TY - GEN
T1 - ECG denoise method based on wavelet function learning
AU - Kang, Won Seok
AU - Yun, Sanghun
AU - Cho, Kookrae
PY - 2012
Y1 - 2012
N2 - In this paper, we propose a new denoise method for noisy electrocardiogram (ECG) signals. We employ an n-gram-based wavelet learning in order to investigate optimal classical wavelet sequences for ECG signals denoise. Our main approach separates the ECG signal of the interest into multi-windows then assigns the optimal wavelet to each window. The wavelet learning approach uses the mean square error(MSE) as a feature to generate an n-gram table. Also, we selected MSE and the signal-to-noise ratio(SNR) for evaluation factors. As a result of simulation, we confirmed that the performance become more precise than the previous approaches.
AB - In this paper, we propose a new denoise method for noisy electrocardiogram (ECG) signals. We employ an n-gram-based wavelet learning in order to investigate optimal classical wavelet sequences for ECG signals denoise. Our main approach separates the ECG signal of the interest into multi-windows then assigns the optimal wavelet to each window. The wavelet learning approach uses the mean square error(MSE) as a feature to generate an n-gram table. Also, we selected MSE and the signal-to-noise ratio(SNR) for evaluation factors. As a result of simulation, we confirmed that the performance become more precise than the previous approaches.
UR - https://www.scopus.com/pages/publications/84873971640
U2 - 10.1109/ICSENS.2012.6411438
DO - 10.1109/ICSENS.2012.6411438
M3 - Conference contribution
AN - SCOPUS:84873971640
SN - 9781457717659
T3 - Proceedings of IEEE Sensors
BT - IEEE SENSORS 2012 - Proceedings
T2 - 11th IEEE SENSORS 2012 Conference
Y2 - 28 October 2012 through 31 October 2012
ER -