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A Deep Learning Approach for Valve Defect Recognition in Heart Acoustic Signal

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 655)


The analysis of phonocardiogram (PCG), although considered as well established in a clinical application, still constitutes the valuable source of diagnostic data. Currently, electronic auscultation provides digital signals which can be processed in order to automatically evaluate the condition of heart or lungs. In this paper, we propose a novel approach for the classification of phonocardiographic signals. We extracted a set of time-frequency parameters which enable to effectively differentiate between normal and abnormal heart beats (with valve defects). These features have constituted an input of the convolutional neural network, which we used for classification of pathological signals. The Aalborg University heart sounds database from PhysioNet/Computing in Cardiology Challenge 2016 was used for verification of developed algorithms. We obtained 99.1% sensitivity and 91.6% specificity on the test data, which is motivational for further research.


  • Deep learning
  • Heart sound classification
  • Convolutional neural network
  • Machine learning
  • Signal processing

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  • DOI: 10.1007/978-3-319-67220-5_1
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  1. Liu, C., Springer, D., Li, Q., Moody, B., Juan, R.A., Chorro, F.J., Castells, F., Roig, J.M., Silva, I., Johnson, A.E.W., Syed, Z., Schmidt, S.E., Papadaniil, C.D., Hadjileontiadis, L., Naseri, H., Moukadem, A., Dieterlen, A., Brandt, C., Tang, H., Samieinasab, M., Samieinasab, M.R., Sameni, R., Mark, R.G., Clifford, G.D.: An open access database for the evaluation of heart sound algorithms. Physiol. Meas. 37, 2181–2213 (2016)

    CrossRef  Google Scholar 

  2. Ray, R., Chambers, J.: Mitral valve disease. Int. J. Clin. Pract. 68, 1216–1220 (2014)

    CrossRef  Google Scholar 

  3. Sun, S., Jiang, Z., Wang, H., Fang, Y.: Automatic moment segmentation and peak detection analysis of heart sound pattern via short-time modified Hilbert transform. Comput. Methods Programs Biomed. 114, 219–230 (2014)

    CrossRef  Google Scholar 

  4. Varghees, V.N., Ramachandran, K.I.: A novel heart sound activity detection framework for automated heart sound analysis. Biomed. Signal Process. Control 13, 174–188 (2014)

    CrossRef  Google Scholar 

  5. Tang, H., Li, T., Qiu, T., Park, Y.: Segmentation of heart sounds based on dynamic clustering. Biomed. Signal Process. Control 7, 509–516 (2012)

    CrossRef  Google Scholar 

  6. Sedighian, P., Subudhi, A.W., Scalzo, F., Asgari, S.: Pediatric heart sound segmentation using Hidden Markov Model. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5490–5493. IEEE (2014)

    Google Scholar 

  7. Uguz, H.: A biomedical system based on artificial neural network and principal component analysis for diagnosis of the heart valve diseases. J. Med. Syst. 36, 61–72 (2012)

    CrossRef  Google Scholar 

  8. Zheng, Y., Guo, X., Ding, X.: A novel hybrid energy fraction and entropy-based approach for systolic heart murmurs identification. Expert Syst. Appl. 42, 2710–2721 (2015)

    CrossRef  Google Scholar 

  9. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 25, pp. 1097–1105. Curran Associates, Inc. (2012)

    Google Scholar 

  10. Karpathy, A., Fei-Fei, L.: Deep visual-semantic alignments for generating image descriptions. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)

    Google Scholar 

  11. Amodei, D., Ananthanarayanan, S., Anubhai, R., Bai, J., Battenberg, E., Case, C., Casper, J., Catanzaro, B., Cheng, Q., Chen, G., et al.: Deep speech 2: end-to-end speech recognition in english and mandarin. In: International Conference on Machine Learning, pp. 173–182 (2016)

    Google Scholar 

  12. Srivastava, N., Hinton, G., Krizhevsky, A.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Res. 15, 1929–1958 (2014)

    MathSciNet  MATH  Google Scholar 

  13. Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 2121–2159 (2011)

    MathSciNet  MATH  Google Scholar 

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Correspondence to Dominik Grochala .

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Kucharski, D., Grochala, D., Kajor, M., Kańtoch, E. (2018). A Deep Learning Approach for Valve Defect Recognition in Heart Acoustic Signal. In: Borzemski, L., Świątek, J., Wilimowska, Z. (eds) Information Systems Architecture and Technology: Proceedings of 38th International Conference on Information Systems Architecture and Technology – ISAT 2017. ISAT 2017. Advances in Intelligent Systems and Computing, vol 655. Springer, Cham.

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