251
Views
7
CrossRef citations to date
0
Altmetric
Original Articles

A clustering based variable sub-window approach using particle swarm optimisation for biomedical sensor data monitoring

ORCID Icon, , , , , & show all
Pages 15-35 | Received 03 Nov 2018, Accepted 17 Mar 2019, Published online: 16 Apr 2019

References

  • Appel, G. 2005. Technical Analysis: Power Tools for Active Investors. Upper Saddle River, NJ: FT Press.
  • Assad, C., M. Wolf, T. Theodoridis, K. Glette, and A. Stoica. 2013. “BioSleeve: A Natural EMG-based Interface for HRI.” In Proceedings of the 8th ACM/IEEE International Conference on Human-Robot Interaction, 69–70. Tokyo: IEEE Press.
  • Azami, H., H. Hassanpour, J. Escudero, and S. Sanei. 2015. “An Intelligent Approach for Variable Size Segmentation of Non-Stationary Signals.” Journal of Advanced Research 6 (5): 687–698. doi:10.1016/j.jare.2014.03.002.
  • Babcock, B., S. Babu, M. Datar, R. Motwani, and J. Widom. 2002. “Models and Issues in Data Stream Systems.” In Proceedings of the Twenty-First ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, 1–16. Madison, WI: ACM.
  • Balkesen, C., and N. Tatbul. 2011. “Scalable Data Partitioning Techniques for Parallel Sliding Window Processing over Data Streams.” In International Workshop on Data Management for Sensor Networks (DMSN), Seattle, WA, USA.
  • Bifet, A., and R. Gavalda. 2007. “Learning from Time-Changing Data with Adaptive Windowing.” In Proceedings of the 2007 SIAM International Conference on Data Mining, 443–448. Minneapolis, MI: SIAM.
  • Bousseljot, R., D. Kreiseler, and A. Schnabel. 1995. “Nutzung der EKG-Signaldatenbank CAR- DIODAT der PTB u¨ber das Internet.” Biomedizinische Technik/Biomedical Engineering 40 (s1): 317–318.
  • Box, G. E. P., G. M. Jenkins, G. C. Reinsel, and G. M. Ljung. 2015. Time Series Analysis: Forecasting and Control. Hoboken, NJ: John Wiley & Sons.
  • Carbone, A., G. Castelli, and H. Eugene Stanley. 2004. “Time-Dependent Hurst Exponent in Financial Time Series.” Physica A: Statistical Mechanics and Its Applications 344 (1–2): 267–271. doi:10.1016/j.physa.2004.06.130.
  • Domingos, P., and G. Hulten. 2000. “Mining High-Speed Data Streams.” In Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 71–80. Boston, MA: ACM.
  • Duan, L., W. Nick Street, and E. Xu. 2011. “Healthcare Information Systems: Data Mining Methods in the Creation of a Clinical Recommender System.” Enterprise Information Systems 5 (2): 169–181. doi:10.1080/17517575.2010.541287.
  • Engle, R. F. 1982. “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation.” Econometrica: Journal of the Econometric Society 987–1007. doi:10.2307/1912773.
  • Fong, S., K. Lan, and R. Wong. 2013. “Classifying Human Voices by Using Hybrid SFX Time-Series Preprocessing and Ensemble Feature Selection.” BioMed Research International 2013. doi:10.1155/2013/720834.
  • Golab, L., and M. Tamer Özsu. 2003. “Issues in Data Stream Management.” ACM Sigmod Record 32 (2): 5–14. doi:10.1145/776985.
  • Hall, M. A. 2000. “Correlation-Based Feature Selection of Discrete and Numeric Class Machine Learning.” (Working paper 00/08). Hamilton: University of Waikato, Department of Computer Science.
  • Ingber, L. 1997. “Statistical Mechanics of Neocortical Interactions: Canonical Momenta Indicatorsof Electroencephalography.” Physical Review E 55 (4): 4578. doi:10.1103/PhysRevE.55.4578.
  • Jain, A. K., M. Narasimha Murty, and P. J. Flynn. 1999. “Data Clustering: A Review.” ACM Computing Surveys (CSUR) 31 (3): 264–323. doi:10.1145/331499.331504.
  • Kennedy, J. 2011. “Particle Swarm Optimization.” In Encyclopedia of Machine Learning, 760–766. New York: Springer.
  • Lin, S.-W., K.-C. Ying, S.-C. Chen, and Z.-J. Lee. 2008. “Particle Swarm Optimization for Parameter Determination and Feature Selection of Support Vector Machines.” Expert Systems with Applications 35 (4): 1817–1824. doi:10.1016/j.eswa.2007.08.088.
  • MacQueen, J. 1967. “Some Methods for Classification and Analysis of Multivariate Observations.” In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability. Vol. 1, 281–297. Oakland, CA: University of California Press.
  • Mandal, I. 2017. “Machine Learning Algorithms for the Creation of Clinical Healthcare Enterprise Systems.” Enterprise Information Systems 11 (9): 1374–1400.
  • R¨Osler, O., and D. Suendermann. 2013. “A First Step Towards Eye State Prediction Using Eeg.” Proc. Of the AIHLS.
  • Raghupathi, W., and V. Raghupathi. 2014. “Big Data Analytics in Healthcare: Promise and Potential.” Health Information Science and Systems 2 (1): 3. doi:10.1186/2047-2501-2-3.
  • Tao, Y., E. C. M. Lam, and Y. Y. Tang. 2001. “Feature Extraction Using Wavelet and Fractal.” Pattern Recognition Letters 22 (3–4): 271–287. doi:10.1016/S0167-8655(01)00003-4.
  • Van-Dai, T., C.-M. Liu, and G. W. Nkabinde. 2016. “Big Data Stream Computing in Healthcare Real-Time Analytics.” In Cloud Computing and Big Data Analysis (IC- CCBDA), 2016 IEEE International Conference, 37–42. Chengdu: IEEE.
  • Xhafa, F. 2016. “Advanced Knowledge Discovery Techniques from Big Data and Cloud Computing.” Enterprise Information Systems 10: 945–946. doi:10.1080/17517575.2016.1198965.
  • Yang, H., L. Peng, H. Zhian, X. Guo, S. Fong, and H. Chen. 2016. “A Decision Support System Using Combined-Classifier for High-Speed Data Stream in Smart Grid.” Enterprise Information Systems 10 (9): 947–958. doi:10.1080/17517575.2015.1086495.
  • Yang, J., and V. Honavar. 1998. “Feature Subset Selection Using a Genetic Algorithm.” In Feature Extraction, Construction and Selection, 117–136. Kluwer: Springer.
  • Yang, Y., and J. O. Pedersen. 1997. “A Comparative Study on Feature Selection in Text Categorization.” In Icml, 412–420. Vol. 97. San Francisco, CA: Morgan Kaufmann Publishers Inc.
  • Zhu, Y., and D. Shasha. 2002. “StatStream: Statistical Monitoring of Thousands of Data Streams in Real Time** Work Supported in Part by US NSF Grants IIS-9988345 and N2010: 0115586.” In VLDB’02: Proceedings of the 28th International Conference on Very Large Databases, 358–369. Hong Kong SAR: Elsevier.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.