Predictive Maintenance Approach in Ventricular Assist Devices: Safeguarding Against Thrombus Formation

7b70ede3-799f-4a81-a28a-27dfcffb848d20240108074156670wseas:wseasmdt@crossref.orgMDT DepositWSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE2224-29021109-951810.37394/23208http://wseas.org/wseas/cms.action?id=40111820241820242110.37394/23208.2024.21https://wseas.com/journals/bab/2024.phpPredictive Maintenance Approach in Ventricular Assist Devices: Safeguarding Against Thrombus FormationThiagoSantosLaboratory of Bioengineering and Biomaterials, Mechanical Department, Federal Institute of Education, Science, and Technology of Sao Paulo, Pedro Vicente Street, 625 - Caninde, Sao Paulo - SP, 01109-010, BRAZILOswaldoMartinsLaboratory of Bioengineering and Biomaterials, Mechanical Department, Federal Institute of Education, Science, and Technology of Sao Paulo, Pedro Vicente Street, 625 - Caninde, Sao Paulo - SP, 01109-010, BRAZILEduardoBockLaboratory of Bioengineering and Biomaterials, Mechanical Department, Federal Institute of Education, Science, and Technology of Sao Paulo, Pedro Vicente Street, 625 - Caninde, Sao Paulo - SP, 01109-010, BRAZILDennisToufenIndustrial Automation Department, Federal Institute of Education, Science, and Technology of Sao Paulo, Salgado Filho Avenue, 3501 – Vila Rio de Janeiro, Guarulhos - SP, 07115-000, BRAZILAffecting millions in the world, cardiovascular diseases are a public health problem. Some patients are not eligible for heart transplantation. Thus, a possibility is to receive a circulatory device known as a ventricular assist device (VAD). This kind of device shows some problems, like thrombogenesis. The thrombus formation in a VAD can cause patient death, and a previous, non-invasive diagnostic is quite complex. The objective of this work is to develop an algorithm to reproduce time signals that indicate the presence and absence of a thrombus, use these signals to train an artificial neural network to classify them, and use these algorithms in a predictive algorithm for early thrombus detection. The results show that it was possible to detect the thrombus formation in its early stages, but the noise level interferes with the accuracy of the ANN, especially when signals in the time domain are used.182024182024191https://wseas.com/journals/bab/2024/a025108-001(2024).pdf10.37394/23208.2024.21.1https://wseas.com/journals/bab/2024/a025108-001(2024).pdf10.1088/1742-6596/1916/1/012208G. Renugadevi, G. Asha Priya, B. Dhivyaa Sankari, and R. Gowthamani, ‘Predicting heart disease using hybrid machine learning model’, J Phys Conf Ser, vol. 1916, no. 1, 2021, doi: 10.1088/1742- 6596/1916/1/012208. 10.1016/j.ihj.2021.10.008J. N. Heaton, S. Singh, M. Li, and S. Vallabhajosyula, ‘Adverse events with HeartMate-3 Left ventricular assist device: Results from the Manufacturer and User Facility Device Experience (MAUDE) database’, Indian Heart Journal, vol. 73, no. 6, pp. 765–767, 2021, doi: 10.1016/j.ihj.2021.10.008. 10.1016/j.jnca.2018.04.011J. I. Glitza, F. Müller-von Aschwege, M. Eichelberg, N. Reiss, T. Schmidt, C. Feldmann, R. Wendl, J. D. Schmitto, and A. Hein, ‘Advanced telemonitoring of Left Ventricular Assist Device patients for the early detection of thrombosis’, Journal of Network and Computer Applications, vol. 118, no. May, pp. 74–82, 2018. 10.1111/joa.13675G. Malone, G. Abdelsayed, F. Bligh, F. Al Qattan, S. Syed, P. Varatharajullu, A. Msellati, D. Mwipatayi, M. Azhar, A. Malone, S. H. Fatimi, C. Conway, and A. Hameed, ‘Advancements in left ventricular assist devices to prevent pump thrombosis and blood coagulopathy’, J Anat, vol. 242, no. 1, pp. 29–49, 2023, doi: 10.1111/joa.13675. 