Title :
Detection of Electrocardiography Based on Wavelet Transform and Extended Kalman Filter
Author_Institution :
Dept. of Comput. & Inf. Eng., Luoyang Inst. of Sci. & Technol., Luoyang, China
Abstract :
This paper combined wavelet transform with extended kalman algorithm. By using wavelet transform which has characteristics of multiresolution to decompose the signal of ECG and then to filter the signal of ECG in each scale. Finally a decision rule is used to determine the QRS wave. The QRS detector has an average sensitivity of Se = 99.34% and a positive predictivity P+= 99.28% by evaluating the algorithm on MIT-BIH Database.
Keywords :
Kalman filters; electrocardiography; medical signal detection; nonlinear filters; signal resolution; wavelet transforms; ECG signal filtering; MIT-BIH database; QRS detector; QRS wave; decision rule; electrocardiography; extended Kalman filter; multiresolution; positive predictivity; signal decomposition; wavelet transform; Electrocardiography; Equations; Filtering theory; Kalman filters; Noise; Wavelet transforms; Electrocardiography; Extended Kalman Filter; Wavelet Transform;
Conference_Titel :
Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2012 4th International Conference on
Conference_Location :
Nanchang, Jiangxi
Print_ISBN :
978-1-4673-1902-7
DOI :
10.1109/IHMSC.2012.40
