Arrhythmia detection using deep learning

  • Authors

    • Niharika Pattanaik
    • Ipsita Mohapatra
    • Mihir Narayan Mohanty
    2018-06-08
    https://doi.org/10.14419/ijet.v7i2.33.15524
  • ECG Signal, Cardiac Heart Failure, Discrete Wavelet Transform (DWT), R-Peaks, Deep Neural Network

  • Abstract

    Currently the demand of hospital services increasing gradually. The smart service to the patients is highly essential that counts the death rate. Cardiac problem is a vital problem and people with Cardiological problem are surviving less. The diagnosis of the heart disease facilitates to storage our data. It motivates the application of Data Mining techniques are useful in Health sectors. In this paper authors have taken an approach to detect Arrhythmia using Wavelet transform and Deep Neural Network (DNN). In first stage R-peaks of Arrhythmia data has been detected using Wavelet Transform. In the next stage the Wavelet coefficients are consider as the input features to the DNN model. The classification result for the arrhythmia detection has been presented in the result section of the paper.

     

     


     

  • References

    1. [1] Dangare, C. S., & Apte, S. S. (2012). Improved study of heart disease prediction system using data mining classification techniques. International Journal of Computer Applications, 47(10), 44-48.

      [2] Gandhi, M., & Singh, S. N. (2015, February). Predictions in heart disease using techniques of data mining. In Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE), 2015 International Conference on (pp. 520-525). IEEE.

      [3] Wu, Z., Ding, X., Zhang, G., Xu, X., Wang, X., Tao, Y., & Ju, C. (2016, December). A novel features learning method for ECG arrhythmias using deep belief networks. In Digital Home (ICDH), 2016 6th International Conference on (pp. 192-196). IEEE.

      [4] De Chazal, P., O'Dwyer, M., & Reilly, R. B. (2004). Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Transactions on Biomedical Engineering, 51(7), 1196-1206.

      [5] Inan, O. T., Giovangrandi, L., & Kovacs, G. T. (2006). Robust neural-network-based classification of premature ventricular contractions using wavelet transform and timing interval features. IEEE Transactions on Biomedical Engineering, 53(12), 2507-2515.

      [6] Martis, R. J., Acharya, U. R., & Min, L. C. (2013). ECG beat classification-using PCA, LDA, ICA and discrete wavelet transform. Biomedical Signal Processing and Control, 8(5), 437-448.

      [7] Kampouraki, A., Manis, G., & Nikou, C. (2009). Heartbeat time series classification with support vector machines. IEEE Transactions on Information Technology in Biomedicine, 13(4), 512-518.

      [8] Gautam, M. K., & Giri, V. K. (2016, March). A Neural Network approach and Wavelet analysis for ECG classification. In Engineering and Technology (ICETECH), 2016 IEEE International Conference on (pp. 1136-1141). IEEE.

      [9] Thuy, L. T. M., Nghia, N. T., Binh, D. V., Hai, N. T., & Hung, N. M. (2017, July). Error-rate analysis for ECG classification in diversity scenario. In System Science and Engineering (ICSSE), 2017 International Conference on (pp. 39-43). IEEE.

      [10] Mohapatra, S.K., Palo, H.K., & Mohanty, M.N. (2018). Detection of Arrhythmia using Neural Network. ICITKM (2018). (pp. 97-100).

      [11] Moody, G. B., & Mark, R. G. (2001). The impact of the MIT-BIH arrhythmia database. IEEE Engineering in Medicine and Biology Magazine, 20(3), 45-50.

      [12] Acharya, U. R., Fujita, H., Oh, S. L., Hagiwara, Y., Tan, J. H., & Adam, M. (2017). Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Information Sciences, 415, 190-198.

      [13] Rashmirekha Ram, Mihir Narayan Mohanty, “Enhancement of Speech using Deep Neural Network with Discrete Cosine Transformâ€, Recent Advances in Machine Learning and Soft Computing, of Journal of Intelligent & Fuzzy Systems, 2018. (Accepted).

      [14] Isin, A., & Ozdalili, S. (2017). Cardiac arrhythmia detection using deep learning. Procedia Computer Science, 120, 268-275.

      [15] Zhang, Q., Yang, L. T., Chen, Z., & Li, P. (2018). A survey on deep learning for big data. Information Fusion, 42, 146-157.

  • Downloads

  • How to Cite

    Pattanaik, N., Mohapatra, I., & Narayan Mohanty, M. (2018). Arrhythmia detection using deep learning. International Journal of Engineering & Technology, 7(2.33), 668-888. https://doi.org/10.14419/ijet.v7i2.33.15524

    Received date: 2018-07-13

    Accepted date: 2018-07-13

    Published date: 2018-06-08