Xin Liu, Jun Zhou, Yongkui Yang, Bo Wang, Jingjing Lan, Chao Wang, Jianwen Luo, W. Goh, T. T. Kim, M. Je
{"title":"A 457-nW cognitive multi-functional ECG processor","authors":"Xin Liu, Jun Zhou, Yongkui Yang, Bo Wang, Jingjing Lan, Chao Wang, Jianwen Luo, W. Goh, T. T. Kim, M. Je","doi":"10.1109/ASSCC.2013.6691002","DOIUrl":null,"url":null,"abstract":"In this paper, a multi-functional ECG signal processor for wearable and implantable real-time monitoring is presented. To enable extremely long-term ambulatory monitoring, several power saving techniques are proposed, including global cognitive clocking, pseudo-downsampling wavelet transform, adaptive storing, and denoising-based run-length compression. An on-chip low-complexity cardiac signal analysis module is proposed to realize comprehensive analysis functions. Near-threshold circuit technique is applied to the overall system. Implemented in 0.18 μm CMOS, the proposed cognitive ECG processor consumes only 457 nW at 0.5 V supply for real-time ambulatory monitoring. Compared with existing designs, the presented ECG processor achieves the lowest power consumption.","PeriodicalId":296544,"journal":{"name":"2013 IEEE Asian Solid-State Circuits Conference (A-SSCC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE Asian Solid-State Circuits Conference (A-SSCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASSCC.2013.6691002","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 15
Abstract
In this paper, a multi-functional ECG signal processor for wearable and implantable real-time monitoring is presented. To enable extremely long-term ambulatory monitoring, several power saving techniques are proposed, including global cognitive clocking, pseudo-downsampling wavelet transform, adaptive storing, and denoising-based run-length compression. An on-chip low-complexity cardiac signal analysis module is proposed to realize comprehensive analysis functions. Near-threshold circuit technique is applied to the overall system. Implemented in 0.18 μm CMOS, the proposed cognitive ECG processor consumes only 457 nW at 0.5 V supply for real-time ambulatory monitoring. Compared with existing designs, the presented ECG processor achieves the lowest power consumption.