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Probabliistic Analysis Of Electrocardiogram (Ecg) Heart Signal

Amjad Gawanmeh1,3,∗, Usman Pervez2, Osman Hasan2,3

1 Department of Electrical and Computer Engineering, Khalifa University of Science and Technology, Abu Dhabi, UAE
2 School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad, Pakistan
3 Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada

* Corresponding Author: E-mail: email

Computer Systems Science and Engineering 2018, 33(1), 21-29. https://doi.org/10.32604/csse.2018.33.021

Abstract

Electrocardiography (ECG) is a heart signal wave that is recorded using medical sensors, which are normally attached to the human body by the heart. ECG waves have repetitive patterns that can be efficiently used in the diagnosis of heart problems as they carry several characteristics of heart operation. Traditionally, the analysis of ECG waves is done using informal techniques, like simulation, which is in-exhaustive and thus the analysis results may lead to ambiguities and life threatening scenarios in extreme cases. In order to overcome such problems, we propose to analyze ECG heart signals using probabilistic model checking, which is a formal methods based quantitative analysis approach. This work presents the formal probabilistic analysis of ECG signal abnormalities where the likelihood of abnormal patterns is studied and analyzed using the PRISM model checker.

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APA Style
Gawanmeh, A., Pervez, U., Hasan, O. (2018). Probabliistic analysis of electrocardiogram (ecg) heart signal. Computer Systems Science and Engineering, 33(1), 21-29. https://doi.org/10.32604/csse.2018.33.021
Vancouver Style
Gawanmeh A, Pervez U, Hasan O. Probabliistic analysis of electrocardiogram (ecg) heart signal. Comput Syst Sci Eng. 2018;33(1):21-29 https://doi.org/10.32604/csse.2018.33.021
IEEE Style
A. Gawanmeh, U. Pervez, and O. Hasan "Probabliistic Analysis Of Electrocardiogram (Ecg) Heart Signal," Comput. Syst. Sci. Eng., vol. 33, no. 1, pp. 21-29. 2018. https://doi.org/10.32604/csse.2018.33.021



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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