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Journal of Healthcare Engineering
Volume 2017 (2017), Article ID 4901017, 14 pages
Research Article

R Peak Detection Method Using Wavelet Transform and Modified Shannon Energy Envelope

1Department of Multimedia, Chonnam National University, 50 Daehak-ro, Yeosu, Jeollanamdo 59626, Republic of Korea
2Department of Software, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam, Gyeonggido 13120, Republic of Korea
3Department of Computer Science & Engineering, Sogang University, 35 Baekbeom-ro, Mapo-gu, Seoul 04107, Republic of Korea

Correspondence should be addressed to Unsang Park

Received 16 December 2016; Revised 3 April 2017; Accepted 23 April 2017; Published 5 July 2017

Academic Editor: Benlian Xu

Copyright © 2017 Jeong-Seon Park et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Rapid automatic detection of the fiducial points—namely, the P wave, QRS complex, and T wave—is necessary for early detection of cardiovascular diseases (CVDs). In this paper, we present an R peak detection method using the wavelet transform (WT) and a modified Shannon energy envelope (SEE) for rapid ECG analysis. The proposed WTSEE algorithm performs a wavelet transform to reduce the size and noise of ECG signals and creates SEE after first-order differentiation and amplitude normalization. Subsequently, the peak energy envelope (PEE) is extracted from the SEE. Then, R peaks are estimated from the PEE, and the estimated peaks are adjusted from the input ECG. Finally, the algorithm generates the final R features by validating R-R intervals and updating the extracted R peaks. The proposed R peak detection method was validated using 48 first-channel ECG records of the MIT-BIH arrhythmia database with a sensitivity of 99.93%, positive predictability of 99.91%, detection error rate of 0.16%, and accuracy of 99.84%. Considering the high detection accuracy and fast processing speed due to the wavelet transform applied before calculating SEE, the proposed method is highly effective for real-time applications in early detection of CVDs.