Detection of valvular heart diseases combining orthogonal non-negative matrix factorization and convolutional neural networks in PCG signals

2023 2023 2023 2023 2023 Alkhodari, 2021, Convolutional and recurrent neural networks for the detection of valvular heart diseases in phonocardiogram recordings, Comput. Methods Programs Biomed., 200, 10.1016/j.cmpb.2021.105940 Wang, 2020, Advanced echocardiography in the evaluation of aortic valve disease, J. Cardiol. Pract., 18 Virani, 2020, American heart association council on epidemiology and prevention statistics committee and stroke statistics subcommittee, Heart Dis. Stroke Statist.-2020 Update: Rep. Am. Heart Assoc. Circul., 141, e139 Wang, 2022, Cross-modality lge-cmr segmentation using image-to-image translation based data augmentation, IEEE/ACM Trans. Comput. Biol. Bioinform. Torre-Cruz, 2022, Unsupervised detection and classification of heartbeats using the dissimilarity matrix in PCG signals, Comput. Methods Programs Biomed., 221, 10.1016/j.cmpb.2022.106909 Chen, 2016, S1 and S2 heart sound recognition using deep neural networks, IEEE Trans. Biomed. Eng., 64, 372 M.F. Khan, M. Atteeq, A.N. Qureshi, Computer aided detection of normal and abnormal heart sound using PCG, in: Proceedings of the 2019 11th International Conference on Bioinformatics and Biomedical Technology, 2019, pp. 94–99. Banerjee, 2020, A semi-supervised approach for identifying abnormal heart sounds using variational autoencoder, 1249 Warriner, 2019, Cardiac auscultation: normal and abnormal, Br. J. Hosp. Med., 80, C28, 10.12968/hmed.2019.80.2.C28 Chauhan, 2008, A computer-aided MFCC-based HMM system for automatic auscultation, Comput. Biol. Med., 38, 221, 10.1016/j.compbiomed.2007.10.006 H. Wu, S. Kim, K. Bae, Hidden Markov model with heart sound signals for identification of heart diseases, in: Proceedings of 20th International Congress on Acoustics (ICA), Sydney, Australia, 2010, pp. 23–27. Saraçoğlu, 2012, Hidden Markov model-based classification of heart valve disease with PCA for dimension reduction, Eng. Appl. Artif. Intell., 25, 1523, 10.1016/j.engappai.2012.07.005 Kwak, 2012, Cardiac disorder classification by heart sound signals using murmur likelihood and hidden Markov model state likelihood, IET Signal Process., 6, 326, 10.1049/iet-spr.2011.0170 Fahad, 2018, Microscopic abnormality classification of cardiac murmurs using ANFIS and HMM, Microsc. Res. Tech., 81, 449, 10.1002/jemt.22998 Ghosh, 2020, Automated detection of heart valve diseases using chirplet transform and multiclass composite classifier with PCG signals, Comput. Biol. Med., 118, 10.1016/j.compbiomed.2020.103632 Vepa, 2009, Classification of heart murmurs using cepstral features and support vector machines, 2539 Nilanon, 2016, Normal/abnormal heart sound recordings classification using convolutional neural network, 585 Das, 2020, Acoustic feature based unsupervised approach of heart sound event detection, Comput. Biol. Med., 126, 10.1016/j.compbiomed.2020.103990 Ediriweera, 2016, Mapping the risk of snakebite in Sri Lanka-a national survey with geospatial analysis, PLoS Negl. Trop. Dis., 10, 10.1371/journal.pntd.0004813 Ahmad, 2009, Classification of phonocardiogram using an adaptive fuzzy inference system, 609 Quiceno-Manrique, 2010, Selection of dynamic features based on time–frequency representations for heart murmur detection from phonocardiographic signals, Ann. Biomed. Eng., 38, 118, 10.1007/s10439-009-9838-3 Maglogiannis, 2009, Support vectors machine-based identification of heart valve diseases using heart sounds, Comput. Methods Programs Biomed., 95, 47, 10.1016/j.cmpb.2009.01.003 Vernekar, 2016, A novel approach for classification of normal/abnormal phonocardiogram recordings