A complete system for an automated ECG diagnosis

• Autorzy: Wójcik Dariusz , Woś Michał , Kłosowski Grzegorz , Rymarczyk Tomasz

Identyfikatory

DOI

10.15199/48.2021.01.32

Warianty tytułu

PL

Kompletny system do zautomatyzowanej diagnozy EKG

• Języki publikacji: EN

Abstrakty

EN

We present a very simple LSTM neural network capable of categorizing heart diseases from the ECG signal. With the use of the ECG simulator we ware able to obtain a large data-set of ECG signal for different diseases that was used for neural network training and validation.

PL

W artykule prezentujemy bardzo prostą sieć LSTM zdolną do rozpoznawania jednostek chorobowych przy chorobach serca. Dodatkowo pokazujemy w jaki sposób stworzyliśmy bazę danych sygnałów pomiarowych użytych do nauki i walidacji sieci neuronowej przy użyciu symulatora EKG.

Słowa kluczowe

EN

neural network; LSTM; ECG

PL

sieć neuronowa; LSTM; EKG

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2021
• Tom: R. 97, nr 1
• Strony: 162--165
• Opis fizyczny: Bibliogr. 31 poz., rys., tab.

Twórcy

autor

Wójcik Dariusz

• Research and Development Center, Netrix S.A., ul. Związkowa 26, 20-148 Lublin, Poland

autor

Woś Michał

• Research and Development Center, Netrix S.A., ul. Związkowa 26, 20-148 Lublin, Poland

autor

Kłosowski Grzegorz

• Lublin University of Technology, Department of organization of Enterprise, ul. Nadbystrzycka 38D, 20-618 Lublin, Poland

autor

Rymarczyk Tomasz

• Research and Development Center, Netrix S.A., ul. Związkowa 26, 20-148 Lublin, Poland
• University of Economics and Innovation, ul. Projektowa 4, 20-209 Lublin, Poland

