Literature DB >> 10650310

Spatial, individual, and temporal variation of the high-frequency QRS amplitudes in the 12 standard electrocardiographic leads.

J Pettersson1, E Carro, L Edenbrandt, C Maynard, O Pahlm, M Ringborn, L Sörnmo, S G Warren, G S Wagner.   

Abstract

BACKGROUND: Analysis of high-frequency QRS amplitudes (HF-QRS) may provide an additional diagnostic tool in patients with heart disease, but the basic properties of these waveforms have not been sufficiently investigated. This study describes the spatial, individual, and temporal variation at rest of HF-QRS recorded with the 12 standard electrocardiographic leads in patients with ischemic heart disease. METHODS AND
RESULTS: Two consecutive electrocardiographic recordings from 67 patients were signal averaged and analyzed within a bandwidth of 150 to 250 Hz. The HF-QRS values were expressed as root mean square values. There was a spatial variation in HF-QRS among the 12 leads, with higher amplitudes in V(2) through V(4), II, aVF, and III. The individual variation among the patients was large for all leads. The sum of the HF-QRS for all 12 leads in each patient ranged from 20 to 75 microV (mean 36 +/- 11 microV). The mean of the temporal variation in HF-QRS for all 12 leads between the 2 recordings was only 0.10 +/- 0. 09 microV.
CONCLUSIONS: Because of the large individual variation, analysis of HF-QRS is probably most applicable in monitoring situations when it is possible to track changes in a patient over time. The temporal variation in HF-QRS at rest is small, both in patients with and those without prior myocardial infarction.

Entities:  

Mesh:

Year:  2000        PMID: 10650310     DOI: 10.1067/mhj.2000.101782

Source DB:  PubMed          Journal:  Am Heart J        ISSN: 0002-8703            Impact factor:   4.749


  4 in total

1.  An electrocardiographic sign of ischemic preconditioning.

Authors:  Loek P B Meijs; Loriano Galeotti; Esther P Pueyo; Daniel Romero; Robert B Jennings; Michael Ringborn; Stafford G Warren; Galen S Wagner; David G Strauss
Journal:  Am J Physiol Heart Circ Physiol       Date:  2014-04-28       Impact factor: 4.733

Review 2.  A Review of Automated Methods for Detection of Myocardial Ischemia and Infarction Using Electrocardiogram and Electronic Health Records.

Authors:  Sardar Ansari; Negar Farzaneh; Marlena Duda; Kelsey Horan; Hedvig B Andersson; Zachary D Goldberger; Brahmajee K Nallamothu; Kayvan Najarian
Journal:  IEEE Rev Biomed Eng       Date:  2017-10-16

3.  A modeling and machine learning approach to ECG feature engineering for the detection of ischemia using pseudo-ECG.

Authors:  Carlos A Ledezma; Xin Zhou; Blanca Rodríguez; P J Tan; Vanessa Díaz-Zuccarini
Journal:  PLoS One       Date:  2019-08-12       Impact factor: 3.240

4.  High arrhythmic risk in antero-septal acute myocardial ischemia is explained by increased transmural reentry occurrence.

Authors:  Hector Martinez-Navarro; Ana Mincholé; Alfonso Bueno-Orovio; Blanca Rodriguez
Journal:  Sci Rep       Date:  2019-11-14       Impact factor: 4.379

  4 in total

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