HUMAN IDENTIFICATION BASED ON HEART SOUND AUSCULTATION POINT
DOI:
https://doi.org/10.11113/jt.v79.8320Keywords:
Identification, verification, heart sound, MFCC, HMMAbstract
The application of human identification and verification has widely been used for over the past few decades. Drawbacks of such system however, are inevitable as forgery sophisticatedly developed alongside the technology advancement. Thus, this study proposed a research on the possibility of using heart sound as biometric. The main aim is to find an optimal auscultation point of heart sounds from either aortic, pulmonic, tricuspid or mitral that will most suitable to be used as the sound pattern for personal identification. In this study, the heart sound was recorded from 92 participants using a Welch Allyn Meditron electronic stethoscope whereas Meditron Analyzer software was used to capture the signal of heart sounds and ECG simultaneously for duration of 1 minute. The system is developed by a combination Mel Frequency Cepstrum Coefficients (MFCC) and Hidden Markov Model (HMM). The highest recognition rate is obtained at aortic area with 98.7% when HMM has 1 state and 32 mixtures, the lowest Equal Error Rate (EER) achieved was 0.9% which is also at aortic area. In contrast, the best average performance of HMM for every location is obtained at mitral area with 99.1% accuracy and 17.7% accuracy of EER at tricuspid area.
References
Dario, F. 2003. Biometrics: Future Abuses. Computer Fraud and Security. ScienceDirect. 2003(10): 12-14. https://doi.org/10.1016/S1361-3723(03)10008-5.
Ortega-Garcia, J., J. Bigun, D. Reynolds and J. Gonzalez-Rodriguez. 2004. Authentication Gets Personal with Biometrics. Signal Processing Magazine. IEEE. 21(2): 50-62. DOI: 10.1109/MSP.2004.1276113.
Close, J. 2006. An Introduction to Biometrics. US: Motorola. 5-7.
Nashwa El-Bendary, Hameed Al-Qaheri, Hossom M. Zawbaa, Mohamed Hamed, Aboul Ella Hassanien, Qiangfu Zhao, Ajith Abraham. 2010. HSAS: Heart Sound Authentication System. Second World Congress on Nature and Biologically Inspired Computing. Kitakyushu, Fukuoka, Japan. 15-17 Dec. IEEE. DOI: 10.1109/NABIC.2010.5716306.
Jain, A. K., A. Ross, S. Prabhakar. 2004. An Introduction to Biometric Recognition. IEEE Trans. Circuits Syst. Video Technol. 14(1): 4-20. DOI: 10.1109/TCSVT.2003.818349.
Gorman, L. O. 2003. Comparing Passwords, Tokens, and Biometrics for User Authentication. Proceedings IEEE. 91(12): 2021 - 2040. DOI: 10.1109/JPROC.2003.819611.
Beritelli, F., A. Spadaccini, et al. 2009. Human Identity Verification based on Mel Frequency Analysis of Digital Heart Sounds. Proceedings of the 16th International Conference on Digital Signal Processing, IEEE. DOI: 10.1109/ICDSP.2009.5201109.
Phua, K., Tran Huy Dat, Jianfeng Chen and Louis Shue. 2008. Human Identification Using Heart Sound. Pattern Recognition Society. Elsevier. 906-919. DOI: 10.1016/j.patcog.2007.07.018.
Beritelli, F., A. Spadaccini. 2010. A Statistical Approach to Biometric identity Verification Based on Heart Sounds. Fourth International Conference on Emerging Security Information, Systems and Technologies. IEEE. DOI: 10.1109/SECURWARE.2010.23.
William, F.G. 1997. Review of Medical Physiology. Prentice-Hall, Englewood Cliffs, NJ.
Leedomwong, T. and P. Woraratsoontorn. 2009. Wavelet Entropy Detection of Heart Sounds. Proceeding of European Computing Conference. Springer US. 27: 737-744. DOI: 10.1007/978-0-387-84814-3_74.
