Neural-network-based adaptive matched filtering for QRS detection

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.