A univariate Cox proportional hazard regression model of selected variables as predictors for composite endpoint showed the following results (Figure 1A-F): 1) 12-lead resting ECG: Q-wave (HR = 2.9; 95%CI = 1.5-5.8; P = 0.002); Q-waves in the anterior-septal leads were significantly associated with the composite endpoint as compared with Q-waves in the inferior leads (HR=18.0; 95%CI=2.0-161.1; P = 0.01); 2) echocardiogram: LVEF<50% (HR=3.4; 95%CI=1.7-4.1; P<0.001); 3) 24-h ambulatory ECG: VT (HR=3.6; 95%CI=1.7-7.1; P<0.001) and SDNN<100 ms (HR=4.1; 95%CI=1.8-8.8; P<0.001); 4) SAECG: DUR>150 ms (HR = 3.0; 95%CI=1.5-5.9; P=0.002) and IVET+ (HR=4.0; 95%CI=1.8-9.3; P<0.001).
SAECG has been used to investigate abnormal electrical transients in surface ECGs, which are considered harbingers of abnormal histopathology underlying ventricular arrhythmia and sudden death (11,12,22,23).
Unfortunately, there is no SAECG
parameter known to be useful in predicting drug efficacy (23).
In principle, the signal-averaged high-resolution electrocardiography (SAECG
) is a technique involving computerized analysis of small segments of a standard electrocardiography (ECG) in order to detect late potentials (1, 2).
The analysis of SAECG was performed by 2 types of filtration: low pass four-pole IIR Butterworth filter and FIR filter with Kaiser window.
Conclusion: Our approach improved risk stratification up to 95% based on SAECG due to the application of FIR filter, 6 new parameters and efficient statistical classifier, the support vector machine.