Neural-network-based adaptive matched filtering for QRS detection

Abstract

We have developed an adaptive matched filtering algorithm based upon an artificial neural network (ANN) for QRS detection. We use an ANN adaptive whitening filter to model the lower frequencies of the 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. We 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. Using this novel approach, the detection rate for a very noisy patient record in the MIT/BIH arrhythmia database is 99.5%, which compares favorably to the 97.5% obtained using a linear adaptive whitening filter and the 96.5% achieved with a bandpass filtering method.

Hardware Prototyping of Neural Network based Fetal Electrocardiogram Extraction

“…Our previous work was mainly on the signal processing algorithm for FECG extraction only but this article is about the hardware prototyping of that algorithm. ADALINE has been considered in our study mainly as one of the robust filtering techniques that use a Neural Network topology and which proved to be one of the best adaptive filtering algorithms [12]. According to the concept of TDL, the MECG signal enters and passes through the N-1 delays and the output of the TDL is an N-dimensional vector, made up of the input signal at the current time, the previous signal that is fed to the ADALINE.…”

Robust R Peak and QRS detection in Electrocardiogram using Wavelet Transform

“…Numerous QRS detection algorithms such as derivative based algorithms [1][2][3][4], wavelet transform [5], Filtering Techniques [6] artificial neural networks [7][8][9], genetic algorithms [10], syntactic methods [11], Hilbert transform [12], Markov models [13] etc. are reported in literature.…”

Vizsgálati módszerek fejlesztése és alkalmazása a neurokardiológiai fluktuációk automatizált feldolgozásában

“…differenciáló módszerek [5][6][7], neurális hálózatok [8], szupport vektor gépek [9], Wavelet-, Hilbert-, ill. hibrid szűrőrendszerek [10][11][12][13][14], rejtett Markov modellek és ICA-PCA [1] dekompozíción alapuló eljárások.…”