A Wavelet-Based ECG Delineation Method: Adaptation to an Experimental Electrograms with Manifested Global Ischemia

Abstract

We present a novel wavelet-based ECG delineation method with robust classification of P wave and T wave. The work is aimed on an adaptation of the method to long-term experimental electrograms (EGs) measured on isolated rabbit heart and to evaluate the effect of global ischemia in experimental EGs on delineation performance. The algorithm was tested on a set of 263 rabbit EGs with established reference points and on human signals using standard Common Standards for Quantitative Electrocardiography Standard Database (CSEDB). On CSEDB, standard deviation (SD) of measured errors satisfies given criterions in each point and the results are comparable to other published works. In rabbit signals, our QRS detector reached sensitivity of 99.87% and positive predictivity of 99.89% despite an overlay of spectral components of QRS complex, P wave and power line noise. The algorithm shows great performance in suppressing J-point elevation and reached low overall error in both, QRS onset (SD = 2.8 ms) and QRS offset (SD = 4.3 ms) delineation. T wave offset is detected with acceptable error (SD = 12.9 ms) and sensitivity nearly 99%. Variance of the errors during global ischemia remains relatively stable, however more failures in detection of T wave and P wave occur. Due to differences in spectral and timing characteristics parameters of rabbit based algorithm have to be highly adaptable and set more precisely than in human ECG signals to reach acceptable performance.

Appendix

In this section, we present some of important formulas which were stated, but not specifically defined, above in order to maintain clarity of the article. Constants that correspond to human-based algorithm are stated in parenthesis and vice versa. Main group contains formulas used to calculate various types of thresholds. Constants used in calculation of P wave and T wave detection time windows are listed in Table 3.

$$ \xi_{{QRS_{\text{on}} }} = 0.33(0.44) \times SD[w_{a} (n)]; a = a_{QRS}^{\text{h}} $$

(7)

$$ \xi_{{QRS_{\text{off}} }} = 0.2(0.1) \times SD [w_{a} (n) ];a = a_{QRS}^{\text{h}} $$

(8)

$$ \xi_{\text{q}} = 1.8(0.83) \times \xi_{{QRS_{\text{on}} }} $$

(9)

$$ \xi_{\text{s}} = 0.1(0.25) \times \xi_{{QRS_{\text{off}} }} $$

(10)

$$ \xi_{\text{P}} = 1.2(1.6) \times SD(w_{2} (n));a = a_{QRS}^{\text{h}} $$

(11)

$$ \xi_{{{\text{P}}_{\text{on}} }} = 0.15(0.15) \times \hbox{max} \left| {w_{a} (n)} \right|;a = a_{\text{P}} $$

(12)

$$ \xi_{{{\text{P}}_{\text{off}} }} = 0.05\left( {0.5} \right) \times \hbox{max} \left| {w_{a} (n)} \right|; a = a_{\text{P}} $$

(13)

$$ \xi_{{{\text{T}}_{\text{end}} }} = 0.55\left( {0.24} \right) \times \hbox{max} \left| {w_{a} (n)} \right|;a = a_{\text{T}} $$

(14)

Table 3 Setting of time window for P wave and T wave in rabbit (human) signals.