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Detection of cardiac arrhythmias in body surface potential mapping (BSMP) measurements

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Warianty tytułu
PL
Detekcja zaburzeń rytmu serca w pomiarach potencjału elektrycznego (BSPM)
Języki publikacji
EN
Abstrakty
EN
The multitude of measurement data obtained from BSPM (Body Surface Potential Mapping) requires automatic detection and classification methods to detect disturbances. The article describes the method of classification of heart rate disorders based on the characteristics of signals from sensors. For the purposes of the research, a coefficient was created that allows the classification of cardiac arrhythmias in the BSPM measurements. In addition, BSPM signals were simulated using a system constructed for testing an innovative measuring vest with 102 measuring electrodes.
PL
Artykuł opisuje problem klasyfikacji zaburzeń rytmu serca sygnałów otrzymanych z pomiarów BSPM. W pracy skonstruowano współczynnik mierzący dynamikę sygnału I sprawdzono możliwości klasyfikacji sygnału opartej na podstawie wyliczonego współczynnika. Pomiary na baize których dokonano analizy pochodzą z symulacji wykonanych na zaprojektowanym urządzeniu symulacyjnych powstałym w celu testowania innowacyjnej kamizelki pomiarowej BSPM ze 102 elektrodami.
Rocznik
Strony
115--118
Opis fizyczny
Bibliogr. 26 poz., rys.,tab.
Twórcy
  • Lublin University of Technology, Nadbystrzycka 38, 20-618 Lublin, Poland
  • Research and Development Center, Netrix S.A., 20-704 Związkowa st. 26, Lublin, Poland
  • University of Economics and Innovation in Lublin, ul. Projektowa 4, 20-209 Lublin, Poland
  • Research and Development Center, Netrix S.A., 20-704 Związkowa st. 26, Lublin, Poland
Bibliografia
  • [1] Lux R.L., Body Surface Potential Mapping Techniques, In: Macfarlane P.W., van Oosterom A., Pahlm O., Kligfield P., Janse M., Camm J. (eds) Comprehensive Electrocardiology. Springer, London, (2010), https://doi.org/10.1007/978-1-84882- 046-3_31
  • [2] Kłosowski G., Rymarczyk T., Wójcik D., Skowron S., Cieplak T. Adamkiewicz P., The Use of Time-Frequency Moments as Inputs of LSTM Network for ECG Signal Classification, Electronics , 9, (2020), 1452, https://doi.org/10.3390/electronics9091452
  • [3] Przysucha B., Rymarczyk T., Wójcik D., Wos M., Vejar A., Improving the Dependability of the ECG Signal for Classification of Heart Diseases, 2020 50th Annual IEEE-IFIP International Conference on Dependable Systems and Networks-Supplemental Volume (DSN-S), Valencia, Spain, (2020), 63-64, doi: 10.1109/DSN-S50200.2020.00034.
  • [4] Gronewegen A. S., Lesh M., D, Roithinger F. X., Ellies W.S., Steiner P. R., Saxon L. A., Lee R. J., Schienman M., Body Surface Mapping of Counterlockwise and Clockwise typical Atrial Flutter: A Comparative Analysis Witch Endocardial Activation Sequence Mapping, Journal of the American College of Cardiology, 35, (2000), No 5, 1276-1287.
  • [5] Korneich F., Montauge T. J., Smet P., Rautaharju P. M., Kovadias M., Multigroup diagnostic classification using body surface potential maps, in Proceedings Computers in Cardiology, Jerusalem, Israel, (1989), 181-184. Doi: 10.1109/CIC.1989.130516
  • [6] Hänninen H., Takala P., Rantonen J., Mäkijärvi M., Virtanen K., Nenonen J., Katila T., Toivonen L., ST-T integral and Twave amplitude in detection of exercise-induced myocardial ischemia evaluated with body surface potential mapping, Journal of Electrocardiology, 36, (2003), No 2, 89-98.
  • [7] Hoekema R., Gurlek C., Brouwer M. A., Chaikovsky I., Verheugt F. W. A., The use of magnetocardiography and body surface potential mapping in the detection of coronary artery disease in chest pain patients with a normal electrocardiogram, Computers in Cardiology, Chicago, IL, USA, (2004), 389-392, doi: 10.1109/CIC.2004.1442954.
  • [8] Polak-Jonkisz D., Laszki-Szcząchor K., Purzyc L., Zwolińska D., Musiał K., Pilecki W., Rusiecki L., Janocha A., Kałka D., Sobieszczańska M., Usefulness of body surface potential mapping for early identification of the intraventricular conduction disorders in young patients with chronic kidney disease, Journal of Electrocardiology, 42, (2009), No 2, 165- 171.
