PL EN
 
Numer specjalny 3/2024 vol. 57
Statystyki
CC BY-SA 3.0
Pobierz cytowanie
PRACA POGLĄDOWA
Measurement channels’ information relationships in body surface potential mapping
Bartosz Przysucha 1
,
 
Marcin Kowalski 2
,
 
Michał Oleszek 3
,
 
Mariusz Sutryk 2
 
 
 
Więcej
Ukryj
1
Lublin University of Technology
 
2
WSEI University
 
3
Netrix S.A.
 
 
Data nadesłania: 21-06-2024
 
 
Data akceptacji: 16-07-2024
 
 
Data publikacji: 20-08-2024
 
 
JoMS 2024;57(Numer specjalny 3):855-877
DOI: https://doi.org/10.13166/jms/191276
 
 
Artykuł (PDF)
Referencje (33)
 
SŁOWA KLUCZOWE
BSPM measurements
Principal component analysis
heart disease classification
vest for BSPM measurement
areas of BSPM heart activity
 
DZIEDZINY
_Inne
 
STRESZCZENIE
Body Surface Potential Mappings (BSPM) measurements allow heart rate monitoring. Based on measurements obtained from BSPM, defects and heart rhythm disorders can be detected. Due to the multitude of data obtained from BSPM measurements, the crucial aspect in the potential test signals analysis on the human body is the automation of the disturbance detection process. This automation process involves the use of advanced algorithms and machine learning techniques to analyze the recorded data and identify patterns indicative of heart rhythm disorders. In the article, the information dependencies in the measurement channels were analyzed and presented. PCA analysis was used to determine informational relationships between the channels. The study was carried out on the data from simulations made for the BSPM measuring vest on a simulation system created for this purpose and based on the ECG signal generator. As a result of the conducted research indicated informational relationships between the channels for signals with disturbances: Atrial fibrillation, Brachycardia, Normal signal, PVC, Tachycardia, Ventricular Fibrillation, and Ventricular Tachycardia.
 
REFERENCJE (33)
1.
Banasiak, R., Wajman, R., Jaworski, T., Fiderek, P., Fidos, H., Nowakowski, J., Sankowski, D. (2014). Study on two-phase flow regime visualisation and identification using 3D electrical capacitance tomography and fuzzy-logic classification. Int. J. Multiph. Flow, 58, s. 1–14.
Google Scholar
 
 
2.
Bonizzi, P. Guillem, M., Castells, F., Climent A., Zarzoso V. (2009). Significance of mixing matrix structure on principal component-based analysis of atrial fibrillation body surface potential maps, Computers in Cardiology, 41-44.
Google Scholar
 
 
3.
Castells F., Laguna P., Sornmo L., BollmannA., Roig J.G. (2007). Principal Component Analysis in ECG Signal Processing, Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing, 21, 074580, doi:10.1155/2007/74580.
CrossRef
 
Google Scholar
 
 
4.
Daly, M.J., Finlay, D.D., Scott, P.J., Nugent, C.D., Adgey, A. J., Harbinson, M.T. (2013). Pre-Hospital Body Surface Potential Mapping Improves Early Diagnosis of Acute Coronary Artery Occlusion in Patients with Ventricular Fibrillation and Cardiac Arrest. Resuscitation, 84, 37–41, doi:10.1016/j.resuscitation.2012.09.008.
CrossRef
 
Google Scholar
 
 
5.
Donnelly, M. P., Nugent, C. D., Finlay, D. D., Rooney, N. F., Black, N. D. (2006). Diagnosing Old MI by Searching for a Linear Boundary in the Space of Principal Components, IEEE Transactions on Information Technology in Biomedicine, 10(3), 476-483, doi: 10.1109/TITB.2006.876033.
CrossRef
 
Google Scholar
 
 
6.
Dusek, J., Mikulka, J. (2021). Measurement-Based Domain Parameter Optimization in Electrical Impedance Tomography Imaging, Sensors, 21(7), 2507; https://doi.org/10.3390/s21072....
CrossRef
 
Google Scholar
 
 
7.
Hänninen, H., Takala, P., Rantonen, J., Mäkijärvi, M., Virtanen, K., Nenonen, J., Katila, T., Toivonen, L. (2003). ST-T Integral and T-Wave Amplitude in Detection of Exercise-Induced Myocardial Ischemia Evaluated with Body Surface Potential Mapping. J Electrocardiol, 36, 89–98, doi:10.1054/jelc.2003.50013.
CrossRef
 
