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Electrocardiogram generation software for testing of parameter extraction algorithms

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Fast and automated ECG diagnosis is of great benefit for treatment of cardi-ovascular 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.
Słowa kluczowe
Rocznik
Strony
37--47
Opis fizyczny
Bibliogr. 23 poz., fig.
Twórcy
  • Lublin University of Technology, Faculty of Electrical Engineering and Computer Science, Institute of Electronics and Information Technology, Nadbystrzycka 36, 20-618 Lublin, Poland
  • Lublin University of Technology, Faculty of Mechanical Engineering, Department of Machine Design and Mechatronics, Nadbystrzycka 36, 20-618 Lublin, Poland
  • Medical University of Lublin, Chair and Department of Traumatology and Emergency Medicine, Staszica 11, 20-081 Lublin, Poland
Bibliografia
  • [1] 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.
  • [2] 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
  • [3] Bronzino, J. D. (2000). The biomedical engineering handbook. Boca Raton, Fla.: CRC Press in cooperation with IEEE Press.
  • [4] Burhan, A. (2011). Einthoven triangle ECG. Retrieved 19 December 2020, from Medicalopedia website: https://mk0medicalopediwjftu.kinstacdn.com/wp-content/uploads/2011/11/einthoven-triangle-ecg.jpg
  • [5] 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.
  • [6] 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
  • [7] 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
  • [8] 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
  • [9] Luthra, A. (2007). ECG made easy. New Delhi; Tunbridge Wells: Jaypee ; Anshan Ltd.
  • [10] 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
  • [11] 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
  • [12] 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
  • [13] 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.
  • [14] 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.
  • [15] 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.
  • [16] 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
  • [17] 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.
  • [18] Reisner, A., Clifford, G., & Mark, R. (2006). The Physiological Basis of the Electrocardiogram.
  • [19] 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.
  • [20] 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.
  • [21] Waechter, J. (2012). Introduction to ECG’s: Rhythm Analysis. Jason Waechter.
  • [22] 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
  • [23] 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
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a3c776ce-7834-48df-bbe3-4f4cbcf3fea4
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