Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
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Research on a Solution for Efficient ECG Data Transmission in u-Healthcare Environment

u-Healthcare 환경에서의 효율적인 ECG 데이터 전송 방안에 관한 연구

  • Cho, Gyoun-Yon (Korea University, BK21+ Program, Department of Public Health Sciences Graduate School) ;
  • Lee, Seo-Joon (Korea University, Department of Healthcare Management, BK21+ Program, Department of Public Health Sciences Graduate School) ;
  • Lee, Tae-Ro (Korea University, Professor, Department of Healthcare Management, BK21+ Program, Department of Public Health Sciences Graduate School)
  • 조균연 (고려대학교 BK21+프로그램) ;
  • 이서준 (고려대학교 BK21+ 프로그램, 일반대학원 보건과학과) ;
  • 이태로 (고려대학교 BK21+ 프로그램, 보건행정학과)
  • Received : 2013.12.13
  • Accepted : 2014.01.20
  • Published : 2014.01.28
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Abstract

In u-Healthcare environment, large amounts of important medical information is processed through wireless communication. Therefore there is a need to increase the efficiency of the network system of sending ECG data. This paper presents a compression solution for efficient ECG data transmission(ECGLZW) in u-Healthcare environment. The results showed that the average compression ratio of ECGLZW was 4.6, which got 200% better than existing methods(Huffman and LZW compression). ECGLZW's high compression ratio can increase the efficiency of wireless channels. As a result, reliable communication and security of u-Healthcare information could be achieved by applying these remaining channels to retransmission and encryption.

u-Healthcare 환경에서는 많은 양의 중요한 의료정보가 무선을 통해 처리된다. 따라서 데이터를 전송하는 네트워크 시스템의 효율 개선이 필요하다. 이 논문은 u-Healthcare 환경에서 ECG 데이터를 효율적으로 전송하기 위한 압축 솔루션(ECGLZW)을 제시한다. 실험 결과, ECGLZW의 평균 압축률은 4.6으로써, 기존의 압축 방식(Huffman 압축 방식과 LZW 압축 방식)에 비해 200% 정도 효율이 개선됨을 알 수 있다. ECGLZW의 높은 압축률로 무선채널의 효율이 개선되고, 이를 네트워크 오류에 대한 재전송 및 암호화에 활용함으로써 데이터에 대한 신뢰성 및 u-Healthcare 정보에 대한 보안을 강화할 수 있다.

Keywords

References

  1. Lee SJ, Lee TR. Design of Remote Infusion Pump Monitoring System Using Wireless Network and RFID Technology. The Journal of Digital Policy & Management. 2013.
  2. Lee SJ, Sim HJ, Lee AR, Lee TR. The Design of Maternity Monitoring System Using USN in Maternity Hospital. The Journal of Digital Policy & Management. 2013.
  3. Cho GY, Lee SJ, Lee TR. Research on Enhancing Reliability of IT Convergence Technology Applied Emergency Management Information System. The Journal of Digital Policy & Management. 2013.
  4. Galiano V, Lopez-Granado O, Malumbres MP, Migallon H. Fast 3D wavelet transform on multicore and many-core computing platforms. Journal of Supercomputing. 2013.
  5. Lee SJ, Roh YH, Kim YK, Lee TR. Geometric detection algorithm design for ECG data analysis using wavelet. International Journal of Bio-Science and Bio-Technology. 2013..
  6. Puthooran E, Anand RS, Mukherjee S. Lossless Compression of Medical Images Using a Dual Level DPCM with Context Adaptive Switching Neural Network Predictor. International Journal of Computational Intelligence Systems. 2013.
  7. Ji TY, Wu QH. Broadband noise suppression and feature identification of ECG waveforms using mathematical morphology and embedding theorem. Computer Methods and Programs in Biomedicine. 2013.
  8. Rahman MZU, Karthik GVS, Fathima SY, Lay-Ekuakille A. An efficient cardiacsignal enhancement using time-frequency realization of leaky adaptive noise cancelers for remote health monitoring systems. Measurement. 2013.
  9. Salomon D. A Consice Introduction to Data Compression: Springer; 2008.
  10. Blelloch G. Introduction to Data Compression, 2001.
  11. Ziv J, Lempel A. UNIVERSAL ALGORITHM FOR SEQUENTIAL DATA COMPRESSION. Ieee Transactions on Information Theory. 1977.
  12. Ziv J, Lempel A. COMPRESSION OF INDIVIDUAL SEQUENCES VIA VARIABLE-RATE CODING. Ieee Transactions on Information Theory. 1978.
  13. Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, et al. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation. 2000.
  14. Chen M, Gonzalez S, Leung V, Zhang Q, Li M. A 2G-RFID-BASED E-HEALTHCARE SYSTEM. Ieee Wireless Communications. 2010.
  15. Touati F, Tabish R. U-Healthcare System: State-of-the-Art Review and Challenges. Journal of medical systems. 2013.
  16. Park S, KimW, Ihm I. Mobile collaborative medical display system. Computer Methods and Programs in Biomedicine. 2008.
  17. Sneha S, Varshney U. A framework for enabling patient monitoring via mobile ad hoc network. Decis Support Syst. 2013.

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