  • [9] Korhonen P., Husa T., Konttila T., Tierala I., Mäkijärvi M., Väänänen H., Toivonen L., Complex T-wave morphology in body surface potential mapping in prediction of arrhythmic events in patients with acute myocardial infarction and cardiac dysfunction, EP Europace, 11, (2009), No 4, 514– 520, https://doi.org/10.1093/europace/eup051
  • [10] Zarychta P., Smith F.E., King S.T., Haigh A.J., Klinge A., Zheng D., Stevens S., Allen J., Okelarin A., Langley P., Murray A., Body Surface Potential Mapping for Detection of Myocardial Infarct Sites, Computers in Cardiology, 34, (2007), 181−184.
  • [11] Dalay M. J, Finaly D. D., Scott P.J., Nugent C. D., Adgey A. A. J., Haribson M. T., Pre-hospital body surface potential mapping improves early diagnosis of acute coronary artery occlusion in patients with ventricular fibrillation and cardiac arrest, Resuscitation, 84, (2013), No 1, 37-41, https://doi.org/10.1016/j.resuscitation.2012. 09.008.
  • [12] Simelius K., Stenroos M., Reinhardt L., Nenonen J., Tierala I., Mäkijärvi M., Toivonen L., Katila T., Spatiotemporal characterisation of paced cardiac activation with body surface potential mapping and self-organising maps, Physiological Measurement, 24, (2003), No 3, 805-8016, https://doi.org/10.1088/0967-3334/24/3/315.
  • [13] Rodrigo M., Climent A.M., Liberos A., Fernández-Aviles F., Atienza F., Guillem M.S., Berenfeld O., Minimal configuration of body surface potential mapping for discrimination of left versus right dominant frequencies during atrial fibrillation, Pacing Clin Electrophysiol. 40, (2017), No 8, 940-946. doi: 10.1111/pace.13133.
  • [14] Wójcik D., Kozłowski E., Woś M., Rymarczyk T., Wośko E., Machine Lerning Pathology Detection with a Body Surface Potential Mapping, UbiComp-ISWC '20 : Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers / gen. chairs: Monica Tentori, Nadir Weibel, Kristof Van Laerhoven.- New York : Association for Computing Machinery (ACM), (2020), 151-155, https://doi.org/10.1145/3410530.3414404
  • [15] Robinson M. R., Curzen N., Electrocardiographic body surface mapping: Potential tool for the detection of transient myocardial ischemia in the 21st century, Annals of Noninvasive Electrocardiology, 2009, 14, (2009), 201-210 DOI: 10.1111/j.1542-474X.2009.00284.x
  • [16] Rymarczyk T., Woś M., Bartosik M., Vejar A., Kozłowski E., Maj M., Electrical activity with ECG analysis for Body Surface Potential Mapping, Przegląd Elektrotechniczny, 96, No 10, (2020), 144-147, doi:10.15199/48.2020.10.26
  • [17] Rymarczyk T.: Characterization of the shape of unknown objects by inverse numerical methods, Przeglad Elektrotechniczny, 88(7B), 138-140,2012.
  • [18] Rymarczyk, T Using electrical impedance tomography to monitoring flood banks 16th International Symposium on Applied Electromagnetics and Mechanics (ISEM),International journal of applied electromagnetics and mechanics 45, 489- 494,2014.
  • [19] Filipowicz, SF and Rymarczyk, The Shape Reconstruction of Unknown Objects for Inverse Problems Przeglad Elektrotechniczny, 88 (3A), 55-57,2012.
  • [20] Koulountzios P., Rymarczyk T., Soleimani M., A quantitative ultrasonic travel-time tomography system for investigation of liquid compounds elaborations in industrial processes, Sensors, 19(23), 5117, 2019.
  • [21] Kłosowski G., Rymarczyk T., Kania K., Świć A., Cieplak T., Maintenance of industrial reactors based on deep learning driven ultrasound tomography, Eksploatacja i Niezawodnosc – Maintenance and Reliability; 22(1), 138–147, 2020.
  • [22] Kłosowski G., Rymarczyk T., Cieplak T., Niderla K., Skowron Ł., Quality Assessment of the Neural Algorithms on the Example of EIT-UST Hybrid Tomography, Sensors, 20(11), 3324, 2020.
  • [23] Łukiański, M., & Wajman, R. (2020). The diagnostic of twophase separation process using digital image segmentation algorithms. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 10(3), 5-8.
  • [24] Duraj, A.; Korzeniewska, E.; Krawczyk, A. Classification algorithms to identify changes in resistance. Przegląd Elektrotechniczny 2015, 1, 82–84.
  • [25] Krawczyk, A.; Korzeniewska, E. Magnetophosphenes–history and contemporary implications. Przegląd Elektrotechniczny 2018, 1, 63–66.
  • [26] Mosorov, V ; Rybak, G ; Sankowski, D, Plug Regime Flow Velocity Measurement Problem Based on Correlability Notion and Twin Plane Electrical Capacitance Tomography: Use Case, Sensors Volume: 21 Issue: 6 Article Number: 2189 DOI: 10.3390/s21062189, 2021.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a22c2a96-c9b9-450f-95e4-ea5ec66dc6a4
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