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.

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SummaryBackground: Atrial fibrillation is often first recognized after a complication such as embolic stroke has occurred. Limited data are available for the prospective identification of patients at risk for developing atrial fibrillation.Hypothesis: Demonstration of areas of slow conduction in the atrium by means of P-wave signal averaging may identify individuals at risk for atrial fibrillation.Methods: P-wave signal averaging from the surface electrocardiogram was performed in 199 normal controls and 81 patients with paroxysmal atrial fibrillation using an automated, P-triggered, high-resolution signal for analysis.Results: Of the variables measured, the filtered P-wave duration and P-wave integral were significantly different between controls and patients (filtered P-wave duration 120 ± 9 vs. 145 ± 21 and P-wave integral 666 ± 208 vs. 868 ± 352), whereas the terminal root-mean-square (RMS) voltages (RMS 20, RMS 30, RMS 40) showed no significant differences between the two groups. Regression analysis of the first and second measurement of the filtered P-wave duration obtained during consecutive tests showed excellent reproducibility (r and r 2 of 0.96 and 0.92). The duration of the filtered P wave showed no age dependence but was shorter in women.

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