using temporal signal analysis and machine learning, 1141 Zhang, 2017, Heart sound classification based on scaled spectrogram and partial least squares regression, Biomed. Signal Process. Control, 32, 20, 10.1016/j.bspc.2016.10.004 Zhang, 2017, Heart sound classification based on scaled spectrogram and tensor decomposition, Expert Syst. Appl., 84, 220, 10.1016/j.eswa.2017.05.014 Tang, 2018, PCG classification using multidomain features and SVM classifier, BioMed Res. Int., 2018, 10.1155/2018/4205027 Demir, 2019, Towards the classification of heart sounds based on convolutional deep neural network, Health Inf. Sci. Syst., 7, 1, 10.1007/s13755-019-0078-0 Ergen, 2012, Time–frequency analysis of phonocardiogram signals using wavelet transform: a comparative study, Comput. Methods Biomech. Biomed. Eng., 15, 371, 10.1080/10255842.2010.538386 Zheng, 2015, A novel hybrid energy fraction and entropy-based approach for systolic heart murmurs identification, Expert Syst. Appl., 42, 2710, 10.1016/j.eswa.2014.10.051 Langley, 2017, Heart sound classification from unsegmented phonocardiograms, Physiol. Meas., 38, 1658, 10.1088/1361-6579/aa724c Dokur, 2008, Heart sound classification using wavelet transform and incremental self-organizing map, Digit. Signal Process., 18, 951, 10.1016/j.dsp.2008.06.001 Varghees, 2017, Effective heart sound segmentation and murmur classification using empirical wavelet transform and instantaneous phase for electronic stethoscope, IEEE Sens. J., 17, 3861, 10.1109/JSEN.2017.2694970 Son, 2018, Classification of heart sound signal using multiple features, Appl. Sci., 8, 2344, 10.3390/app8122344 Boutana, 2011, Segmentation and identification of some pathological phonocardiogram signals using time-frequency analysis, IET Signal Process., 5, 527, 10.1049/iet-spr.2010.0013 Papadaniil, 2013, Efficient heart sound segmentation and extraction using ensemble empirical mode decomposition and kurtosis features, IEEE J. Biomed. Health Inform., 18, 1138, 10.1109/JBHI.2013.2294399 Pretorius, 2010, Development of a pediatric cardiac computer aided auscultation decision support system, 6078 El Badlaoui, 2020, Novel PCG analysis method for discriminating between abnormal and normal heart sounds, Irbm, 41, 223, 10.1016/j.irbm.2019.12.003 Schmidt, 2015, Acoustic features for the identification of coronary artery disease, IEEE Trans. Biomed. Eng., 62, 2611, 10.1109/TBME.2015.2432129 Zhang, 2017, Heart sound classification based on scaled spectrogram and partial least squares regression, Biomed. Signal Process. Control, 32, 20, 10.1016/j.bspc.2016.10.004 Petschenka, 2016, How herbivores coopt plant defenses: natural selection, specialization, and sequestration, Curr. Opin. Insect Sci., 14, 17, 10.1016/j.cois.2015.12.004 Son, 2018, Classification of heart sound signal using multiple features, Appl. Sci., 8, 2344, 10.3390/app8122344 Potes, 2016, Ensemble of feature-based and deep learning-based classifiers for detection of abnormal heart sounds, 621 Rubin, 2016, Classifying heart sound recordings using deep convolutional neural networks and mel-frequency cepstral coefficients, 813 Maknickas, 2017, Recognition of normal–abnormal phonocardiographic signals using deep convolutional neural networks and mel-frequency spectral coefficients, Physiol. Meas., 38, 1671, 10.1088/1361-6579/aa7841 Singh, 2019, Classification of short unsegmented heart sound based on deep learning, 1 Li, 2020, Classification of heart sounds using convolutional neural network, Appl. Sci., 10, 3956, 10.3390/app10113956 Xiao, 2020, Heart sounds classification using a novel 1-D convolutional neural network with extremely low parameter consumption, Neurocomputing, 392, 153, 10.1016/j.neucom.2018.09.101 Kiranyaz, 2020, Real-time