Abstract
Many real-time QRS detection algorithms have been proposed in the literature. However these algorithms usually either exhibit too long a response time or lack robustness. An algorithm has been developed which offers a balance between these two traits, with a very low response time yet with performance comparable to the other algorithms. The wavelet based derivative approach achieved better detection. In the first step, the clean ECG signal is obtained. then, QRS complexes are detected and each complex is used to locate the peaks of the individual waves, including onsets and offsets of the P and T waves which are present in one cardiac cycle. The algorithm was evaluated on MIT-BIH Database, the manually annotated database, for validation purposes. The proposed QRS detector achieved sensitivity of 98.91% and a positive predictivity of 99.65% over the validation database.
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P., A., Talele, K.T. (2011). ECG Feature Extraction Using Wavelet Based Derivative Approach. In: Shah, K., Lakshmi Gorty, V.R., Phirke, A. (eds) Technology Systems and Management. Communications in Computer and Information Science, vol 145. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20209-4_34
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DOI: https://doi.org/10.1007/978-3-642-20209-4_34
Publisher Name: Springer, Berlin, Heidelberg
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