- About this Journal ·
- Aims and Scope ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
ISRN Artificial Intelligence
Volume 2013 (2013), Article ID 380239, 11 pages
Health Monitoring for Elderly: An Application Using Case-Based Reasoning and Cluster Analysis
Center for Applied Autonomous Sensor Systems, Örebro University, 701 82 Örebro, Sweden
Received 24 March 2013; Accepted 18 April 2013
Academic Editors: T.-C. Chen, G. L. Foresti, Z. Liu, and R. Rada
Copyright © 2013 Mobyen Uddin Ahmed et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- S. Youm, G. Lee, S. Park, and W. Zhu, “Development of remote healthcare system for measuring and promoting healthy lifestyle,” Expert Systems with Applications, vol. 38, no. 3, pp. 2828–2834, 2011.
- J. Gong, S. Lu, R. Wang, and L. Cui, “PDhms: pulse diagnosis via wearable healthcare sensor network,” in Proceedings of the IEEE International Conference on Communications (ICC '11), pp. 1–5, 2011.
- M. U. Ahmed, A. M. Islam, and A. Loutfi, “A case-based patient identification system using pulse oximeter and a personalized health profile,” in Proceedings of the Health Sciences at 20th International Conference on Case-Based Reasoning (ICCBR '12), B. Isabelle, M. Stefania, and M. Cindy, Eds., Springer, Lyon, France, September 2012.
- Z. Jin, J. Oresko, S. Huang, and A. C. Cheng, “HeartToGo: a personalized medicine technology for cardiovascular disease prevention and detection,” in Proceedings of the IEEE/NIH Life Science Systems and Applications Workshop (LiSSA '09), pp. 80–83, April 2009.
- N. Krupa, M. A. MA, E. Zahedi, S. Ahmed, and F. M. Hassan, “Antepartum fetal heart rate feature extraction and classification using empirical mode decomposition and support vector machine,” BioMedical Engineering Online, vol. 10, article 6, 2011.
- A. Aamodt and E. Plaza, “Case-based reasoning: foundational issues, methodological variations, and system approaches,” AI Communications, vol. 7, no. 1, pp. 39–59, 1994.
- S. Begum, M. U. Ahmed, P. Funk, N. Xiong, and B. Von Schéele, “A case-based decision support system for individual stress diagnosis using fuzzy similarity matching,” Computational Intelligence, vol. 25, no. 3, pp. 180–195, 2009.
- J. M. Corchado, J. Bajo, and A. Abraham, “GerAmi: improving healthcare delivery in geriatric residences,” IEEE Intelligent Systems, vol. 23, no. 2, pp. 19–25, 2008.
- M. U. Ahmed, S. Begum, P. Funk, N. Xiong, and B. von Schéele, “A three phase computer assisted biofeedback training system using case-based reasoning,” in Proceedings of the 9th European Conference on Case-based Reasoning, Trier, Germany, 2008.
- S. Montani, L. Portinale, G. Leonardi, R. Bellazzi, and R. Bellazzi, “Case-based retrieval to support the treatment of end stage renal failure patients,” Artificial Intelligence in Medicine, vol. 37, no. 1, pp. 31–42, 2006.
- I. Bichindaritz, “Prototypical case mining from biomedical literature for bootstrapping a case base,” Applied Intelligence, vol. 28, no. 3, pp. 222–237, 2008.
- M. U. Ahmed, S. Begum, P. Funk, N. Xiong, and B. von Schéele, “Case-based reasoning for diagnosis of stress using enhanced cosine and fuzzy similarity,” International Journal of Transactions on Case-Based Reasoning on Multimedia Data, vol. 1, no. 1, pp. 3–19, 2008.
- M. U. Ahmed, S. Begum, P. Funk, N. Xiong, and B. von Schéele, “A multi-module case based biofeedback system for stress treatment,” International Journal of Artificial Intelligence in Medicine, vol. 51, no. 2, pp. 107–115, 2010.
- M. U. Ahmed and P. Funk, “A computer aided system for post-operative pain treatment combining knowledge discovery and case-based reasoning,” in Case-Based Reasoning Research and Development, vol. 7466 of Lecture Notes in Computer Science, pp. 3–16, 2012.
- S. Begum, M. U. Ahmed, P. Funk, N. Xiong, and M. Folke, “Case-based reasoning systems in the health sciences: a survey of recent trends and developments,” IEEE Transactions on Systems, Man and Cybernetics C, vol. 41, no. 4, pp. 421–434, 2011.
