1. Nabih-Ali M, El-Dahshan EL-S. A, and Yahia A. S. A review of intelligent systems for heart sound signal analysis, Journal of Medical Engineering & Technology, 2017: 41(7):1-11. [ DOI:10.1080/03091902.2017.1382584] [ PMID] 2. World Health Organization. Cardiovascular diseases (CVDs) 2021. [Available from: https://www.who.int/en/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)]. 3. Deng M, Meng T, Cao J, et al. Heart sound classification based on improved MFCC features and convolutional recurrent neural networks, Neural Networks 2020; 130, 22-32. [ DOI:10.1016/j.neunet.2020.06.015] [ PMID] 4. Seah J. J, Zhao J, Wang D. Y, & Lee H. P. Review on the Advancements of Stethoscope Types in Chest Auscultation, Diagnostics, 2023: 13(9), 1545. [ DOI:10.3390/diagnostics13091545] [ PMID] [ ] 5. Homsi M. N, Medina N, Hernandez M, et al. Automatic heart sound recording classification using a nested set of ensemble algorithms, In Proceedings of the 2016 Computing in Cardiology Conference (CinC), Vancouver, BC, Canada 2016:817-820. 6. Potes C, Parvaneh S, Rahman A, et al. Ensemble of feature-based and deep learning-based classifiers for detection of abnormal heart sounds, In Proceedings of the 2016 Computing in Cardiology Conference (CinC), Vancouver, BC, Canada 2016:621-624. [ DOI:10.22489/CinC.2016.182-399] 7. Hassani K, Jafarian K, and Doyle D. J, Heart Sounds Features Usage for Classification of Ventricular Septal Defect Size in Children, In Proceedings of the 61th International Conference on Biomedical Engineering, Springer: Singapore 2016:28-31. [ DOI:10.1007/978-981-10-4220-1_6] 8. Ghaffari M, Ashourian M, İnce E. A, et al. Phonocardiography signal processing for automatic diagnosis of ventricular septal defect in newborns and children, In Proceedings of the 2017 9th International Conference on Computational Intelligence and Communication Networks (CICN), Girne, Cyprus 2017:62-66. [ DOI:10.1109/CICN.2017.8319357] 9. Ahmad M. S, Mir J, Obaid Ullah M, et al. An efficient heart murmur recognition and cardiovascular disorders classification system, Australasian Physical & Engineering Sciences in Medicine, Springer 2019; 42:733-743. [ DOI:10.1007/s13246-019-00778-x] [ PMID] 10. Aziz S, Khan M.U, Alhaisoni M, et al. Phonocardiogram Signal Processing for Automatic Diagnosis of Congenital Heart Disorders through Fusion of Temporal and Cepstral Features, Sensors 2020; 20(13),3790. [ DOI:10.3390/s20133790] [ PMID] [ ] 11. Mohammadi E, Kermani S, Nourian-Zavareh M, et al. A New Approach of Phonocardiogram Analysis for Screening Some of Cardio-vascular Diseases Based on Deep Learning, Journal of Isfahan Medical School 2022; 40(661):109-114 [In Persian] 12. Liu C, Springer D, Li Q, et al. An open access database for the evaluation of heart sound algorithms, Physiol Meas 2016; 37(12):2181-2213. [ DOI:10.1088/0967-3334/37/12/2181] [ PMID] [ ] 13. Nourian-Zavareh M, Kermani S, Hashemi-Jazi S. M, et al. Estimation and evaluation of new features from phonocardiogram for detecting cardiovascular abnormalities, Journal of Isfahan Medical School 2019; 36(506):1444-1449. [In Persian] 14. Borghi P. H, Borges R. C, and Teixeira J. P. Atrial fibrillation classification based on MLP networks by extracting Jitter and Shimmer parameters, Procedia Computer Science 2021; 181:1-939. [ DOI:10.1016/j.procs.2021.01.249] 15. Springer D. B, Brennan T, Ntusi N, et al. Automated signal quality assessment of mobile phone-recorded heart sound signals, Journal of Medical Engineering Technol 2016; 40(7-8):342-355. [ DOI:10.1080/03091902.2016.1213902] [ PMID] 16. Schmidt S. E, Holst-Hansen C, Graff C, et al. Segmentation of heart sound recordings by a duration-dependent hidden Markov model, Physiol. Meas 2010; 31(4):513-29. [ DOI:10.1088/0967-3334/31/4/004] [ PMID] 17. Springer D. B, Tarassenko L, and Clifford G. D. Logistic Regression-HSMM-based Heart Sound Segmentation, IEEE Transactions on Biomedical Engineering 2016; 63(4):822-32. 18. Zhang Y, Jiang J. J, Biazzo L, et al. Perturbation and Nonlinear Dynamic Analyses of Voices from Patients with Unilateral Laryngeal Paralysis, Journal of Voice 2004; 19(4):519-28. [ DOI:10.1016/j.jvoice.2004.11.005] [ PMID] 19. Teixeira J. P, and Goncalves A. Algorithm for jitter and shimmer measurement in pathologic voices, Procedia Computer Science, 2016; 100:271-279. [ DOI:10.1016/j.procs.2016.09.155] 20. Randhawa S. K, and Singh M. Classification of heart sound signals using multi-modal features, Procedia Computer Science, 2015; 58: 165-171. [ DOI:10.1016/j.procs.2015.08.045] 21. SINGH S. A, and MAJUMDER S. Short unsegmented PCG classification based on ensemble classifier, Turkish Journal of Electrical Engineering and Computer Sciences 2020; 28(2):875-889. [ DOI:10.3906/elk-1905-165] 22. Zabihi M, Rad A. B, Kiranyaz S, et al. Heart sound anomaly and quality detection using ensemble of neural networks without segmentation, Computing in Cardiology Conference (CinC), IEEE 2016; 43:613-616. [ DOI:10.22489/CinC.2016.180-213] 23. Kay E, and Agarwal A, Dropconnected neural network trained with diverse features for classifying heart sounds, Computing in Cardiology Conference (CinC), IEEE 2016; 617-620. [ DOI:10.22489/CinC.2016.181-266]
|