Sınam Ajıtkumar SINGH, Swanırbhar MAJUMDER

Short unsegmented PCG classification based on ensemble classifier

Diseases associated with the heart are one of the main reasons of death worldwide. Hence, early examination of the heart is important. For analysis of cardiac disorders, a study of heart sounds is a crucial and beneficial approach. Still, automated classification of heart sounds is a challenging task that mainly depends on segmentation of heart sounds and derivation of features using segmented samples. In the literature available for PCG classification provided by PhysioNet/CinC Challenge 2016, most of the research has focused on enhancing the accuracy of the classification model based on complicated segmentation processes and has failed to improve the sensitivity. In this paper, we present an automated heart sound classification by eliminating the segmentation steps using multidomain features, which results in enhanced sensitivity. The study is based on homomorphic envelogram, mel frequency cepstral coefficient MFCC , power spectral density PSD , and multidomain feature extraction. The extracted features are trained using the 5-fold cross-validation method based on an ensemble boosting algorithm over 100 independent iterations. Our proposed design is evaluated using public datasets published in PhysioNet/Computers in Cardiology Challenge 2016. Accuracy of 92.47% with improved sensitivity of 94.08% and specificity of 91.95% is achieved using our model. The output performance proves that our proposed model offers superior performance results.

Keywords:

Phonocardiogram, mel frequency cepstral coefficient, homomorphic filtering, ensemble classifier feature extraction, machine learning,

