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http://dx.doi.org/10.7776/ASK.2010.29.2.133

New Temporal Features for Cardiac Disorder Classification by Heart Sound  

Kwak, Chul (충북대학교 전자정보대학 제어로봇공학과)
Kwon, Oh-Wook (충북대학교 전자정보대학 제어로봇공학과)
Publication Information
The Journal of the Acoustical Society of Korea / v.29, no.2, 2010 , pp. 133-140 More about this Journal
Abstract
We improve the performance of cardiac disorder classification by adding new temporal features extracted from continuous heart sound signals. We add three kinds of novel temporal features to a conventional feature based on mel-frequency cepstral coefficients (MFCC): Heart sound envelope, murmur probabilities, and murmur amplitude variation. In cardiac disorder classification and detection experiments, we evaluate the contribution of the proposed features to classification accuracy and select proper temporal features using the sequential feature selection method. The selected features are shown to improve classification accuracy significantly and consistently for neural network-based pattern classifiers such as multi-layer perceptron (MLP), support vector machine (SVM), and extreme learning machine (ELM).
Keywords
Heart Sound Classification; Feature Extraction; Extreme Learning Machine (ELM);
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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1 F. Schwenker, "Hierarchical support vector machines for multi-class pattern recognition," International Conference on Knowledge-based Intelligent Engineering Systems & Allied Technologies, Brighton, UK, vol. 2, pp. 561 565, 2000.
2 Y. Liu and Y.F. Zheng, "One-against-all multi-class SVM classification using reliability measures," IEEE International Joint Conference on Neural Networks, vol. 2, pp. 849-854, 2005.
3 Simon Haykin, Neural Networks: A comprehensive foundation, 2nd ed, Prentice Hall, 1999.
4 University of Washington department of medicine, Technique: Heart Sounds & Murmurs,http://depts.washington.edu/physdx/heart/tech.html
5 Texas heart institute at St. Luke's episcopal hospital, Heart Sound and Murmur Recordings, http://www.texasheart.org/Education/CME/explore/events/e ventdetail_5469.cfm
6 R. A. Azra'ai, M. N. bin Taib, and N. M. Tahir, "Artificial neural network for identification of heart problem," in Proc. Signal Processing and Communication Systems 2008, Gold Coast, Australia, pp. 1-6, Dec. 2008.
7 A. Ricke, R. Provinelli, and M. Johnson, "Automatic segmentation of heart sound signals using hidden Markov models," in Proc. Computers in Cardiology 2005, pp. 953-956, 2005.
8 김희근, 정용주, "Ergodic hidden Markov model을 이용한 연속 심음분류에 관한 연구," 대한의료정보학회지, 13권, 1호, 35-41쪽, 2007.
9 M. Obayya and F. Abou-Chadi, "Classifying some cardiac abnormalities using heart rate variability signals," in Proc. 25th National Radio Science Conference 2008, Tanta Univ., Egypt, pp. 1-8, March. 2008.
10 S. E. Schmidt, E. Toft, C. Holst-Hansen, C. Graff, and J. J. Struijk, "Segmentation of heart sound recordings from an electronic stethoscope by a duration dependent hidden- Markov model," in Proc. Computers in Cardiology 2008, Bologna, Italy, pp. 345-348, Sep. 2008.
11 C. Kwak and O.-W. Kwon, "Cardiac disorder classification based on extreme learning machine," in Proc. World Academy of Science, Engineering and Technology 2008, Bangkok, Thailand, vol. 36, pp. 1260-1263, 2008.
12 곽철, 권오욱, "자동 분할과 ELM을 이용한 심장질환 분류 성능 개선," 한국음향학회지, 28권, 1호, 32-43쪽, 2009.   과학기술학회마을
13 G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, "Extreme learning machine: A new learning scheme of feedforward neural networks," 2004 International Joint Conference on Neural Networks (IJCNN'2004), vol. 2, pp. 985-990, July. 2004.
14 G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, "Extreme learning machine: Theory and applications," Neurocomputing, vol. 70, pp. 489-501, 2006.   DOI   ScienceOn
15 Daniel Mason, Listening to the Heart: A Comprehensive Collection of Heart Sounds and Murmurs, F. A. Davis Company, Philadelphia, 2000.
16 M. Leshno, V. Y. Lin, A. Pinkus, and S. Schocken, "Multilayer feedforward networks with a nonpolynomial activation function can approximate any function," Neural Networks, vol. 6, pp. 861-867, 1993.   DOI   ScienceOn
17 M.F. Moller, "A scaled conjugate gradient algorithm for fast supervised learning," Neural Networks, vol. 4, no. 4, pp. 525-533, 1993.
18 R. K. Sinha, Y. Aggarwal, and B. N. Das, "Backpropagation artificial neural network classifier to detect changes in heart sound due to mitral valve regurgitation," Journal of Medical Systems, vol. 31, pp. 205-209, 2007.   DOI   ScienceOn
19 C. N. Gupta, R. Palaniappan, S. Swaminathan, and S. M. Krishnan, "Neural network classification of homomorphic segmented heart sounds," Applied Soft Computing, vol. 7, pp. 286-297, 2007.   DOI   ScienceOn