طبقه‌بندی صداهای طبیعی و غیرطبیعی ضبط‌شده قلب با استفاده از آنالیز زمان- فرکانس سیگنال‌های ‌PCG

[1]           K. Rezaee and J. Haddadnia, “Design and performance evaluation of intelligent system to segregate and classify the phonocardiograph abnormalities using matched filter and multilayer perceptron-back propagation neural networks, ” Pajoohandeh Journal, vol. 18, no. 5, pp. 277-286, 2013.

[2]           F. Javed, P. Venkatachalam, and A. F. MH, “A signal processing module for the analysis of heart sounds and heart murmurs, ” in Journal of Physics: Conference Series, 2006, vol. 34, no. 1, p. 1098: IOP Publishing.

[3]           G. D. Cliffordet al., “Classification of normal/abnormal heart sound recordings: The PhysioNet/Computing in Cardiology Challenge 2016, ” in Computing in Cardiology Conference (CinC), 2016, 2016, pp. 609-612: IEEE.

[4]           D. B. Springer, L. Tarassenko, and G. D. Clifford, “Logistic regression-hsmm-based heart sound segmentation, ” IEEE Transactions on Biomedical Engineering,vol. 63, no. 4, pp. 822-832, 2016.

[5]           M. Nabih-Ali, E.-S. A. El-Dahshan, and A. S. Yahia, “Heart diseases diagnosis using intelligent algorithm based on PCG signal analysis, ” Circuits Syst,vol. 8, no. 7, pp. 184-190, 2017.

[6]           J. Xu, L. Durand, and P. Pibarot, “Nonlinear transient chirp signal modeling of the aortic and pulmonary components of the second heart sound, ” IEEE Transactions onBiomedical Engineering,vol. 47, no. 10, pp. 1328-1335, 2000.

[7]           J. Xu, L.-G. Durand, and P. Pibarot, “Extraction of the aortic and pulmonary components of the second heart sound using a nonlinear transient chirp signal model, ” IEEE Transactions on Biomedical Engineering, vol. 48, no. 3, pp. 277-283, 2001.

[8]           T. Leung, P. White, W. Collis, A. Salmon, and E. Brown, “Time-frequency methods for analysing paediatric heart murmurs, ” Appl Sign Process,vol. 4, no. 3, pp. 154-167, 1997.

[9]           L. Huiying, L. Sakari, and H. Iiro, “A heart sound segmentation algorithm using wavelet decomposition and reconstruction, ” in Engineering in Medicine and Biology Society, 1997. Proceedings of the 19th Annual International Conference of the IEEE, 1997, vol. 4, pp. 1630-1633: IEEE.

[10]         H. Liang and I. Nartimo, “A feature extraction algorithm based on wavelet packet decomposition for heart sound signals, ” in Time-Frequency and Time-Scale Analysis, 1998. Proceedings of the IEEE-SP International Symposium on, 1998, pp. 93-96: IEEE.

[11]         T. Nilanon, J. Yao, J. Hao, S. Purushotham, and Y. Liu, “Normal/abnormal heart sound recordings classification using convolutional neural network, ” in Computing in Cardiology Conference (CinC), 2016, 2016, pp. 585-588: IEEE.

[12]         M. Zabihi, A. Bahrami Rad, S. Kiranyaz, M. Gabbouj, and A. Katsaggelos, “Heart sound anomaly and quality detection using ensemble of neural networks without segmention,” in Computing in Cardiology Conference (CinC), 2016, 2016, pp. 813-816: IEEE.

[13]         J. Rubin, R. Abreu, A. Ganguli, S. Nelaturi, L. Matei and K. Sircharan, “classifying heart sound recordings using deep convolutional neural networks and mel-frequency cepstral Cofficients, ” in Computing in Cardiology Conference (CinC), 2016, 2016, pp.1141-1144: IEEE.

[14]         H. Shino, H. Yoshida, K. Yana, K. Harada, J. Sudoh, and E. Harasewa, “Detection and classification of systolic murmur for phonocardiogram screening, ” in Engineering in Medicine and Biology Society, 1996. Bridging Disciplines for Biomedicine. Proceedings of the 18th Annual International Conference of the IEEE, 1996, vol. 1, pp. 123-124: IEEE.

[15]         S. Jabbari and H. Ghassemian, “Modeling of heart systolic murmurs based on multivariate matching pursuit for diagnosis of valvular disorders, ” Computers in biology and medicine, vol. 41, no. 9, pp. 802-811, 2011.

