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Neural Processing Letters
Published in: Neural Processing Letters 4/2023

25-02-2022

Classification of Phonocardiogram Based on Multi-View Deep Network

Authors: Guangyang Tian, Cheng Lian, Bingrong Xu, Junbin Zang, Zhidong Zhang, Chenyang Xue

Published in: Neural Processing Letters | Issue 4/2023

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Abstract

A phonocardiogram (PCG) is a plot of high-fidelity recording of the sounds of the heart obtained using an electronic stethoscope that is highly valuable in clinical medicine. It can help cardiologists diagnose cardiovascular diseases quickly and accurately. In this paper, we propose a multi-view deep network for the classification of PCG signals that can extract rich multi-view features from different modalities of PCG for classification. The model is mainly composed of two branches. In the first branch, we divide each PCG signal into three equal-length sub-signals, using Gramian Angular Fields to encode them from audio modality to two-dimensional image modality, and then Res2Net is applied to extract the image view features. In the second branch, we propose MobileNet-LSTM to extract the features of another view from preprocessed PCG signals. Finally, the features from these two views are fused and fed into the classifier for classification. Experiments show that our proposed method achieves 97.99% accuracy on the 2016 PhysioNet/CinC Challenge dataset, which is very competitive compared with the existing baseline models. In addition, the ablation experiment proves the necessity and effectiveness of our proposed method.
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Literature
1.
World Health Organization (2021) Cardiovascular diseases (CVDs) fact sheet. World Health Organization, Geneva
2.
Yang Z-J, Liu J, Ge J-P et al (2011) Prevalence of cardiovascular disease risk factor in the Chinese population: the 2007–2008 China national diabetes and metabolic disorders study. Eur Heart J 33(2):213–220CrossRef
3.
Arpitha Y, Madhumathi GL, Balaji N (2021) Spectrogram analysis of ECG signal and classification efficiency using MFCC feature extraction technique. J Ambient Intell Human Comput 19:1–10
4.
Ghosh SK, Ponnalagu RN, Tripathy RK, Acharya UR (2020) Automated detection of heart valve diseases using chirplet transform and multiclass composite classifier with PCG signals. Comput Biol Med 118:103632CrossRef
5.
6.
Randhawa SK, Singh M (2015) Classification of heart sound signals using multi-modal features. Procedia Comput Sci 58:165–171CrossRef
7.
Choi S, Cho SH, ParkCW Shin JH (2015) A novel cardiac spectral envelope extraction algorithm using a single-degree-of-freedom vibration model. Biomed Signal Process Control 18:169–173CrossRef
8.
Ren Z, Cummins N, Pandit V et al (2018) Learning image-based representations for heart sound classification. In: Proceedings of the 2018 international conference on digital health, pp 143–147
9.
Meintjes A, Lowe A, Legget M (2018) Fundamental heart sound classification using the continuous wavelet transform and convolutional neural networks. In: 2018 40th Annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 409–412
10.
Alaskar H, Alzhrani N, Hussain A, Almarshed F (2019) The implementation of pretrained AlexNet on PCG classification. In: International conference on intelligent computing, pp 784–794
11.
Kumar A, Shaikh AM, Li Y et al (2020) Pruning filters with L1-norm and capped L1-norm for CNN compression. Appl Intell 51:1152–1160CrossRef
12.
Dwivedi AK, Imtiaz SA, Rodriguez-Villegas E (2018) Algorithms for automatic analysis and classification of heart sounds-a systematic review. IEEE Access 7:8316–8345CrossRef
13.
Deperlioglu O, Kose U, Gupta D et al (2020) Diagnosis of heart diseases by a secure internet of health things system based on autoencoder deep neural network. Comput Commun 162:31–50CrossRef
14.
Deng M, Meng T, Cao J et al (2020) Heart sound classification based on improved MFCC features and convolutional recurrent neural networks. Neural Netw 130:22–32CrossRef
15.
Chen Q, Sun S (2009) Hierarchical multi-view fisher discriminant analysis. Lect Notes Comput Sci 5864:289–298CrossRef
16.
Zhao J, Xie X, Xu X, Sun S (2017) Multi-view learning overview: recent progress and new challenges. Inf Fus 38:43–54CrossRef
17.
Liu Q, Wu X, Ma X (2018) An automatic segmentation method for heart sounds. Biomed Eng Online 17(1):106CrossRef
18.
