Abstract
Myocardial infarction (MI) is a type of cardiovascular diseases (CVDs) with high mortality. Early diagnosis and treatment are crucial to improve survival rate. At present, electrocardiogram (ECG) is a common method for clinical diagnosis of MI, but it requires rich experiences. Hence it is meaningful to design an approach that can screen the MI automatically. In this paper, a feature fusion network is proposed for MI screening based on ECG images, which is composed of heartbeat detection module, local and global feature extraction module, feature fusion and classification module. Firstly, heartbeats are detected from ECG images. Then, heartbeats features are extracted as local features, at the same time, the features extracted from the corresponding ECG image are thought as global features. Finally, classification is designed for judging the input ECG is MI or normal based on fused features. Experiments on two ECG image datasets show the robust performance of the proposed method for MI screening at 99.34% of accuracy, 99.78% of specificity, and 99.64% of sensitivity.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Acharya, U.R., Fujita, H., Oh, S.L., Hagiwara, Y., Tan, J.H., Adam, M.: Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Inf. Sci. 415, 190–198 (2017)
Acharya, U.R., et al.: Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads. Knowl.-Based Syst. 99, 146–156 (2016)
Acharya, U.R., et al.: A deep convolutional neural network model to classify heartbeats. Comput. Biol. Med. 89, 389–396 (2017)
Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)
Goldberger, A., et al.: Components of a new research resource for complex physiologic signals. PhysioToolkit Physionet PhysioBank (2000)
Güler, İ, Übeylı, E.D.: ECG beat classifier designed by combined neural network model. Pattern Recogn. 38(2), 199–208 (2005)
Hannun, A.Y., et al.: Cardiologist-level arrhythmia detection with convolutional neural networks. Nat. Med. 25(1), 65–69 (2019)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 770–778. IEEE Computer Society (2016)
Ince, T., Kiranyaz, S., Gabbouj, M.: A generic and robust system for automated patient-specific classification of ECG signals. IEEE Trans. Biomed. Eng. 56(5), 1415–1426 (2009)
Kumar, M., Pachori, R.B., Rajendra Acharya, U.: Automated diagnosis of atrial fibrillation ECG signals using entropy features extracted from flexible analytic wavelet transform. Biocybernetics Biomed. Eng. 38(3), 564–573 (2018)
Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Lodhi, A.M., Qureshi, A.N., Sharif, U., Ashiq, Z.: A novel approach using voting from ECG leads to detect myocardial infarction. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) IntelliSys 2018. AISC, vol. 869, pp. 337–352. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-01057-7_27
Lu, W., Hou, H., Chu, J.: Feature fusion for imbalanced ECG data analysis. Biomed. Signal Process. Control 41, 152–160 (2018)
Lui, H.W., Chow, K.L.: Multiclass classification of myocardial infarction with convolutional and recurrent neural networks for portable ECG devices. Inf. Med. Unlocked 13, 26–33 (2018)
Martis, R.J., Acharya, U.R., Min, L.C.: ECG beat classification using PCA, LDA, ICA and discrete wavelet transform. Biomed. Signal Process. Control 8(5), 437–448 (2013)
Ponomaryov, V., Li, J., Si, Y., Xu, T., Jiang, S.: Deep convolutional neural network based ECG classification system using information fusion and one-hot encoding techniques. Math. Probl. Eng. 2018, 7354081–7354091 (2018)
Roth, G.A., et al.: Global, regional, and national burden of cardiovascular diseases for 10 causes, 1990 to 2015. J. Am. Coll. Cardiol. 70(1), 1–25 (2017)
Sadhukhan, D., Pal, S., Mitra, M.: Automated identification of myocardial infarction using harmonic phase distribution pattern of ECG data. IEEE Trans. Instrum. Meas. 67(10), 2303–2313 (2018)
Sharma, L.D., Sunkaria, R.K.: Inferior myocardial infarction detection using stationary wavelet transform and machine learning approach. SIViP 12(2), 199–206 (2017). https://doi.org/10.1007/s11760-017-1146-z
Tan, J.H., et al.: Application of stacked convolutional and long short-term memory network for accurate identification of cad ECG signals. Comput. Biol. Med. 94, 19–26 (2018)
Yildirim, Ö.: A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification. Comput. Biol. Med. 96, 189–202 (2018)
Acknowledgements
This work is supported by National Natural Science Foundation of China under grants No.61801428.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Hao, P., Yin, X., Wu, F., Zhang, F. (2021). A Novel Feature Fusion Network for Myocardial Infarction Screening Based on ECG Images. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12889. Springer, Cham. https://doi.org/10.1007/978-3-030-87358-5_44
Download citation
DOI: https://doi.org/10.1007/978-3-030-87358-5_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87357-8
Online ISBN: 978-3-030-87358-5
eBook Packages: Computer ScienceComputer Science (R0)