Analysis of unpredictable intra-QRS potentials in signal-averaged electrocardiograms using an autoregressive moving average prediction model.

Instead of extracting the abnormal intra-QRS potentials (AIQP) waveform, this study proposes the analysis of the unpredictable intra-QRS potentials (UIQP) based on an autoregressive moving average (ARMA) prediction model to detect the signals with sudden slope change within the QRS complex for the diagnosis of high-risk patients with ventricular tachycardia (VT). The UIQP is detected as the slope changes at slope discontinuities by the prediction error of the ARMA prediction model. Because of the linearity of the ARMA prediction model, the UIQP is also proportional to the amplitude of the QRS complex if the input QRS waves have the same shapes. Hence this study further defines the UIQP-to-QRS ratio to normalize the UIQP by the root-mean-square (RMS) value of the QRS complex. The study subjects were composed of 42 normal Taiwanese and 30 patients with sustained VT. The clinical results show that the UIQP-to-QRS ratios of the VT patients in leads X, Y and Z were significantly higher than those of the normal subjects. The logical combination of any 4 of the UIQP-to-QRS ratios and conventional time-domain parameters can increase the diagnosis performance of VT patients to 92.9% specificity, 93.3% sensitivity and 93.1% total prediction accuracy.