phonocardiogram anomaly detection by adaptive 1d convolutional neural networks, Neurocomputing, 411, 291, 10.1016/j.neucom.2020.05.063 Baghel, 2020, Automatic diagnosis of multiple cardiac diseases from PCG signals using convolutional neural network, Comput. Methods Programs Biomed., 197, 10.1016/j.cmpb.2020.105750 Khan, 2022, Cardi-net: A deep neural network for classification of cardiac disease using phonocardiogram signal, Comput. Methods Programs Biomed., 219, 10.1016/j.cmpb.2022.106727 Thomae, 2016, Using deep gated RNN with a convolutional front end for end-to-end classification of heart sound, 625 Raza, 2019, Heartbeat sound signal classification using deep learning, Sensors, 19, 4819, 10.3390/s19214819 Deng, 2020, Heart sound classification based on improved MFCC features and convolutional recurrent neural networks, Neural Netw., 130, 22, 10.1016/j.neunet.2020.06.015 Wang, 2020, Automatic recognition of murmurs of ventricular septal defect using convolutional recurrent neural networks with temporal attentive pooling, Sci. Rep., 10, 1, 10.1038/s41598-020-77994-z Ahmad, 2021, Automatic classification of heart sounds using long short-term memory, 1 Megalmani, 2021, Unsegmented heart sound classification using hybrid CNN-LSTM neural networks, 713 Chen, 2022, Automatic classification of normal–abnormal heart sounds using convolution neural network and long-short term memory, Electronics, 11, 1246, 10.3390/electronics11081246 Carabias-Orti, 2011, Musical instrument sound multi-excitation model for non-negative spectrogram factorization, IEEE J. Sel. Top. Sign. Proces., 5, 1144, 10.1109/JSTSP.2011.2159700 Nie, 2018, Deep learning based speech separation via NMF-style reconstructions, IEEE/ACM Trans. Audio Speech Lang. Process., 26, 2043, 10.1109/TASLP.2018.2851151 Muñoz-Montoro, 2022, Multichannel blind music source separation using directivity-aware MNMF with harmonicity constraints, IEEE Access, 10, 17781, 10.1109/ACCESS.2022.3150248 Liu, 2008, Reducing microarray data via nonnegative matrix factorization for visualization and clustering analysis, J. Biomed. Inform., 41, 602, 10.1016/j.jbi.2007.12.003 Kumar, 2018, Hyperspectral tissue image segmentation using semi-supervised NMF and hierarchical clustering, IEEE Trans. Med. Imaging, 38, 1304, 10.1109/TMI.2018.2883301 Aonishi, 2022, Imaging data analysis using non-negative matrix factorization, Neurosci. Res., 179, 51, 10.1016/j.neures.2021.12.001 Canadas-Quesada, 2017, A non-negative matrix factorization approach based on spectro-temporal clustering to extract heart sounds, Appl. Acoust., 125, 7, 10.1016/j.apacoust.2017.04.005 Dia, 2019, Heart rate estimation from phonocardiogram signals using non-negative matrix factorization, 1293 Cruz, 2020, Combining a recursive approach via non-negative matrix factorization and Gini index sparsity to improve reliable detection of wheezing sounds, Expert Syst. Appl., 147 De La Torre Cruz, 2021, Monophonic and polyphonic wheezing classification based on constrained low-rank non-negative matrix factorization, Sensors, 21, 1661, 10.3390/s21051661 Cruz, 2021, An incremental algorithm based on multichannel non-negative matrix partial co-factorization for ambient denoising in auscultation, Appl. Acoust., 182 Alzubaidi, 2021, Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions, J. Big Data, 8, 1, 10.1186/s40537-021-00444-8 Lee, 1999, Learning the parts of objects by non-negative matrix factorization, Nature, 401, 788, 10.1038/44565 Laroche, 2015, A structured nonnegative matrix factorization for source separation, 2033 LeCun, 1998, Gradient-based learning applied to document recognition, Proc. IEEE, 86, 2278, 10.1109/5.726791 Krizhevsky, 2017, Imagenet classification with deep convolutional neural networks, Commun. ACM, 60, 84, 10.1145/3065386 Simonyan, 2014 K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770–778. