Journal Home > Volume 28 , Issue 2

Severe cardiovascular diseases can rapidly lead to death. At present, most studies in the deep learning field using electrocardiogram (ECG) are performed on intra-patient experiments for the classification of coronary artery disease (CAD), myocardial infarction, and congestive heart failure (CHF). By contrast, actual conditions are inter-patient experiments. In this study, we proposed a deep learning network, namely, CResFormer, with dual feature extraction to improve accuracy in classifying such diseases. First, fixed segmentation of dual-lead ECG signals without preprocessing was used as input data. Second, one-dimensional convolutional layers performed moderate dimensionality reduction to accommodate subsequent feature extraction. Then, ResNet residual network block layers and transformer encoder layers sequentially performed feature extraction to obtain key associated abstract features. Finally, the Softmax function was used for classifications. Notably, the focal loss function is used when dealing with unbalanced datasets. The average accuracy, sensitivity, positive predictive value, and specificity of four classifications of severe cardiovascular diseases are 99.84%, 99.68%, 99.71%, and 99.90% in intra-patient experiments, respectively, and 97.48%, 93.54%, 96.30%, and 97.89% in inter-patient experiments, respectively. In addition, the model performs well in unbalanced datasets and shows good noise robustness. Therefore, the model has great application potential in diagnosing CAD, MI, and CHF in the actual clinical environment.


menu
Abstract
Full text
Outline
About this article

Intra-Patient and Inter-Patient Multi-Classification of Severe Cardiovascular Diseases Based on CResFormer

Show Author's information Dengao Li1( )Changcheng Shi1Jumin Zhao2Yi Liu2Chunxia Li3
College of Data Science, Taiyuan University of Technology, Jinzhong 030600, China
College of Information and Computer, Taiyuan University of Technology, Jinzhong 030600, China
Shanxi Bethune Hospital, Taiyuan 030032, China

Abstract

Severe cardiovascular diseases can rapidly lead to death. At present, most studies in the deep learning field using electrocardiogram (ECG) are performed on intra-patient experiments for the classification of coronary artery disease (CAD), myocardial infarction, and congestive heart failure (CHF). By contrast, actual conditions are inter-patient experiments. In this study, we proposed a deep learning network, namely, CResFormer, with dual feature extraction to improve accuracy in classifying such diseases. First, fixed segmentation of dual-lead ECG signals without preprocessing was used as input data. Second, one-dimensional convolutional layers performed moderate dimensionality reduction to accommodate subsequent feature extraction. Then, ResNet residual network block layers and transformer encoder layers sequentially performed feature extraction to obtain key associated abstract features. Finally, the Softmax function was used for classifications. Notably, the focal loss function is used when dealing with unbalanced datasets. The average accuracy, sensitivity, positive predictive value, and specificity of four classifications of severe cardiovascular diseases are 99.84%, 99.68%, 99.71%, and 99.90% in intra-patient experiments, respectively, and 97.48%, 93.54%, 96.30%, and 97.89% in inter-patient experiments, respectively. In addition, the model performs well in unbalanced datasets and shows good noise robustness. Therefore, the model has great application potential in diagnosing CAD, MI, and CHF in the actual clinical environment.

Keywords: dual-lead ECG signals, coronary artery disease, myocardial infarction, congestive heart failure, inter-patient experiments, intra-patient experiments, CResFormer

References(57)

