title: [QRS detection based on neural-network].
abstract: In this paper, we have developed an adaptive matched filtering algorithm based upon an artificial network (ANN) for QRS detection. We used an ANN adaptive whitening filter to model the lower frequencies of the ECG signals which are inherently nonlinear and non-stationary. The residual signal which contained mostly higher frequency QRS complex energy was then passed through a linear matched filter to detect the location of the QRS complex. The results demonstrate that this ANN whitenting filter is very effective for removing the time-varying, nonlinear noise characteristic of ECG signals. With this novel approach, the detection rate for a very noisy patient record in the MIT/BIH arrhythmia database in 99.2%, which compares favorably to the 97.8% achieved with a band-pass filtering method.
authors.affiliation: Electronic Science and Engineering Department, Nanjing University.
authors.forename: X
authors.initials: X
authors.lastname: Yu
authors.forename: X
authors.initials: X
authors.lastname: Xu
mesh_terms: D004562:Electrocardiography; D006801:Humans; D016571:Neural Networks, Computer