Iwata, A., N. Ishii and N. Suzumura. 1980. Algorithm for Detection the First and the Second Heart Sounds by Spectral Tracking. Med. & Bio. Eng. & Comp. 19-26. DOI: 10.1007/BF02442475.
Lehner, R. J. and R. M. Rangayyan. 1987. A Three channel microcomputer System for Segmentation and Characterization of the Phonocardiogram. IEEE Transactions on Biomedical Engineering. 34: 485-9. DOI: 10.1109/TBME.1987.326060.
Groch, M. W., J. R. Domnanovich and W. D. Erwin. 1992. A New Heart Sound Gating Devices for Medical Imaging. IEEE Transaction on Biomedical Engineering. 39(3): 307-10. DOI: 10.1109/10.125016.
Davis, S. B. and P. Mermelstein. 1980. Comparison of Parametric Representations for Monosyllabic Word Recognition in Continuously Spoken Sentences. IEEE Transactions on Acoustics, Speech and Signal Processing. 28(4):357-366. DOI: 10.1.1.462.5073.
Garcia, J. O. and C. A. R. Garcia. 2003. Mel-frequency Cepstrum Coefficients Extraction from Infant Cry for Classification of Normal and Pathological Cry with Feed-Forward Neural Networks. Proceedings of the International Joint Conference on Neural Network. 20-24 July. 3140-3145. DOI: 10.1109/IJCNN.2003.1224074.
Rabiner, L. and B. H. Juang. 1993. Fundamentals of Speech Recognition. Prentice Hall Signal Processing Series. Englewood Cliffs.
Picone, J. W. 1993. Signal Modeling Techniques in Speech Recognition. Proceedings of the IEEE. 81(9): 1215-1247. DOI: 10.1109/5.237532.
Li, X., M. Parizeau, R. Plamondon. 2000. Training Hidden Markov Models with Multiple Observations – A Combinatorial Method. IEEE Transactions Pattern Analysis Machinery Intelligent. 22: 371-377. DOI: 10.1109/34.845379.
Yong-Joo Chung. 2007. Classification of Continuous Heart Sound Signals using the Ergodic Hidden Markov Model. Springer-Verlag Berlin Heidelberg. DOI: 10.1007/978-3-540-72847-4_72.
Ricke, A. D., R. J. Povinelli, M. T. Johnson. 2005. Automatic Segmentation of Heart Sound Signals Using Hidden Markov Models. Computers in Cardiology. 953-956. DOI: 10.1109/CIC.2005.1588266.
Chung Y. 2006. A Classification Approach for the Heart Sound Signals Using Hidden Markov Models. SSPR/SPR. 375-383. DOI: 10.1007/11815921_41.
Gamero, L. G. 2003. Detection of the First and Second Heart Sound Using Probabilistic Models. Proceedings of the 25' Annual Intemational Conference of the IEEE EMBS. Cancun, Mexico. 2877-2880. DOI: 10.1109/IEMBS.2003.1280519.
Georgiadis, S. R. 2005. Single-trial Dynamical Estimation of Event-related Potentials: A Kalman Filter-based Approach. IEEE Transcations on Biomedical Engineering. 52(8): 1397-1406. DOI: 10.1109/TBME.2005.851506.
Sh-Hussain Salleh, Hadrina Sheikh Hussain, T. Tian Swee, Chee-Ming Ting, Alias Mohd Noor et al. 2012. Acoustic Cardiac Signal Analysis: A Kalman Filter based Approach. International Journal Nanomedicine. Dove Medical Press Ltd. 7: 2873-2881. DOI: 10.2147/IJN.S32315.
Zhao, Z. and J. Wang. 2011. Heart Sound Identification System. IEEE. 2079-2082. DOI: 10.1109/ICECC.2011.60676
Downloads
Published
Issue
Section
License
Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.
