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

Automatic optimum order selection of parametric modelling for the evaluation of abnormal intra-QRS signals in signal-averaged electrocardiograms. C.-C. Lin C.-M. Chen I.-F. Yang T.-F. Yang

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

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  1. Automatic optimum order selection ofparametric modelling for theevaluation of abnormal intra-QRSsignals in signal-averagedelectrocardiograms C.-C. Lin C.-M. Chen I.-F. Yang T.-F. Yang MEDICALAND BIOLOGICAL ENGINEERING AND COMPUTING Volume 43,Number 2,  218-224, SpringerLink

  2. Outline • Introduction • Materials and methods • Results • Conclusions

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

  4. Introduction(Cont.) • 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)

  5. Introduction(Cont.) • 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.

  6. Introduction(Cont.) • Original signal and the QRS estimate to evaluate the modelling accuracy and determine the optimum order without the effect derived from the database.

  7. Materials and methods • 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)

  8. Materials and methods(Cont.)

  9. Materials and methods(Cont.) • 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

  10. Materials and methods(Cont.) • ARMA(2M,2M)

  11. Materials and methods(Cont.)

  12. Materials and methods(Cont.) • The cross-correlation coefficient p between the original signal x(n) and the QRS estimate

  13. Results

  14. Results(Cont.)

  15. Results(Cont.)

  16. Results(Cont.)

  17. Results(Cont.)

  18. Conclusions • 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.

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