A portable Raspberry Pi-based system for diagnosis of heart valve diseases using automatic segmentation and artificial neural networks

References

• Aggarwal, C. (2018). Neural networks and deep learning: A textbook (1st ed. 2018 ed.). Springer.
   Google Scholar
• Ahmad, W. (2011). Heart murmur detection/classification using cochlea-like pre-processing and artificial intelligence. International Journal of Biomedical Engineering and Technology, 7(1), 87–16. https://doi.org/10.1504/IJBET.2011.042500
   Google Scholar
• Alfarhan, K., Mashor, M., Saad, A., & Omar, M. (2018). Wireless heart abnormality monitoring kit based on Raspberry Pi. Journal of Biomimetics, Biomaterials and Biomedical Engineering, 35, 96–108. https://doi.org/10.4028/www.scientific.net/JBBBE.35.96
   Web of Science ®Google Scholar
• Amirjani, A., Yousefi, M., & Cheshmaroo, M. (2014). Parametrical optimization of stent design; a numerical-based approach. Computational Materials Science, 90, 210–220. https://doi.org/10.1016/j.commatsci.2014.04.002
   Web of Science ®Google Scholar
• Cabrera, M., Sanders, B., Goor, O., Driessen-Mol, A., Oomens, C., & Baaijens, F. (2017). Computationally designed 3D printed self-expandable polymer stents with biodegradation capacity for minimally invasive heart valve implantation: A proof-of-concept study. 3D Printing and Additive Manufacturing, 4(1), 19–29. https://doi.org/10.1089/3dp.2016.0052
   PubMedGoogle Scholar
• Classifying Heart Sounds Challenge. (2016). Peterjbentley.com. http://www.peterjbentley.com/heartchallenge
   Google Scholar
• Darwish, A. H., Joukhadar, A., & Kashkash, M. (2018). Using the bees algorithm for wheeled mobile robot path planning in an indoor dynamic environment. Cogent Engineering, 5(1), 1426539.
   Web of Science ®Google Scholar
• Emre, G., & Uguz, H. (2011). Classification of heart sounds based on the least squares support vector machine. International Journal of Innovative Computing, Information & Control, 7(12), 7131–7144.
   Web of Science ®Google Scholar
• Firuzbakht, F., Fallah, A., Rashidi, S., & Khoshnood, E. (2018). Abnormal heart sound diagnosis based on phonocardiogram signal processing. 26th Iranian Conference on Electrical Engineering (pp. 1450–1455). IEEE. https://doi.org/10.1109/ICEE.2018.8472410
   Google Scholar
• Grzegorczyk, I., Solinski, M., Lepek, M., Perka, A., Rosinski, J., Rymko, J., Stepien, K., & Gieraltowski, J. (2016). PCG classification using a neural network approach. Computing in Cardiology Conference. Vancouver: IEEE. https://doi.org/10.22489/CinC.2016.323-252
   Google Scholar
• Hofmann, S., Groß, V., & Dominik, A. (2016). Recognition of abnormalities in phonocardiograms for computer-assisted diagnosis of heart failures. Computing in Cardiology Conference. Vancouver: IEEE. https://doi.org/10.22489/CinC.2016.161-187
   Google Scholar
• I. eGeneralMedical. (2016). Cardiac auscultation of heart murmurs/CD -user license download only. Egeneralmedical.com. [ Online]. http://www.egeneralmedical.com/listohearmur.html
   Google Scholar
• Joukhadar, A., Hanna, D. K., & Al-Izam, E. A. (2020). UKF-based image filtering and 3D reconstruction. In Machine Vision and Navigation (pp. 267–289). Cham: Springer.
