An approximate compressor for wearable biomedical healthcare monitoring systems

2015 International Conference on Hardware/Software Codesign and System Synthesis, CODES+ISSS 2015

Volumn , Issue , 2015, Pages 133-142

  Samie, Farzad ^a   Bauer, Lars ^a   Henkel, Jorg ^a  

^a KARLSRUHE INSTITUTE OF TECHNOLOGY   (Germany)

Author keywords

Approximate Computing; Biomedical Healthcare; Compression; Internet of Things; IoT; Wearable Devices

Indexed keywords

CODES (SYMBOLS); COMPACTION; COMPRESSORS; DATA COMPRESSION; DIGITAL STORAGE; ELECTRIC BATTERIES; ELECTRIC POWER SUPPLIES TO APPARATUS; ENERGY UTILIZATION; ERRORS; HARDWARE; HARDWARE-SOFTWARE CODESIGN; HEALTH CARE; INTERNET; INTERNET OF THINGS; MEDICAL APPLICATIONS; MONITORING; RECONFIGURABLE HARDWARE; WIRELESS TELECOMMUNICATION SYSTEMS;

APPROXIMATE COMPUTING; BIOMEDICAL APPLICATIONS; COMPRESSION TECHNIQUES; HARDWARE MODIFICATIONS; TOTAL ENERGY CONSUMPTION; ULTRA-LOW POWER DESIGN; WEARABLE DEVICES; WIRELESS COMMUNICATIONS;

WEARABLE TECHNOLOGY;

EID: 84963770129     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CODESISSS.2015.7331376     Document Type: Conference Paper

References (32)

