Abstract
Myocardial infarction (MI) is an acute interruption of blood flow to the heart, which causes the heart to suffer from a deficiency of blood and ischemia, so the heart muscle is damaged, and cells can die and lose their function. Despite the low incidence of MI in the world, it is still a common disease-causing death. Therefore, detecting the MI signals early can reduce mortality. This paper presented a method based on a deep convolutional neural network (CNN) for the detection of MI automatically. The proposed CNN is an end-to-end model without requiring any stages of machine learning and requires only one stage to detect MI from the input signals. In the case of imbalanced data, we optimize our deep model with a new loss function named the focal loss to deal with this case by constituting the loss indirectly the focus in those difficult classes. The Physikalisch-Technische Bundesanstalt (PTB) dataset was employed in the validation to classify the signals to normal and MI. The performance of our technique alongside state-of-the-art in the area shows an increase in terms of average accuracy and F1 score. Results show that focal loss improves the detection accuracy by 9% for detecting MI signals. In summary, the proposed method achieved an overall accuracy, precision, F1 score, and recall of 98.84%, 98.31%, 97.92%, and 97.63, respectively using focal loss and overall accuracy of 89.72%, a precision of 88.52%, a recall of 81.11% and F1 score of 83.02% without using focal loss. Our method using focal loss is an effective tool to perform a fast and reliable MI diagnosis to assist the cardiologists in detecting MI early.
Similar content being viewed by others
References
Yang, H.: Multiscale recurrence quantification analysis of spatial cardiac vectorcardiogram signals. IEEE Trans. Biomed. Eng. 58(2), 339–347 (2010)
Vogel, B., Claessen, B.E., Arnold, S.V., Chan, D., Cohen, D.J., Giannitsis, E., et al.: ST-segment elevation myocardial infarction. Nat. Rev. Dis. Prim. 5(1), 1–20 (2019)
Doig, D., Turner, E.L., Dobson, J., Featherstone, R.L., Lo, R.T.H., Gaines, P.A., et al.: Predictors of stroke, myocardial infarction or death within 30 days of carotid artery stenting: results from the International Carotid Stenting Study. Eur. J. Vasc. Endovasc. Surg. 51(3), 327–334 (2016)
Das, M.K., Khan, B., Jacob, S., Kumar, A., Mahenthiran, J.: Significance of a fragmented QRS complex versus a Q wave in patients with coronary artery disease. Circulation 113(21), 2495–2501 (2006)
Clarkson: S T Elevation (Online) (2020). http://www.nataliescasebook.com/tag/s-t-elevation. Accessed: 1 Oct 2020
Wang, H., Li, Z., Li, Y., Gupta, B.B., Choi, C.: Visual saliency guided complex image retrieval. Pattern Recogn. Lett. 130, 64–72 (2020)
Zhang, J., Lin, F., Xiong, P., Du, H., Zhang, H., Liu, M., et al.: Automated detection and localization of myocardial infarction with staked sparse autoencoder and treebagger. IEEE Access 7, 70634–70642 (2019)
Zhang, X., Li, R., Dai, H., Liu, Y., Zhou, B., Wang, Z.: Localization of myocardial infarction with multi-lead bidirectional gated recurrent unit neural network. IEEE Access 7, 161152–161166 (2019)
AlZu’bi, S., Shehab, M., Al-Ayyoub, M., Jararweh, Y., et al.: Parallel implementation for 3d medical volume fuzzy segmentation. Pattern Recogn. Lett. 130, 312–318 (2020)
Zhang, G., Si, Y., Wang, D., Yang, W., Sun, Y.: Automated detection of myocardial infarction using a gramian angular field and principal component analysis network. IEEE Access 7, 171570–171583 (2019)
Han, C., Shi, L.: Automated interpretable detection of myocardial infarction fusing energy entropy and morphological features. Comput. Methods Programs Biomed. 175, 9–23 (2019)
Tao, R., Zhang, S., Huang, X., Tao, M., Ma, J., Ma, S., et al.: Magnetocardiography-based ischemic heart disease detection and localization using machine learning methods. IEEE Trans. Biomed. Eng. 66(6), 1658–1667 (2018)
Baloglu, U.B., Talo, M., Yildirim, O., San Tan, R., Acharya, U.R.: Classification of myocardial infarction with multi-lead ECG signals and deep CNN. Pattern Recogn. Lett. 122, 23–30 (2019)
Jafarian, K., Vahdat, V., Salehi, S., Mobin, M.: Automating detection and localization of myocardial infarction using shallow and end-to-end deep neural networks. Appl. Soft Comput. 93, 106383 (2020)
Liu, W., Huang, Q., Chang, S., Wang, H., He, J.: Multiple-feature-branch convolutional neural network for myocardial infarction diagnosis using electrocardiogram. Biomed. Signal Process. Control 45, 22–32 (2018)
