Abstract
Electrocardiogram signal is principally used for the interpretation and assessment of heart’s condition. The main criteria in ECG signal analysis is interpretation of QRS complex and obtaining its feature information. R wave is the most significant segment of this QRS complex, which has a prominent role in finding HRV (Heart Rate Variability) features and in determining its characteristic features. This paper intends to propose a novel approach for the analysis of ECG signals. The ECG signal is preprocessed using stationary wavelet transform (SWT) with interval dependent thresholding integrated with the wiener filter and is then subjected to Hilbert transform along with a window to enhance the presence of QRS complexes, to detect R-Peaks by setting a threshold. The proposed algorithm is validated with different parameters like Sensitivity, +Predictivity and Accuracy. The proposed method yields promising results with 99.94 % Sensitivity, 99.92 % +Predictivity, 99.87 % Accuracy. Finally the proposed method is compared with other methods to show the efficiency of the proposed technique for the analysis of ECG Signal.
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References
Afonso, X., Tompkins, W.J., Nguyen, T., Luo, S.: ECG beat detection using filter banks. IEEE Trans. Biomed. Eng. 46, 230–236 (1999)
Al Mahmdy, M., Bryan Riley, H.: Performance study of different denoising methods for ECG signals, Science Direct. Procedia Comput. Sci. 37, 325–332 (2014)
Kohler, B.U., et al.: The principles of software QRS detection. IEEE Eng. Medicine Biol. Mag. 21, 42–57 (2002)
Arzeno, N.M., Deng, Z.D., Poon, C.S.: Analysis of first-derivative based QRS detection algorithms. IEEE Trans. Biomed. Eng. 55(2), 478–484 (2008)
Thulasi Prasad, S., Varadarajan, S.: Heart rate detection using Hilbert transform. Int. J. Res. Eng. Technol. 2, 508–513 (2013)
Benitez, D., Gaydecki, P.A., Zaidi, A.: The use of the Hilbert transform in ECG signal analysis. Comput. Biol. Med. 31, 399–406 (2001)
Ruchita, G., Sharma, A.K.: Detection of QRS complexes of ECG recording based on wavelet transform using Matlab. Int. J. Eng. Sci. 7(2), 3038–3034 (2010)
Arbateni, Khaled, Bennia, Abdelhak: Sigmoidal radial basis function ANN for QRS complex detection. Neurocomputing, Elsevier 145, 438–450 (2014)
Shivappriya, S.N., Shanthaselvakumari, R., Gowrishankar, T.:ECG delineation using stationary wavelet transform. In: International Conference on Advanced Computing and Communications (ADCOM), IEEE, pp. 271–274 (2006)
Li, G.: Noise removal of Raman spectra using interval thresholing method. In: Second International Symposium on intelligent Information Technology Application, IITA’08. Shanghai, China, vol. 1, pp. 535–539 (2008)
Nikolev, N., Nikolov, Z., Gotchev, A., Egiazarian, K.: Wavelet domain Wiener filtering for ECG denoising using improved signal estimate. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), vol. 6, pp. 3578–3581 (2000)
Mark, R., Moody, G.: MIT-BIH arrhythmia database. http://www.physionet.org/physiobank/database/mitdb
Pan, J., Tompkins, W.J.: A real time QRS detection algorithm. IEEE Trans. Biomed. Eng. BME-32(3) March (1985)
Chen, S.W., Chen, H.C., Chan, H.L.: A real-time QRS detection method based on moving-averaging incorporating with wavelet denoising. Comput. Methods Programs Biomed. 82, 187–195 (2006)
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Sahu, G., Biswal, B., Choubey, A. (2016). Analysis of ECG Signals Using Advanced Wavelet Filtering Approach. In: Behera, H., Mohapatra, D. (eds) Computational Intelligence in Data Mining—Volume 2. Advances in Intelligent Systems and Computing, vol 411. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2731-1_40
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DOI: https://doi.org/10.1007/978-81-322-2731-1_40
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