Real-Time 12-Lead High-Frequency QRS Electrocardiography for Enhanced Detection of Myocardial Ischemia and Coronary Artery Disease

Abstract

Several studies have shown that diminution of the high-frequency (HF; 150-250 Hz) components present within the central portion of the QRS complex of an electrocardiogram (ECG) is a more sensitive indicator for the presence of myocardial ischemia than are changes in the ST segments of the conventional low-frequency ECG. However, until now, no device has been capable of displaying, in real time on a beat-to-beat basis, changes in these HF QRS ECG components in a continuously monitored patient. Although several software programs have been designed to acquire the HF components over the entire QRS interval, such programs have involved laborious off-line calculations and postprocessing, limiting their clinical utility. We describe a personal computer-based ECG software program developed recently at the National Aeronautics and Space Administration (NASA) that acquires, analyzes, and displays HF QRS components in each of the 12 conventional ECG leads in real time. The system also updates these signals and their related derived parameters in real time on a beat-to-beat basis for any chosen monitoring period and simultaneously displays the diagnostic information from the conventional (low-frequency) 12-lead ECG. The real-time NASA HF QRS ECG software is being evaluated currently in multiple clinical settings in North America. We describe its potential usefulness in the diagnosis of myocardial ischemia and coronary artery disease.

Detection of hypertrophic cardiomyopathy is improved when using advanced rather than strictly conventional 12-lead electrocardiogram

“…Advanced ECG parameters derived from signal averaging. Signal averaging was performed using software developed by the authors 20,21,25,28 to generate results for parameters of: 1) 12-lead HF QRS ECG; 20 2) derived 3-dimensional ECG, using the Frank-lead reconstruction technique of Kors et al 29 to derive several vectocardiographic parameters previously described by Draper et al, 30 including for example the spatial mean QRS-T angle 17,28,31 and the magnitude, 30 azimuth /elevation 30 and beat-to-beat variation 16 of the spatial ventricular gradient and its components; and 3) QRS and Twaveform complexity via SVD, for example to derive parameters such as the principal component analysis (PCA) ratio, 21,22,32 the "relative residuum" 21,23 and the dipolar and nondipolar voltages 19,25 of the QRS and T waveforms. The majority of the parameters studied and their related detailed methods have been described in other recent publications.…”

“…These techniques include beat-to-beat QT variability (QTV) 8-10, 12, 13 and R-wave to R-wave variability (RRV); 14, 15 "3-dimensional" (spatial and spatiotemporal) ECG; [16][17][18][19] highfrequency (HF) QRS ECG; 20 and detailed studies of waveform complexity by singular value decomposition (SVD). 7,19,[21][22][23] A theoretical advantage of computerized ECG systems is that they allow for multiple conventional and advanced ECG techniques to be performed in software during a single digital recording.…”

High-frequency electrocardiogram analysis in the ability to predict reversible perfusion defects during adenosine myocardial perfusion imaging

“…A RAZ score, ranging from 0 to 108 was calculated (5,7). This score was calculated from 2 subscores: the "general RAZ burden" (0-36) and the "RAZ contiguity" subscore (0-72).…”

Evidence of cardiac electrical remodeling in patients with Huntington disease

“…Besides the measurement of HRV, which has been used predominantly for the assessment of sympathovagal balance, these techniques include indices of the electrical remodeling of the ventricles (e.g., the QRS‐T angle [Kardys et al., 2003]), ventricular depolarization (e.g., high‐frequency QRS [HFQRS] ECG [Abboud, Berenfeld, & Sadeh, 1991; Schlegel et al., 2004]), and ventricular repolarization. The indices of ventricular repolarization comprise various indices of QT interval variability (QTV), such as the QT variability index (QTVI) (Atiga et al., 1998; Berger et al., 1997; Starc & Schlegel, 2006) and the unexplained part of QTV (unexplained QTV), which represents an intrinsic part of QTV independent of simultaneous heart rate variability, the influence of the heart rate on QTV having been eliminated (Starc & Schlegel, 2008).…”