logo

SCIENCE CHINA Information Sciences, Volume 59, Issue 4: 042401(2016) https://doi.org/10.1007/s11432-015-5371-1

Value locality based storage compression memory architecture for ECG sensor node

More info
  • ReceivedDec 24, 2014
  • AcceptedApr 29, 2015
  • PublishedMar 1, 2016

Abstract

This paper proposes a value compression memory architecture for QRS detection in ultra-low-power ECG sensor nodes. Based on the exploration of value spatial locality in the most critical preprocessing stage of the ECG algorithm, a cost efficient compression strategy, which reorganizes several adjacent sample values into a base value with several displacements, is proposed. The displacements will be half or quarter scale quantifications; as a result, the storage size is reduced. The memory architecture saves memory space by storing compressed data with value spatial locality into a compressed memory section and by using a small, uncompressed memory section as backup to store the uncompressed data when a value spatial locality miss occurs. Furthermore, a low-power accession strategy is proposed to achieve low-power accession. An embodiment of the proposed memory architecture has been evaluated using the MIT/BIH database, the proposed memory architecture and a low-power accession strategy to achieve memory space savings of 32.5% and to achieve a 68.1\% power reduction with a negligible performance reduction of 0.2%.


References

[1] Pop V, de Francisco R, Pflug H, et al. Human++: wireless autonomous sensor technology for body area networks. In:~Proceedings of the 16th Asia and South Pacific Design Automation Conference, Pacifico Yokohama, 2011. 561--566. Google Scholar

[2] Liu X, Zheng Y, Phyu M W, et al. Power and area efficient wavelet-based on-chip ECG processor for WBAN. In:~Proceedings of International Conference on Body Sensor Networks (BSN), Singapore, 2010. 124--130. Google Scholar

[3] Liu X, Zheng Y, Phyu M W, et al. IEEE Trans Biomed Eng, 2011, 58: 380-389 CrossRef Google Scholar

[4] Kim H, Yazicioglu R F, Torfs T, et al. A low power ECG signal processor for ambulatory arrhythmia monitoring system. In:~Proceedings of IEEE Symposium on VLSI Circuits (VLSIC), Honolulu, 2010. 19--20. Google Scholar

[5] Ieong C I, Mak P I, Lam C P, et al. 83-QRS detection processor using quadratic spline wavelet transform for wireless ECG acquisition in 0.35-CMOS. IEEE Trans Biomed Circ Syst, 2012, 6: 586-595 Google Scholar

[6] Liu X, Zhou J, Yang Y, et al. A 457-nW cognitive multi-functional ECG processor. In:~Proceedings of IEEE Asian Solid-State Circuits Conference (A-SSCC), Singapore, 2013. 141--144. Google Scholar

[7] Yazicioglu R F, Kim S, Torfs T, et al. IEEE J Solid-State Circ, 2011, 46: 209-223 CrossRef Google Scholar

[8] Kim H, Yazicioglu R F, Kim S, et al. A configurable and low-power mixed signal SoC for portable ECG monitoring applications. In:~Proceedings of Symposium on VLSI Circuits (VLSIC), Kyoto, 2011. 142--143. Google Scholar

[9] Jeon D, Chen Y P, Lee Y, et al. An implantable 64nW ECG-monitoring mixed-signal SoC for arrhythmia diagnosis. In:~Proceedings of IEEE Solid-State Circuits Conference Digest of Technical Papers (ISSCC), San Francisco, 2014. 416--417. Google Scholar

[10] Ashouei M, Hulzink J, Konijnenburg M, et al. A voltage-scalable biomedical signal processor running ECG using 13pJ/cycle at 1MHz and 0.4 V. In:~Proceedings of IEEE Solid-State Circuits Conference Digest of Technical Papers (ISSCC), San Francisco, 2011. 332--334. Google Scholar

[11] Kim H, Choi S, Yoo H J. A low power 16-bit RISC with lossless compression accelerator for body sensor network system. In: Proceedings of Asian Solid-State Circuits Conference, Hangzhou, 2006. 207--210. Google Scholar

[12] Kwong J, Chandrakasan A P. IEEE J Solid-State Circ, 2011, 46: 1742-1753 CrossRef Google Scholar

[13] ATMEL. 8-bit Atmel Microcontroller with 128Kbytes in-System Programmable Flash Rev 2467X-AVR-06/11. 2011. Google Scholar

[14] Texas Instruments. MSP430x2xx Family User's Guide. 2013. Google Scholar

[15] Boichat N, Atienza D, Khaled N. Wavelet-based ECG delineation on a wearable embedded sensor platform. In:~Proceedings of 6th International Wearable and Implantable Body Sensor Networks, Berkeley, 2009. 256--261. Google Scholar

[16] Yseboodt L, De Nil M, Huisken J, et al. J Signal Process Syst, 2009, 57: 107-119 CrossRef Google Scholar

[17] Mamaghanian H, Khaled N, Atienza D, et al. IEEE Trans Biomed Eng, 2011, 58: 2456-2466 CrossRef Google Scholar

[18] Polastre J, Szewczyk R, Culler D. Telos: enabling ultra-low power wireless research. In:~Proceedings of 4th International Symposium on Information Processing in Sensor Networks, Los Angeles, 2005. 364--369. Google Scholar

[19] Lee K H, Verma N. A 1.2-0.55 V general-purpose biomedical processor with configurable machine-learning accelerators for high-order, patient-adaptive monitoring. In:~Proceedings of European Solid State Circuits Conference, Bordeaux, 2012. 285--288. Google Scholar

[20] Ekanayake V, Kelly IV C, Manohar R. ACM SIGARCH Comput Architect News, 2004, 32: 27-36 Google Scholar

[21] Duarte F, Hulzink J, Zhou J, et al. ACM Trans Des Automat Electron Syst, 2011, 16: 51-36 Google Scholar

[22] Nazhandali L, Minuth M, Zhai B, et al. A second-generation sensor network processor with application-driven memory optimizations and out-of-order execution. In:~Proceedings of International Conference on Compilers, Architectures and Synthesis for Embedded Systems, San Francisco, 2005. 249--256. Google Scholar

[23] Pan J, Tompkins W J. IEEE Trans Biomed Eng, 1985, 32: 230-236 Google Scholar

[24] Torfs T, Yazicioglu R F, Kim S, et al. Ultra low power wireless ECG system with beat detection and real time impedance measurement. In:~Proceedings of Biomedical Circuits and Systems Conference (BioCAS), Paphos, 2010. 33--36. Google Scholar

[25] Martínez J P, Almeida R, Olmos S, et al. IEEE Trans Biomed Eng, 2004, 51: 570-581 CrossRef Google Scholar

[26] Moody G B, Mark R G, Goldberger A L. IEEE Eng Med Biol Mag, 2001, 20: 70-75 CrossRef Google Scholar

[27] Teman A, Pergament L, Cohen O, et al. IEEE J Solid-State Circ, 2011, 46: 2713-2726 CrossRef Google Scholar

[28] Lutkemeier S, Jungeblut T, Berge H K O, et al. IEEE J Solid-State Circ, 2013, 48: 8-19 CrossRef Google Scholar

[29] Kücük G, Basaran C. J Comput, 2007, 2: 67-74 Google Scholar

[30] Hanson S, Seok M, Lin Y S, et al. IEEE J Solid-State Circ, 2009, 44: 1145-1155 CrossRef Google Scholar

Copyright 2019 Science China Press Co., Ltd. 《中国科学》杂志社有限责任公司 版权所有

京ICP备18024590号-1