Delineation of ECG characteristic features using multiresolution wavelet analysis method

A discrete wavelet transform (DWT) based feature extraction technique in the QT segment of digitized electrocardiograph recordings is proposed. At first, the signal is denoised by decomposing it using DWT technique and discarding the coefficients corresponding to the noise components. A multiresolution approach along with an adaptive thresholding is used for the detection of R-peaks. Then Q, S peak, QRS onset and offset points are identified. Finally, the T wave is detected. By detecting the baseline of the ECG data, height of R, Q, S and T wave are calculated. For R-peak detection, proposed algorithm yields sensitivity and positive predictivity of 99.8% and 99.6% respectively with MIT BIH Arrhythmia database, 99.84% and 99.98% respectively with PTB diagnostic ECG database. For time plane features, an average coefficient of variation of 3.21 is obtained over 150 leads tested from PTB data, each with 10,000 samples.

► Use of DWT for multiresolution analysis of the ECG data.
► Denoising is performed by discarding detailed coefficients D1 and D2.
► QRS zone is selected by D4 + D5 and adaptive thresholding.
► Fiducial points are determined by amplitude and slope reversal point detection.
► Average sensitivity and predictivity of 99.8% and 99.6% with mit-db obtained.