   Google Scholar
• Kameswari, M., Vera, R., Maurice, E., & Robert, B. (2010). Valvular heart disease: Diagnosis and management. Mayo Clinic Proceedings, 85(5), 483–500. https://doi.org/10.4065/mcp.2009.0706
   PubMed Web of Science ®Google Scholar
• Kononenko, I., SImek, E., & Sikonja, M. (1997). Overcoming the myopia of inductive learning algorithms with RELIEFF. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.56.474020
   Google Scholar
• Kumar, D., Jadeja, R., & Pande, S. (2018). Wavelet bispectrum-based nonlinear features for cardiac murmur identification. Cogent Engineering. https://doi.org/10.1080/23311916.2018.1502906
   Google Scholar
• MathWorks. Signal processing ToolboxTM user’s guide. R2017a. https://mafiadoc.com/signal-processing-toolbox_59aefbfb1723ddbec5e2c665.html
   Google Scholar
• MathWorks. Neural network ToolboxTM user’s guide. R2017b. https://www.academia.edu/34938587/Neural_Network_Toolbox_Users_Guide
   Google Scholar
• Meziani, F., Debbal, S., & Atbi, A. (2012). Analysis of phonocardiogram signals using wavelet transform. Journal of Medical Engineering & Technology, 36(6), 283–302. https://doi.org/10.3109/03091902.2012.684830
   PubMedGoogle Scholar
• Mohebali, B., Tahmassebi, A., Meyer-Baese, A., & Gandomi, A. (2020). Probabilistic neural networks: A brief overview of theory, implementation, and application. Elsevier. https://doi.org/10.1016/B978-0-12-816514-0.00014-X
   Google Scholar
• Noman, F., Ghani, U., Saba, T., Rehman, A., & Iqbal, S. (2018). Microscopic abnormality classification of cardiac murmurs using ANFIS and HMM. Microscopy Research and Technique, 81(5), 449–457. https://doi.org/10.1002/jemt.22998
   PubMed Web of Science ®Google Scholar
• Patidar, S., & Pachori, R. (2013). Segmentation of cardiac sound signals by removing murmurs usingconstrained tunable-Q wavelet transform. ELSEVIER. Biomedical Signal Processing and Control, 8(6), 559–567. https://doi.org/10.1016/j.bspc.2013.05.004
   Web of Science ®Google Scholar
• Rick, N., Catherine, O., Robert, B., Blase, C., John, E., Robert, G., Patrick, O., Carlos, R., Nikolaos, S., Paul, S., Thoralf, S., & James, T. (2014). 2014 AHA/ACC guideline for the management of patients with valvular heart disease: Executive summary. AHA, Circulation, 129(23), 2440–2492. https://doi.org/10.1161/CIR.0000000000000029
   PubMed Web of Science ®Google Scholar
• Safara, F., Doraisamy, S., Azman, A., Jantan, A., & Ranga, S. (2013). Diagnosis of heart valve disorders through trapezoidal features and hybrid classifier. International Journal of Bioscience, Biochemistry and Bioinformatics, 3(6), 262–265. https://doi.org/10.7763/IJBBB.2013.V3.298
   Google Scholar
• Singh, A., Dutta, M., & Travieso, K. (2017). Analysis of heart sound for automated diagnosis of cardiac disorders. International Conference and Workshop on Bioinspired Intelligence. Funchal, Portugal: IEEE. https://doi.org/10.1109/IWOBI.2017.7985528
   Google Scholar
• Souliman, A., Joukhadar, A., Alturbeh, H., & Whidborne, J. F. (2013). Real time control of multi-agent mobile robots with intelligent collision avoidance system. In 2013 Science and Information Conference (pp. 93–98). IEEE.
   Google Scholar
• Souliman, A., Joukhadar, A., Alturbeh, H., & Whidborne, J. F. (2014). Intelligent collision avoidance for multi agent mobile robots. In Intelligent Systems for Science and Information (pp. 297–315). Cham: Springer.
   Google Scholar
• Suboh, M., Mashor, M., Saad, A., & Mohamed, M. (2008). Classification of heart valve diseases using correlation analysis. 5th International Conference on Electrical Engineering, Computing Science and Automatic Control (pp. 173–176). IEEE https://doi.org/10.1109/ICEEE.2008.4723402.
   Google Scholar
• Suhas, K., Hemanth, R., Nayak, S., & Niranjana, B. (2017). A Hybrid Model for Recognizing Cardiac Murmurs From Phonocardiogram Signal. Annual India Conference. IEEE. https://doi.org/10.1109/INDICON.2016.7839002.
   Google Scholar
• Uğuz, H. (2012). A biomedical system based on artificial neural network and principal component analysis for diagnosis of the heart valve diseases. Journal of Medical Systems, 36(1), 61–72. https://doi.org/10.1007/s10916-010-9446-7
   PubMed Web of Science ®Google Scholar
• http://www.3m.com/healthcare/littmann/mmm-library.html
   Google Scholar
• https://physionet.org/content/challenge-2016/1.0.0
   Google Scholar
• Zeng, Y., Sun, R., Xianchi, L., Liu, M., Chen, S., & Zhang, P. (2016). Pathophysiology of valvular heart disease (Review). Experimental and Therapeutic Medicine, 11(4), 1184–1188. https://doi.org/10.3892/etm.2016.3048
   PubMed Web of Science ®Google Scholar