[16]         C. Potes, S. Parvaneh, A. Rahman, and B. Conroy, “Ensemble of feature-based and deep learning-based classifiers for detection of abnormal heart sounds, ” in Computing in Cardiology Conference (CinC), 2016, 2016, pp. 621-624: IEEE.

[17]         M. N. Homsi et al., “Automatic heart sound recording classification using a nested set of ensemble algorithms, ” in Computing in Cardiology Conference (CinC), 2016, 2016, pp. 817-820: IEEE.

[18]         D. BARSCHDORFF, S. ESTER, and E. MOST, “Phonocardiogram analysis of congenital and acquired heart diseases using artificial neural networks, ” in Comparative Approaches To Medical Reasoning: World Scientific, 1995, pp. 271-288.

[19]         I. Grzegorczyk et al., “PCG classification using a neural network approach, ” in Computing in Cardiology Conference (CinC), 2016, 2016, pp. 1129-1132: IEEE.

[20]         M. A. Goda and P. Hajas, “Morphological determination of pathological PCG signals by time and frequency domain analysis, ” in Computing in Cardiology Conference (CinC), 2016, 2016, pp. 1133-1136: IEEE.

[21]         B. Boashash, G. Azemi, and N. A. Khan, “Principles of time–frequency feature extraction for change detection in non-stationary signals: Applications to newborn EEG abnormality detection, ” Pattern Recognition, vol. 48, no. 3, pp. 616-627, 2015.

[22]         J. J. G. Ortiz, C. P. Phoo, and J. Wiens, “Heart sound classification based on temporal alignment techniques, ” in Computing in Cardiology Conference (CinC), 2016, 2016, pp. 589-592: IEEE.

[23]         N. Verbiest, C. Cornelis, and R. Jensen, “Fuzzy rough positive region based nearest neighbour classification, ” in Fuzzy Systems (FUZZ-IEEE), 2012 IEEE International Conference on, 2012, pp. 1-7: IEEE.

[24]         J. M. Keller, M. R. Gray, and J. A. Givens, “A fuzzy k-nearest neighbor algorithm, ” IEEE transactions on systems, man, and cybernetics, no. 4, pp. 580-585, 1985.

[25]         W.-C. Kao and C.-C. Wei, “Automatic phonocardiograph signal analysis for detecting heart valve disorders, ” Expert Systems with Applications, vol. 38, no. 6, pp. 6458-6468, 2011.

[26]         B. Boashash, G. Azemi, and J. M. O'Toole, “Time-frequency processing of nonstationary signals: Advanced TFD design to aid diagnosis with highlights from medical applications, ” IEEE Signal Processing Magazine, vol. 30, no. 6, pp. 108-119, 2013.

[27]         B. Boashash, Time-frequency signal analysis and processing: a comprehensive reference. Academic Press, 2015.

[28]         C. Liu, D. Springer, and G. D. Clifford, “Performance of an open-source heart sound segmentation algorithm on eight independent databases, ” Physiological measurement,vol. 38, no. 8, p. 1730, 2017.

[29]         C. Liu et al., “An open access database for the evaluation of heart sound algorithms, ” Physiological Measurement, vol. 37, no. 12, p. 2181,2016.

]30[     مرتضی خرّم کشکولی، مریم دهقانی، « تشخیص، شناسایی و جداسازی عیب توربین گاز پالایشگاه دوم پارس جنوبی با استفاده از روش‌های ترکیبی داده‌کاوی، k-means، تحلیل مؤلفه‌های اصلی (PCA) و ماشین بردار پشتیبان (SVM) »، مجله مهندسی برق دانشگاه تبریز، دوره 47، شماره 2، صص 501-515، تابستان 1396.

]31[      صفر ایراندوست پاکچین، سعید مشگینیو سجاد نصیرزاده، « بازشناسی چهره با استفاده از آنالیز تفکیک خطی بر پایه موجک‌های هار و گابور و ماشین بردار پشتیبان»، مجله مهندسی برق دانشگاه تبریز، دوره 47، شماره 4، صص 1317-1327، زمستان 1396.

[32]         L. G. Valiant, “A theory of the learnable," Communications of the ACM,vol. 27, no. 11, pp. 1134-1142, 1984.

[33]         S. Abney, R. E. Schapire, and Y. Singer, “Boosting applied to tagging and PP attachment, ” in 1999 Joint SIGDAT Conference onEmpirical Methods in Natural Language Processing and Very Large Corpora, 1999.

[34]         F. Smeraldi, M. Defoin-Platel, and M. Saqi, “Handling Missing Features with Boosting Algorithms for Protein–Protein Interaction Prediction, ” in International Conference on Data Integration in the Life Sciences, 2010, pp. 132-147: Springe.