Automated Auscultative Diagnosis System for Evaluation of Phonocardiogram Signals Associated with Heart Murmur Diseases

Cardiac auscultation that is a still widely used technique to diagnose heart murmurs induced by heart disorders. Taking into account that this method is quite subjective and time consuming, the enhancement of diagnosis techniques would contribute significantly to clinical auscultation. Development of computer-aided auscultative diagnosis systems, which provide more objective and reliable results would be beneficial to reduce the classification errors for the cardiac disorder categories. The presented study uses a combination of Mel–frequency cepstral coefficient (MFCC) and Hidden Markov Model (HMM. Classification experiments were conducted on the 84 heart sound data made up of 6 different types of heart sound. From this, average correct classification rate of 98.8% was achieved when the HMM has 5 states and frame size is 25ms.

Keywords:

Classification, Phonocardiograpy Heart Sounds, Computer-Assisted Diagnosis,

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Bereksi F, Debbal SM. Computerized heart sounds analysis. Comput Biol Med 2008; 38: 263-280.
Kwak C, Kwon OW. Cardiac disorder classification by heart sound signals using murmur likelihood and Hidden Markov Model state likelihood. Signal Process 2012; 6: 326-334.
Eroğlu A. Üfürümlü Çocuğa Yaklaşım. Türk Pediatri Arşivi 2009; 44: 48-52. Topol EJ, Califf RM, editors. Textbook of Cardiovascular Medicine. Philadelphia: Lippincott Williams & Wilkins; 2007.
Abbas KA, Bassam R. Phonocardiography Signal Processing. In: Enderle JD, editors. Synthesis Lectures on Biomedical Engineering. United States. Morgan & Claypool; 2009. p.2-27.
Liu J. Liu W. Wang H. Tao T. Zhang J. A novel envelope extraction method for multichannel heart sounds signal detection. In: Ko FIS, Kwack KD, Hwang S,
Kawata S, Chen YW, editors. ICCIT 2011. International Conference on Computer Science and Information Technology; 2011 Nov 29 - Dec 1; Seogwipo: South-Korea; 2011. p. 630-638.
Arslan A.Yıldız O. Cardiac Arrhythmia Analysis Using Hidden Markov Model and Murmur Diagnosis. In: SIU 2014. 2014 IEEE 22nd Signal Processing and Communications Applications Conference; 2014 Apr 23-25; Trabzon: Turkey; 2014.p. 2031-4.
3M littman heart sounds data set information, 2015. Available: http://www.littmann.com/wps/portal/3M/en_US/3M-Littmann/stethoscope/littmann-learning-institute/heart-lung-sounds/
Ellis D, Mandel M. Automatic analysis of heart sounds using speech recognition techniques. Speech Commun 2005; 48: 1162-81.
Gupta S, Jaafar J, Ahmad W, Bansal A. Feature extraction using MFCC. Signal Image Process 2013; 4: 101-8.
Schafer RW, Rabiner LR. Digital representations of speech signals. Proc IEEE 1975; 63: 662-77.
Vyas G, Kumari B. Speaker recognition system based on MFCC and DCT. International Journal of Engineering and Advanced Technology 2013; 2: 167-9.
Verma S, Kashyap T. Analysis of hearts sounds as biometric using MFCC&Linear SVM classifier. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering 2014; 3, 6626-33.
Gupta CN, Palaniappan S, Swaminathan S, Krishnan S. Neural network classification of homomorphic segmented heart sounds. Appl Soft Comput 2007; 7: 286–97.
Abdel I, Akula R. Artificial intelligence algorithm for heart disease diagnosis using phonocardiogram signals. IEEE T on Bio-med Eng 2012; 51: 1196-1202.
Geranmayeh A, Babaei S. Heart sound reproduction based on neural network classification of cardiac valve disorders using wavelet transforms of PCG signals. Comput Biol Med 2009; 39: 8-15.
Alajarin JM, Candel JL, Merino RR. Classification and diagnosis of heart sounds and murmurs using artificial neural networks. In: Mira J, Alvarez JR, editors. IWINAC '07 .Proceedings of the 2nd International Work-conference on The Interplay Between Natural and Artificial Computation; 2007 Jun 18-21; Berlin: Germany; 2007.p.303-12.
Uğuz H. A biomedical system based on artificial neural network and principal component analysis for diagnosis of the heart valve diseases. J Med Syst 2012; 36: 61-72.