Bibliografia

• [1] Grudzien, K.; Chaniecki, Z.; Romanowski, A.; Sankowski, D.; Nowakowski, J.; Niedostatkiewicz, M. Application of twin-plane ECT sensor for identification of the internal imperfections inside concrete beams. In Proceedings of the 2016 IEEE International Instrumentation and Measurement Technology Conference Proceedings, Taipei, Taiwan, 23–26 May 2016; 1–6
• [2] Romanowski, A. Big Data-Driven Contextual Processing Methods for Electrical Capacitance Tomography. IEEE Trans. Ind. Informatics, 15 (2019), 1609–1618
• [3] Kryszyn J., Smolik W., Toolbox for 3d modelling and image reconstruction in electrical capacitance tomography, Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska (IAPGOŚ), 2017, (1), 137-145
• [4] Rymarczyk T., Characterization of the shape of unknown objects by inverse numerical methods, Przegląd Elektrotechniczny, 88 (2012), No. 7b, 138-140
• [5] Rymarczyk T., Kłosowski G., Tchórzewski P., Cieplak T., Kozłowski E.: Area monitoring using the ERT method with multisensor electrodes, Przegląd Elektrotechniczny, 95 (2019), No. 1, 153-156
• [6] Rymarczyk T., Nita P., Vejar A., Woś M., Stefaniak B., Adamkiewicz P.: Wearable mobile measuring device based on electrical tomography, Przegląd Elektrotechniczny, 95 (219), No. 4, 211-214
• [7] 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 (2020), No 1, 138–147
• [8] Kłosowski G., Rymarczyk T., Wójcik D., Skowron S., Adamkiewicz P., The Use of Time-Frequency Moments as Inputs of LSTM Network for ECG Signal Classification, Electronics, 9 (2020), No. 9, 1452
• [9] 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 (2020), No. 11, 3324
• [10] Koulountzios P., Rymarczyk T., Soleimani M., A quantitative ultrasonic travel-time tomography system for investigation of liquid compounds elaborations in industrial processes, Sensors, 19 (2019), No. 23, 5117
• [11] Szczesny, A.; Korzeniewska, E. Selection of the method for the earthing resistance measurement. Przegląd Elektrotechniczny, 94 (2018), 178–181
• [12] Korzeniewska, E., Sekulska-Nalewajko, J., Gocawski, J., Droż Dż, T., Kiebasa, P., Analysis of changes in fruit tissue after the pulsed electric field treatment using optical coherence tomography, EPJ Applied Physics, 91 (2020), No.3, 30902
• [13] Sekulska-Nalewajko, J., Gocławski, J., Korzeniewska, E., A method for the assessment of textile pilling tendency using optical coherence tomography, Sensors (Switzerland), 20 (2020), No.13, 1-19, 3687
• [14] Pawłowski, S., Plewako, J., Korzeniewska, E., Field modeling the impact of cracks on the electroconductivity of thin-film textronic structures, Electronics (Switzerland), 9 (2020), No.3, 402
• [15] Kosinski, T.; Obaid, M.; Wozniak, P.W.; Fjeld, M.; Kucharski, J. A fuzzy data-based model for Human-Robot Proxemics. In Proceedings of the 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), New York, NY, USA, 26–31 August 2016; 335–340
• [16] Fraczyk, A.; Kucharski, J. Surface temperature control of a rotating cylinder heated by moving inductors. Appl. Therm. Eng., 125 (2017), 767–779
• [17] Majchrowicz M., Kapusta P., Jackowska-Strumiłło L., Sankowski D., Acceleration of image reconstruction process in the electrical capacitance tomography 3d in heterogeneous, multi-gpu system, Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska (IAPGOŚ), 7( 2017), No. 1, 37-41
• [18] Goetzke-Pala A., Hoła A., Sadowski Ł., A non-destructive method of the evaluation of the moisture in saline brick walls using artificial neural networks. Archives of Civil and Mechanical Engineering, 18 (2018), No. 4, 1729-1742
• [19] Kozłowski E., Mazurkiewicz D., Żabiński T., Prucnal S., Sęp J., Assessment model of cutting tool condition for real-time supervision system, Eksploatacja i Niezawodnosc – Maintenance and Reliability, 21 (2019), No. 4, 679–685
• [20] Kozłowski E., Mazurkiewicz D., Żabiński T., Prucnal S., Sęp J., Machining sensor data management for operation-level predictive model. Expert Systems with Applications 159 (2020), 1-22
• [21] Daniewski K., Kosicka E., Mazurkiewicz D., Analysis of the correctness of determination of the effectiveness of maintenance service actions. Management and Production Engineering Review 9 (2018); No. 2, 20-25.
• [22] Véjar A., Charpentier P., Generation of an adaptive simulation driven by product trajectories, J Intell Manuf, 23 (2012), No. 6, 2667–2679
• [23] Ribeiro, A.H., Ribeiro, M.H., Paixão, G.M.M. et al. Automatic diagnosis of the 12-lead ECG using a deep neural network. Nat Commun 11, 1760 (2020). https://doi.org/10.1038/s41467-020- 15432-4
• [24] El-Khafif SH.,El-Brawany M.A. ,Artificial Neural Network-Based Automated ECG Signal Classifier, International Scholarly Research Notices, 261917, https://doi.org/10.1155/2013/261917
• [25] Li, J., Si, Y., Xu, T., Jiang, Saibiao, Deep Convolutional Neural Network Based ECG Classification System Using Information Fusion and One-Hot Encoding Techniques, Mathematical Problems in Engineering, 2018, 7354081, https://doi.org/10.1155/2018/7354081
• [26] Gao J., Zhang H., Lu P., Wang Z.: An Effective LSTM Recurrent Network to Detect Arrhythmia on Imbalanced ECG Dataset, 320651, 2019
• [27] Graves A.: Generating Sequences With Recurrent Neural Networks, arXiv:1308.0850, 2014
• [28] Sutskever I., Martens J., Hinton G.: Generating text with recurrent neuralnetworks, ICML’11, 2011
• [29] Vinyals O., Kaiser L., Koo T., Petrov S., Sutskever I., Hinton G.: Grammar as a Foreign Language, http://arxiv.org/abs/1412.7449, arXiv: 1412.7449, 2015
• [30] Fluke website, www.flukebiomedical.com
• [31] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23):e215-e220 [Circulation Electronic Pages; http://circ.ahajournals.org/content/101/23/e215.full]; 2000 (June 13). PMID: 10851218; doi: 10.1161/01.CIR.101.23.e215

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).