Abstract
Electrocardiogram (ECG) plays an essential role in the medical field, it records the electrical activity of the heart over time and provides information about the heart condition. Hence, the cardiologist uses it to detect the abnormalities of the heart and to diagnose the heart diseases. Convolutional Neural Networks (CNNs) have proven their ability in extracting the most important features, Long Short-Term Memory (LSTM) has the capabilities of learning the temporal dependencies between the sequential data. In this paper, a novel method based on the combination of CNN and LSTM is proposed to classify 15 classes of the MIT-BIH dataset automatically without any hand-engineering feature extraction methods. The proposed method consists of data filtering, dynamic technique for heartbeat segmentation, and CNN-LSTM model consists of 12 layers.
Our experimental results of the proposed method achieved promising overall accuracy of 98.16% in classification between 15 classes of the MIT-BIH dataset, which outperforms several heartbeat classification methods.
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World Health Organization: Cardiovascular diseases (CVDs) (2017). http://www.who.int/mediacentre/factsheets/fs317/en/
World Health Organization: About cardiovascular diseases (CVDs) (2017). https://www.who.int/cardiovascular_diseases/about_cvd/en/
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. Springer, Heidelberg, vol 70, pp. 349–382 (2014)
Alfonso, V.X., Tompkins, J.: Detecting ventricular fibrillation. IEEE Trans. Biomed. Eng. 54(1), 174–177 (2007)
Moody, G.B., Mark, R.G.: The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol. Mag. 20, 45–50 (2001)
Kastor, J.A.: Arrhythmias, 2nd edn. W.B. Saunders, London (1994)
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
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
Min, S., Lee, B., Yoon, S.: Deep learning in bioinformatics. Brief. Bioinf. 18, 851–869 (2017)
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)
Abiwinanda, N., Hanif, M., Hesaputra, S.T., Handayani, A., Mengko, T.R.: Brain tumor classification using convolutional neural network. In: Lhotska, L., Sukupova, L., Lacković, I., Ibbott, G. (eds.) World Congress on Medical Physics and Biomedical Engineering 2018. IFMBE Proceedings, vol. 68, no. 1. Springer, Singapore (2019)
Karthik, S., Srinivasa Perumal, R., Chandra Mouli, P.V.S.S.R.: Breast cancer classification using deep neural networks (2018). https://doi.org/10.1007/978-981-10-6680-1_12
Liu, J., Pan, Y., Li, M., Chen, Z., Tang, L., Lu, C., Wang, J.: Applications of deep learning to MRI images: a survey. Big Data Min. Anal. 1, 1–18 (2018)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2, 1–127 (2009)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735
Wu, Y., et al.: Google’s neural machine translation system: bridging the gap between human and machine translation (2016)
Kim, M., et al.: Speaker-independent silent speech recognition from flesh-point articulatory movements using an LSTM neural network. IEEE/ACM Trans. Audio Speech Lang. Process. 25(12), 2323–2336 (2017)
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)
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)
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)
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
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)
Acharya, U.R., Oh, S.L., Hagiwara, Y., Tan, J.H., Adam, M., Gertych, A., San, T.R.: A deep convolutional neural network model to classify heartbeats. Comput. Biol. Med. (2017). https://doi.org/10.1016/j.compbiomed.2017.08.022
Li, D., Zhang, J., Zhang, Q., Wei, X.: Classification of ECG signals based on 1D convolution neural network. In: 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom) (2017). https://doi.org/10.1109/healthcom.2017.8210784
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
Rectifier (neural networks). https://en.wikipedia.org/wiki/Rectifier_(neural_networks)
MIT-BIH Arrhythmias Database. http://www.physionet.org/physiobank/database/mitdb/. Accessed 5 Sept 2019
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)
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). Combination of Convolutional and Recurrent Neural Networks for Heartbeat Classification. In: Hassanien, AE., Azar, A., Gaber, T., Oliva, D., Tolba, F. (eds) Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020). AICV 2020. Advances in Intelligent Systems and Computing, vol 1153. Springer, Cham. https://doi.org/10.1007/978-3-030-44289-7_34
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DOI: https://doi.org/10.1007/978-3-030-44289-7_34
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