A Fully Automatic Method for Accurate Parametrization and Reconstruction of ECG Waveforms
Parameterization and synthesis of electrocardiogram (ECG) recordings are some of the most challenging problems in biomedical signal processing owing to the fact that ECG signals commonly exhibit complex temporal morphology and contain numerous artifacts of data collection process. In this paper, we present a fully automatic framework for accurate and robust parameterization and reconstruction of ECG waveforms. The method uses the observed signal to ascertain a nondeterministic model for the ECG signal and employs the Dynamic Time warping (DTW) algorithm to determine a non-linear temporal relationship between the established ECG model and the individual pulses in the ECG signal. The results of parameterization provide a set of data that accurately describe the morphology of the ECG pulses. The proposed signal synthesis algorithm is able to independently account for the temporal and spatial dynamics of consecutive ECG pulses and provide a faithful reconstruction of ECG signals. Performance evaluation experiments are conducted on a database of 135 one-minute ECG recordings. The percentage rootmean-square difference measure is employed to evaluate the quality of signal reconstruction and also validate the results of signal parameterization.