Liu, W., Zhang, M., Zhang, Y., Liao, Y., Huang, Q., Chang, S., et al.: Real-time multilead convolutional neural network for myocardial infarction detection. IEEE J. Biomed. Health Inf. 22(5), 1434–1444 (2017)
Alghamdi, A., Hammad, M., Ugail, H., Abdel-Raheem, A., Muhammad, K., Khalifa, H.S., Abd El-Latif, A.A.: Detection of myocardial infarction based on novel deep transfer learning methods for urban healthcare in smart cities. Multimedia Tools Appl. (2020). https://doi.org/10.1007/s11042-020-08769-x
Feng, K., Pi, X., Liu, H., Sun, K.: Myocardial infarction classification based on convolutional neural network and recurrent neural network. Appl. Sci. 9(9), 1879 (2019)
Chen, M., Fang, L., Zhuang, Q., Liu, H.: Deep learning assessment of myocardial infarction from MR image sequences. IEEE Access 7, 5438–5446 (2019)
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)
Iqbal, U., Wah, T.Y., ur Rehman, M.H., Shah, J.H. Prediction analytics of myocardial infarction through model-driven deep deterministic learning. Neural Comput. Appl., 1–20 (2019)
Han, C., Shi, L.: ML–ResNet: a novel network to detect and locate myocardial infarction using 12 leads ECG. Comput. Methods Programs Biomed. 185, 105138 (2020)
Fu, L., Lu, B., Nie, B., Peng, Z., Liu, H., Pi, X.: Hybrid network with attention mechanism for detection and location of myocardial infarction based on 12-lead electrocardiogram signals. Sensors 20(4), 1020 (2020)
Prabhakararao, E., Dandapat, S.: Myocardial infarction severity stages classification from ecg signals using attentional recurrent neural network. IEEE Sens. J. 20(15), 8711–8720 (2020)
Eckle, K., Schmidt-Hieber, J.: A comparison of deep networks with ReLU activation function and linear spline-type methods. Neural Netw. 110, 232–242 (2019)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift (2015). arXiv:1502.03167
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Dwivedi, R.K., Kumar, R., Buyya, R.: Gaussian distribution-based machine learning scheme for anomaly detection in healthcare sensor cloud. Int. J. Cloud Appl. Comput. 11(1), 52–72 (2020)
Hammad, M., Zhang, S., Wang, K.: A novel two-dimensional ECG feature extraction and classification algorithm based on convolution neural network for human authentication. Future Gener. Comput. Syst. 101, 180–196 (2019)
Hammad, M., Liu, Y., Wang, K.: Multimodal biometric authentication systems using convolution neural network based on different level fusion of ECG and fingerprint. IEEE Access 7, 26527–26542 (2018)
Hammad, M., Pławiak, P., Wang, K., Acharya, U.R.: ResNet-Attention model for human authentication using ECG signals. Expert Syst. (2020). https://doi.org/10.1111/exsy.12547
Ortega-Delcampo, D., Conde, C., Palacios-Alonso, D., Cabello, E.: Border control morphing attack detection with a convolutional neural network de-morphing approach. IEEE Access 8, 92301–92313 (2020)
Kumar, A.: Design of secure image fusion technique using cloud for privacy-preserving and copyright protection. Int. J. Cloud Appl. Comput. 9(3), 22–36 (2019)
Liu, T., Tian, Y., Zhao, S., Huang, X., Wang, Q.: Residual convolutional neural network for cardiac image segmentation and heart disease diagnosis. IEEE Access 8, 82153–82161 (2020)
Li, D., Deng, L., Gupta, B.B., Wang, H., Choi, C.: A novel CNN based security guaranteed image watermarking generation scenario for smart city applications. Inf. Sci. 479, 432–447 (2019)
He, J., Li, K., Liao, X., Zhang, P., Jiang, N.: Real-time detection of acute cognitive stress using a convolutional neural network from electrocardiographic signal. IEEE Access 7, 42710–42717 (2019)
Liu, H., Chu, W., Wang, H.: Automatic segmentation algorithm of ultrasound heart image based on convolutional neural network and image saliency. IEEE Access (2020)
Sedik, A., Iliyasu, A.M., El-Rahiem, A., Abdel Samea, M.E., Abdel-Raheem, A., Hammad, M., et al.: Deploying machine and deep learning models for efficient data-augmented detection of COVID-19 infections. Viruses 12(7), 769 (2020)
Ramchoun, H., Idrissi, M.A.J., Ghanou, Y., Ettaouil, M.: New modeling of multilayer perceptron architecture optimization with regularization: an application to pattern classification. IAENG Int. J. Comput. Sci. 44(3), 261–269 (2017)
Bousseljot R, Kreiseler D, Schnabel, A.: Nutzung der EKG-Signaldatenbank CARDIODAT der PTB über das Internet. Biomed. Tech. Band 40 Ergänzungsband 1, S317 (1995)
Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P.C., Mark, R., et al.: PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online] 101(23), e215–e220 (2000)