10.1007/978-3-030-45124-0_33M. Barboza, F. Junqueira, E. Bock, T. Leão, J. Dias, J. Dias, M. Pessoa, J. R. Souza, and D. dos Santos, ‘Ventricular Assist Device in Health 4.0 Context’, IFIP Adv Inf Commun Technol, vol. 577, pp. 347–354, 2020. 10.1177/175114371301400309K. Salaunkey, J. Parameshwar, K. Valchanov, and A. Vuylsteke, Mechanical support for heart failure 2C04 3C00, vol. 14, no. 3. 2013. doi: 10.1177/175114371301400309. A. I. Fiorelli, J. de Lima, O. Junior, H. B. Coelho, and D. Cristo, ‘Mechanical circulatory support : why and when’, vol. 87, no. 1, pp. 1–15, 2008. 10.1016/j.jchf.2015.06.015U. P. Jorde, K. D. Aaronson, S. S. Najjar, F. D. Pagani, C. Hayward, D. Zimpfer, T. Schlöglhofer, D. T. Pham, D. J. Goldstein, K. Leadley, M. J. Chow, M. C. Brown, and N. Uriel, ‘Identification and Management of Pump Thrombus in the HeartWare Left Ventricular Assist Device System: A Novel Approach Using Log File Analysis’, JACC Heart Fail, vol. 3, no. 11, pp. 849–856, 2015. 10.1016/s0735-1097(19)32800-1F. Ortiz and T. Thenappan, ‘Low Flow Alarm in a Patient With Left Ventricular Assist Device: What Went Wrong?’, Journal of the American College of Cardiology, vol. 73, no. 9, p. 2194, 2019, doi: 10.1016/s0735- 1097(19)32800-1. 10.1016/j.athoracsur.2020.12.038E. J. Molina, P. Shah, M. S. Kiernan, W. K. Cornwell, H. Copeland, K. Takeda, F. G. Fernandez, V. Badhwar, R. H. Habib, J. P. Jacobs, D. Koehl, J. K. Kirklin, F. D. Pagani, and J. A. Cowger, ‘The Society of Thoracic Surgeons Intermacs 2020 Annual Report’, Annals of Thoracic Surgery, vol. 111, no. 3, pp. 778–792, 2021. 10.1007/s10047-020-01228-2T. Gyoten, M. Morshuis, S. V. Rojas, M. A. Deutsch, R. Schramm, J. F. Gummert, and H. Fox, ‘Identification of characteristics, risk factors, and predictors of recurrent LVAD thrombosis: conditions in HeartWare devices’, Journal of Artificial Organs, vol. 24, no. 2, pp. 173–181, 2021, doi: 10.1007/s10047-020-01228-2. 10.1016/j.jtcvs.2007.08.048A. L. Meyer, C. Kuehn, J. Weidemann, D. Malehsa, C. Bara, S. Fischer, A. Haverich, and M. Strüber, ‘Thrombus formation in a HeartMate II left ventricular assist device’, Journal of Thoracic and Cardiovascular Surgery, vol. 135, no. 1, pp. 203–204, 2008, doi: 10.1016/j.jtcvs.2007.08.048. 10.1016/j.healun.2013.05.002D. J. Goldstein, R. John, C. Salerno, S. Silvestry, and N. Moazami, ‘Algorithm for the diagnosis and management of suspected pump thrombus’, Journal of Heart and Lung Transplantation, vol. 32, no. 7, pp. 667–670, 2013, doi: 10.1016/j.healun.2013.05.002. T. D. Rossing, Shock and Vibration Handbook , 2nd ed. , vol. 45, no. 7. 1977. doi: 10.1119/1.10796. 10.1007/s40430-020-02560-7S. Neto, S. Sobrinho, C. Costa, T. Leão, S. Senra, E. Bock, G. Santos, S. Souza, M. Silva, C. Frajuca, and M. Souza, ‘Investigation of MEMS as accelerometer sensor in an Implantable Centrifugal Blood Pump prototype’, Journal of the Brazilian Society of Mechanical Sciences and Engineering, vol. 42, no. 9, pp. 1–10, 2020. 10.1109/access.2022.3179517T. Guo, T. Zhang, E. Lim, M. LopezBenitez, F. Ma, and L. Yu, ‘A Review of Wavelet Analysis and Its Applications: Challenges and Opportunities’, IEEE Access, vol. 10, pp. 58869–58903, 2022, doi: 10.1109/ACCESS.2022.3179517. G. M. Khan, ‘Artificial neural network (ANNs)’, Studies in Computational Intelligence, vol. 725, pp. 39–55, 2018, doi: 10.1007/978-3-319-67466-7_4. 10.1109/iic.2015.7150869S. Biswal and G. R. Sabareesh, ‘Design and development of a wind turbine test rig for condition monitoring studies’, 2015 International Conference on Industrial Instrumentation and Control, ICIC 2015, no. Icic, pp. 891–896, 2015, doi: 10.1109/IIC.2015.7150869. D. P. Kingma and J. L. Ba, ‘Adam: A method for stochastic optimization’, 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, pp. 1–15, 2015.