Phonocardiogram classification using deep neural networks and weighted probability comparisons

References

• The top 10 causes of death [Internet]. Geneva: World Health Organization; 2016 [cited 2018 Jun 26]. Available from: https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death
   Google Scholar
• Hunt SA, Abraham WT, Chin MH, et al. 2009 focused update incorporated into the ACC/AHA 2005 guidelines for the diagnosis and management of heart failure in adults. Circulation. 2009;119:e391–e479.
   PubMed Web of Science ®Google Scholar
• Adams HP, Del Zoppo G, Alberts MJ, et al. Guidelines for the early management of adults with ischemic stroke: a guideline from the American Heart Association/American Stroke Association Stroke Council, Clinical Cardiology Council, Cardiovascular Radiology and Intervention Council, and the Atherosclerotic Peripheral Vascular Disease and Quality of Care Outcomes in Research Interdisciplinary Working Groups: the American Academy of Neurology affirms the value of this guideline as an educational tool for neurologists. Circulation. 2007;115:e478–e534.
   PubMed Web of Science ®Google Scholar
• Hoffman JIE, Kaplan S. The incidence of congenital heart disease. J Am Coll Cardiol. 2002;39:1890–1900.
   PubMed Web of Science ®Google Scholar
• Members AF, Dickstein K, Cohen-Solal A, et al. ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure 2008: the Task Force for the Diagnosis and Treatment of Acute and Chronic Heart Failure 2008 of the European Society of Cardiology. Developed in collaboration with the Heart. Eur Heart J. 2008;29:2388–2442.
   PubMed Web of Science ®Google Scholar
• Stevenson LW, Perloff JK. The limited reliability of physical signs for estimating hemodynamics in chronic heart failure. JAMA. 1989;261:884–888.
   PubMed Web of Science ®Google Scholar
• Ishmail AA, Wing S, Ferguson J, et al. Interobserver agreement by auscultation in the presence of a third heart sound in patients with congestive heart failure. Chest. 1987;91:870–873.
   PubMed Web of Science ®Google Scholar
• Spiteri MA, Cook DG, Clarke SW. Reliability of eliciting physical signs in examination of the chest. Lancet. 1988;1:873–875.
   PubMed Web of Science ®Google Scholar
• Marcus GM, Gerber IL, McKeown BH, et al. Association between phonocardiographic third and fourth heart sounds and objective measures of left ventricular function. JAMA. 2005;293:2238–2244.
   PubMed Web of Science ®Google Scholar
• Lok CE, Morgan CD, Ranganathan N. The accuracy and interobserver agreement in detecting the 'gallop sounds' by cardiac auscultation. Chest. 1998;114:1283–1288.
   PubMed Web of Science ®Google Scholar
• Ari S, Hembram K, Saha G. Detection of cardiac abnormality from PCG signal using LMS based least square SVM classifier. Expert Syst. Appl. 2010;37:8019–8026.
   Web of Science ®Google Scholar
• Chen Q, Zhang W, Tian X, et al. Automatic heart and lung sounds classification using convolutional neural networks. Proceedings of the 2016 Signal and Information Processing Association Annual Summit and Conference (APSIPA); 2016 Dec 13–15; Jeju (Korea). New York (NY): IEEE; 2016. p. 1–4.
   Google Scholar
• Wang Y, Li W, Zhou J, et al. Identification of the normal and abnormal heart sounds using wavelet-time entropy features based on OMS-WPD. Futur Gener Comput Syst. 2014;37:488–495.
   Web of Science ®Google Scholar
• Deng SW, Han JQ. Towards heart sound classification without segmentation via autocorrelation feature and diffusion maps. Futur Gener Comput Syst. 2016;60:13–21.
   Web of Science ®Google Scholar
• Gavrovska A, Zajić G, Reljin I, et al. Classification of prolapsed mitral valve versus healthy heart from phonocardiograms by multifractal analysis. Comput Math Methods Med. 2013;2013:1.
   Web of Science ®Google Scholar
• Low JX, Choo KW. Automatic classification of periodic heart sounds using convolutional neural network. World Acad Sci Eng Technol Int J Electr Comput Eng. 2018;5:100–105.
   Google Scholar
• Quiceno-Manrique AF, Godino-Llorente JI, Blanco-Velasco M, et al. Selection of dynamic features based on time-frequency representations for heart murmur detection from phonocardiographic signals. Ann Biomed Eng. 2010;38:118–137.
   PubMed Web of Science ®Google Scholar
• Randhawa SK, Singh M. Classification of heart sound signals using multi-modal features. Procedia Comput Sci. 2015;58:165–171.
   Google Scholar
• Schmidt SE, Holst-Hansen C, Hansen J, et al. Acoustic features for the identification of coronary artery disease. IEEE Trans Biomed Eng. 2015;62:2611–2619.
   PubMed Web of Science ®Google Scholar
• Sinha RK, Aggarwal Y, Das BN. Backpropagation artificial neural network classifier to detect changes in heart sound due to mitral valve regurgitation. J Med Syst. 2007;31:205–209.
   PubMed Web of Science ®Google Scholar
• Banerjee R, Choudhury AD, Deshpande P, et al. A robust dataset-agnostic heart disease classifier from phonocardiogram. Conf Proc IEEE Eng Med Biol Soc. 2017;2017:4582–4585.
   PubMedGoogle Scholar
• Bouril A, Aleinikava D, et al. Automated classification of normal and abnormal heart sounds using support vector machines. In: Murray A, editor. Proceedings of Computing in Cardiology 2016; 2016 Sep 11–14; Vancouver, Canada. p. 549–552.