Google Scholar
 
 
8.
Hoekema, R., Gurlek, C., Brouwer, M.A., Chaikovsky, I., Verheugt, F.W.A. (2004). The Use of Magnetocardiography and Body Surface Potential Mapping in the Detection of Coronary Artery Disease in Chest Pain Patients with a Normal Electrocardiogram. In Proceedings of the Computers in Cardiology, 2004; 389–392.
Google Scholar
 
 
9.
Jolliffe, I.T. (2002). Principal Components in Regression Analysis. In Principal Component Analysis; Ed.; Springer Series in Statistics; Springer: New York, NY, 167–198. ISBN 978-0-387-22440-4.
Google Scholar
 
 
10.
Kłosowski, G., Rymarczyk, T., Wójcik, D., Skowron, S., Cieplak, T., Adamkiewicz, P. (2020). The Use of Time-Frequency Moments as Inputs of LSTM Network for ECG Signal Classification. Electronics 2020, 9, s. 1452, doi:10.3390/electronics9091452.
CrossRef
 
Google Scholar
 
 
11.
Korhonen, P., Husa, T., Konttila, T., Tierala, I., Mäkijärvi, M., Väänänen, H., Toivonen, L. (2009). 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, 514–520, doi:10.1093/europace/eup051.
CrossRef
 
Google Scholar
 
 
12.
Korzeniewska, E., Krawczyk, A., Stando, J. (2021). Torsion field – an example of pseudo-scientific concept in physics, Przeglad Elektrotechniczny, Volume97, Issue1, Page196-199, DOI10.15199/48.2021.01.41.
Google Scholar
 
 
13.
Korzeniewska, E., Szczesny, A., Lipinski, P., Drozdz, T., Kielbasa, P., Miernik, A. (2021). Prototype of a Textronic Sensor Created with a Physical Vacuum Deposition Process for Staphylococcus aureus Detection, SENSORS Volume: 21 Issue: 1 Article Number: 183 DOI: 10.3390/s21010183.
CrossRef
 
Google Scholar
 
 
14.
Koulountzios, P., Rymarczyk, T., Soleimani, M. (2019). A quantitative ultrasonic travel-time tomography system for investigation of liquid compounds elaborations in industrial processes, Sensors, vol. 19, no. 23, 5117.
Google Scholar
 
 
15.
Kryszyn, J., Wanta, D., Smolik, W. T. (2019). Evaluation of the electrical capacitance tomography system for measurement using 3d sensor. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska,, 9(4), 52-59.
Google Scholar
 
 
16.
Lux, R.L. (2010). Body Surface Potential Mapping Techniques. In Comprehensive Electrocardiology; Macfarlane, P.W., van Oosterom, A., Pahlm, O., Kligfield, P., Janse, M., Camm, J., Eds.; Springer: London, 1361–1374. ISBN 978-1-84882-046-3.
Google Scholar
 
 
17.
Google Scholar
 
 
18.
Polak-Jonkisz, D., Laszki-Szczachor, K., Purzyc, L., Zwolińska, D., Musiał, K., Pilecki, W., Rusiecki, L., Janocha, A., Kałka, D., Sobieszczańska, M. (2009). Usefulness of Body Surface Potential Mapping for Early Identification of the Intraventricular Conduction Disorders in Young Patients with Chronic Kidney Disease. J Electrocardiol, 42, 165–171, doi:10.1016/j.jelectrocard.2008.09.007.
CrossRef
 
Google Scholar
 
 
19.
Potse M., Puyo S., Bear L., Hocini M., Ha¨ıssaguerre M., Dubois R. (2017). Non-Invasive Assessment of Spatiotemporal Organization of Ventricular Fibrillation Through Principal Component Analysis, Computing in Cardiology, 44, doi:10.22489/CinC.2017.101-051.
CrossRef
 
Google Scholar
 
 
20.
Przysucha, B., Rymarczyk, T., Wójcik, D., Wos, M., Vejar, A. (2020). Improving the Dependability of the ECG Signal for Classification of Heart Diseases.; IEEE Computer Society, 63–64.
Google Scholar
 
 
21.
Randazzo, V., Ferretti, J., Pasero, E. (2021). Anytime ECG Monitoring through the Use of a Low-Cost, User-Friendly, Wearable Device. Sensors, 21, 6036, doi:10.3390/s21186036.
CrossRef
 
Google Scholar
 
 
22.
Robinson, M.R., Curzen, N. (2009). Electrocardiographic Body Surface Mapping: Potential Tool for the Detection of Transient Myocardial Ischemia in the 21st Century? Ann Noninvasive Electrocardiol , 14, 201–210, doi:10.1111/j.1542-474X.2009.00284.x.
CrossRef
 