[1]
O. Sayadi and M. B. Shamsollahi, Multiadaptive bionic wavelet transform: Application to ECG denoising and baseline wandering reduction, EURASIP J. Adv. Signal Process., vol. 2007, no. 1, p. 041274, 2007.
[2]
E. J. Benjamin, S. S. Virani, C. W. Callaway, A. M. Chamberlain, A. R. Chang, S. S. Cheng, S. E. Chiuve, M. Cushman, F. N. Delling, R. Deo, et al., Heart disease and stroke statistics-2018 update: A report from the American Heart Association, Circulation, vol. 137, no. 12, pp. e67–e492, 2018.
[3]
L. M. Buja and J. T. Willerson, The role of coronary artery lesions in ischemic heart disease: Insights from recent clinicopathologic, coronary arteriographic, and experimental studies, Hum. Pathol., vol. 18, no. 5, pp. 451–461, 1987.
[4]
E. D. Grech, ABC of Interventional Cardiology. 2nd ed. New York, NY, USA: John Wiley Sons, 2011.
[5]
A. L. Bui, T. B. Horwich, and G. C. Fonarow, Epidemiology and risk profile of heart failure, Nat. Rev. Cardiol., vol. 8, no. 1, pp. 30–41, 2011.
[6]
A. P. Ambrosy, G. C. Fonarow, J. Butler, O. Chioncel, S. J. Greene, M. Vaduganathan, S. Nodari, C. S. P. Lam, N. Sato, A. N. Shah, et al., The global health and economic burden of hospitalizations for heart failure: Lessons learned from hospitalized heart failure registries, J. Am. Coll. Cardiol., vol. 63, no. 12, pp. 1123–1133, 2014.
[7]
E. J. Benjamin, P. Muntner, A. Alonso, M. S. Bittencourt, C. W. Callaway, A. P. Carson, A. M. Chamberlain, A. R. Chang, S. S. Cheng, S. R. Das, et al., Heart disease and stroke statistics-2019 update: A report from the American Heart Association, Circulation, vol. 139, no. 10, pp. 56–528, 2019.
[8]
K. Thygesen, J. S. Alpert, A. S. Jaffe, M. L. Simoons, B. R. Chaitman, H. D. White, Writing Group on the Joint ESC/ACCF/AHA/WHF Task Force for the Universal Definition of Myocardial Infarction, Third universal definition of myocardial infarction, Eur. Heart J., vol. 33, no. 20, pp. 2551–2567, 2012.
[9]
J. E. Madias, ECG changes in response to diuresis in an ambulatory patient with congestive heart failure, Congest. Heart Fail., vol. 12, no. 5, pp. 277–283, 2006.
[10]
R. C. Schlant, R. J. Adolph, J. P. DiMarco, L. S. Dreifus, M. I. Dunn, C. Fisch, A. Garson Jr, L. J. Haywood, H. J. Levine, J. A. Murray, et al., Guidelines for electrocardiography. A report of the American College of Cardiology/American Heart Association Task Force on assessment of diagnostic and therapeutic cardiovascular procedures (Committee on Electrocardiography), J. Am. Coll. Cardiol., vol. 19, no. 3, pp. 473–481, 1992.
[11]
R. B. Schnabel, X. Y. Yin, P. Gona, M. G. Larson, A. S. Beiser, D. D. Mcmanus, C. Newton-Cheh, S. A. Lubitz, J. W. Magnani, P. T. Ellinor, et al., 50 year trends in atrial fibrillation prevalence, incidence, risk factors, and mortality in the Framingham Heart Study: A cohort study, Lancet, vol. 386, no. 9989, pp. 154–162, 2015.
[12]
W. G. Morrison and I. J. Swann, Electrocardiograph interpretation by junior doctors, Arch. Emerg. Med., vol. 7, no. 2, pp. 108–110, 1990.
[13]
F. G. Liu, Z. W. Zhang, and R. L. Zhou, Automatic modulation recognition based on CNN and GRU, Tsinghua Science and Technology, vol. 27, no. 2, pp. 422–431, 2022.
[14]
J. J. Pang, Y. Huang, Z. Z. Xie, J. B. Li, and Z. P. Cai, Collaborative city digital twin for the COVID-19 pandemic: A federated learning solution, Tsinghua Science and Technology, vol. 26, no. 5, pp. 759–771, 2021.
