ECG feature extraction using differentiation, Hilbert transform, variable threshold and slope reversal approach

An accurate and reliable ECG feature extraction algorithm is presented in this paper. ECG samples are de-noised and its first derivative and Hilbert transform are computed. Sample having maximum amplitude in the transformed domain is found out and those samples having amplitudes within a lead wise specified threshold of that maximum are marked. In the original signal, where these marked samples undergo slope reversals are spotted as R-peak. On the left and right side of the R-peak, slope reversals are identified as Q and S peak, respectively. QRS onset-offset points, T and P waves are also detected. ECG baseline modulation correction is done after detecting characteristics points. The algorithm offers a good level of Sensitivity, Positive Predictivity and accuracy of R peak detection. Each wave and segment duration and each peak height is measured. Measurement errors of extracted ECG features are calculated. The algorithm is implemented on MATLAB 7.1 environment.

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