Segmentation of heart sound signals based on duration hidden Markov model_Journal of Biomedical Engineering

Authors：

KUI Haoran ¹ , PAN Jiahua ² , ZONG Rong ¹ , YANG Hongbo ² , SU Wei ¹ ,  WANG Weilian ¹

1. School of Information Science and Engineering, Yunnan University, Kunming 650504, P.R.China;
2. Yunnan Fuwai Cardiovascular Disease Hospital, Kunming 650102, P.R.China;

Corresponding author：

WANG Weilian, Email: wlwang_47@126.com

Keywords：

heart sound segmentation; autocorrelation estimation; Gaussian mixture distribution; duration hidden Markov model; Viterbi algorithm

DOI：

10.7507/1001-5515.201911061

Video：

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Abstract Full text Figures/Tables Video References Cited by

Heart sound segmentation is a key step before heart sound classification. It refers to the processing of the acquired heart sound signal that separates the cardiac cycle into systolic and diastolic, etc. To solve the accuracy limitation of heart sound segmentation without relying on electrocardiogram, an algorithm based on the duration hidden Markov model (DHMM) was proposed. Firstly, the heart sound samples were positionally labeled. Then autocorrelation estimation method was used to estimate cardiac cycle duration, and Gaussian mixture distribution was used to model the duration of sample-state. Next, the hidden Markov model (HMM) was optimized in the training set and the DHMM was established. Finally, the Viterbi algorithm was used to track back the state of heart sounds to obtain S₁, systole, S₂ and diastole. 500 heart sound samples were used to test the performance of our algorithm. The average evaluation accuracy score (F₁) was 0.933, the average sensitivity was 0.930, and the average accuracy rate was 0.936. Compared with other algorithms, the performance of our algorithm was more superior. It is proved that the algorithm has high robustness and anti-noise performance, which might provide a novel method for the feature extraction and analysis of heart sound signals collected in clinical environments.

Citation： KUI Haoran, PAN Jiahua, ZONG Rong, YANG Hongbo, SU Wei, WANG Weilian. Segmentation of heart sound signals based on duration hidden Markov model. Journal of Biomedical Engineering, 2020, 37(5): 765-774. doi: 10.7507/1001-5515.201911061 Copy

1.	Yadav A, Dutta M K, Travieso C M, et al. Automatic classification of normal and abnormal PCG recording heart sound recording using Fourier transform// 2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI). San Carlos: IEEE, 2018: 1-9.
2.	Roy J K, Roy T S, Mandal N, et al. A Simple technique for heart sound detection and identification using Kalman filter in real time analysis// 2018 International Symposium in Sensing and Instrumentation in IoT Era (ISSI). Shanghai: IEEE, 2018: 1-8.
3.	Liang H, Lukkarinen S, Hartimo I. Heart sound segmentation algorithm based on heart sound envelogram//Computers in Cardiology 1997. Lund: IEEE, 1997: 105-108.
4.	Hamidi M, Ghassemian H, Imani M. Classification of heart sound signal using curve fitting and fractal dimension. Biomed Signal Process Control, 2018, 39: 351-359.
5.	Clifford G D, Liu C, Moody B, et al. Classification of normal/abnormal heart sound recordings: The PhysioNet/Computing in Cardiology Challenge 2016// 2016 Computing in Cardiology Conference (CinC). Vancouver: IEEE, 2016: 609-612.
6.	Vepa J, Tolay P, Jain A. Segmentation of heart sounds using simplicity features and timing information// 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. Las Vegas: IEEE, 2008: 469-472.
7.	Moukadem A, Dieterlen A, Hueber N, et al. A robust heart sounds segmentation module based on S-transform. Biomed Signal Process Control, 2013, 8(3): 273-281.
8.	Sepehri A, Gharehbaghi A, Dutoit T, et al. A novel method for pediatric heart sound segmentation without using the ECG. Comput Meth Prog Bio, 2010, 99(1): 43-48.
9.	Messner E, Zöhrer M, Pernkopf F. Heart sound segmentation—An event detection approach using deep recurrent neural networks. IEEE Trans Biomed Eng, 2018, 65(9): 1964-1974.
10.	Yan Z, Jiang Z, Miyamoto A, et al. The moment segmentation analysis of heart sound pattern. Comput Meth Prog Bio, 2010, 98(2): 140-150.
11.	Sharma L N. Multiscale analysis of heart sound for segmentation using multiscale Hilbert envelope// 2015 13th International Conference on ICT and Knowledge Engineering (ICT & Knowledge Engineering 2015). Bangkok: IEEE, 2015: 33-37.
12.	Gill D, Gavrieli N, Intrator N. Detection and identification of heart sounds using homomorphic envelogram and self-organizing probabilistic model// Computers in Cardiology, 2005. Lyon: IEEE, 2005: 957-960.
13.	Oliveira J H S. Subject-driven supervised and unsupervised Hidden Markov Models for heart sound segmentation in real noisy environments. 2018.
14.	Alexander B, Nallathambi G, Selvaraj N. Screening of heart sounds using hidden Markov and gammatone filterbank models// 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). Orlando: IEEE, 2018: 1460-1465.
15.	Illanes-Manriquez A, Zhang Q. An algorithm for QRS onset and offset detection in single lead electrocardiogram records// 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Lyon: IEEE, 2007: 541-544.
16.	Laguna P, Jané R, Caminal P. Automatic detection of wave boundaries in multilead ECG signals: Validation with the CSE database. Comput Biomed Res, 1994, 27(1): 45-60.
17.	Stein P D. Understanding heart sounds and murmurs, third edition. Clin Cardiol, 2010, 17(8): 458-459.
18.	Hassani K, Bajelani K, Navidbakhsh M, et al. Heart sound segmentation based on homomorphic filtering. Perfusion, 2014, 29(4): 351-359.
19.	Amrutha B, Arora M. Comparison of envelope detection and signal normalization methods for foetal heart rate extraction from foetal heart sound// 2018 Fourth International Conference on Biosignals, Images and Instrumentation (ICBSII). Chennai: IEEE, 2018: 206-212.
20.	Rasmussen C E. The infinite Gaussian mixture model// Solla S A, Leen T K, Müller K R. Advances in Neural Information Processing Systems 12. Cambridge: MIT Press, 2000, 11: 554-560.
21.	Rabiner L R. A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE, 1989, 77(2): 257-286.
22.	Ross S M. Introduction to probability models. New York: Academic Press, 2014.
23.	Hughes N P, Term H. Probabilistic models for automated ECG interval analysis. Oxford: University of Oxford, 2006.
24.	Lalmas M, van Rijsbergen C J. Information retrieval: Uncertainty and logics: Advanced models for the representation and retrieval of information. Kluwer Academic Publishers, 1998.