[15]
S. Patidar, R. B. Pachori, and U. R. Acharya, Automated diagnosis of coronary artery disease using tunable-Q wavelet transform applied on heart rate signals, Knowl.-Based Syst., vol. 82, pp. 1–10, 2015.
[16]
M. Kumar, R. B. Pachori, and U. R. Acharya, Characterization of coronary artery disease using flexible analytic wavelet transform applied on ECG signals, Biomed. Signal Process. Control, vol. 31, pp. 301–308, 2017.
[17]
G. Altan, N. Allahverd, and Y. Kutlu, Diagnosis of coronary artery disease using deep belief networks, Eur. J. Eng. Nat. Sci., vol. 2, no. 1, pp. 29–36, 2017.
[18]
U. R. Acharya, H. Fujita, M. Adam, O. S. Lih, V. K. Sudarshan, T. J. Hong, J. E. Koh, Y. Hagiwara, C. K. Chua, C. K. Poo, et al., Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals: A comparative study, Information Sciences, vol. 377, pp. 17–29, 2017.
[19]
A. Caliskan and M. E. Yuksel, Classification of coronary artery disease data sets by using a deep neural network, The EuroBiotech Journal, vol. 1, no. 4, pp. 271–277, 2017.
[20]
A. D. Dolatabadi, S. E. Z. Khadem, and B. M. Asl, Automated diagnosis of coronary artery disease (CAD) patients using optimized SVM, Comput. Methods Programs Biomed., vol. 138, pp. 117–126, 2017.
[21]
J. H. Tan, Y. Hagiwara, W. Pang, I. Lim, S. L. Oh, M. Adam, R. S. Tan, M. Chen, and U. R. Acharya, Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals, Comput. Biol. Med., vol. 84, pp. 19–26, 2018.
[22]
U. R. Acharya, H. Fujita, O. S. Lih, M. Adam, J. H. Tan, and C. K. Chua, Automated detection of coronary artery disease using different durations of ECG segments with convolutional neural network, Knowl.-Based Syst., vol. 132, pp. 62–71, 2017.
[23]
M. Abdar, W. Ksia̧żek, U. R. Acharya, R. S. Tan, V. Makarenkov, and P. Pławiak, A new machine learning technique for an accurate diagnosis of coronary artery disease, Comput. Methods Programs Biomed., vol. 179, p. 104992, 2019.
[24]
K. Chandrakar, A new approach to detect congestive heart failure using detrended fluctuation analysis of electrocardiogram signals, J. Eng. Sci. Technol., vol. 10, no. 2, pp. 145–159, 2015.
[25]
K. Chandrakar, Entropy measures of irregularity and complexity for surface electrocardiogram time series in patients with congestive heart failure, J. Adv. Comput. Res., vol. 6, no. 4, pp. 1–11, 2015.
[26]
U. R. Acharya, H. Fujita, V. K. Sudarshan, S. L. Oh, A. Muhammad, J. E. W. Koh, J. H. Tan, C. K. Chua, K. P. Chua, and R. S. Tan, Application of empirical mode decomposition (EMD) for automated identification of congestive heart failure using heart rate signals, Neural Comput. Appl., vol. 28, no. 10, pp. 3073–3094, 2017.
[27]
Z. Masetic and A. Subasi, Congestive heart failure detection using random forest classifier, Comput. Methods Programs Biomed., vol. 130, pp. 54–64, 2016.
[28]
V. K. Sudarshan, U. R. Acharya, S. L. Oh, M. Adam, J. H. Tan, C. K. Chua, K. P. Chua, and R. S. Tan, Automated diagnosis of congestive heart failure using dual tree complex wavelet transform and statistical features extracted from 2 s of ECG signals, Comput. Biol. Med., vol. 83, pp. 48–58, 2017.
[29]
M. Kumar, R. B. Pachori, and U. R. Acharya, Use of accumulated entropies for automated detection of congestive heart failure in flexible analytic wavelet transform framework based on short-term HRV signals, Entropy, vol. 19, no. 3, p. 92, 2017.
