Abstract
Electrocardiogram (ECG) is an essential source of information for heart diseases classification. Hence, it is used by the cardiologist to diagnose heart attacks and detect the abnormalities of the heart. The automatic classification of the ECG signals is playing a vital role in the clinical diagnosis of heart diseases. In this paper, an end-to-end classification method is proposed using 1D Convolution Neural Networks (CNN) to extract the important features from the input signals and classify it automatically. The main advantage of CNN compared to the related work methods is that it gets rid of the hand-crafted features by combining the feature extraction and the classification into a single learning method without any human supervision. The proposed solution consists of data filtering, dynamic heartbeat segmentation, and 1D-CNN consisting of 10 layers without the input and the output layers.
Our experimental results on 14 classes of the public MIT-BIH arrhythmia dataset achieved a promising classification accuracy of 97.8% which outperforms several ECG classification methods.
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Tantawi, M., Revett, K., Salem, A.-B., Tolba, M.F.: Electrocardiogram (ECG): a new burgeoning utility for biometric recognition. In: Hassanien, A., Kim, T.H., Kacprzyk, J., Awad, A. (eds.) Bio-Inspiring Cyber Security and Cloud Services: Trends and Innovations, Intelligent Systems Reference Library, vol. 70, pp. 349–382. Springer, Heidelberg (2014)
Artis, S.G., Mark, R.G., Moody, G.B.: Detection of atrial fibrillation using artificial neural networks. In: Proceedings of the Computers in Cardiology, Venice, Italy, 23–26 September 1991, IEEE, Piscataway, NJ, USA, pp. 173–176 (1991)
Ye, C., Kumar, B.V.K.V., Coimbra, M.T.: Heartbeat classification using morphological and dynamic features of ECG signals. IEEE Trans. Biomed. Eng. 59(10), 2930–2941 (2012)
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)
Alfonso, V.X., Tompkins, J.: Detecting ventricular fibrillation. IEEE Trans. Biomed. Eng. 54(1), 174–177 (2007)
Minami, K., Nakajima, H., Toyoshima, T.: Real-time discrimination of ventricular tachyarrhythmia with Fouriertransform neural network. IEEE Trans. Biomed. Eng. 46(2), 179–185 (1999)
Ye, C., Vijaya Kumar, B.V.K., Coimbra, M.T.: Heartbeat classification using morphological and dynamic features of ECG signals. IEEE Trans. Biomed. Eng. (2012)
Kastor, J.A.: Arrhythmias, 2nd edn. W.B. Saunders, London (1994)
Moody, G.B., Mark, R.G.: The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol. Mag. 20, 45–50 (2001). [CrossRef] [PubMed]
El-Saadawy, H., Tantawi, M., Shedeed, H.A., Tolba, M.F.: Electrocardiogram (ECG) classification based on dynamic beats segmentation. In: Proceedings of the 10th International Conference on Informatics and Systems - INFOS 2016 (2016). https://doi.org/10.1145/2908446.2908452
Gautam, M.K., Giri, V.K.: A neural network approach and wavelet analysis for ECG classification. In: Proceedings of the 2016 IEEE International Conference on Engineering and Technology (ICETECH), Coimbatore, India, 17–18 March 2016, IEEE: Piscataway, NJ, USA, pp. 1136–1141 (2016)
Zebardast, B., Ghaffari, A., Masdari, M.: A new generalized regression artificial neural networks approach for diagnosing heart disease. Int. J. Innov. Appl. Stud. 4, 679 (2013)
Min, S., Lee, B., Yoon, S.: Deep learning in bioinformatics. Briefings Bioinform. 18, 851–869 (2017)
Bakator, M., Radosav, D.: Deep learning and medical diagnosis: a review of literature. Multimodal Technol. Interact. 2, 47 (2018). https://doi.org/10.3390/mti2030047
Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2, 1–127 (2009)
Yu, S.N., Chou, K.T.: Integration of independent component analysis and neural networks for ECG beat classification. Expert Syst. Appl. 34, 2841–2846 (2008)
Sahoo, S., Kanungo, B., Behera, S., Sabut, S.: Multiresolution wavelet transform based feature extraction and ECG classification to detect cardiac abnormalities. Measurement 108, 55–66 (2017)
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, 437–448 (2013)
Martis, R.J., Acharya, U.R., Mandana, K., Ray, A.K., Chakraborty, C.: Application of principal component analysis to ECG signals for automated diagnosis of cardiac health. Expert Syst. Appl. 39, 11792–11800 (2012)
Yazdanian, H., Nomani, A., Yazdchi, M.R.: Autonomous detection of heartbeats and categorizing them by using support vector machines. IEEE (2013)
Elhaj, F.A., Salim, N., Harris, A.R., Swee, T.T., Ahmed, T.: Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals. Comput. Meth. Programs Biomed. 127, 52–63 (2016)
Osowski, S., Hoai, L.T., Markiewicz, T.: Support vector machine-based expert system for reliable heartbeat recognition. IEEE Trans. Biomed. Eng. 51, 582–589 (2004)
Khazaee, A.: Combining SVM and PSO for PVC detection. Int. J. Adv. Eng. Sci. 3(4) (2013)
Khalaf, A.F., Owis, M.I., Yassine, I.A.: A novel technique for cardiac arrhythmia classification using spectral correlation and support vector machines. Expert Syst. Appl. 42, 8361–8368 (2015)
El-Saadawy, H., Tantawi, M., Shedeed, H.A., Tolba, M.F.: Hybrid hierarchical method for electrocardiogram heartbeat classification. IET Signal Process. 12(4), 506–513 (2018). https://doi.org/10.1049/iet-spr.2017.0108
MIT-BIH Arrhythmias Database. http://www.physionet.org/physiobank/database/mitdb/. Accessed 12 Oct 2017
Testing and reporting performance results of cardiac rhythm and ST segment measurement algorithms. ANSI/AAMI EC57:1998 standard, Association for the Advancement of Medical Instrumentation (1998)
Thakor, N.V., Webster, J.G., Tompkins, W.J.: Estimation of QRS Complex Power Spectra for Design of a QRS Filter. IEEE Trans. Biomed. Eng. BME-31(11), 702–706 (1984). https://doi.org/10.1109/tbme.1984.325393
Yang, W., Si, Y., Wang, D., Guo, B.: Automatic recognition of arrhythmia based on principal component analysis network and linear support vector machine. Comput. Biol. Med. 101, 22–32 (2018)
Acharya, U.R., Oh, S.L., Hagiwara, Y., Tan, J.H., Adam, M., Gertych, A., Tan, R.S.: A deep convolutional neural network model to classify heartbeats. Comput. Biol. Med. 89, 389–396 (2017)
Yildirim, Ö.: A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification. Comput. Biol. Med. 96, 189–202 (2018). https://doi.org/10.1016/j.compbiomed.2018.03.016
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR, abs/1412.6980 (2014)
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Shaker, A.M., Tantawi, M., Shedeed, H.A., Tolba, M.F. (2020). Heartbeat Classification Using 1D Convolutional Neural Networks. In: Hassanien, A., Shaalan, K., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2019. AISI 2019. Advances in Intelligent Systems and Computing, vol 1058. Springer, Cham. https://doi.org/10.1007/978-3-030-31129-2_46
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DOI: https://doi.org/10.1007/978-3-030-31129-2_46
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