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

نوع مقاله : علمی-پژوهشی

نویسندگان

دانشکده مهندسی برق و کامپیوتر - دانشگاه رازی

چکیده

سیگنال صوتی تولیدشده ناشی از فعالیت‌های مکانیکی قلب، اطلاعات مفیدی در رابطه با عملکرد دریچه‌های قلبی فراهم می‌کند. اما به دلیل محدودیت شنوایی انسان، ماهیت گذرا و غیر ایستان سیگنال صدای قلب و انرژی پایین‌تر صداهای پاتولوژیک نسبت به صداهای طبیعی، یافتن نشانه‌های بیماری و تصمیم‌گیری برمبنای صداهای شنیده‌شده از طریق گوشی پزشکی کار دشواری بوده و نیاز به تمرین و تکرار زیادی دارد. به دلیل محتوای تشخیصی بالای سوفل‌ها در هر دو حوزه زمان و فرکانس، استخراج ویژگی‌های زمان- فرکانس مناسب‌ترین روش برای پردازش این صداهای غیرطبیعی به شمار می‌روند. در این تحقیق به‌منظور طبقه‌بندی صداهای قلبی، ویژگی‌های حوزه زمان- فرکانس از سیگنال‌های صدای قلب استخراج شده است. در مرحله طبقه‌بندی، از ترکیب دو طبقه‌بند AdaBoost و شبکه عصبی کانولوشن استفاده شده و درنهایت عملکرد روش پیشنهادی با استفاده از روش leave-one-out روش پیشنهادی مورد ارزیابی قرارگرفته است. این‌روش بر روی پایگاه داده چالش 2016 فیزیونت پیاده‌سازی شده است. نتایج حاصل نشان‌دهنده عملکرد بهتر راهکار پیشنهادی در مقایسه با بهترین روش موجود در چالش 2016 فیزیونت و دست‌یابی به حساسیت %93.27 و اختصاصیت 81.96% در طبقه‌بندی صداهای قلبی است درحالی‌که بهترین روش در چالش 2016 فیزیونت به حساسیت% 93.48 و اختصاصیت %80.36 دست یافته است.

کلیدواژه‌ها

عنوان مقاله [English]

Classification of Normal/Abnormal Heart Sound Recordings Using Time –Frequency PCG Signal Analysis

نویسندگان [English]

  • H. Hazeri
  • Gh. Azemi
  • P. Zarjam
Faculty of Electrical and Computer Engineering, Razi University, Kermanshah, Iran
چکیده [English]

The heart’s acoustic signal produced by its mechanical activity can provide useful information on the condition of heart valves. The heart sound auscultation, i.e. listening to the heart sounds with a stethoscope, is therefore a primary method for evaluating the cardiovascular function. This method has advantages of being fast, inexpensive, easy to use and noninvasive. On the other hand, due to the transient and non-stationary nature of PCG signals and auscultatory limitations, the correct medical diagnosis based on the heart sound through a stethoscope requires a lot of expertise and needs referral of the patient to a cardiologist.  This is not only time-consuming but also imposes a financial burden on the medical system. Thus, automated detection and analysis of the recorded heart sound auscultation has received a lot of attentions in recent years. Even, this was put to the challenge by the PhysioNet/CinC in 2016.This research is based on the non-stationary nature of PCG signals and proposes a new method based on time–frequency analysis of such signals with the aim to classify heart sounds into normal and abnormal sounds. The proposed methodology uses time-frequency features and two classifiers; AdaBoost and CNN. The publicly available 2016 PhysioNet/CinC 2016 Challenge database was used to evaluate the performance of the proposed method using a leave-one-out cross validation. The experimental results show that the proposed method performs very well and has 1.5% higher sensitivity compared to the best existing method.

کلیدواژه‌ها [English]

  • PCG signal
  • Feature Extraction
  • Classifirers
  • Valve diseases
  • Time –Frequency Analysis

مراجع

[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.

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