Analysis of abnormal signals within the QRS complex of the high-resolution electrocardiogram.
This paper presents a new, quantitative approach to measuring abnormal intra-QRS signals, using the high-resolution electrocardiogram (HRECG). These signals are conventionally known as QRS "notches and slurs." They are measured qualitatively and form the basis for the ECG identification of myocardial infarction. The HRECG is used for detection of ventricular late potentials (LP), which are linked with the presence of a reentry substrate for ventricular tachycardia (VT) after a myocardial infarction. LP's are defined as signals from areas of delayed conduction which outlast the normal QRS period. Our objective is to quantify very low-level abnormal signals that may not outlast the normal QRS period. In this work, abnormal intra-QRS potentials (AIQP) were characterized by removing the predictable, smooth part of the QRS from the original waveform. This was represented as the impulse response of an ARX parametric model, with model order selected empirically from a training data set. AIQP were estimated using the residual of the modeling procedure. Critical AIQP parameters to separate VT and non-VT subjects were obtained using discriminant functions. Results suggest that AIQP indexes are a new predictive index of the HRECG for VT. The concept of abnormal intra-QRS potentials permits the characterization of pathophysiological signals contained wholly within the normal QRS period, but related to arrhythmogenesis. The new method may have other applications, such as detection of myocardial ischemia and improved ECG identification of the site of myocardial infarction, particularly in the absence of Q waves.
Gomis
P
P
Industrial Technology Department, University Simón Bolívar, La Guaira, Venezuela.
Jones
D L
DL
Caminal
P
P
Berbari
E J
EJ
Lander
P
P
D004562:Electrocardiography; D005583:Fourier Analysis; D006801:Humans; D016018:Least-Squares Analysis; D016014:Linear Models; D008955:Models, Cardiovascular; D009203:Myocardial Infarction / Q000175:diagnosis; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted