Comparative analysis on wavelet-based detection of finite duration low-amplitude signals related to ventricular late potentials.
Ventricular late potentials (VLPs) are considered as a noninvasive marker of patients with myocardial infarction, who are prone to the development of ventricular tachycardia. This paper investigates the effects of variations in physical properties of myocardial infarcts in terms of their effects on the parametric variations in VLP analysis. A sufficiently large set of signals underlining the behavior of physical parameters was employed to represent the effect of physical size, position, orientation and type of infarct. The approximated signals are variations from real electrocardiography signals by adding potentials representing late potentials based on duration, frequency, amplitude and position. The aim is not to exactly model VLP but rather to generate an approximate set of signals to examine the performance of the standard methods for different possibilities in infarct dynamics. We investigate some of the detection approaches together with their related assumptions, and try to pinpoint the drawbacks and inaccuracies of these methods and also their assumptions. The three widely accepted criteria--QRS duration, root-mean-square and duration of the signal at the end of QRS for VLP detection--were used in the investigation. Results from the application of these parameters to the set of signals are presented. In addition we investigate the physical nature of an infarct and list a number of possible reasons that might be the cause of a low success rate for the detection of additive potentials. To improve the performance of the common methods, two more wavelet transform parameters are added to those of the standard methods. The method derived from this analysis is presented as an alternative means for the detection of late signals named as delayed potentials, a more general class that includes VLP as a subset.
Mousa
Ayad
A
Computer Engineering Department, Cankaya University, Ankara, Turkey. amousa@cankaya.edu.tr
Yilmaz
Atila
A
D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D006801:Humans; D009203:Myocardial Infarction / Q000150:complications / Q000175:diagnosis* / Q000503:physiopathology; D010363:Pattern Recognition, Automated / Q000379:methods*; D011379:Prognosis; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D017180:Tachycardia, Ventricular / Q000175:diagnosis* / Q000209:etiology / Q000503:physiopathology; D013997:Time Factors