1. Daniele Bortolotti, Mauro Mangia, Andrea Bartolini, et al. An ultra-low power dual-mode ECG monitor for healthcare and wellness. In Design, Automation and Test in Europe (DATE), pp. 1611-1616, 2015.
2. Ruben Braojos, Hossein Mamaghanian, Alair Dias Junior, et al. Ultra-low power design of wearable cardiac monitoring systems. In Design Automation Conference (DAC), pp. 1-6, 2014.
3. M Magno and C Spagnol and L Benini et al. A low power wireless node for contact and contactless heart monitoring. Microelectronics Journal, 45(12):1656-1664, 2014.
4. Wen-Shiung Chen and Lili Hsieh and Shang-Yuan Yuan. High performance data compression method with pattern matching for biomedical ECG and arterial pulse waveforms. Computer methods and programs in Biomedicine, 74(1):11-27, 2004.
5. Ke Li, Yun Pan, and Fangjian Chen, et al. Real-Time lossless ECG compression for low-power wearable medical devices based on adaptive region prediction. Electronics Letters, 50(25):1904-1906, 2014.
6. Amit Pande and Eilwoo Baik and Prasant Mohapatra. Efficient health data compression on mobile devices. In ACM MobiHoc workshop on Pervasive wireless healthcare, pp. 25-30, 2013.
7. Lawrence K Au, Winston H Wu, and Maxim A Batalin, et al. Microleap: Energy-Aware wireless sensor platform for biomedical sensing applications. In Biomedical Circuits and Systems Conference (BioCAS), pp. 158-162, 2007.
8. SangJoon Lee and Jungkuk Kim and Jong-Ho Lee. A real-Time ECG data compression and transmission algorithm for an ehealth device. IEEE Transactions on Biomedical Engineering, 58(9):2448-2455, 2011.
9. Hoi-Jun Yoo and Chris Van Hoof. Introduction to Bio-Medical CMOS IC. In Bio-Medical CMOS ICs, pp. 1-9. Springer, 2011.
10. Hyejung Kim, Refet Firat Yazicioglu, and Patrick Merken, et al. ECG signal compression and classification algorithm with quad level vector for ECG holter system. IEEE Transactions on Information Technology in Biomedicine, 14(1):93-100, 2010.
11. Catalina Monica Fira and Liviu Goras. An ECG signals compression method and its validation using NNs. IEEE Transactions on Biomedical Engineering, 55(4):1319-1326, 2008.
12. M Sabarimalai Manikandan and S Dandapat. Wavelet threshold based ECG compression using USZZQ and Huffman coding of DSM. Biomedical Signal Processing and Control, 1(4):261-270, 2006.
13. M Sabarimalai Manikandan and S Dandapat. ECG signal compression using discrete sinc interpolation. In International Conference on Intelligent Sensing and Information Processing (ICISIP), pp. 14-19, 2005.
14. Shun-Ren Siao and Chih-Cheng Hsu and Mark Po-Hung Lin et al. A novel approach for ECG data compression in healthcare monitoring system. In International Symposium on Bioelectronics and Bioinformatics (ISBB), pp. 1-4, 2014.
15. S-L Chen and J-G Wang. VLSI implementation of low-power cost-efficient lossless ECG encoder design for wireless healthcare monitoring application. Electronics Letters, 49(2):91-93, 2013.
16. Swagath Venkataramani and Srimat T. Chakradhar and Kaushik Roy et al. Computing approximately, and efficiently. In Design, Automation and Test in Europe (DATE), pp. 748-751, 2015.
17. Lakshmi NB Chakrapani, Kirthi Krishna Muntimadugu, and Avinash Lingamneni, et al. Highly energy and performance efficient embedded computing through approximately correct arithmetic: A mathematical foundation and preliminary experimental validation. In International Conference on Compilers, Architectures and Synthesis for Embedded Systems (CASES), pp. 187-196, 2008.
18. Shyamsundar Venkataraman and Akash Kumar and Jeremy Schlachter et al. Designing inexact systems efficiently using elimination heuristics. In Design, Automation and Test in Europe (DATE), pp. 758-763, 2015.
19. Junjun Hu and Weikang Qian. A new approximate adder with low relative error and correct sign calculation. In Design, Automation and Test in Europe (DATE), pp. 1449-1454, 2015.
20. Vaibhav Gupta, Debabrata Mohapatra, and Sang Phill Park, et al. IMPACT: imprecise adders for low-power approximate computing. In ISLPED, pp. 409-414, 2011.
21. Jin Miao and Ku He and Andreas Gerstlauer et al. Modeling and synthesis of quality-energy optimal approximate adders. In International Conference on Computer-Aided Design (ICCAD), pp. 728-735, 2012.
22. Chia-Hao Lin and Chao Lin. High accuracy approximate multiplier with error correction. In International Conference on Computer Design (ICCD), pp. 33-38, 2013.
23. Ku He and Andreas Gerstlauer and Michael Orshansky. Circuitlevel timing-error acceptance for design of energy-efficient DCT/IDCT-based systems. IEEE Transactions on Circuits and Systems for Video Technology, 23(6):961-974, 2013.
24. Vaibhav Gupta and Debabrata Mohapatra and Anand Raghunathan et al. Low-power digital signal processing using approximate adders. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 32(1):124-137, 2013.
25. Daniele Bortolotti, Hossein Mamaghanian, Andrea Bartolini, et al. Approximate compressed sensing: ultra-low power biosignal processing via aggressive voltage scaling on a hybrid memory multi-core processor. In ISLPED, volume 1, pp. 40-45, 2014.
26. Adrian Sampson and Jacob Nelson and Karin Strauss et al. Approximate storage in solid-state memories. ACM Transactions on Computer Systems (TOCS), 32(3):9, 2014.
27. Qian Zhang and Feng Yuan and Rong Ye et al. ApproxIt: An approximate computing framework for iterative methods. In Design Automation Conference (DAC), pp. 1-6, 2014.
28. Adrian Sampson, Werner Dietl, Emily Fortuna, et al. EnerJ: Approximate data types for safe and general low-power computation. ACM SIGPLAN Notices, 46(6):164-174, 2011.
29. Zainul Charbiwala and Jonathan Friedman and Mani B Srivastava et al. Filters that remember: duty cycling analog circuits for long term medical monitoring. In Wireless Health, p. 3, 2011.
30. Michael Buro. On the maximum length of Huffman codes. Information processing letters, 45(5):219-223, 1993.
31. Ary L Goldberger, Luis AN Amaral, Leon Glass, et al. Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals. Circulation, 101(23):e215-e220, 2000.
32. Hyejung Kim, Sunyoung Kim, Nick Van Helleputte, et al. A configurable and low-power mixed signal SoC for portable ECG monitoring applications. IEEE Transactions on Biomedical Circuits and Systems, 8(2):257-267, 2014.