Browse > Article
http://dx.doi.org/10.7776/ASK.2011.30.3.149

Heart Sound-Based Cardiac Disorder Classifiers Using an SVM to Combine HMM and Murmur Scores  

Kwak, Chul (충북대학교 전자정보대학 제어로봇공학과)
Kwon, Oh-Wook (충북대학교 전자정보대학 제어로봇공학과)
Publication Information
The Journal of the Acoustical Society of Korea / v.30, no.3, 2011 , pp. 149-157 More about this Journal
Abstract
In this paper, we propose a new cardiac disorder classification method using an support vector machine (SVM) to combine hidden Markov model (HMM) and murmur existence information. Using cepstral features and the HMM Viterbi algorithm, we segment input heart sound signals into HMM states for each cardiac disorder model and compute log-likelihood (score) for every state in the model. To exploit the temporal position characteristics of murmur signals, we divide the input signals into two subbands and compute murmur probability of every subband of each frame, and obtain the murmur score for each state by using the state segmentation information obtained from the Viterbi algorithm. With an input vector containing the HMM state scores and the murmur scores for all cardiac disorder models, SVM finally decides the cardiac disorder category. In cardiac disorder classification experimental results, the proposed method shows the relatively improvement rate of 20.4 % compared to the HMM-based classifier with the conventional cepstral features.
Keywords
Cardiac disorder classification; HMM state score; Murmur score;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 S. Young, G. Evermann, M. Gales, T. Hain, D. Kershaw, X. Liu, G. Moore, J. Odell, D. Ollason, D. Povey, V. Valtchev and P. Woodland, Hidden Markov Model Toolkit v3.4, Cambridge University, 2006.
2 D. Mason, Listening to the Heart: A Comprehensive Collection of Heart Sounds and Murmurs, F.A. Davis Company, Philadelphia, 2000.
3 University of Washington department of medicine, Technique: Heart Sounds & Murmurs, http://depts.washington.edu/physdx/heart/tech.html
4 Texas heart institute as St. Luke's episcopal hospital, Heart Sound and Murmur Recordings, http://www.texasheart.org/Education/CME/explore/events/eventdetail_5469.cfm
5 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, Dec. 2008.
6 곽철, 권오욱, "자동 분할과 ELM을 이용한 심장질환 분류 성능 개선", 한국음향학회지, 28권, 1호, 32-43쪽, 2009.
7 곽철, 권오욱, "심음 기반의 심장질환 분류를 위한 새로운 시간영역 특징", 한국음향학회지, 29권, 2호, 133-140쪽, 2010.
8 C. Donghui and L. Zhijing, "A new text categorization method based on HMM and SVM," in Proc. 2010 2nd International Conference on Computer Engineering and Technology, Chengdu, China, pp. 383-386, Apr. 2010.
9 J.D. Cai and R.W. Yan, "Application of FCM-HMM-SVM based mixed method for fault diagnosis of power electronic circuit," in Proc. International Conference on Mechanic Automation and Control Engineering, Wuhan, China, pp. 3982-3985, Jun. 2010.
10 S. Haykin, Neural Networks: A comprehensive foundation, 2nd ed., Prentice Hall, 1999.
11 S.M. Arcasoy, J.D. Christie, V.A. Ferrari, M.S. Sutton, D.A. Zisman, N.P. Blumenthal, A. Pochettino and R.M. Kotloff, "Echocardiographic assessment of pulmonary hypertension in patients with advanced lung disease," American journal of respiratory and critical care medicine, vol. 167, pp. 735-740, 2003.   DOI   ScienceOn
12 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
13 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
14 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.
15 A. Ricke, R. Provinelli and M. Johnson, "Automatic segmentation of heart sound signals using hidden Markov models," Proc. Computers in Cardiology 2005, Lyon, France, pp. 953-956, Sep. 2005.
16 김희근, 정용주, "Ergodic hidden Markov model을 이용한 연속심 음분류에 관한 연구," 대한의료정보학회지, 13권, 1호, 35-41쪽, 2007.
17 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, Mar. 2008.
18 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.