UM  > Faculty of Science and Technology
Residential Collegefalse
Status已發表Published
A 2.63 μw ECG Processor with Adaptive Arrhythmia Detection and Data Compression for Implantable Cardiac Monitoring Device
Yin, Yue1; Abubakar, Syed Muhammad1; Tan, Songyao1; Shi, Jiahua1; Yang, Peilin1; Yang, Wendi1; Jiang, Hanjun1; Wang, Zhihua1,2; Jia, Wen2; Ua, Seng Pan3
2021-08-01
Source PublicationIEEE Transactions on Biomedical Circuits and Systems
ISSN1932-4545
Volume15Issue:4Pages:777-790
Abstract

An ultra-low power ECG processor ASIC (application specific integrated circuit) with R-wave detection and data compression is presented, which is designed for the long-term implantable cardiac monitoring (ICM) device for arrhythmia diagnosis. An adaptive derivative-based detection algorithm with low computation overhead for potential arrhythmia recording is proposed to detect arrhythmia with the occasional abnormal heart beats. In order to save as much as possible cardiac information with the limited memory size available in the ICM device, a hierarchical data buffer structure is proposed which saves 3 types of data, including the raw ECG data segments of 2 seconds, compressed ECG data segments of 45 seconds, and R-peak values and interval lengths of >2000 beat cycles. A modified swinging-door-trending (SDT) method is proposed for the ECG data compression. The ASIC has been implemented based on fully-customized near-threshold standard cells using the thick-gate transistors in 65-nm CMOS technology for low dynamic power consumption and leakage. The ASIC core occupies a die area of 1.77 mm2. The measured total power is 2.63 μW, which is among the ECG processors with the lowest core power consumption. It exhibits a relatively high positive precision rate (P+) of 99.3% with a sensitivity of 98.2%, in contrast to the similar designs in literature with the same core power consumption level. Also, an ECG data compression ratio (CR) of up to 17.0 has been achieved, with a good trade-off between the compression efficiency and loss.

KeywordAdaptive Arrhythmia Detection Data Compression Ecg Processor Implantable Cardiac Monitoring Swinging Door Trending
DOI10.1109/TBCAS.2021.3100434
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaEngineering
WOS SubjectEngineering, Biomedical ; Engineering, Electrical & Electronic
WOS IDWOS:000696078800017
Scopus ID2-s2.0-85111603136
Fulltext Access
Citation statistics
Document TypeJournal article
CollectionFaculty of Science and Technology
THE STATE KEY LABORATORY OF ANALOG AND MIXED-SIGNAL VLSI (UNIVERSITY OF MACAU)
DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING
Corresponding AuthorJiang, Hanjun; Jia, Wen
Affiliation1.Tsinghua Beijing Innovation Center for Future Chips, School of Integrated Circuits, Tsinghua University, Beijing, 100084, China
2.Guangdong Engineering Research Center on ICs for Wireless Healthcare, Research Institute of Tsinghua University in Shenzhen, Guangdong, 518057, China
3.State-Key Laboratory of Analog and Mixed-Signal VLSI, IME/ECE/FST, University of Macau, 999078, Macao
Recommended Citation
GB/T 7714
Yin, Yue,Abubakar, Syed Muhammad,Tan, Songyao,et al. A 2.63 μw ECG Processor with Adaptive Arrhythmia Detection and Data Compression for Implantable Cardiac Monitoring Device[J]. IEEE Transactions on Biomedical Circuits and Systems, 2021, 15(4), 777-790.
APA Yin, Yue., Abubakar, Syed Muhammad., Tan, Songyao., Shi, Jiahua., Yang, Peilin., Yang, Wendi., Jiang, Hanjun., Wang, Zhihua., Jia, Wen., & Ua, Seng Pan (2021). A 2.63 μw ECG Processor with Adaptive Arrhythmia Detection and Data Compression for Implantable Cardiac Monitoring Device. IEEE Transactions on Biomedical Circuits and Systems, 15(4), 777-790.
MLA Yin, Yue,et al."A 2.63 μw ECG Processor with Adaptive Arrhythmia Detection and Data Compression for Implantable Cardiac Monitoring Device".IEEE Transactions on Biomedical Circuits and Systems 15.4(2021):777-790.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Yin, Yue]'s Articles
[]'s Articles
[Tan, Songyao]'s Articles
Baidu academic
Similar articles in Baidu academic
[Yin, Yue]'s Articles
[Abubakar, Syed ...]'s Articles
[Tan, Songyao]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Yin, Yue]'s Articles
[Abubakar, Syed ...]'s Articles
[Tan, Songyao]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.