Sinha R, Aggarwal Y, Das B. Backpropagation artificial neural network classifier to detect changes in heart sound due to mitral valve regurgitation. J Med Syst 2007; 31: 205-9.
Phatiwuttipat P. Kongprawechon W. Tungpimolrut K. Yuenyong S. Cardiac auscultation analysis system with neural network and SVM technique. In: ECTI-CON 2011.Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology 8th International Conference; 2011 May 17-19; Khon Kaen: Thailand; 2011.p.1027-30.
Rios-Gutierrez F. Alba-Flores R. Strunic S. Recognition and classification of cardiac murmurs using ANN and segmentation. In: Gonzalez JLV, editor. CONIELECOMP 2012.Electrical Communications and Computers 22nd International Conference; 2012 Feb 27-29; Puebla:Mexico; 2012.p.219-23.
Salman AH. Mengko TR. Mengko RKW. Langi AZR. Review of digital heart sound classification methods via artificial neural networks. In: ICICI-BME 2013.Instrumentation, Communications, Information Technology, and Biomedical Engineering 3rd International Conference; 2013 Nov 7-8; Bandung: Indonesia; 2013.p.425-30.
Zhuravlev YI, editor. Pattern Recognition and Image Analysis. Russia:Springer; 2007.
Schmidt SE, Holst-Hansen C, Graff C, Toft E, Struijk JJ. Segmentation of heart sound recordings from an electronic stethoscope by a duration dependent hidden markov model. Physiol Meas 2010; 31, 513-29.
Ricke AD, Johnson MT, Povinelli RJ. Automatic segmentation of heart sound signals using hidden markov models. Comput Cardiol 2005; 953−6.
Hermansky H, Malayath N. Data-driven spectral basis functions for automatic speech recognition. Speech Commun 2003; 40: 449-66.
Hussain S. Salleh Kamarulafizam I. Noor AM. Harris AA. Oemar H. Yusoff K. Classification of heart sound based on multipoint auscultation system. In:Bouabdallah A, editor. WoSSPA 2013.Systems, Signal Processing and their Applications 8th International Workshop; 2013 May 12-15; Zeralda: Algeria; 2013.p.174-9.
Sengur A. Support vector machine ensembles for intelligent diagnosis of valvular heart disease. Journal of Medical Systems 2012; 36: 2649-55.
Markaki M. Germanakis I. Stylianou Y. Automatic classification of systolic heart murmurs. In: Ward RK, editor. ICASSP. 2013.Acoustics, Speech and Signal Processing IEEE International Conference; 2013 May 26-31; Vancouver: Canada; 2013.p.1301-5.
Chatunapalak I. Phatiwuttipat P. Kongprawechnon W. Tungpimolrut K. Childhood musical murmur classification with support vector machine technique. In: ECTI-CON 2012.Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology 9th International Conference; 2012 May 16-18; Phetchaburi: Thailand; 2012. p.1-4.
Kumar D. Carvalho P. Antunes M. Paiva RP. Henriques J. Heart murmur classification with feature selection. In: Herrera L, editor. 32nd Annual International Conference of the IEEE EMBS; 2010 Aug 31- Sep 4; Buenos Aires: Argentina; 2010. 4566-9.
Fu W, Yang X, Wang Y. Heart sound diagnosis based on DTW and MFCC. IEE P-Vis Image Sign 2010; 6: 2920-3.
Shen C, Choy F, Chen Y, Wang S. A modular approach to computer-aided auscultation: analysis and parametric characterization of murmur acoustic qualities. Comput Biol Med 2013; 43: 798–805.
Bilge HŞ, Volkan V. Ultrason Görüntülerinde Prostat Sınırının Bulunması. J Fac Eng Archit Gazi Uni 2007; 22: 407-13.
Yıldız O, Tez M, Bilge HŞ, Akçayol MA, Güler İ. Meme Kanseri Sınıflandırması için Veri Füzyonu ve Genetik Algoritma Tabanlı Gen Seçimi. J Fac Eng Archit Gazi Uni 2012; 27: 659-68.
Wang P, Lim CS, Chauhan S, Foo JY, Anantharaman, V. Phonocardiographic Signal Analysis Method Using a Modified Hidden Markov Model. Anals of Biomedical Engineering 2007; 35: 367-74.
Lima CS. Cardoso MJ. Phonocardiogram Segmentation By Using Hidden Markov Models. In: Gardner, JW, editor. ICBEB 2007.Proceedings of the Fifth IASTED International Conference on Biomedical Engineering; 2007 Feb 14-16; Innsbruck: Austria; 2007. p. 415-8.
Hang W. Sahong K. Keunsung B. Hidden Markov Model With Heart Sound Signals For Identification of Heart Diseases. In: Burgess, M, editor. ICA 2010. Proceedings of 20th International Congress on Acoustics; 2010 Aug 23-27; Syndey: Autralia; 2010.