A remote electrocardiogram monitoring system with good swiftness and high reliablility

Computers and Electrical Engineering

Volumn 53, Issue , 2016, Pages 191-202

  Chen, Hanlin ^a   Liu, Hongwei ^b  

^a BEIJING ELECTRONIC SCIENCE AND TECHNOLOGY INSTITUTE   (China)

^b RESEARCH INSTITUTE OF HIGHWAY MINISTRY OF TRANSPORT   (China)

Author keywords

A2F500; Dynamically Variable Time Interval; MD5; Parallel PCA ICA Algorithm; Two level Load balance Monitoring Strategy

Indexed keywords

APPLICATION SPECIFIC INTEGRATED CIRCUITS; COMPUTER AIDED DIAGNOSIS; DISTRIBUTED COMPUTER SYSTEMS; EIGENVALUES AND EIGENFUNCTIONS; ELECTROCARDIOGRAPHY; FEATURE EXTRACTION; INDEPENDENT COMPONENT ANALYSIS; PRINCIPAL COMPONENT ANALYSIS; SECURITY OF DATA; SYSTEM-ON-CHIP; TIME DELAY;

A2F500; ELECTROCARDIOGRAM SIGNAL; ICA ALGORITHMS; LONG TIME DELAYS; MONITORING CENTERS; MONITORING STRATEGY; MONITORING SYSTEM; TIME INTERVAL;

MONITORING;

EID: 84973168979     PISSN: 00457906     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compeleceng.2016.02.004     Document Type: Article

References (27)

1. [1] Zhang, Y., Sun, J., Zhao, C., A remote real-time ECG monitoring system based on 3G network. Appl Mech Mater 16:4 (2013), 794–797.
2. [2] Wu, W.H., Bui, A.A.T., Batalin, M.A., Au, L.K., Binney, J.D., Kaiser, W.J., MEDIC: medical embedded device for individualized care. Artif Intell Med 42:2 (2008), 137–152.
3. [3] Shih, D.H., Chiang, H.S., Lin, B., Lin, S.B., An embedded mobile ECG reasoning system for elderly patients. IEEE Trans Inf Technol Biomed 14:3 (2010), 854–865.
4. [4] Noh, Y.H., Jeong, D.U., Implementation of a data packet generator using pattern matching for wearable ECG monitoring systems. Sensors 14:7 (2014), 12623–12639.
5. [5] Chen, J., Wang, F., Zhang, Y., Shi, X., ECG compression using uniform scalar dead-zonequantization and conditional entropy coding. Med Eng Phys 30:4 (2008), 523–530.
6. [6] Sufi, F., Khalil, I., Faster person identification using compressed ECG in time critical wireless telecardiology applications. J Netw Comput Appl 34:1 (2011), 282–293.
7. [7] Sufi, F., Fang, Q., Khalil, I., Mahmoud, S.S., novel methods of faster cardiovascular diagnosis in wireless telecardiology. IEEE J Sel. Areas Commun 27:4 (2009), 537–552.
8. [8] Tan, Y., Shen, L., Wang, H., Tan, Y., Study of portable ecg monitor base on S3C244B0X. Proceedings of the 8th international symposium on distributed computing and applications to business, 2009, IEEE Press, 360–363.
9. [9] Zou, B., Chai, J., Based on ARM processor's ECG blood pressure monitor development. Med Equip 1:18 (2005), 7–10.
10. [10] Ebrahimzadeh, A., Shakiba, B., Khazaee, A., Detection of electrocardiogram signals using an efficient method. Appl Soft Comput 22:5 (2014), 108–117.
11. [11] Chawla, M.P.S., Multidimensional independent component analysis for statistical estimations of indeterminacies in electrocardiograms. J Mech Med Biol 9:3 (2009), 345–375.
12. [12] Chawla, M.P.S., Segment classification of ECG data and construction of scatter plots using principal component analysis. J Mech Med Biol 8:3 (2008), 421–458.
13. [13] Yan, J., Lu, Y., Liu, J., Wub, X., Xu, Y., Self-adaptive model-based ECG de-noising using features extracted by mean shift algorithm. Biomed Signal Process Control 5:2 (2010), 103–113.
14. [14] Sahin, I., Yilmazer, N., Simaan, M.A., A method for subsample fetal heart rate estimation under noisy conditions. IEEE Trans Biomed Eng 57:4 (2010), 875–883.
15. [15] Atoui, H., Fayn, J., Rubel, P., A novel neural-network model for deriving standard 12-lead ECGs from serial three-lead ECGs: application to self-care. IEEE Trans Inf Technol Biomed 14:3 (2010), 883–890.
16. [16] Ȕbeyli, E.D., Adaptive neuro-fuzzy inference system for classification of ECG signals using Lyapunov exponents. Comput Methods Programs Biomed 93:3 (2009), 313–321.
17. [17] Sufi, F., Khalil, I., Mahmood, A.N., A clustering based system for instant detection of cardiac abnormalities from compressed ECG. Expert Syst Appl, 38:5 (2011), 4705–4713.
18. [18] Huang, W.C., Hung, S.H., Chung, J.F., Chang, M.H., Van, L.D., FPGA implementation of 4-channel ICA for on-line EEG signal separation. Proceedings of the IEEE biomedical circuits & systems conference, 2008, 65–68.
19. [19] Elrharras, A., Moukhlis, S.E., Saadane, R., Wahbi, M., Hamdoun, A., FPGA-based fully parallel PCA-ANN for spectrum sensing. Comput Inf Sci 8:1 (2015), 108–118.
20. [20] Jewajinda, Y., Chongstitvatana, P., A parallel genetic algorithm for adaptive hardware and its application to ECG signal classification. Neural Comput Appl 22:7–8 (2013), 1609–1626.
21. [21] Wang, L., Yu, Y., Huang, Q., Yang, J., Zhao, Z., The design and implementation of the MD5 algorithm based on FPGA. Appl Mech Mater 427-429 (2013), 806–809.
22. [22] Noaman, M.A., A VHDL model for implementation of MD5 hash algorithm. Eng Technol J 31:6 (2013), 1107–1116.
23. [23] Wang, B., Han, G., Zhang, X., Design and implementation of MD5 algorithm based on FPGA. Commun Technol 43:1 (2010), 69–71.
24. [24] Shao, B., Embedded real-time operating system μC/OS-II. 2003, Beijing University of Aeronautics and Astronautics Press, Beijing.
25. [25] H., Zhou, Hou, K.M., Zuo, D., Real-time automatic ECG diagnosis method dedicated to pervasive cardiac care. Wirel Sens Netw 1:4 (2009), 276–283.
26. [26] Lao, N., Wen, J., Ma, W., Wang, Y., Combine high level symptom and low level state information for configuration fault diagnosis. Proceedings of the 18th USENIX conference on system administration, 2004, 153–159.
27. [27] Chawla, M.P.S., PCA and ICA processing methods for removal of artifacts and noise in electrocardiograms: a survey and comparison. Appl Soft Comput 11:2 (2011), 2216–2226.