Method to filter ecgs and evaluate clinical parameter distortion using realistic ECG model parameter fitting

By fitting a previously published nonlinear model for generating realistic ECG to waveforms collected from a healthy human subject, and using a nonlinear least-squares optimization procedure, the authors demonstrate that significant points (P, Q, R, S, and T) on the ECG can be determined to an arbitrary accuracy. The model-fitting routine runs in real-time on a 3 GHz PC coloured (1/f^beta) noise is then added to the ECG in order to evaluate the fitting accuracy under a variety of recording conditions. A method for determining noise levels (and colour) in real ECGs using the residual of a singular valued decomposition is described. Furthermore, a method for evaluating the filter is described which allows an application-specific evaluation of the filter in terms of the distortion in the QRS width and amplitude, the ST-level, the QT interval, the PR-interval, and the fiducial point location. Using these methods, the model-based filter is shown to introduce insignificant clinical distortion in the QT interval and QRS width down to an SNRges0 dB for beta<2. The fiducial point location is shown to be insignificantly distorted (<1ms) for an SNRges2 dB, and the ST-level is stable down to SNR>12 dB. PR interval is more sensitive to noise due to the low amplitude nature of the P-wave. In general, the filter performance is degraded by increasing beta