-Power Techniques in Wearable Medical Devices for Healthcare Applications;Electronics;2024-08-05

2. PSCS: A Physiological Sound Compression System Based on Compressive Sensing with Self-Adaptive Compression Ratio and Optimized DCT;2024 IEEE International Symposium on Circuits and Systems (ISCAS);2024-05-19

3. Reducing Energy Consumption Using Bidirectional Long Short Term Memory-ECG Compression;2024 Third International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE);2024-04-26

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3