Abd Elrahman, S.M., Abraham, A.: A review of class imbalance problem. J. Netw. Innov. Comput. 1(2013), 332–340 (2013)
Chen, Z., Lin, T., Xia, X., Xu, H., Ding, S.: A synthetic neighborhood generation based ensemble learning for the imbalanced data classification. Appl. Intell. 48(8), 2441–2457 (2018)
Koziarski, M., Krawczyk, B., Woźniak, M.: Radial-Based oversampling for noisy imbalanced data classification. Neurocomputing 343, 19–33 (2019)
Khatami, A., Babaie, M., Khosravi, A., Tizhoosh, H.R., Nahavandi, S.: Parallel deep solutions for image retrieval from imbalanced medical imaging archives. Appl. Soft Comput. 63, 197–205 (2018)
Jegierski, H., Saganowski, S.: An “outside the box” solution for imbalanced data classification. IEEE Access 8, 125191–125209 (2020)
Goyal, P., Kaiming, H.: Focal loss for dense object detection. IEEE Trans. Pattern Anal. Mach. Intell. 39, 2999–3007 (2018)
Brownlee, J.: A gentle introduction to cross-entropy for machine learning. Machine Learning Mastery, 20 Oct 2019. https://machinelearningmastery.com/cross-entropy-for-machine-learning/. Accessed: 20 May 2020
Qiu, S.: Global weighted average pooling bridges pixel-level localization and image-level classification (2018). arXiv:1809.08264
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014). arXiv:1412.6980
Diamantidis, N.A., Karlis, D., Giakoumakis, E.A.: Unsupervised stratification of cross-validation for accuracy estimation. Artif. Intell. 116(1–2), 1–16 (2000)
Hammad, M., Wang, K.: Fingerprint classification based on a Q-Gaussian multiclass support vector machine. In: Proceedings of the 2017 International Conference on biometrics engineering and application, pp. 39–44 (2017)
Hammad, M., Maher, A., Wang, K., Jiang, F., Amrani, M.: Detection of abnormal heart conditions based on characteristics of ECG signals. Measurement 125, 634–644 (2018)
Tuncer, T., Dogan, S., Pławiak, P., Acharya, U.R.: Automated arrhythmia detection using novel hexadecimal local pattern and multilevel wavelet transform with ECG signals. Knowl. Based Syst. 186, 104923 (2019)
Pławiak, P., Abdar, M.: Novel methodology for cardiac arrhythmias classification based on long-duration ECG signal fragments analysis. In: Biomedical signal processing, pp 225–272. Springer, Singapore
McAllister, B.S., Haghighat, K.: Bone augmentation techniques. J. Periodontol. 78(3), 377–396 (2007)
Nakajima, K., Okuda, K., Watanabe, S., Matsuo, S., Kinuya, S., Toth, K., Edenbrandt, L.: Artificial neural network retrained to detect myocardial ischemia using a Japanese multicenter database. Ann. Nucl. Med. 32(5), 303–310 (2018)
Sharma, M., Patel, S., Acharya, U.R.: Automated detection of abnormal EEG signals using localized wavelet filter banks. Pattern Recogn. Lett. (2020)
Ghosh, S.K., Ponnalagu, R.N., Tripathy, R.K., Acharya, U.R.: Automated detection of heart valve diseases using chirplet transform and multiclass composite classifier with PCG signals. Comput. Biol. Med. 118, 103632 (2020)
Vicnesh, J., Wei, J.K.E., Oh, S.L., Arunkumar, N., Abdulhay, E.W., Ciaccio, E.J., Acharya, U.R.: Autism spectrum disorder diagnostic system using HOS bispectrum with EEG signals. Int. J. Environ. Res. Public Health 17(3), 971 (2020)
Ay, B., Yildirim, O., Talo, M., Baloglu, U.B., Aydin, G., Puthankattil, S.D., Acharya, U.R.: Automated depression detection using deep representation and sequence learning with EEG signals. J. Med. Syst. 43(7), 205 (2019)
Pławiak, P., Abdar, M., Pławiak, J., Makarenkov, V., Acharya, U.R.: DGHNL: a new deep genetic hierarchical network of learners for prediction of credit scoring. Inf. Sci. 516, 401–418 (2020)
Pławiak, P., Abdar, M., Acharya, U.R.: Application of new deep genetic cascade ensemble of SVM classifiers to predict the Australian credit scoring. Appl. Soft Comput. 84, 105740 (2019)
Pławiak, P., Acharya, U.R.: Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals. Neural Comput. Appl. 32(15), 11137–11161 (2020)
Tuncer, T., Ertam, F., Dogan, S., Aydemir, E., Pławiak, P.: Ensemble residual network-based gender and activity recognition method with signals. J. Supercomput. 76(3), 2119–2138 (2020)
Author information
Authors and Affiliations
Corresponding authors
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Hammad, M., Alkinani, M.H., Gupta, B.B. et al. Myocardial infarction detection based on deep neural network on imbalanced data. Multimedia Systems 28, 1373–1385 (2022). https://doi.org/10.1007/s00530-020-00728-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00530-020-00728-8