Abstract
Auscultation is the primary tool for detection and diagnosis of cardiovascular diseases in hospitals and home visits. This fact has led in the recent years to the development of automatic methods for heart sound classification, thus allowing for detecting cardiovascular pathologies in an effective way. The aim of this paper is to review recent methods for automatic classification and to apply several signal processing techniques in order to evaluate them in the PhysioNet/CinC Challenge 2016 results. For this purpose, the records of the open database PysioNet/Computing are modified by segmentation or filtering methods and the results were tested using the challenge best ranked algorithms. Results show that an adequate preprocessing of data and subsequent feature selection may improve the performance of machine learning and classification techniques.
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References
WHO. 2016 world statistics on cardiovascular disease. Technical report (2016). http://www.who.int/mediacentre/factsheets/fs317/en/
Clifford, G.D., Liu, C., Moody, B., Springer, D., Silva, I., Li, Q., Mark, R.G.: Classification of normal/abnormal heart sound recordings: the physionet/computing in cardiology challenge 2016. Comput. Cardiol, pp. 609–612 (2016)
Ortiz, J.J.G., Phoo, C.P., Wiens, J.: Heart sound classification based on temporal alignment techniques. In: International Conference on Computing in Cardiology 2016 (2016)
Liu, C., Springer, D., Li, Q., Moody, B., Juan, R.A., Chorro, F.J., Castells, F., Roig, J.M., Silva, I., Johnson, A.E., et al.: An open access database for the evaluation of heart sound algorithms. Physiol. Meas. 37(12), 2181 (2016)
Classification of normal/abnormal heart sound recordings: the physionet/cin cardiology challenge 2016 (2016). http://physionet.org/challenge/2016/
Akay, Y., Akay, M., Welkowitz, W., Kostis, J.: Noninvasive detection of coronary artery disease. IEEE Eng. Med. Biol. Mag. 13(5), 761–764 (1994)
Liang, H., Nartimo, I.: A feature extraction algorithm based on wavelet packet decomposition for heart sound signals. In: Proceedings of the IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis, pp. 93–96. IEEE (1998)
Uguz, H.: Adaptive neuro-fuzzy inference system for diagnosis of the heart valve diseases using wavelet transform with entropy. Neural Comput. Appl. 21(7), 1617–1628 (2012)
Zheng, Y., Guo, X., Ding, X.: A novel hybrid energy fraction and entropy-based approach for systolic heart murmurs identification. Expert Syst. Appl. 42(5), 2710–2721 (2015)
Ari, S., Hembram, K., Saha, G.: Detection of cardiac abnormality from PCG signal using LMS based least square SVM classifier. Expert Syst. Appl. 37(12), 8019–8026 (2010)
Wang, P., Lim, C.S., Chauhan, S., Foo, J.Y.A., Anantharaman, V.: Phonocardiographic signal analysis method using a modified hidden Markov model. Ann. Biomed. Eng. 35(3), 367–374 (2007)
Saracoglu, R.: Hidden Markov model-based classification of heart valve disease with PCA for dimension reduction. Eng. Appl. Artif. Intell. 25(7), 1523–1528 (2012)
Bentley, P., Grant, P., McDonnell, J.: Time-frequency and time-scale techniques for the classification of native and bioprosthetic heart valve sounds. IEEE Trans. Bio-med. Eng. 45(1), 125 (1998)
Quiceno-Manrique, A., Godino-Llorente, J., Blanco-Velasco, M., Castellanos-Dominguez, G.: Selection of dynamic features based on time-frequency representations for heart murmur detection from phonocardiographic signals. Ann. Biomed. Eng. 38(1), 118–137 (2010)
Zabihi, M., Rad, A.B., Kiranyaz, S., Gabbouj, M., Katsaggelos, A.K.: Heart sound anomaly and quality detection using ensemble of neural networks without segmentation. In: International Conference on Computing in Cardiology 2016 (2016)
Moukadem, A., Dieterlen, A., Hueber, N., Brandt, C.: A robust heart sounds segmentation module based on S-transform. Biomed. Signal Process. Control 8(3), 273–281 (2013)
Varghees, V.N., Ramachandran, K.: A novel heart sound activity detection framework for automated heart sound analysis. Biomed. Signal Process. Control 13, 174–188 (2014)
Tang, H., Li, T., Qiu, T., Park, Y.: Segmentation of heart sounds based on dynamic clustering. Biomed. Signal Process. Control 7(5), 509–516 (2012)
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)
Yuenyong, S., Nishihara, A., Kongprawechnon, W., Tungpimolrut, K.: A framework for automatic heart sound analysis without segmentation. Biomed. Eng. Online 10(1), 1 (2011)
Deng, S.W., Han, J.Q.: Towards heart sound classification without segmentation via autocorrelation feature and diffusion maps. Future Gener. Comput. Syst. 60, 13–21 (2016)
Balili, C.C., Sobrepena, M.C.C., Naval, P.C.: Classification of heart sounds using discrete and continuous wavelet transform and random forests. In: 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), pp. 655–659. IEEE (2015)
Moukadem, A., Dieterlen, A., Brandt, C.: Shannon entropy based on the S-transform spectrogram applied on the classification of heart sounds. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 704–708. IEEE (2013)
Potes, C., Parvaneh, S., Rahman, A., Conroy, B.: Ensemble of feature-based and deep learning-based classifiers for detection of abnormal heart sounds. In: International Conference on Computing in Cardiology 2016 (2016)
Godino-Llorente, J.I., Gomez-Vilda, P.: Automatic detection of voice impairments by means of short-term cepstral parameters and neural network based detectors. IEEE Trans. Biomed. Eng. 51(2), 380–384 (2004)
Rubin, J., Abreu, R., Ganguli, A., Nelaturi, S., Matei, I., Sricharan, K.: Classifying heart sound recordings using deep convolutional neural networks and mel-frequency cepstral coefficients. In: International Conference on Computing in Cardiology 2016 (2016)
Homsi, M.N., Medina, N., Hernandez, M., Quintero, N., Perpiñan, G., Warrick, A.Q.P.: Automatic heart sound recording classification using a nested set of ensemble algorithms. In: International Conference on Computing in Cardiology 2016 (2016)
Goda, M.A., Hajas, P.: Morphological determination of pathological PCG signals by time and frequency domain analysis. In: International Conference on Computing in Cardiology 2016 (2016)
Springer, D.B., Tarassenko, L., Clifford, G.D.: Logistic regression-HSMM-based heart sound segmentation. IEEE Trans. Biomed. Eng. 63(4), 822–832 (2016)
Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier, Amsterdam (2011)
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This work has been partially supported by the project TIN2015-67020-P of the Spanish Ministry of Economy and Competitiveness.
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Pérez-Guzmán, R.E., García-Bermúdez, R., Rojas-Ruiz, F., Céspedes-Pérez, A., Ojeda-Riquenes, Y. (2017). Evaluation of Algorithms for Automatic Classification of Heart Sound Signals. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2017. Lecture Notes in Computer Science(), vol 10208. Springer, Cham. https://doi.org/10.1007/978-3-319-56148-6_48
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DOI: https://doi.org/10.1007/978-3-319-56148-6_48
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