[30]
U. R. Acharya, H. Fujita, S. L. Oh, Y. Hagiwara, J. H. Tan, M. Adam, and R. S. Tan, Deep convolutional neural network for the automated diagnosis of congestive heart failure using ECG signals, Appl. Intell., vol. 49, no. 1, pp. 16–27, 2019.
[31]
R. K. Tripathy, M. R. A. Paternina, J. G. Arrieta, A. Zamora-Méndez, and G. R. Naik, Automated detection of congestive heart failure from electrocardiogram signal using Stockwell transform and hybrid classification scheme, Comput. Methods Programs Biomed., vol. 173, pp. 53–65, 2019.
[32]
S. Khade, A. Subhedar, K. Choudhary, T. Deshpande, and U. Kulkarni, A system to detect heart failure using deep learning techniques, Int. Res. J. Eng. Technol., vol. 6, no. 6, pp. 384–387, 2019.
[33]
L. Hussain, I. A. Awan, W. Aziz, S. Saeed, A. Ali, F. Zeeshan, and K. S. Kwak, Detecting congestive heart failure by extracting multimodal features and employing machine learning techniques, BioMed Res. Int., vol. 2020, p. 4281243, 2020.
[34]
N. Safdarian, N. J. Dabanloo, and G. Attarodi, A new pattern recognition method for detection and localization of myocardial infarction using T-wave integral and total integral as extracted features from one cycle of ECG signal, J. Biomed. Sci. Eng., vol. 7, no. 10, pp. 818–824, 2014.
[35]
P. Kora and S. R. Kalva, Improved Bat algorithm for the detection of myocardial infarction, SpringerPlus, vol. 4, no. 1, p. 666, 2015.
[36]
U. R. Acharya, H. Fujita, S. L. Oh, Y. Hagiwara, J. H. Tan, and M. Adam, Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals, Information Sciences, vol. 415–416, pp. 190–198, 2017.
[37]
M. Sharma, R. S. Tan, and U. R. Acharya, A novel automated diagnostic system for classification of myocardial infarction ECG signals using an optimal biorthogonal filter bank, Comput. Biol. Med., vol. 102, pp. 341–356, 2018.
[38]
K. Feng, X. T. Pi, H. Y. Liu, and K. Sun, Myocardial infarction classification based on convolutional neural network and recurrent neural network, Appl. Sci., vol. 9, no. 9, p. 1879, 2019.
[39]
C. Han and L. Shi, ML-ResNet: A novel network to detect and locate myocardial infarction using 12 leads ECG, Comput. Methods Programs Biomed., vol. 185, p. 105138, 2020.
[40]
W. H. Liu, F. Wang, Q. J. Huang, S. Chang, H. Wang, and J. He, MFB-CBRNN: A hybrid network for MI detection using 12-lead ECGs, IEEE J. Biomed. Health Inform., vol. 24, no. 2, pp. 503–514, 2020.
[41]
U. B. Baloglu, M. Talo, O. Yildirim, R. S. Tan, and U. R. Acharya, Classification of myocardial infarction with multi-lead ECG signals and deep CNN, Pattern Recognit. Lett., vol. 122, pp. 23–30, 2019.
[42]
W. Y. Yang, Y. J. Si, D. Wang, G. Zhang, X. Liu, and L. L. Li, Automated intra-patient and inter-patient coronary artery disease and congestive heart failure detection using EFAP-Net, Knowl.-Based Syst., vol. 201–202, p. 106083, 2020.
[43]
W. Y. Yang, Y. J. Si, G. Zhang, D. Wang, M. Q. Sun, W. Fan, X. Liu, and L. L. Li, A novel method for automated congestive heart failure and coronary artery disease recognition using THC-Net, Information Sciences, vol. 568, pp. 427–447, 2021.
[44]
H. Fujita, V. K. Sudarshan, M. Adam, S. L. Oh, J. H. Tan, Y. Hagiwara, K. C. Chua, K. P. Chua, and U. R. Acharya, Characterization of cardiovascular diseases using wavelet packet decomposition and nonlinear measures of electrocardiogram signal, in International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, S. Benferhat, K. Tabia, and M. Ali, eds. Arras, France: Springer, 2017, pp. 259–266.
