Implementation of electrical tomography in a medical measurement system for innovative imaging and area monitoring

LETS (Lung Electrical Tomography System) is an innovative medical system enabling comprehensive heart and lung function monitoring. It utilizes advanced techniques such as electrical impedance tomography (EIT), body surface potential mapping (BSPM), and electrical capacitance tomography (ECT) to provide accurate information about physiological parameters. The LETS system allows one-time examinations and continuous monitoring 24 hours a day. Remotely measuring and monitoring parameters such as the electrical activity of the heart muscle, blood flow, blood pressure, pulse, and lung aeration enables rapid identification and intervention in case of abnormalities. The system also allows for the detection of cardiac arrhythmias and other heart disorders, as well as the assessment of lung respiratory capacity and optimization of ventilation strategies. However, it's important to note that the LETS system has some limitations. For example, it may not be suitable for patients with certain pacemakers or other implanted devices. Overall, LETS represents a breakthrough in medical imaging, opening up new possibilities in diagnosing and monitoring heart and lung diseases. Its integration may contribute to improving patient care and increasing the effectiveness of medical interventions.

REFERENCJE (14)

Abdou, M. A. (2022). ‘Literature Review: Efficient Deep Neural Networks Techniques for Medical Image Analysis’. Neural Computing and Applications 34, no. 8 (1 April 2022): 5791–5812. https://doi.org/10.1007/s00521....

CrossRef

Google Scholar

Goncalves, J. P. N., De Waal, G. M., Page, M. J., Venter, C., Roberts, T., Holst, F., Pretorius, E., Bester J., (2021). ‘The Value of Detecting Pathological Changes During Clot Formation in Early Disease Treatment-Naïve Breast Cancer Patients’. Microscopy and Microanalysis 27, no. 2 (April 2021): 425–36. https://doi.org/10.1017/S14319....

CrossRef

Google Scholar

Goncalves, J. P. N., De Waal, G., Page, M.J., Venter, C., Roberts, T., Holst, F., Pretorius, E., Bester, J., (2021). ‘The Value of Detecting Pathological Changes During Clot Formation in Early Disease Treatment-Naïve Breast Cancer Patients’. Microscopy and Microanalysis 27, no. 2 (1 April 2021): 425–36. https://doi.org/10.1017/S14319....

CrossRef

Google Scholar

He, B.,(2004). Modeling and Imaging of Bioelectrical Activity Principles and Applications.

Google Scholar

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 9. https://doi.org/10.3390/electr....

CrossRef

Google Scholar

Heines, S. J. H., Becher, T. B., Van der Horst, I. C. C., Bergmans, D. J. J, (2023). ‘Clinical Applicability of Electrical Impedance Tomography in Patient-Tailored Ventilation: A Narrative Review’. Tomography 9, no. 5 (October 2023): 1903–32. https://doi.org/10.3390/tomogr....

CrossRef

Google Scholar

Macfarlane, P. W., (2010). Comprehensive Electrocardiology, t. 1. London: Springer London.

Google Scholar

Papik, K., Molnar, B., Schaefer, R., Dombovari, Z., Tulassay, Z., Feher, J., (1998). ‘Application of Neural Networks in Medicine — a Review’, n.d.

Google Scholar

Rymarczyk, T., Kłosowski, G., Niderla, K., (2024). Advantages of Convolutional Neural Network Compared to Multilayer Perceptron in Electrical Tomography. Przeglad Elektrotechniczny, EBSCOhost’. Accessed.

Google Scholar

10.

Rymarczyk, T., Kłosowski, G., Kozłowski, E., Tchórzewski, P. (2019). Comparison of Selected Machine Learning Algorithms for Industrial Electrical Tomography. Sensors 19, no. 7 (January 2019): 1521. https://doi.org/10.3390/s19071....

CrossRef

Google Scholar

11.

Shimazaki, A., Ueda, D., Choppin, A., Yamamoto, A., Honjo, T., Shimahara, Y., Miki, Y., (2022). ‘Deep Learning-Based Algorithm for Lung Cancer Detection on Chest Radiographs Using the Segmentation Method’. Scientific Reports 12, no. 1 (14 January 2022): 727. https://doi.org/10.1038/s41598....

CrossRef

Google Scholar

12.

Wójcik, D., Rymarczyk, T., Oleszek, M., Maciura, Ł., Bednarczuk, P., (2021). ‘Diagnosing Cardiovascular Diseases with Machine Learning on Body Surface Potential Mapping Data’. In Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems, 379–81. SenSys ’21. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/348573....

CrossRef

Google Scholar

13.

Wócik, D., Stefaniak, B., Woś, M., Kiczek, B., Rymarczyk, T., (2022). Image Reconstruction for Lung Monitoring in Wearable Electrical Impedance Tomography. | Przeglad Elektrotechniczny |EBSCOhost’. Accessed 27 April 2024. https://openurl.ebsco.com/EPDB....

Google Scholar

14.

Vargas-Luna, F. M., Delgadillo-Cano,M. I., Riu-Costa, J. P., Kashina, S., Balleza-Ordaz J. M., (2024). ‘Assessing Pulmonary Function Parameters Non-Invasively by Electrical Bioimpedance Tomography’. Journal of Medical and Biological Engineering 44, no. 1 (1 February 2024): 67–78. https://doi.org/10.1007/s40846....

Archiwum

Udostępnij

Wyślij mailem

ARTYKUŁ POWIĄZANY

Use of electrical impedance tomography for lung volume reconstruction

Analysis of the effectiveness of two different loss functions in training a neural network in lung image reconstruction using impedance tomography

Indeksy

Indeks słów kluczowych

Indeks dziedzin

Indeks autorów

eISSN:	2391-789X
ISSN:	1734-2031