Automatic identification of a stable QRST complex for non-invasive evaluation of human cardiac electrophysiology

Abstract

Background A vectorcardiography approach to electrocardiology contributes to the non-invasive assessment of electrical heterogeneity in the ventricles of the heart and to risk stratification for cardiac events including sudden cardiac death. The aim of this study was to develop an automatic method that identifies a representative QRST complex (QRSonset to Tend) from a Frank vectorcardiogram (VCG). This method should provide reliable measurements of morphological VCG parameters and signal when such measurements required manual scrutiny. Methods Frank VCG was recorded in a population-based sample of 1094 participants (550 women) 50-65 years old as part of the Swedish CArdioPulmonary bioImage Study (SCAPIS) pilot. Standardized supine rest allowing heart rate stabilization and adaptation of ventricular repolarization preceded a recording period lasting �5 minutes. In the Frank VCG a recording segment during steady-state conditions and with good signal quality was selected based on QRST variability. In this segment a representative signal-averaged QRST complex from cardiac cycles during 10s was selected. Twenty-eight morphological parameters were calculated including both conventional conduction intervals and VCG-derived parameters. The reliability and reproducibility of these parameters were evaluated when using completely automatic and automatic but manually edited annotation points.

Limits of the spatial ventricular gradient and QRST angles in patients with normal electrocardiograms and no known cardiovascular disease stratified by age, sex, and race

“…Bioimaging Study (SCAPIS) and found similar gender differences with larger SVG magnitude and larger QRST angles in men compared with women. 17 SVG orientation (azimuth and elevation) was not reported.…”

“…15 Our study is the first to show that the distribution of SVG and QRST angles in presumed healthy patients with normal ECGs vary by race; prior studies included almost entirely White participants. [16][17][18] Although 45% of our study population was non-White, we did not have enough patients to assess how these VCG measurements varied among different non-White races. Our study also has a much wider age range than other prior studies, and this allowed us to demonstrate the novel finding that all SVG components except SVG azimuth changed with age, and that age-related changes were modulated by sex.…”

“…8,9 Other VCG measures of electrical heterogeneity, including the spatial QRST angle, 10,11 have also been correlated with elevated SCD risk and adverse cardiac remodeling in the general population 6,8,12 as well as other adverse cardiovascular events. 13,14 Although studies of normal limits of these parameters exist, and there have been efforts to quantify the influence of sex and body size on normal limits, [15][16][17][18] these studies were performed primarily in healthy, White volunteers, [15][16][17][18] which limits the generalizability of these results to the more diverse populations encountered in cardiovascular medicine. The influence of age and race on SVG and QRST angle normal limits, however, is unclear.…”

Accelerated QT adaptation following atropine‐induced heart rate increase in LQT1 patients versus healthy controls: A sign of disturbed hysteresis

“…The system calculates a global QRST complex from the three QRST complexes in the orthogonal X, Y, and Z directions with automatically set annotation points for onset, offset, and peak of the QRS complex and T wave. The QT peak interval was measured from QRS onset to T peak (maximum T amplitude) and the QT interval from QRS onset to T‐wave end defined by the tangent method (Lundahl et al, 2020 ; Vink et al, 2018 ). QTc was HR corrected according to Bazett (Dahlberg et al, 2021 ).…”

A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges

“…The recent increase in the availability of low-cost portable ECG sensors has opened doors to new areas such as fitness monitoring [85] and wearable biometric authentication devices [86]- [89], resulting in the pervasive acquisition of ECG data. While the interested reader can consult [90]- [93] for a detailed description of the physiological principles of cardiac electrophysiology, we now describe the (single-lead) ECG for one cardiac cycle illustrated in Fig. 1.…”