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.
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
Bronzino, J. D. (2000). The biomedical engineering handbook. Boca Raton, Fla.: CRC Press in cooperation with IEEE Press.
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
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
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
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
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
Luthra, A. (2007). ECG made easy. New Delhi; Tunbridge Wells: Jaypee ; Anshan Ltd.
DOI: https://doi.org/10.5005/jp/books/10248
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
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
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
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.
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
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
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
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
Reisner, A., Clifford, G., & Mark, R. (2006). The Physiological Basis of the Electrocardiogram.
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.
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.
Waechter, J. (2012). Introduction to ECG’s: Rhythm Analysis. Jason Waechter.
DOI: https://doi.org/10.3917/eufor.364.0005
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
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
