C.-C. Lin C.-M. Chen I.-F. Yang T.-F. Yang

MEDICALAND BIOLOGICAL ENGINEERING AND COMPUTING Volume 43,Number 2,  218-224, SpringerLink

Introduction Materials and methods Results Conclusions

Abnormal intra-QRS potentials (AIQPs) in signal-averaged electrocardiograms have been proposed as a risk evaluation index for ventricular arrhythmias.

Three standardised time-domain SAECG to detection of ventricular late potentials(VLPs)◦ Filtered total QRS duration (FQRSD)◦ RMS voltage of the last QRS 40 ms (RMS40)◦ Low-amplitude signals below 40 μ V(LAS40)

Several methods developed in other domains◦ Frequency-domain analysis◦ Spectro temporal mapping analysis (STM)◦ Spectral turbulence analysis(STA)

Gomis and Lander proposed a new concept, they developed a parametric model to esti-mate the AIQP.

The optimum model order depends on the clinical classifications and results, and so the database collected may critically affect the AIQP detection.

Original signal and the QRS estimate to evaluate the modelling accuracy and determine the optimum order without the effect derived from the database.

Group I (the normal group) consisted of 130 normal Taiwanese (62 men and 68 women, aged 35±16 years old)

Group II(the VPC group) consisted of 87 ventricular premature contraction (VPC) patients (42 men and 45 women, aged 65±12 years old)

Group III (the VT group) consisted of 23 patients (13 men and 10 women, aged 68±15)

Autoregressive moving average (ARMA)◦ DCT domain was used to simulate the normal QRS

system

ARMA(2,2) with a conjugate pole pair at r∠

c = a1a2G/r^2, a=G － c, b=2crcos － (a1 ＋ a2) G

ARMA(2M,2M)

The cross-correlation coefficient p between the original signal x(n) and the QRS estimate )(s n

A significant correlation existed between RMS40 and AIQP in lead Y.

Automatically determining the optimum order improves the feasibility of AIQP analysis in clinical diagnosis.