Abstract:

Detecting peaks and boundaries of ECG characteristic waves supplies fundamental features for extracting clinically useful information. In this paper, an accurate threshold-independent multi-lead ECG delineation system is presented. Detection of QRS complexes, P and T waves is based on wavelet transform using Haar function as prototype wavelet and analyzing the first scale details coefficients.The delineator is performed on certain selected channels. Afterwards, a method, using a special histogram-based estimation, yields the exact positions of the significant points in all multi-channel ECG signals. The algorithm is applied on MIT-BIH Arrhythmia database signals and on multi-channel ECG signals measured at our institute.The single-channel delineation method was tested on MIT-BIH Arrythmia database signals. Sensitivity and positive predictivity were greater than 99.84% and 99.89% respetively for more than 15,990 beats. Furthermore, an overall mean error of less than two sampling intervals (1 ms) is obtained comparing the manual and the automatic method, whereas the standard deviation does not exceed three sampling intervals.

Abstract:

In this paper, a new offline method for automatic baseline drift correction in Electrocardiogram is presented. It is based on Discrete Wavelet Transform (DWT) and analyzing high scale Approximation Coefficients (AC). A set of 650 noisy ECG signals was created by mixing different artificially generated noise-free ECGs and baseline wanders. By applying different mother Wavelets on each noisy signal, twelve stage DWT decomposition was carried out and twelve filtered ECGs were reconstructed by canceling the highest level AC at each stage. The similarities between initially generated baseline and canceled AC, as well as between the corresponding noise-free and reconstructed ECGs were examined every time by means of Correlation technique. The results from all 650 signals were considered in order to find the suitable Wavelet and AC level. The highest correlations, better than 99.9% for baseline and 99.99% for filtered ECG, were found with the ninth scale approximation coefficients when using Daubechies11 or Symlet12 as prototype wavelet. The algorithm was applied on various MIT-BIH and Multi-channel ECG signals. Furthermore, the baseline elimination results were considered to be very promising.