- S. Begum, M. U. Ahmed, and P. Funk, “Case-based systems in health sciences: a case study in the field of stress management,” WSEAS Transactions on Systems, vol. 8, no. 3, pp. 344–354, 2009.
- I. Watson, Applying Case-Based Reasoning: Techniques For Enterprise Systems, Morgan Kaufmann Publishers, San Fransisco, Calif, USA, 1997.
- M. Stacey and C. McGregor, “Temporal abstraction in intelligent clinical data analysis: a survey,” Artificial Intelligence in Medicine, vol. 39, no. 1, pp. 1–24, 2007.
- I. Yoo, P. Alafaireet, M. Marinov et al., “Data mining in healthcare and biomedicine: a survey of the literature,” Journal of Medical Systems, vol. 36, no. 4, pp. 2431–2448, 2011.
- S. Jeong, C. H. Youn, E. B. Shim, M. Kim, Y. M. Cho, and L. Peng, “An integrated healthcare system for personalized chronic disease care in home-hospital environments,” IEEE Transactions on Information Technology in Biomedicine, vol. 16, no. 4, pp. 572–585, 2012.
- G. K. Pang, “Health monitoring of elderly in independent and assisted living,” in Proceedings of the International Conference on Biomedical Engineering (ICoBE '12), pp. 553–556, February 2012.
- R. M. Rahman and F. R. M. Hasan, “Using and comparing different decision tree classification techniques for mining ICDDR,B Hospital Surveillance data,” Expert Systems with Applications, vol. 38, no. 9, pp. 11421–11436, 2011.
- C. M. Chen, “Web-based remote human pulse monitoring system with intelligent data analysis for home health care,” Expert Systems with Applications, vol. 38, no. 3, pp. 2011–2019, 2011.
- G. Koshmak, An Android Based Monitoring and Alarm System for Patients with Chronic Obtrusive Disease [M.S. thesis], Department of Technology at Örebro University.
- K. Dingli, T. Assimakopoulos, P. K. Wraith, I. Fietze, C. Witt, and N. J. Douglas, “Spectral oscillations of RR intervals in sleep apnoea/hypopnoea syndrome patients,” European Respiratory Journal, vol. 22, no. 6, pp. 943–950, 2003.
- C. Zamarrón, F. Gude, J. Barcala, J. R. Rodriguez, and P. V. Romero, “Utility of oxygen saturation and heart rate spectral analysis obtained from pulse oximetric recordings in the diagnosis of sleep apnea syndrome,” Chest, vol. 123, no. 5, pp. 1567–1576, 2003.
- D. Cvetkovic, E. D. Übeyli, and I. Cosic, “Wavelet transform feature extraction from human PPG, ECG, and EEG signal responses to ELF PEMF exposures: a pilot study,” Digital Signal Processing, vol. 18, no. 5, pp. 861–874, 2008.
- R. Xu and D. Wunsch, “Survey of clustering algorithms,” IEEE Transactions on Neural Networks, vol. 16, no. 3, pp. 645–678, 2005.
- T. W. Liao, “Clustering of time series data: survey,” Pattern Recognition, vol. 38, pp. 1857–1874, 2005.
- C. A. Ratanamahatana, J. Lin, D. Gunopulos, E. J. Keogh, M. Vlachos, and G. Das, “Mining time series data,” in Data Mining and Knowledge Discovery Handbook, pp. 1049–1077, 2010.
- L. Vendramin, R. J. G. B. Campello, and E. R. Hruschka, “On the comparison of relative clustering validity criteria,” in Proceedings of the 9th SIAM International Conference on Data Mining (SDM '09), pp. 729–740, May 2009.
- Y. Liu, Z. Li, H. Xiong, X. Gao, and J. Wu, “Understanding of internal clustering validation measures,” in Proceedings of the 10th IEEE International Conference on Data Mining (ICDM '10), pp. 911–916, December 2010.
- Q. Zhao, M. Xu, and P. Fränti, “Sum-of-squares based cluster validity index and significance analysis,” in Adaptive and Natural Computing Algorithms, vol. 5495 of Lecture Notes in Computer Science, pp. 313–322, 2009.
- K. R. Žalik and B. Žalik, “Validity index for clusters of different sizes and densities,” Pattern Recognition Letters, vol. 32, no. 2, pp. 221–234, 2011.
- M. U. Ahmed and A. Loutfi, “Physical activity classification for elderly based on pulse rate,” in Proceedings of the 10th International Conference on Wearable Micro and Nano Technologies for Personalized Health Tallinn, June 2013.