An R-peak detection method based on peaks of Shannon energy envelope

2013 ◽  
Vol 8 (5) ◽  
pp. 466-474 ◽  
Author(s):  
Honghai Zhu ◽  
Jun Dong
2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Jeong-Seon Park ◽  
Sang-Woong Lee ◽  
Unsang Park

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.


IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 51227-51237 ◽  
Author(s):  
Miran Lee ◽  
Dajeong Park ◽  
Suh-Yeon Dong ◽  
Inchan Youn

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 48529-48542
Author(s):  
Xiaoyuan Wei ◽  
Yuan Yang ◽  
Jesus Urena ◽  
Jiaxuan Yan ◽  
Haozhen Wang

2014 ◽  
Vol 26 (01) ◽  
pp. 1450007 ◽  
Author(s):  
Xiuling Liu ◽  
Jianli Yang ◽  
Xiaoyu Zhu ◽  
Suiping Zhou ◽  
Hongrui Wang ◽  
...  

QRS complex is the most important part in electrocardiogram (ECG) as it contains the most important information of heart activities. R-peak detection is the first, yet crucial, step in most ECG automatic diagnose methods. Due to the existence of noise in ECG signals and changes in QRS morphology, most existing methods are not robust in different conditions. In the field of intelligent remote health caring, in addition to the detection accuracy, timeliness is also an important research issue. In this paper, wavelet transform and energy window transform are introduced, which form the basis of a novel R-peak detection method. Wavelet transform is used to efficiently reduce noise and highlight useful ECG signal for it has good time-frequency resolution characters, and energy window transform converts time domain signal to energy domain, which makes it easier to isolate QRS complex from other signals. As a result, influence from QRS morphology changes can be effectively alleviated. To validate the effectiveness of this new method, ECG records of MIT-BIH arrhythmia database are used in the experiments. The experiment results show that the proposed method is efficient and robust to noise and QRS morphology changes. The computational cost of the proposed method has also been evaluated.


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