Performance assessment of standard algorithms for dynamic R-T interval measurement: comparison between R-Tapex and R-Tend approach

Abstract

Three automatic approaches to ventricular repolarisation duration measurement (R-Tapex, R-T(end threshold) and R-T(end fitting) methods) are compared on computer-generated and real ECG signals, in relation to their reliability in the presence of the most common electrocardiographic artefacts (i.e. additive broadband noise and additive and multiplicative periodical disturbances). Simulations permit the evaluation of the amount of R-T beat-to-beat variability induced by the artefacts. The R-T(end threshold) method performs better than the R-T(end fitting) one, and, hence, the latter should be used with caution when R-T(end) variability is addressed. Whereas the R-Tapex method is more robust with regard to broadband noise than the R-T(end threshold) one, the reverse situation is observed in the presence of periodical amplitude modulations. A high level of broadband noise dose not prevent the detection of the central frequency of underlying R-T periodical changes. Comparison between the power spectra of the beat-to-beat R-T variability series obtained from three orthogonal ECG leads (X,Y,Z) is used to assess the amount of real and artefactual variability in 13 normal subjects at rest. The R-Tapex series displays rhythms at high frequency (HF) with a percentage power on the Z lead (57.1 +/- 4.9) greater than that on the X and Y leads (41.9 +/- 4.6 and 46.1 +/- 4.9, respectively), probably because of respiratory-related artefacts affecting the Z lead more remarkably. More uniform HF power distributions over X,Y,Z leads are observed in the R-T(end threshold) series (31.8 +/- 3.8, 39.2 +/- 4.1 and 35.1 +/- 4.2, respectively), thus suggesting minor sensitivity of the R-T(end threshold) measure to respiratory-related artefacts.

RT variability unrelated to heart period and respiration progressively increases during graded head-up tilt

“…ECG traces were preprocessed according to Porta et al (30) to limit noise and cancel baseline wandering. The heart period was computed as RR interval.…”

“…The T-wave apex was searched in a predefined temporal window, the duration of which depended on the preceding RR interval. The T-wave apex was located using parabolic interpolation (30). The T-wave end was located according to a threshold on the first derivative set as a fraction (i.e., 30%) of the absolute maximal first derivative value computed on the T-wave downslope (30).…”

“…Indeed, QT variability depends on RR changes (5,18,21,28,34) and on influences synchronous with respiration (R) capable of directly modifying the QT interval [e.g., respiratory-related artifacts capable of distorting the T-wave such as cardiac axis movements (18,30)]. …”

A Refined Multiscale Self-Entropy Approach for the Assessment of Cardiac Control Complexity: Application to Long QT Syndrome Type 1 Patients

“…HP was computed as the time distance between two consecutive R-wave peaks after fixing their location via parabolic interpolation [22]. QT was taken as the time distance between the R-wave interpolated apex and T-wave offset, delineated as the point where the absolute value of the first derivative on the T-wave downslope went below a threshold set as the 30% of the maximum absolute value, locally computed over each T-wave decline [22]. After the extraction of the HP and QT measures on a beat-to-beat basis, the two series were manually checked for values coming from ectopic beats or misdetections.…”

“…The QT regulation is highly complex involving differential modulations of the atria and ventricles, variable set points depending on the region of the heart and a more anatomically distributed network compared to the one of HP, mainly mediated by the autonomic innervation of sinus node, thus resulting in a smaller degree of predictability of the QT series and negligible QT information storage compared to HP, especially at short and medium time scales [13,14,[38][39][40]. However, the higher complexity of QT at short time scale is compatible with the low signal-to-noise ratio characterizing its variability as well [22,41]. Remarkably, at long time scale the information storage of QT and HP were similar, thus supporting the observation that the high level of unpredictability of QT dynamics is due to fast components unexplained by past QT values and even by past HP samples.…”