   Google Scholar
• Zabihi M, Bahrami Rad A, Kiranyaz S, et al. Heart sound anomaly and quality detection using ensemble of neural networks without segmentation. In: Murray A, editor. Proceedings of Computing in Cardiology 2016; 2016 Sep 11–14; Vancouver, Canada. p. 613–616.
   Google Scholar
• Zhang W, Han J, Deng S. Heart sound classification based on scaled spectrogram and partial least squares regression. Biomed Signal Process. Control. 2017;32:20–28.
   Web of Science ®Google Scholar
• Dominguez-Morales JP, Jimenez-Fernandez AF, Dominguez-Morales MJ, et al. Deep neural networks for the recognition and classification of heart murmurs using neuromorphic auditory sensors. IEEE Trans Biomed Circuits Syst. 2017;12:1–11.
   Web of Science ®Google Scholar
• Gonzalez Ortiz JJ, Phoo CP, Wiens J, Heart Sound Classification Based on Temporal Alignment Techniques. 2016; Available from: http://www.cinc.org/archives/2016/pdf/170-317.pdf
   Google Scholar
• Langley P, Murray A. Heart sound classification from unsegmented phonocardiograms. Physiol Meas. 2017;38:1658–1670.
   PubMed Web of Science ®Google Scholar
• Maknickas V, Maknickas A. Recognition of normal – abnormal phonocardiographic signals using deep convolutional neural networks and mel-frequency spectral coefficients. Physiol. Meas 2017;38:1671–1684.
   PubMed Web of Science ®Google Scholar
• Potes C, Parvaneh S, Rahman A, et al. Ensemble of feature-based and deep learning-based classifiers for detection of abnormal heart sounds. In: Murray A, editor. Proceedings of Computing in Cardiology 2016; 2016 Sep 11–14; Vancouver, Canada. p. 621–624.
   Google Scholar
• Rubin J, Abreu R, Ganguli A, et al. Recognizing Abnormal Heart Sounds Using Deep Learning. arXiv Prepr. arXiv1707.04642 [Internet]. 2017; Available from: http://arxiv.org/abs/1707.04642
   Google Scholar
• Tschannen M, Kramer T, Marti G, et al. Heart sound classification using deep structured features. In: Murray A, editor. Proceedings of Computing in Cardiology 2016; 2016 Sep 11–14; Vancouver, Canada. p. 565–568.
   Google Scholar
• Yang T, Hsieh H. Classification of acoustic physiological signals based on deep learning neural networks with augmented features. 2016 Comput Cardiol Conf. 2016;43:569–572.
   Google Scholar
• Liu C, Springer D, Li Q, et al. An open access database for the evaluation of heart sound algorithms. Physiol Meas. 2016;37:2181.
   PubMed Web of Science ®Google Scholar
• Lalkhen AG, McCluskey A. Clinical tests: sensitivity and specificity. Contin Educ Anaesth Crit Care Pain. 2008;8:221–223.
   Google Scholar
• Springer DB, Tarassenko L, Clifford GD. Logistic regression-HSMM-based heart sound segmentation. IEEE Trans Biomed Eng. 2016;63:822–832.
   PubMed Web of Science ®Google Scholar
• Reed TR, Reed NE, Fritzson P. Heart sound analysis for symptom detection and computer-aided diagnosis. Simul Model Pract Theory. 2004;12:129–146.
   Web of Science ®Google Scholar
• El-Segaier M, Lilja O, Lukkarinen S, et al. Computer-based detection and analysis of heart sound and murmur. Ann Biomed Eng. 2005;33:937–942.
   PubMed Web of Science ®Google Scholar
• Ahlstrom C, Hult P, Rask P, et al. Feature extraction for systolic heart murmur classification. Ann Biomed Eng. 2006;34:1666–1677.
   PubMed Web of Science ®Google Scholar
• Tzanetakis G, Cook P. Musical genre classification of audio signals. IEEE Trans Speech Audio Process. 2002;10:293–302.
   Google Scholar
• Clark V, Kruse JA. Ejection clicks. In: Walker H, Hall W, Hurst J, editors. Clinical methods: The history, physics, and laboratory examinations. 3rd ed. Boston: Butterworths; 1990. p. 142–143.
   Google Scholar
• Devereux RB, Perloff JK, Reichek N, et al. Mitral valve prolapse. Circulation. 1976;54:3–14.
   PubMed Web of Science ®Google Scholar
• Ergen B, Tatar Y, Gulcur HO. Time-frequency analysis of phonocardiogram signals using wavelet transform: a comparative study. Comput Methods Biomech Biomed Engin. 2012;15:371–381.
   PubMed Web of Science ®Google Scholar
• Clifford G, Liu C, Springer D, et al. Classification of Normal/Abnormal Heart Sound Recordings: the PhysioNet/Computing in Cardiology Challenge 2016. 2016;3–6. Available from: http://www.cinc.org/archives/2016/pdf/179-154.pdf
   Google Scholar
• Keras [Internet]. San Francisco (CA): GitHub - François Chollet; [cited 2018 Jun 26]. Available from: https://github.com/keras-team/keras
   Google Scholar
• Abadi M, Barham P, Chen J, et al. TensorFlow: a system for large-scale machine learning. OSDI. 2016;16:265–283.
   Google Scholar
• Friedman J, Hastie T, Tibshirani R, The elements of statistical learning. New York, NY: Springer series in statistics New York; 2009.
   Google Scholar
• Singh M, Cheema A. Heart sounds classification using feature extraction of phonocardiography signal. Int J Comput Apl. 2013;77:13–17. Available from: http://research.ijcaonline.org/volume77/number4/pxc3891001.pdf.
   Google Scholar
• Japkowicz N, Stephen S. The class imbalance problem: a systematic study. Intel Data Anal. 2002;6:429–449.
   Google Scholar