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http://dx.doi.org/10.3745/KTCCS.2014.3.10.371

Research on a Solution for Efficient ECG Data Transmission in IoT Environment  

Cho, Gyoun Yon (고려대학교 보건과학연구소)
Lee, Seo Joon (고려대학교 보건과학과)
Lee, Tae Ro (고려대학교 보건정책관리학부)
Publication Information
KIPS Transactions on Computer and Communication Systems / v.3, no.10, 2014 , pp. 371-376 More about this Journal
Abstract
Consistently collecting a variety of vital signs is crucial in u-Healthcare. In order to do so, IoT is being considered as a top solution nowadays as an efficient network environment between the sensor and the server. This paper proposes a transmission method and compression algorithm which are appropriate for IoT environment. Results were compared to widely used compression methods, and were compared to other prior researches. The results showed that the compression ratio of our proposed algorithm was 11.7.
Keywords
u-Healthcare; IoT; Vital Information Transmission Method; Compression Solution;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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1 Cho, G.Y., "Research on a Method for Efficient u-Healthacer Data Transmission in M2M Environment", in Journal of Digital Convergence, pp.251-257, 2014.
2 Finsterer, J., C. Stollberger, and R. Hoftberger, "Left ventricular hypertrabeculation/noncompaction in hereditary inclusion body myopathy", in International Journal of Cardiology, pp.67-69, 2011.
3 WHO, Global Status Report on Noncommunicable Diseases, 2010.
4 Dabby, R., et al., "Myotonia in DNM2-related centronuclear myopathy", in Journal of Neural Transmission, pp.549-553. 2014.
5 Wilde, E.T., "Do emergency medical system response times matter for health outcomes?", in Health Econ, pp.790-806, 2013.
6 Trabuco, M.H., M.V. Chaffim Costa, and F.A. de Oliveira Nascimento, "S-EMG signal compression based on domain transformation and spectral shape dynamic bit allocation", in Biomedical Engineering Online, 2014.
7 Li, S.H., et al., "Developing an active emergency medical service system based on WiMAX technology", in J Med Syst, pp.3177-3193. 2012.
8 El-Masri, S. and B. Saddik, "An emergency system to improve ambulance dispatching, ambulance diversion and clinical handover communication-a proposed model", in J Med Syst, pp.3917-3923, 2012.
9 Lee Cc Fau - Hsu, C.-W., et al., "An enhanced mobilehealthcare emergency system based on extended chaotic maps", in Journal of Medical Systems, 2013.
10 Chen, M., et al., "A 2G-RFID-BASED E-HEALTHCARE SYSTEM", in Ieee Wireless Communications, pp.37-43, 2010.
11 Sneha, S. and U. Varshney, "A framework for enabling patient monitoring via mobile ad hoc network", in Decision Support Systems, pp.218-234, 2013.
12 Park, S., W. Kim, and I. Ihm, "Mobile collaborative medical display system", in Computer Methods and Programs in Biomedicine, pp.248-260, 2008.
13 Lee, S.J., et al., "The Design of Maternity Monitoring System Using USN in Maternity Hospital", in The Journal of Digital Policy & Management, pp.347-354, 2013.
14 Lee, S.J. and T.R. Lee, "Design of Remote Infusion Pump Monitoring System Using Wireless Network and RFID Technology", in The Journal of Digital Policy & Management, pp.159-167, 2013.
15 Tu, Y.-J., W. Zhou, and S. Piramuthu, "Identifying RFIDembedded objects in pervasive healthcare applications", in Decision Support Systems, pp.586-593, 2009.
16 de Carvalho Junior, H.H., et al., "A heart disease recognition embedded system with fuzzy cluster algorithm", in Computer Methods and Programs in Biomedicine, pp.447-454, 2013.
17 Nilsen, W., et al., "Advancing the Science of mHealth", in Journal of Health Communication, pp.5-10, 2012.
18 Puthooran, E., R.S. Anand, and S. Mukherjee, "Lossless Compression of Medical Images Using a Dual Level DPCM with Context Adaptive Switching Neural Network Predictor", in International Journal of Computational Intelligence Systems, pp.1082-1093, 2013.
19 Berger, P.D.A., et al., "Compression of EMG signals with wavelet transform and artificial neural networks", in Physiological Measurement, pp.457-465, 2006.
20 Lee, S.J., et al., "Geometric detection algorithm design for ECG data analysis using wavelet", in International Journal of Bio-Science and Bio-Technology, pp.11-23, 2013.
21 Ku, C.-T., et al., "Wavelet-Based ECG Data Compression System With Linear Quality Control Scheme", in Ieee Transactions on Biomedical Engineering, pp.1399-1409, 2010.
22 Peric, Z., et al., "DPCM quantizer adaptation method for efficient ECG signal compression", in Journal of Communications Technology and Electronics, pp.1241-1250, 2013.
23 Weeks, M., "Digital Signal Processing Using Matlab and Wavelets", Infinity Science Press, 2007.
24 Galiano, V., et al., "Fast 3D wavelet transform on multicore and many-core computing platforms", in Journal of Supercomputing, pp.848-865, 2013.
25 Mao, Y., et al. "Medical data mining for early deterioration warning in general hospital wards", in IEEE International Conference on Data Mining, 2011.
26 Salomon, D., "A Consice Introduction to Data Compression", Springer, 2008.
27 Blelloch, G., "Introduction to Data Compression", 2001.
28 Ziv, J. and A. Lempel, "UNIVERSAL ALGORITHM FOR SEQUENTIAL DATA COMPRESSION", in Ieee Transactions on Information Theory, pp.337-343, 1977.
29 Goldberger, A.L., et al., "PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation", pp.215-220, 2000.
30 Ziv, J. and A. Lempel, "COMPRESSION OF INDIVIDUAL SEQUENCES VIA VARIABLE-RATE CODING", Ieee Transactions on Information Theory, pp.530-536, 1978.
31 Mukhopadhyay, S.K., S. Mitra, and M. Mitra, "ECG signal compression using ASCII character encoding and transmission via SMS", in Biomedical Signal Processing and Control, pp.354-363, 2013.