PDF

___

[1] Lloyd-Jones D, Adams RJ, Brown TM, Carnethon M, Dai S et al. Heart disease and stroke statistics–2010 update: a report from the American Heart Association. Circulation 2010; 121 (7): e46
[2] Etchells E, Bell C, Robb K. Does this patient have an abnormal systolic murmur? JAMA 1997; 277 (7): 564-571.
[3] Jabbari S, Ghassemian H. Modeling of heart systolic murmurs based on multivariate matching pursuit for diagnosis of valvular disorders. Computers in Biology and Medicine 2011; 41 (9): 802-811.
[4] Ahlstrom C, Hult P, Rask P, Karlsson JE, Nylander E et al. Feature extraction for systolic heart murmur classification. Annals of Biomedical Engineering 2006; 34 (11): 1666-1677.
[5] Maglogiannis I, Loukis E, Zafiropoulos E, Stasis A. Support vectors machine-based identification of heart valve diseases using heart sounds. Computer Methods and Programs in Biomedicine 2009; 95 (1): 47-61.
[6] Hassani K, Bajelani K, Navidbakhsh M, Doyle DJ, Taherian F. Heart sound segmentation based on homomorphic filtering. Perfusion 2014; 29 (4): 351-359.
[7] Gupta CN, Palaniappan R, Swaminathan S, Krishnan SM. Neural network classification of homomorphic segmented heart sounds. Applied Soft Computing 2007; 7 (1): 286-297.
[8] Springer DB, Tarassenko L, Clifford GD. Logistic regression-HSMM-based heart sound segmentation. IEEE Transactions on Biomedical Engineering 2015; 63 (4): 822-832.
[9] Schmidt SE, Holst-Hansen C, Graff C, Toft E, Struijk JJ. Segmentation of heart sound recordings by a durationdependent hidden Markov model. Physiological Measurement 2010; 31 (4): 513.
[10] Golpaygani AT, Abolpour N, Hassani K, Bajelani K, Doyle DJ. Detection and identification of S1 and S2 heart sounds using wavelet decomposition method. International Journal of Biomathematics 2015; 8 (6): 1550078.
[11] Whitaker BM, Suresha PB, Liu C, Clifford GD, Anderson DV. Combining sparse coding and time-domain features for heart sound classification. Physiological Measurement 2017; 38 (8): 1701.
[12] Dominguez-Morales JP, Jimenez-Fernandez AF, Dominguez-Morales MJ, Jimenez-Moreno G. Deep neural networks for the recognition and classification of heart murmurs using neuromorphic auditory sensors. IEEE Transactions on Biomedical Circuits and Systems 2017; 12 (1): 24-34.
[13] Abdollahpur M, Ghaffari A, Ghiasi S, Mollakazemi MJ. Detection of pathological heart sounds. Physiological Measurement 2017; 38 (8): 1616.
[14] Homsi MN, Warrick P. Ensemble methods with outliers for phonocardiogram classification. Physiological Measurement 2017; 38 (8): 1631.
[15] Zhang W, Han J, Deng S. Heart sound classification based on scaled spectrogram and tensor decomposition. Expert Systems with Applications 2017; 84: 220-231.
[16] Messner E, Zöhrer M, Pernkopf F. Heart sound segmentation—an event detection approach using deep recurrent neural networks. IEEE Transactions on Biomedical Engineering 2018; 65 (9): 1964-1974.
[17] Plesinger F, Viscor I, Halamek J, Jurco J, Jurak P. Heart sounds analysis using probability assessment. Physiological Measurement 2017; 38 (8): 1685.
[18] Kamson AP, Sharma LN, Dandapat S. Multi-centroid diastolic duration distribution based HSMM for heart sound segmentation. Biomedical Signal Processing and Control 2019; 48: 265-272.
[19] Tang H, Dai Z, Jiang Y, Li T, Liu C. PCG classification using multidomain features and SVM classifier. BioMed Research International 2018; 1: 1-14. doi: 10.1155/2018/4205027
[20] Maknickas V, Maknickas A. Recognition of normal–abnormal phonocardiographic signals using deep convolutional neural networks and mel-frequency spectral coefficients. Physiological Measurement 2017; 38 (8): 1671.
[21] Han W, Yang Z, Lu J, Xie S. Supervised threshold-based heart sound classification algorithm. Physiological Measurement 2018; 39 (11): 115011.
[22] Deng SW, Han JQ. Towards heart sound classification without segmentation via autocorrelation feature and diffusion maps. Future Generation Computer Systems 2016; 60: 13-21.
[23] Hamidi M, Ghassemian H, Imani M. Classification of heart sound signal using curve fitting and fractal dimension. Biomedical Signal Processing and Control 2018; 39; 351-359.
[24] Langley P, Murray A. Heart sound classification from unsegmented phonocardiograms. Physiological Measurement 2017; 38 (8): 1658.
[25] Langley P, Murray A. Abnormal heart sounds detected from short duration unsegmented phonocardiograms by wavelet entropy. In: IEEE 2016 Computing in Cardiology Conference (CinC); Vancouver, Canada; 2016. pp. 545- 548.
[26] Clifford GD, Liu C, Moody B, Springer D, Silva I et al. Classification of normal/abnormal heart sound recordings: the PhysioNet/Computing in Cardiology Challenge 2016. In: IEEE 2016 Computing in Cardiology Conference; Vancouver, Canada; 2016. pp. 609-612.
[27] Liu C, Springer D, Li Q, Moody B, Juan RA et al. An open access database for the evaluation of heart sound algorithms. Physiological Measurement 2016; 37 (12): 2181.
[28] Das R, Sengur A. Evaluation of ensemble methods for diagnosing of valvular heart disease. Expert Systems with Applications 2010; 37 (7): 5110-5115.
[29] Freund Y, Schapire RE. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 1997; 55 (1): 119-139.
[30] Duba RO, Hart PE. Pattern Classification and Scene Analysis. New York, NY, USA: Wiley, 1973.
[31] Arnott PJ, Pfeiffer GW, Tavel ME. Spectral analysis of heart sounds: relationships between some physical characteristics and frequency spectra of first and second heart sounds in normals and hypertensives. Journal of Biomedical Engineering 1984; 6 (2): 121-128.
[32] Zhao L, Wei S, Zhang C, Zhang Y, Jiang X et al. Determination of sample entropy and fuzzy measure entropy parameters for distinguishing congestive heart failure from normal sinus rhythm subjects. Entropy 2015; 17 (9): 6270-6288.
[33] Singh SA, Majumder S. Classification of unsegmented heart sound recording using KNN classifier. Journal of Mechanics in Medicine and Biology 2019; 19 (4): 1950025.
[34] Sun Y, Kamel MS, Wong, AK, Wang Y. Cost-sensitive boosting for classification of imbalanced data. Pattern Recognition 2007; 40 (12): 3358-3378.

Turkish Journal of Electrical Engineering and Computer Science-Cover

ISSN: 1300-0632
Yayın Aralığı: Yılda 6 Sayı
Yayıncı: TÜBİTAK

Arşiv