Identification of post-myocardial infarction patients prone to ventricular tachycardia using time–frequency analysis of QRS and ST segments

Couderc, Jean‐Philippe; Chevalier, Philippe; Fayn, Jocelyne; Rubel, P.; Touboul, P

doi:10.1053/eupc.2000.0091

Cited by 15 publications

(5 citation statements)

References 31 publications

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“…The time-frequency data of each QRS complex were collected using the Wavelet transform (Morlet wavelet). According to previous definitions of high frequency content in other works [ 14 , 15 , 16 , 17 ], data were analyzed in the defined range of high frequencies (85–130 Hz), with an upper period of 11.5 ms and lower period of 7.7 ms ( Figure 3 A). A temporal definition of 1 kHz and a frequency resolution of 1/125 suboctaves were used.…”

Section: Methodsmentioning

confidence: 99%

“…Some studies have demonstrated a robust link between QRS fragmentation and late potentials [ 14 ]. Overall, fragmentation would represent a more prominent phenomenon that will allow for their identification by visual inspection of the time domain record of the QRS complex, while less prominent forms will be masked within the total energy/voltage of the normal myocardium, thus requiring signal processing for appropriate identification.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of the High-Frequency Content in Human QRS Complexes by the Continuous Wavelet Transform: An Automatized Analysis for the Prediction of Sudden Cardiac Death

Iglesias

Gutiérrez

Cos

et al. 2018

Sensors

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Background: Fragmentation and delayed potentials in the QRS signal of patients have been postulated as risk markers for Sudden Cardiac Death (SCD). The analysis of the high-frequency spectral content may be useful for quantification. Methods: Forty-two consecutive patients with prior history of SCD or malignant arrhythmias (patients) where compared with 120 healthy individuals (controls). The QRS complexes were extracted with a modified Pan-Tompkins algorithm and processed with the Continuous Wavelet Transform to analyze the high-frequency content (85–130 Hz). Results: Overall, the power of the high-frequency content was higher in patients compared with controls (170.9 vs. 47.3 103nV2Hz−1; p = 0.007), with a prolonged time to reach the maximal power (68.9 vs. 64.8 ms; p = 0.002). An analysis of the signal intensity (instantaneous average of cumulative power), revealed a distinct function between patients and controls. The total intensity was higher in patients compared with controls (137.1 vs. 39 103nV2Hz−1s−1; p = 0.001) and the time to reach the maximal intensity was also prolonged (88.7 vs. 82.1 ms; p < 0.001). Discussion: The high-frequency content of the QRS complexes was distinct between patients at risk of SCD and healthy controls. The wavelet transform is an efficient tool for spectral analysis of the QRS complexes that may contribute to stratification of risk.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of the High-Frequency Content in Human QRS Complexes by the Continuous Wavelet Transform: An Automatized Analysis for the Prediction of Sudden Cardiac Death

Iglesias

Gutiérrez

Cos

et al. 2018

Sensors

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“…In an analysis of 856 post-infarction patients enrolled in the MADIT-II trial, PRD was shown to be an independent predictor of SCD (adjusted HR = 1.40; p = 0.003), suggesting that assessment of repolarization dynamics can be used for SCD risk stratification in IHD patients [ 15 ]. Late potentials (LPs), detecting alterations in high-frequency components within the QRS and ST segment, can identify IHD patients with increased risk for VA or SCD [ 16 ]. Further trials are ongoing to validate these scores and to employ machine learning tools to the ECG for advanced risk stratification.…”

Section: Advanced Analysis Of Ecgmentioning

confidence: 99%

Sudden Cardiac Death Prevention in Patients with Ischemic Heart Disease—Beyond the Ejection Fraction

Steiner¹,

Sharabi²,

Goldenberg³

2022

Rev. Cardiovasc. Med.

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Sudden cardiac death (SCD) in patients with ischemic heart disease remains a leading cause of death. Prediction of who is at risk is based on the left ventricular ejection fraction (EF). However, the majority of victims of SCD have a normal EF, and the majority of patients implanted with an implantable cardioverter-defibrillator based on their EF are never treated by their device. Several parameters could allow better prediction of SCD. Several signs on the ECG and Periodic Repolarization Dynamics have been associated with increased risk. Elevated serum biomarkers such as pro-B type natriuretic peptides and serum soluble suppression of tumorigenicity 2 protein (sST2) are predictive of SCD. On the echocardiogram, global longitudinal strain, speckle tracking and relative wall thickness have been implicated. Programmed ventricular stimulation studies and cardiac magnetic resonance are promising modalities that could be further investigated. In conclusion, the EF is an imperfect tool for predicting SCD. Using the modalities reviewed, a model could be created for better prediction of patients at risk.

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“…The most obvious and not yet fully characterized physiological factor influencing the VR is the HR. In clinical studies, power-law models QTc(i )Z aCb!RR(iK1) g are used despite the fact that the QT-RR relationship is described as a subject-dependent phenomenon (Couderc et al 2000). Fridericia's (QTcZQT!RR K1/3 ) and Bazett's (QTcZQT!RR K1/2 ) are the most common examples of rate correction formulae, where RR is expressed in seconds and is the interval from the beats prior to the QT interval.…”

Section: (I) Cardiac Repolarization and Heart Ratementioning

confidence: 99%

Measurement and regulation of cardiac ventricular repolarization: from the QT interval to repolarization morphology

Couderc

2009

Phil. Trans. R. Soc. A.

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Ventricular repolarization (VR) is a crucial step in cardiac electrical activity because it corresponds to a recovery period setting the stage for the next heart contraction. Small perturbations of the VR process can predispose an individual to lethal arrhythmias. In this review, I aim to provide an overview of the methods developed to analyse static and dynamic aspects of the VR process when recorded from a surface electrocardiogram (ECG). The first section describes the list of physiological and clinical factors that can affect the VR. Technical aspects important to consider when digitally processing ECGs are provided as well. Special attention is given to the analysis of the effect of heart rate on the VR and its regulation by the autonomic nervous system. The final section provides the rationale for extending the analysis of the VR from its duration to its morphology. Several modelling techniques and measurement methods will be presented and their role within the arena of cardiac safety will be discussed.

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Identification of post-myocardial infarction patients prone to ventricular tachycardia using time–frequency analysis of QRS and ST segments

Cited by 15 publications

References 31 publications

Analysis of the High-Frequency Content in Human QRS Complexes by the Continuous Wavelet Transform: An Automatized Analysis for the Prediction of Sudden Cardiac Death

Analysis of the High-Frequency Content in Human QRS Complexes by the Continuous Wavelet Transform: An Automatized Analysis for the Prediction of Sudden Cardiac Death

Sudden Cardiac Death Prevention in Patients with Ischemic Heart Disease—Beyond the Ejection Fraction

Measurement and regulation of cardiac ventricular repolarization: from the QT interval to repolarization morphology

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