About the Journal
About the Journal
Editorial Board
Scientific Council
Reviewers
Ethical Code
Special Issue
Terms of the journal
GDRP - information on the processing of personal data
Indexing
Licenses & Access
Archive
For Authors
Publishing Policy
Technical instruction for the authors
Agreement CC BY-SA
Copyright statement
Review procedure
The procedure of reviewing
Reviewer's form
List of reviewers
Contact
About the Journal
About the Journal
Editorial Board
Scientific Council
Reviewers
Ethical Code
Special Issue
Terms of the journal
GDRP - information on the processing of personal data
Indexing
Licenses & Access
REVIEW PAPER
Measurement channels’ information relationships in body surface potential mapping
1
Lublin University of Technology
2
WSEI University
3
Netrix S.A.
Submission date: 2024-06-21
Acceptance date: 2024-07-16
Publication date: 2024-08-20
JoMS 2024;57(Numer specjalny 3):855-877
KEYWORDS
BSPM measurementsPrincipal component analysisheart disease classificationvest for BSPM measurementareas of BSPM heart activity
TOPICS
ABSTRACT
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.
REFERENCES (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.
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.
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.
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.
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.
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....
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
| eISSN: | 2391-789X |
| ISSN: | 1734-2031 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
