Spatial, temporal and wavefront direction characteristics of 12-lead T-wave morphology

Abstract

Three new approaches for the analysis of ventricular repolarisation in 12-lead electrocardiograms (ECGs) are presented: the spatial and temporal variations in T-wave morphology and the wavefront direction difference between the ventricular depolarisation and repolarisation waves. The spatial variation characterises the morphology differences between standard leads. The temporal variation measures the change in interlead relationships. A minimum dimensional space, constructed by ECG singular value decomposition, is used. All descriptors are measured using the ECG vector in the constructed space and the singular vectors that define this space. None of the descriptors requires time domain measurements (e.g. the precise detection of the T-wave offset), and so the inaccuracies associated with conventional QT interval related parameters are avoided. The new descriptors are compared with the conventional measurements provided by a commercial system for an automatic evaluation of QT interval and QT dispersion in digitally recorded 12-lead ECGs. The basic comparison uses a set of 1100 normal ECGs. The short-term intrasubject reproducibility of the new descriptors is compared with that of the conventional measurements in a set of 760 ECGs recorded in 76 normal subjects and a set of 630 ECGs recorded in 63 patients with hypertrophic cardiomyopathy (ten serial recordings in each subject of both these sets). The discriminative power of the new and conventional parameters to distinguish normal and abnormal repolarisation patterns is compared using the same set. The results show that the new parameters do not correlate with the conventional QT interval-related descriptors (i.e. they assess different ECG qualities), are generally more reproducible than the conventional parameters, and lead to a more significant separation between normal and abnormal ECGs, both univariately and in multivariate regression models.

Evidence of cardiac electrical remodeling in patients with Huntington disease

“…However, numerous studies indicate that conventional ECG variables may not be the most reliable predictors of heart failure (Acar, Yi, Hnatkova, & Malik, 1999; Kardys et al., 2003; Man et al., 2012; Bacharova & Estes, 2017; Schlegel et al., 2010). …”

“…Specific variability analyses included:

the standard deviation of normal‐to‐normal RR and QT intervals (SDNN_RR and SDNN_QT in all ECG leads, respectively);

several other time and frequency domain indices of RR interval variability, including the very low (0.0–0.04 Hz), low (0.04–0.15 Hz), high (0.15–0.40 Hz), and total (0.0–0.40 Hz) frequency powers of RR interval variability in natural log‐transformed units (ln ms2/Hz) calculated using autoregression (lnAR) and the Lomb periodogram method (lnLo) (Schlegel et al., 2010);

the QT variability index (QTVI) (Atiga et al., 1998), using the means and variances of the RR interval (Piccirillo et al., 2007) rather than those of the heart rate (Berger et al., 1997) in the denominator of the QTVI equation; and

the “unexplained” part of QTV (Solaimanzadeh et al, 2008; Starc & Schlegel, 2008), wherein the QTV signal is decomposed into two parts, one being described by the concomitant RR interval HRV and/or by the concomitant variability of the QRS‐T angle and the other representing the “unexplained” part of QTV. Decomposition is performed according to a model (Solaimanzadeh et al, 2008; Starc & Schlegel, 2008) that takes into account the hysteresis‐like properties of QT interval dynamics (Lang, Flapan, & Nielsen, 2001) as also the fact that while changes in QT intervals are predominantly driven by changes in RR intervals (Almeida et al, 2006), they can also occur in response to changes in QT wavefront direction descriptors, such as in the QRS‐T angle or equivalent (Acar, Yi, Hnatkova, & Malik, 1999; Kors, van Herpen, & Bemmel, 1999). In a specific manner, we determined the “unexplained” part of SDNN_QT (unexplained SDNN_QT) and the corresponding “unexplained” part of QTV (unexplained QTV).

…”

“…Decomposition is performed according to a model (Solaimanzadeh et al, 2008; Starc & Schlegel, 2008) that takes into account the hysteresis‐like properties of QT interval dynamics (Lang, Flapan, & Nielsen, 2001) as also the fact that while changes in QT intervals are predominantly driven by changes in RR intervals (Almeida et al, 2006), they can also occur in response to changes in QT wavefront direction descriptors, such as in the QRS‐T angle or equivalent (Acar, Yi, Hnatkova, & Malik, 1999; Kors, van Herpen, & Bemmel, 1999). In a specific manner, we determined the “unexplained” part of SDNN_QT (unexplained SDNN_QT) and the corresponding “unexplained” part of QTV (unexplained QTV).…”

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

“…Therefore, any resting ECG technique that might increase sensitivity for detecting HCM [7][8][9][10][11] as well as increase specificity for distinguishing between HCM and athlete's heart would be clinically relevant.…”

“…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. Related results can then be integrated (scored) automatically by using statistical or pattern recognition techniques to maximize diagnostic or predictive accuracy.…”