1. Yadav A, Dutta M K, Travieso C M, et al. Automatic classification of normal and abnormal PCG recording heart sound recording using Fourier transform// 2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI). San Carlos: IEEE, 2018: 1-9.
2. Roy J K, Roy T S, Mandal N, et al. A Simple technique for heart sound detection and identification using Kalman filter in real time analysis// 2018 International Symposium in Sensing and Instrumentation in IoT Era (ISSI). Shanghai: IEEE, 2018: 1-8.
3. Liang H, Lukkarinen S, Hartimo I. Heart sound segmentation algorithm based on heart sound envelogram//Computers in Cardiology 1997. Lund: IEEE, 1997: 105-108.
4. Hamidi M, Ghassemian H, Imani M. Classification of heart sound signal using curve fitting and fractal dimension. Biomed Signal Process Control, 2018, 39: 351-359.
5. Clifford G D, Liu C, Moody B, et al. Classification of normal/abnormal heart sound recordings: The PhysioNet/Computing in Cardiology Challenge 2016// 2016 Computing in Cardiology Conference (CinC). Vancouver: IEEE, 2016: 609-612.
6. Vepa J, Tolay P, Jain A. Segmentation of heart sounds using simplicity features and timing information// 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. Las Vegas: IEEE, 2008: 469-472.
7. Moukadem A, Dieterlen A, Hueber N, et al. A robust heart sounds segmentation module based on S-transform. Biomed Signal Process Control, 2013, 8(3): 273-281.
8. Sepehri A, Gharehbaghi A, Dutoit T, et al. A novel method for pediatric heart sound segmentation without using the ECG. Comput Meth Prog Bio, 2010, 99(1): 43-48.
9. Messner E, Zöhrer M, Pernkopf F. Heart sound segmentation—An event detection approach using deep recurrent neural networks. IEEE Trans Biomed Eng, 2018, 65(9): 1964-1974.
10. Yan Z, Jiang Z, Miyamoto A, et al. The moment segmentation analysis of heart sound pattern. Comput Meth Prog Bio, 2010, 98(2): 140-150.
11. Sharma L N. Multiscale analysis of heart sound for segmentation using multiscale Hilbert envelope// 2015 13th International Conference on ICT and Knowledge Engineering (ICT & Knowledge Engineering 2015). Bangkok: IEEE, 2015: 33-37.
12. Gill D, Gavrieli N, Intrator N. Detection and identification of heart sounds using homomorphic envelogram and self-organizing probabilistic model// Computers in Cardiology, 2005. Lyon: IEEE, 2005: 957-960.
13. Oliveira J H S. Subject-driven supervised and unsupervised Hidden Markov Models for heart sound segmentation in real noisy environments. 2018.
14. Alexander B, Nallathambi G, Selvaraj N. Screening of heart sounds using hidden Markov and gammatone filterbank models// 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). Orlando: IEEE, 2018: 1460-1465.
15. Illanes-Manriquez A, Zhang Q. An algorithm for QRS onset and offset detection in single lead electrocardiogram records// 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Lyon: IEEE, 2007: 541-544.
16. Laguna P, Jané R, Caminal P. Automatic detection of wave boundaries in multilead ECG signals: Validation with the CSE database. Comput Biomed Res, 1994, 27(1): 45-60.
17. Stein P D. Understanding heart sounds and murmurs, third edition. Clin Cardiol, 2010, 17(8): 458-459.
18. Hassani K, Bajelani K, Navidbakhsh M, et al. Heart sound segmentation based on homomorphic filtering. Perfusion, 2014, 29(4): 351-359.
19. Amrutha B, Arora M. Comparison of envelope detection and signal normalization methods for foetal heart rate extraction from foetal heart sound// 2018 Fourth International Conference on Biosignals, Images and Instrumentation (ICBSII). Chennai: IEEE, 2018: 206-212.
20. Rasmussen C E. The infinite Gaussian mixture model// Solla S A, Leen T K, Müller K R. Advances in Neural Information Processing Systems 12. Cambridge: MIT Press, 2000, 11: 554-560.
21. Rabiner L R. A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE, 1989, 77(2): 257-286.
22. Ross S M. Introduction to probability models. New York: Academic Press, 2014.
23. Hughes N P, Term H. Probabilistic models for automated ECG interval analysis. Oxford: University of Oxford, 2006.
24. Lalmas M, van Rijsbergen C J. Information retrieval: Uncertainty and logics: Advanced models for the representation and retrieval of information. Kluwer Academic Publishers, 1998.

Journal of Biomedical Engineering

Segmentation of heart sound signals based on duration hidden Markov model

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