ELECTROCARDIOGRAM GENERATION SOFTWARE FOR TESTING OF PARAMETER EXTRACTION ALGORITHMS
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
Independent researcher, Lublin (Poland)
Robert KARPIŃSKI
Lublin University of Technology, Faculty of Mechanical Engineering, Department of Machine Design and Mechatronics, Nadbystrzycka 36, 20-618 Lublin (Poland)
Przemysław KRAKOWSKI
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, heartReferences
Barill, T., & SlikkStat Learning Inc. (2012). The six second ECG: A practical guide to basic and 12 lead ECG interpretation. Palm Springs, Calif.: SkillStat Learning Inc.
Google Scholar
Boulakia, M., Cazeau, S., Fernández, M. A., Gerbeau, J.-F., & Zemzemi, N. (2010). Mathematical Modeling of Electrocardiograms: A Numerical Study. Annals of Biomedical Engineering, 38(3), 1071–1097. https://doi.org/10.1007/s10439-009-9873-0
DOI: https://doi.org/10.1007/s10439-009-9873-0
Google Scholar
Bronzino, J. D. (2000). The biomedical engineering handbook. Boca Raton, Fla.: CRC Press in cooperation with IEEE Press.
Google Scholar
Burhan, A. (2011). Einthoven triangle ECG. Retrieved 19 December 2020, from Medicalopedia website: https://mk0medicalopediwjftu.kinstacdn.com/wp-content/uploads/2011/11/einthoven-triangleecg.jpg
Google Scholar
Clifford, G. D., Azuaje, F., & Mcsharry, P. (2006). ECG statistics, noise, artifacts, and missing data. Advanced Methods and Tools for ECG Data Analysis, 6, 18.
DOI: https://doi.org/10.1186/1475-925X-6-18
Google Scholar
Costa, C. M. (2016). Computational Modeling of Bioelectrical Activity of the Heart at Microscopic and Macroscopic Size Scales (Doctoral dissertation). Karl-Franzens Universit ̈at Graz, Graz. https://doi.org/10.13140/RG.2.2.26259.99365
Google Scholar
Karpiński, R., Machrowska, A., & Maciejewski, M. (2019). Application of acoustic signal processing methods in detecting differences between open and closed kinematic chain movement for the knee joint. Applied Computer Science, 15(1), 36–48. https://doi.org/10.23743/acs-2019-03
Google Scholar
Ławicki, T., & Zhirnova, O. (2015). Application of curvelet transform for denoising of CT images. In Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2015, (966226). International Society for Optics and Photonics. https://doi.org/10.1117/12.2205483
DOI: https://doi.org/10.1117/12.2205483
Google Scholar
Luthra, A. (2007). ECG made easy. New Delhi; Tunbridge Wells: Jaypee ; Anshan Ltd.
DOI: https://doi.org/10.5005/jp/books/10248
Google Scholar
Machrowska, A., Karpiński, R., Krakowski, P., & Jonak, J. (2019). Diagnostic factors for opened and closed kinematic chain of vibroarthrography signals. Applied Computer Science, 15(3), 34-44. http://doi.org/10.23743/acs-2019-19
Google Scholar
Maciejewski, M. (2019). Information technology implementations and limitations in medical research. Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, 5(1), 66–72. https://doi.org/10.5604/20830157.1148052
DOI: https://doi.org/10.5604/20830157.1148052
Google Scholar
Maciejewski, M., & Dzida, G. (2017). ECG parameter extraction and classification in noisy signals. 2017 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) (pp. 243–248). IEEE. https://doi.org/10.23919/SPA.2017.8166872
DOI: https://doi.org/10.23919/SPA.2017.8166872
Google Scholar
Maciejewski, M., Surtel, W., Wójcik, W., Masiak, J., Dzida, G., & Horoch, A. (2014). Telemedical systems for home monitoring of patients with chronic conditions in rural environment. Ann Agric Environ Med., 21(1), 167-73.
Google Scholar
Omiotek, Z. (2017). Improvement of the classification quality in detection of Hashimoto’s disease with a combined classifier approach. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, 231(8), 774–782.
DOI: https://doi.org/10.1177/0954411917702682
Google Scholar
Omiotek, Z., Dzierżak, R., & Uhlig, S. (2019). Fractal analysis of the computed tomography images of vertebrae on the thoraco-lumbar region in diagnosing osteoporotic bone damage. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, 233(12), 1269–1281.
DOI: https://doi.org/10.1177/0954411919880695
Google Scholar
Pan, J., & Tompkins, W. J. (1985). A Real-Time QRS Detection Algorithm. IEEE Transactions on Biomedical Engineering, BME-32(3), 230–236. https://doi.org/10.1109/TBME.1985.325532
DOI: https://doi.org/10.1109/TBME.1985.325532
Google Scholar
Rehman, A., Mustafa, M., & Israr, I. (2013). Survey of wearable sensors with comparative study of noise reduction ecg filters. International Journal of Computing and Network Technology, 221(1249), 1–21.
DOI: https://doi.org/10.12785/ijcnt/010105
Google Scholar
Reisner, A., Clifford, G., & Mark, R. (2006). The Physiological Basis of the Electrocardiogram.
Google Scholar
Rincón, F. J., Gutiérrez, L., Jiménez, M., Díaz, V., Khaled, N., Atienza, D., … Micheli, G. D. (2009). Implementation of an Automated ECG-based Diagnosis Algorithm for a Wireless Body Sensor Plataform. Proceedings of the International Conference on Biomedical Electronics and Devices (BIODEVICES 2009) (pp. 88–96). Porto, Springer.
Google Scholar
Surtel, W., Maciejewski, M., & Maciejewska, B. (2013). Processing of simultaneous biomedical signal data in circulatory system conditions diagnosis using mobile sensors during patient activity. 2013 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) (pp. 163–167). IEEE.
Google Scholar
Waechter, J. (2012). Introduction to ECG’s: Rhythm Analysis. Jason Waechter.
DOI: https://doi.org/10.3917/eufor.364.0005
Google Scholar
Xavax. (2016). A Wiggers diagram, showing the cardiac cycle events occuring in the left ventricle. Wikimedia Commons: Wiggers Diagram.svg. Retrieved from https://commons.wikimedia.org/w/index.php?curid=50317988
Google Scholar
Zhou, H., Hou, K.-M., & Zuo, D. (2009). Real-Time Automatic ECG Diagnosis Method Dedicated to Pervasive Cardiac Care. Wireless Sensor Network, 01(04), 276–283. https://doi.org/10.4236/wsn.2009.14034
DOI: https://doi.org/10.4236/wsn.2009.14034
Google Scholar
Authors
Marcin MACIEJEWSKIm.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
Authors
Barbara MACIEJEWSKAIndependent researcher, Lublin Poland
Authors
Robert KARPIŃSKILublin University of Technology, Faculty of Mechanical Engineering, Department of Machine Design and Mechatronics, Nadbystrzycka 36, 20-618 Lublin Poland
Authors
Przemysław KRAKOWSKIMedical University of Lublin, Chair and Department of Traumatology and Emergency Medicine, Staszica 11, 20-081 Lublin Poland
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. Einthoven triangle ECG. Retrieved 19 December 2020, from Medicalopedia website: https://mk0medicalopediwjftu.kinstacdn.com/wp-content/uploads/2011/11/einthoven-triangleecg.jpg){kind=link}
. A Wiggers diagram, showing the cardiac cycle events occuring in the left ventricle. Wikimedia Commons: Wiggers Diagram.svg. Retrieved from https://commons.wikimedia.org/w/index.php?curid=50317988){kind=link}