DocumentCode :
888635
Title :
Neural-network-based adaptive matched filtering for QRS detection
Author :
Xue, Qiuzhen ; Hu, Yu Hen ; Tompkins, Willis J.
Author_Institution :
Dept. of Electr. & Comput. Eng., Wisconsin Univ., Madison, WI, USA
Volume :
39
Issue :
4
fYear :
1992
fDate :
4/1/1992 12:00:00 AM
Firstpage :
317
Lastpage :
329
Abstract :
The authors have developed an adaptive matched filtering algorithm based upon an artificial neural network (ANN) for QRS detection. They use an ANN adaptive whitening filter to model the lower frequencies of the electrocardiogram (ECG) which are inherently nonlinear and nonstationary. The residual signal which contains mostly higher frequency QRS complex energy is then passed through a linear matched filter to detect the location of the QRS complex. The authors developed an algorithm to adaptively update the matched filter template from the detected QRS complex in the ECG signal itself so that the template can be customized to an individual subject. This ANN whitening filter is very effective at removing the time-varying, nonlinear noise characteristic of ECG signals. The detection rate for a very noisy patient record in the MIT/BIH arrhythmia database is 99.5% with this approach, which compares favorably to the 97.5% obtained using a linear adaptive whitening filter and the 96.5% achieved with a bandpass filtering method.
Keywords :
electrocardiography; neural nets; signal processing; ECG lower frequencies modelling; ECG signal noise characteristic; QRS detection; adaptive matched filtering algorithm; adaptive whitening filter; artificial neural network; higher frequency QRS complex energy; linear matched filter; residual signal; the MIT/BIH arrhythmia database; very noisy patient record; Adaptive filters; Adaptive signal detection; Artificial neural networks; Band pass filters; Databases; Electrocardiography; Filtering algorithms; Frequency; Matched filters; Nonlinear filters; Algorithms; Arrhythmias, Cardiac; Artifacts; Electrocardiography; Humans; Linear Models; Neural Networks (Computer); Sensitivity and Specificity;
fLanguage :
English
Journal_Title :
Biomedical Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9294
Type :
jour
DOI :
10.1109/10.126604
Filename :
126604
Link To Document :
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