Ricke AD, Povinelli RJ, Johnson MT (2005) Automatic segmentation of heart sound signals using hidden Markov models. Comput Cardiol 32:953–956CrossRef
19.
Papadaniil CD, Hadjileontiadis LJ (2014) Efficient heart sound segmentation and extraction using ensemble empirical mode decomposition and kurtosis features. IEEE J Biomed Health Inform 18(4):1138–1152CrossRef
20.
Sepehri AA, Gharehbaghi A, Dutoit T et al (2010) A novel method for pediatric heart sound segmentation without using the ECG. Comput Methods Progr Biomed 99(1):43–48CrossRef
21.
Springer DB, Tarassenko L, Clifford GD (2015) Logistic regression-HSMM-based heart sound segmentation. IEEE Trans Biomed Eng 63(4):822–832
22.
Tschannen M, Kramer T, Marti G et al (2016) Heart sound classification using deep structured features. In: 2016 Computing in cardiology conference, pp 565–568
23.
Tang H, Dai Z, Jiang Y et al (2018) PCG classification using multidomain features and SVM classifier. BioMed Res Int 9:4205027
24.
Thomae C, Dominik A (2016) Using deep gated RNN with a convolutional front end for end-to-end classification of heart sound. In: 2016 Computing in cardiology conference, IEEE, pp 625–628
25.
Nilanon T, Yao J, Hao J et al (2016) Normal/abnormal heart sound recordings classification using convolutional neural network. In: 2016 Computing in cardiology conference, IEEE, pp 585–588
26.
Latif S, Usman M, Rana R, Qadir J (2018) Phonocardiographic sensing using deep learning for abnormal heartbeat detection. IEEE Sens J 18(22):9393–9400CrossRef
27.
28.
29.
Yang Q, Wu X (2006) 10 challenging problems in data mining research. Inf Technol Decis Mak 05(04):597–604CrossRef
30.
Faloutsos C, Ranganathan M, Manolopoulos Y (1994) Fast subsequence matching in time-series databases. In: ACM SIGMOD international conference on management of data, pp 419–429
31.
Keogh E, Ratanamahatana CA (2005) Exact indexing of dynamic time warping. Knowl Inf Syst 7(3):358–386CrossRef
32.
Ye L, Keogh E (2011) Time series shapelets: a novel technique that allows accurate, interpretable and fast classification. Data Min Knowl Discov 22(1):149–182MathSciNetCrossRefMATH
33.
Rakthanmanon T, Keogh E (2013) Fast-shapelets: a scalable algorithm for discovering time series shapelets. In: Proceedings of the 13th SIAM international conference on data mining (SDM)
34.
Dau HA, Bagnall A, Kamga K et al (2019) The UCR time series archive. IEEE/CAA J Autom Sinica 6(6):1293–1305CrossRef
35.
Wang Z, Yan W, Oates T (2017) Time series classification from scratch with deep neural networks: a strong baseline. In: 2017 International joint conference on neural networks, pp 1578–1585
36.
Karim F, Majumdar S, Darabi H, Chen S (2017) LSTM fully convolutional networks for time series classification. IEEE Access 6:1662–1669CrossRef
37.
Serrà J, Pascual S, Karatzoglou A (2018) Towards a universal neural network encoder for time series. Artif Intell Res Dev Curr Chall New Trends Appl 308:120
38.
Zheng Y, Liu Q, Chen E et al (2014) Time series classification using multi-channels deep convolutional neural networks. In: Web-age information management, pp 298–310
39.
Fawaz HI, Lucas B, Forestier G et al (2020) InceptionTime: finding AlexNet for time series classification. Data Min Knowl Disc 34:1936–1962MathSciNetCrossRef
40.
Wang Z, Oates T (2015) Encoding time series as images for visual inspection and classification using tiled convolutional neural networks. In: Workshops at the twenty-ninth AAAI conference on artificial intelligence, pp 40–46
41.
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 770–778
42.
Gao S-H, Cheng M-M, Zhao K et al (2019) Res2Net: a new multi-scale backbone architecture. IEEE Trans Pattern Anal Mach Intell 43(2):652–662CrossRef
43.
Howard AG, Zhu M, Chen B et al (2017) MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:​1704.​04861
44.
Hu J, Li S, Sun G, Albanie S (2020) Squeeze-and-excitation networks. EEE Trans Pattern Anal 42(8):2011–2023CrossRef
45.
Liu C, Springer D, Li Q et al (2016) An open access database for the evaluation of heart sound algorithms. Physiol Meas 37(12):2181–2213CrossRef
Metadata
Title
Classification of Phonocardiogram Based on Multi-View Deep Network
Authors
Guangyang Tian
Cheng Lian
Bingrong Xu
Junbin Zang
Zhidong Zhang
Chenyang Xue
Publication date
25-02-2022
Publisher
Springer US
Published in
Neural Processing Letters / Issue 4/2023
Print ISSN: 1370-4621
Electronic ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-022-10771-3

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