DOI
[45]
U. R. Acharya, H. Fujita, V. K. Sudarshan, S. L. Oh, M. Adam, J. H. Tan, J. H. Koo, A. Jain, C. M. Lim, and K. C. Chua, Automated characterization of coronary artery disease, myocardial infarction, and congestive heart failure using contourlet and shearlet transforms of electrocardiogram signal, Knowl.-Based Syst., vol. 132, pp. 156–166, 2017.
[46]
O. S. Lih, V. Jahmunah, T. R. San, E. J. Ciaccio, T. Yamakawa, M. Tanabe, M. Kobayashi, O. Faust, and U. R. Acharya, Comprehensive electrocardiographic diagnosis based on deep learning, Artif. Intell. Med., vol. 103, p. 101789, 2020.
[47]
G. Zhang, Y. J. Si, W. Y. Yang, and D. Wang, A robust multilevel DWT densely network for cardiovascular disease classification, Sensors, vol. 20, no. 17, p. 4777, 2020.
[48]
P. De Chazal, M. O’Dwyer, and R. B. Reilly, Automatic classification of heartbeats using ECG morphology and heartbeat interval features, IEEE Trans. Biomed. Eng., vol. 51, no. 7, pp. 1196–1206, 2004.
[49]
H. F. Huang, J. Liu, Q. Zhu, R. P. Wang, and G. S. Hu, A new hierarchical method for inter-patient heartbeat classification using random projections and RR intervals, Biomed. Eng. Online, vol. 13, p. 90, 2014.
[50]
G. S. Yan, S. Liang, Y. C. Zhang, and F. Liu, Fusing transformer model with temporal features for ECG heartbeat classification, presented at the 2019 IEEE Int. Conf. Bioinformatics and Biomedicine (BIBM), San Diego, CA, USA, 2019, pp. 898–905.
DOI
[51]
A. L. Goldberger, L. A. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C. K. Peng, and H. E. Stanley, PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals, Circulation, vol. 101, no. 23, pp. e215–e220, 2000.
[52]
K. M. He, X. Y. Zhang, S. Q. Ren, and J. Sun, Deep residual learning for image recognition, presented at the 2016 IEEE Conf. Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 2016, pp. 770–778.
DOI
[53]
S. Ioffe and C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, in Proc. 32nd Int. Conf. Machine Learning, Lille, France, 2015, pp. 448–456.
[54]
H. Song, D. Rajan, J. Thiagarajan, and A. Spanias, Attend and diagnose: Clinical time series analysis using attention models, Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, no. 1, pp. 4091–4098, 2018.
[55]
R. P. Lippmann, Pattern classification using neural networks, IEEE Commun. Mag., vol. 27, no. 11, pp. 47–50, 1989.
[56]
X. Glorot and Y. Bengio, Understanding the difficulty of training deep feedforward neural networks, in Proc. 13th Int. Conf. Artificial Intelligence and Statistics, Sardinia, Italy, 2010, pp. 249–256.
[57]
K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv: 1409.1556, 2015.
Publication history
Copyright
Acknowledgements
Rights and permissions

Publication history

Received: 01 March 2022
Accepted: 30 March 2022
Published: 29 September 2022
Issue date: April 2023

Copyright

© The author(s) 2023.

Acknowledgements

This paper was supported by the National Major Scientific Research Instrument Development Project (No. 62027819); the General Project of National Natural Science Foundation of China (No. 62076177); Shanxi Province Key Technology and Generic Technology R&D Project (No. 2020XXX007).

Rights and permissions

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

Return