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, Going deeper with convolutions, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1–9. Ding, 2005, On the equivalence of nonnegative matrix factorization and spectral clustering, 606 Li, 2001, Learning spatially localized, parts-based representation, 207 Cañadas-Quesada, 2016, Constrained non-negative matrix factorization for score-informed piano music restoration, Digit. Signal Process., 50, 240, 10.1016/j.dsp.2016.01.004 Wang, 2012, Nonnegative matrix factorization: A comprehensive review, IEEE Trans. Knowl. Data Eng., 25, 1336, 10.1109/TKDE.2012.51 C. Ding, T. Li, W. Peng, H. Park, Orthogonal nonnegative matrix t-factorizations for clustering, in: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2006, pp. 126–135. Yoo, 2010, Nonnegative matrix factorization with orthogonality constraints, J. Comput. Sci. Eng., 4, 97, 10.5626/JCSE.2010.4.2.097 Grais, 2013, Discriminative nonnegative dictionary learning using cross-coherence penalties for single channel source separation, 808 Géron, 2017 Tian, 2020, Attention-guided CNN for image denoising, Neural Netw., 124, 117, 10.1016/j.neunet.2019.12.024 Chollet, 2021 Hernandez-Olivan, 2021, A comparison of deep learning methods for timbre analysis in polyphonic automatic music transcription, Electronics, 10, 810, 10.3390/electronics10070810 Oh, 2020, Classification of heart sound signals using a novel deep WaveNet model, Comput. Methods Programs Biomed., 196, 10.1016/j.cmpb.2020.105604 Kim, 2009, Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap, Comput. Statist. Data Anal., 53, 3735, 10.1016/j.csda.2009.04.009 G. Vanwinckelen, H. Blockeel, On estimating model accuracy with repeated cross-validation, in: BeneLearn 2012: Proceedings of the 21st Belgian-Dutch Conference on Machine Learning, 2012, pp. 39–44. Berrar, 2019, Cross-validation, 542 Mang, 2023, Cochleogram-based adventitious sounds classification using convolutional neural networks, Biomed. Signal Process. Control, 82, 10.1016/j.bspc.2022.104555 Kok, 2019, A novel method for automatic identification of respiratory disease from acoustic recordings, 2589 Demir, 2020, Classification of lung sounds with CNN model using parallel pooling structure, IEEE Access, 8, 105376, 10.1109/ACCESS.2020.3000111 Petmezas, 2022, Automated lung sound classification using a hybrid CNN-LSTM network and focal loss function, Sensors, 22, 1232, 10.3390/s22031232 Ghosh, 2019, Automated detection of heart valve disorders from the PCG signal using time-frequency magnitude and phase features, IEEE Sens. Lett., 3, 1, 10.1109/LSENS.2019.2949170 Milani, 2021, Abnormal heart sound classification using phonocardiography signals, Smart Health, 21, 10.1016/j.smhl.2021.100194 Nersisson, 2017, Heart sound and lung sound separation algorithms: a review, J. Med. Eng. Technol., 41, 13, 10.1080/03091902.2016.1209589 Neili, 2022, A comparative study of the spectrogram, scalogram, melspectrogram and gammatonegram time-frequency representations for the classification of lung sounds using the ICBHI database based on CNNs, Biomed. Eng./Biomed. Tech., 67, 367, 10.1515/bmt-2022-0180 Lee, 2000, Algorithms for non-negative matrix factorization, Adv. Neural Inf. Process. Syst., 13 Févotte, 2009, Nonnegative matrix factorization with the itakura-saito divergence: With application to music analysis, Neural Comput., 21, 793, 10.1162/neco.2008.04-08-771 Liutkus, 2015, Cauchy nonnegative matrix factorization, 1 Russakovsky, 2015, Imagenet large scale visual recognition challenge, Int. J. Comput. Vis., 115, 211, 10.1007/s11263-015-0816-y