title: ECG feature extraction using differentiation, Hilbert transform, variable threshold and slope reversal approach.
abstract: 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.
authors.affiliation: Department of Applied Physics, University of Calcutta, Kolkata, India. sonartoritag@yahoo.co.in
authors.forename: S K
authors.initials: SK
authors.lastname: Mukhopadhyay
authors.forename: M
authors.initials: M
authors.lastname: Mitra
authors.forename: S
authors.initials: S
authors.lastname: Mitra
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D004562:Electrocardiography; D006321:Heart / Q000502:physiology; D006801:Humans; D009203:Myocardial Infarction / Q000503:physiopathology; D012815:Signal Processing, Computer-Assisted; D012984:Software