aVR vs. -aVR: should aVR be left where it is?

Madias, John E.

doi:10.1093/europace/euq507

Cited by 2 publications

(1 citation statement)

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“…3 involving the features from a Goldberger and from an Einthoven lead traces back to the fact that aVR, aVL and aVF can be calculated with a linear combination of the signals from lead I, II and III and are therefore dependent on each other. The negative correlation coefficients involving amplitude features from the aVR lead is due to the predominantly negative signal in this lead [5].…”

Section: Discussionmentioning

confidence: 94%

Quantification of Interpatient 12-lead ECG Variabilities within a Healthy Cohort

Nagel

Pilia

Loewe

et al. 2020

Current Directions in Biomedical Engineering

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The morphology of the electrocardiogram (ECG) varies among different healthy subjects due to anatomical and structural reasons, such as for example the shape of the heart geometry or the position and size of surrounding organs in the torso. Knowledge about these ECG morphology changes could be used to parameterize electrophysiological simulations of the human heart. In this work, we detected the boundaries of ECG waveforms, i.e. the P-wave, the QRS-complex and the T-wave, in 12- lead ECGs from 918 healthy subjects in the Physionet Computing in Cardiology Challenge 2020 Database with the IBT openECG toolbox. Subsequently, we obtained the onset, the peak and the offset of each P-wave, QRS-complex and T-wave in the signal. In this way, the duration of the P-wave, the QRScomplex and the T-wave, the PQ-, RR- and the QT-interval as well as the amplitudes of the P-wave, the Q-, R- and Speak and the T-wave in each lead were extracted from the 918 healthy ECGs. Their statistical distributions and correlation between each other were assessed. The highest variabilities among the 918 healthy subject were found for the RR interval and the amplitudes of the QRScomplex. The highest correlation was observed for feature pairs that represent the same feature in different leads. Especially the R-peak amplitudes showed a strong correlation across different leads. The calculated feature distributions can be used to optimize the parameters of populations of cardiac electrophysiological models. In this way, realistic in-silico generated surface ECGs can be simulated in large scale and could be used as input data for machine learning algorithms for a classification of cardiovascular diseases.

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Section: Discussionmentioning

confidence: 94%