ELECTROCARDIOGRAM GENERATION SOFTWARE FOR TESTING OF PARAMETER EXTRACTION ALGORITHMS

Marcin  MACIEJEWSKI; Barbara  MACIEJEWSKA; Robert  KARPIŃSKI; Przemysław  KRAKOWSKI

doi:10.23743/acs-2020-27

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Published: Dec 30, 2020

DOI: https://doi.org/10.23743/acs-2020-27

Issue Vol. 16 No. 4 (2020)

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ELECTROCARDIOGRAM GENERATION SOFTWARE FOR TESTING OF PARAMETER EXTRACTION ALGORITHMS
Marcin MACIEJEWSKI, Barbara MACIEJEWSKA, Robert KARPIŃSKI, Przemysław KRAKOWSKI

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DOI

https://doi.org/10.23743/acs-2020-27

Authors

Marcin MACIEJEWSKI

m.maciejewski@pollub.pl

* Lublin University of Technology, Faculty of Electrical Engineering and Computer Science, Institute of Electronics and Information Technology, Nadbystrzycka 36, 20-618 Lublin, Poland

Barbara MACIEJEWSKA

barbarasmaciejewska@gmail.com

Independent researcher, Lublin, Poland

Robert KARPIŃSKI

r.karpinski@pollub.pl

Lublin University of Technology, Faculty of Mechanical Engineering, Department of Machine Design and Mechatronics, Nadbystrzycka 36, 20-618 Lublin, Poland

Przemysław KRAKOWSKI

przemyslawkrakowski@umlub.pl

Medical University of Lublin, Chair and Department of Traumatology and Emergency Medicine, Staszica 11, 20-081 Lublin, Poland

Abstract

Fast and automated ECG diagnosis is of great benefit for treatment of cardiovascular and other conditions. The algorithms used to extract parameters need to be precise, robust and efficient. Appropriate training and testing methods for such algorithms need to be implemented for optimal results. This paper presents a software solution for computer ECG generation and a simplified concept of testing process. All the parameters of the resulting generated signal can be tweaked and set properly. Such software can also be beneficial for training and educational use.

Keywords:

ECG, software, algorithm testing, heart

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MACIEJEWSKI, M. ., MACIEJEWSKA, B. ., KARPIŃSKI, R. ., & KRAKOWSKI, P. . (2020). ELECTROCARDIOGRAM GENERATION SOFTWARE FOR TESTING OF PARAMETER EXTRACTION ALGORITHMS. Applied Computer Science, 16(4), 37–47. https://doi.org/10.23743/acs-2020-27

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