A Cloud-based Learnable Agent-oriented Approach to Control and improve Pacemaker Operation
Subject Areas : electrical and computer engineering
H.
Banki
1
نگار
مجمع
2
A.
Monadjemi
3
Keywords: Verification pacemaker colored fuzzy Petri-nets software agent reinforcement learning,
Abstract :
This paper aims to present a cloud-based learning agent-oriented approach for verification of the pacemaker behavior by monitoring and heart rate adjustment of an arrhythmic patient. In case of the pacemaker failure or inappropriate heart rate generation, the patient is put at risk. Using the proposed approach, one can directs the pacemaker rate to correct one when it is incorrect. Using a learnable software agent, the proposed approach is able to learn un-predefined situations and operates accordingly. The proposed approach is cloud based meaning that it sends a message through cloud in case of a critical situation. After determining the patient heart rate by pacemaker, the proposed method verifies this rate against the predefined physician suggestion and automatically corrects it based on a reinforcement learning mechanism if there is some conflict. The proposed method was implemented and installed on a tablet as a patient mobile device for monitoring the pacemaker implanted in the patient chest. The contrast between results of our approach and expected results existing in the dataset showed our approach improved the pacemaker accuracy until 13.24%. The use of the software agent with reinforcement learning is able to play a significant role in improving medical devices in case of critical situations.
[1] N. K. Singh, M. Lawford, T. S. E. Maibaum and A. Wassynd, "Formalizing the cardiac pacemaker resynchronization therapy," in Proc. Int. Conf. on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management, pp. 374-386, Oct. 2014.
[2] J. Gao, X. Bai, and W. Tsai, "Cloud testing-issues, challenges, needs and practice," An Int. J. Softw. Eng., vol. 1, no. 1, pp. 9-23, Sept. 2015.
[3] T. M. King and A. S. Ganti, "Migrating autonomic self-testing to the cloud," in Proc.3rd Int. Conf. Softw. Testing, Verif. Valid. Work., pp. 438-443, Apr. 2010.
[4] K. Divi and H. Liu, "Modeling of WBAN and cloud integration for secure and reliable healthcare," in Proc. 8th Int. Conf. Body Area Networks, pp. 8-11, Boston, Massachusetts, USA, 30 Sept. 30-02 Oct. 2013.
[5] J. Wan, C. Zou, S. Ullah, C. F. Lai, M. Zhou, and X. Wang, "Cloud-enabled wireless body area networks for pervasive healthcare," IEEE Network, vol. 27, no. 5, pp. 56-61, Sept.-Oct. 2013.
[6] R. P. Padhy, M. R. Patra, S. C. Satapathy, and O. Corporation, "Design and implementation of a cloud based rural healthcare information system model," Univeral J. Appl. Comput. Sci. Technol., vol. 2, no. 1, pp. 149-157, 2012.
[7] Y. S. Jeong, H. W. Kim, and J. H. Park, "Visual scheme monitoring of sensors for fault tolerance on wireless body area networks with cloud service infrastructure," Int. J. Distrib. Sens. Networks, vol. 20, no. 4, pp. 154-180, Apr. 2014.
[8] K. Karnavel, V. Divya, and P. Karthika, "Agent based software testing framework (ABSTF) for application maintenance," arXiv:1307.3398., 4 pp., 2013.
[9] D. Ponnurangam and U. Anbarasan, "Evolution of agent-oriented distributed model for software testing," Int. Arab J. Inf. Technol., vol. 3, no. 2, pp. 111-117, 2006.
[10] S. Yu and J. Ai, "Software test data generation based on multi-agent," Int. J. Softw. Eng. Its Appl., vol. 4, no. 1, pp. 67-74, Dec. 2010.
[11] S. Greenwood, "A multi-agent software engineering environment for testing web-based applications," in Proc. 27th Annual Int. Computer Software and Applications Conf., pp. 210-215, 3-6 Nov. 2003.
[12] Y. Qi, D. Kung, and E. Wong, "An agent-based testing approach for web applications," in Proc. 29th Annu. Int. Comput. Softw. Appl. Conf., vol. 2, pp. 45-50, 26-28 Jul. 2005.
[13] S. Enyedi, L. Miclea, and I. Stefan, "Agent-based testing and repair of heterogeneous distributed systems," in Proc. Int. Conf. Autom. Qual. Testing, Robot., vol. 1, pp. 104-108, 22-25 May. 2008.
[14] B. Athamena and Z. Houhamdi, "A petri net based multi-agent system behavioral testing," Mod. Appl. Sci., vol. 6, no. 3, pp. 46-57, 22-25 May 2012.
[15] Z. Zhang, J. Thangarajah, and L. Padgham, "Automated unit testing for agent systems," in Proc. Second Int. Conf. Eval. Nov. Approaches to Softw. Eng., pp. 10-18, Budapest, Hungary, 11-12 May 2007.
[16] R. Coelho, U. Kulesza, A. von Staa, and C. Lucena, "Unit testing in multi-agent systems using mock agents and aspects," in Proc. Int. Work. Softw. Eng. Large-Scale Multi-Agent Syst., pp. 83-90, Shanghai, China, 22-23 May, 2006.
[17] L. Padgham, Z. Zhang, J. Thangarajah, and T. Miller, "Model-based test oracle generation for automated unit testing of agent systems," IEEE Trans. on Software Engineering, vol. 39, no. 9, pp. 1230-1244, Sept. 2013.
[18] N. Sivakumar, "Agent oriented software testing-role oriented approach," Int. J. Adv. Comput. Sci. Appl., vol. 3, no. 12, pp. 156-163, Dec. 2012.
[19] T. Drew and M. Gini, "Implantable medical devices as agents and part of multiagent systems," in Proc. 5th Int. Jt. Conf. Auton. Agents Multiagent Syst., AAMAS'06, pp. 1534-1541, Hakodate, Japan, 8-12 May 2006.
[20] C. J. Su, "Mobile multi-agent based, distributed information platform (MADIP) for wide-area e-health monitoring," Comput. Ind., vol. 59, no. 1, pp. 55-68, Jan. 2008.
[21] N. Majma, S. M. Babamir, and A. Monadjemi, "Runtime verification of pacemaker functionality using hierarchical fuzzy colored petri-nets," J. Med. Syst., vol. 41, no. 2, pp. 1-21, Feb. 2017.
[22] R. Sutton and A. Barto, Reinforcement Learning: An Introduction, 2012.
[23] M. Negnevitsky, Artificial Intelligence, p. 407, 2005.
[24] E. Braunwald and R. O. Bonow, Braunwald's Heart Disease: A Textbook of Cardiovascular, Elsevier Saunders, pp. 18-44, 2012.
[25] M. Lichman, "{UCI} Machine Learning Repository," [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science, 2013.