TY - GEN
T1 - R-wave detection
T2 - 10th International Conference on Information Sciences, Signal Processing and their Applications, ISSPA 2010
AU - Dong, Shiying
AU - Xu, Fangfei
AU - Lingwood, Barbara
AU - Mesbah, Mostefa
AU - Boashash, Boualem
PY - 2010
Y1 - 2010
N2 - In the electrocardiograph (ECG), R-wave is the positive upward deflection in the QRS complex which represents the depolarization of both left and right ventricles. Accurate detection of the R-wave peaks in the ECG plays a primary role in the construction and analysis of the heart rate variability (HRV). Numerous methods have been proposed to enhance the robustness and accuracy of the automatic detection. The majority of these methods have been developed for adult ECG and may not perform adequately in the case of the newborn. In this study, we analysed the performance of four R-wave detection methods that were applied on newborn piglet ECG data. These methods are based on: first derivative, wavelet transform, and nonlinear transform. The results of our performance analysis showed that the nonlinear approach based on the Hilbert transform marginally outperformed the others, with the highest sensitivity (Se) of 99 .95%, the lowest detection error(ER) of 0.12% and a high positive prediction (+P) of 99.93%.
AB - In the electrocardiograph (ECG), R-wave is the positive upward deflection in the QRS complex which represents the depolarization of both left and right ventricles. Accurate detection of the R-wave peaks in the ECG plays a primary role in the construction and analysis of the heart rate variability (HRV). Numerous methods have been proposed to enhance the robustness and accuracy of the automatic detection. The majority of these methods have been developed for adult ECG and may not perform adequately in the case of the newborn. In this study, we analysed the performance of four R-wave detection methods that were applied on newborn piglet ECG data. These methods are based on: first derivative, wavelet transform, and nonlinear transform. The results of our performance analysis showed that the nonlinear approach based on the Hilbert transform marginally outperformed the others, with the highest sensitivity (Se) of 99 .95%, the lowest detection error(ER) of 0.12% and a high positive prediction (+P) of 99.93%.
KW - Electrocardiograph
KW - Heart rate variability
KW - Newborn
KW - QRS complex
KW - R-wave detection
UR - http://www.scopus.com/inward/record.url?scp=78650286395&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78650286395&partnerID=8YFLogxK
U2 - 10.1109/ISSPA.2010.5605472
DO - 10.1109/ISSPA.2010.5605472
M3 - Conference contribution
AN - SCOPUS:78650286395
SN - 9781424471676
T3 - 10th International Conference on Information Sciences, Signal Processing and their Applications, ISSPA 2010
SP - 320
EP - 323
BT - 10th International Conference on Information Sciences, Signal Processing and their Applications, ISSPA 2010
Y2 - 10 May 2010 through 13 May 2010
ER -