Google Scholar
 
 
23.
Rodrigo, M., Climent, A., Liberos, A., Fernández-Aviles, F., Atienza, F., Guillem, M., Berenfeld, O. (2017). Minimal Configuration of Body Surface Potential Mapping for Discrimination of Left versus Right Dominant Frequencies during Atrial Fibrillation. Pacing and Clinical Electrophysiology , 40, 940–946, doi:10.1111/pace.13133.
CrossRef
 
Google Scholar
 
 
24.
Rymarczyk, T., Kłosowski, G., Hoła A., Sikora, J., Wołowiec, T., Tchórzewski, P., Skowron, S. (2021). Comparison of Machine Learning Methods in Electrical Tomography for Detecting Moisture in Building Walls, Energies, 14(10), 2777.
Google Scholar
 
 
25.
Rymarczyk, T., Nita, P., Vejar, A., Woś, M., Stefaniak, B., Adamkiewicz, P. (2019). Wearable Mobile Measuring Device Based on Electrical Tomography. Electrotechnical Review, 1, 213–216, doi:10.15199/48.2019.04.39.
CrossRef
 
Google Scholar
 
 
26.
Rymarczyk, T., Stanikowski, A., Nita, P. (2019). Wearable sensor array for biopotential measurements. Applications of Electromagnetics in Modern Engineering and Medicine (PTZE),184–187.
Google Scholar
 
 
27.
Rymarczyk, T., Woś, M., Bartosik, M., Vejar, A., Kozłowski, E., Maj, M. (2020). Electrical Activity with ECG Analysis for Body Surface Potential Mapping. Electrotechnical Review R. 96, nr 10, s. 144–147, doi:10.15199/48.2020.10.26.
CrossRef
 
Google Scholar
 
 
28.
Sadrawi, M., Lin, C.-H., Lin, Y.-T., Hsieh, Y., Kuo, C.-C., Chien, J.C., Haraikawa, K., Abbod, M.F., Shieh, J.-S. (2017). Arrhythmia Evaluation in Wearable ECG Devices. Sensors (Basel), 17, 2445, doi:10.3390/s17112445.
CrossRef
 
Google Scholar
 
 
29.
Simelius, K., Stenroos, M., Reinhardt, L., Nenonen, J., Tierala, I. Mäkijärvi, M., Toivonen, L., Katila, T. (2003). Spatiotemporal Characterization of Paced Cardiac Activation with Body Surface Potential Mapping and Self-Organizing Maps. Physiol. Meas., 24, 805–816, doi:10.1088/0967-3334/24/3/315.
CrossRef
 
Google Scholar
 
 
30.
SippensGroenewegen, A., Lesh, M.D., Roithinger, F.X., Ellis, W.S., Steiner, P.R., Saxon, L.A., Lee, R.J., Scheinman, M.M. (2000). Body Surface Mapping of Counterclockwise and Clockwise Typical Atrial Flutter: A Comparative Analysis with Endocardial Activation Sequence Mapping. J Am Coll Cardiol, 35, 1276–1287, doi:10.1016/s0735-1097(00)00549-0.
CrossRef
 
Google Scholar
 
 
31.
Zarychta, P., Smith, F.E., King, S.T., Haigh, A.J., Klinge, A., Zheng, D., Stevens, S., Allen, J., Okelarin, A., Langley, P., et al. (2007). Body Surface Potential Mapping for Detection of Myocardial Infarct Sites. In Proceedings of the 2007 Computers in Cardiology; 181–184.
Google Scholar
 
 
32.
Weber, F. M., Keller, D. U. J., Bauer, S., Seemann, G., Lorenz, C., Dössel, O. (2011). Predicting Tissue Conductivity Influences on Body Surface Potentials—An Efficient Approach Based on Principal Component Analysis, IEEE Transactions on Biomedical Engineering, 58(2), 265–273. doi:10.1109/tbme.2010.2090151.
CrossRef
 
Google Scholar
 
 
33.
Wójcik, D., Kozłowski, E., Woś, M., Rymarczyk, T., Wośko, E. (2020). Machine Learning Pathology Detection with a Body Surface Potential Mapping. In Proceedings of the 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; Association for Computing Machinery: New York, NY, USA, September 10, 151–155.
Google Scholar
 
 
Udostępnij
Wyślij mailem
Indeksy
 
Indeks słów kluczowych
Indeks dziedzin
Indeks autorów
eISSN:2391-789X
ISSN:1734-2031
Journals System - logo
© 2006-2024 Journal hosting platform by Bentus
Scroll to top