Literature DB >> 22516167

Novel set of vectorcardiographic parameters for the identification of ischemic patients.

Raúl Correa1, Pedro D Arini, Max E Valentinuzzi, Eric Laciar.   

Abstract

New signal processing techniques have enabled the use of the vectorcardiogram (VCG) for the detection of cardiac ischemia. Thus, we studied this signal during ventricular depolarization in 80 ischemic patients, before undergoing angioplasty, and 52 healthy subjects with the objective of evaluating the vectorcardiographic difference between both groups so leading to their subsequent classification. For that matter, seven QRS-loop parameters were analyzed, i.e.: (a) Maximum Vector Magnitude; (b) Volume; (c) Planar Area; (d) Maximum Distance between Centroid and Loop; (e) Angle between XY and Optimum Plane; (f) Perimeter and, (g) Area-Perimeter Ratio. For comparison, the conventional ST-Vector Magnitude (ST(VM)) was also calculated. Results indicate that several vectorcardiographic parameters show significant differences between healthy and ischemic subjects. The identification of ischemic patients via discriminant analysis using ST(VM) produced 73.2% Sensitivity (Sens) and 73.9% Specificity (Spec). In our study, the QRS-loop parameter with the best global performance was Volume, which achieved Sens=64.5% and Spec=74.6%. However, when all QRS-loop parameters and ST(VM) were combined, we obtained Sens=88.5% and Spec=92.1%. In conclusion, QRS loop parameters can be accepted as a complement to conventional ST(VM) analysis in the identification of ischemic patients.
Copyright © 2012 IPEM. Published by Elsevier Ltd. All rights reserved.

Entities:  

Mesh:

Year:  2012        PMID: 22516167     DOI: 10.1016/j.medengphy.2012.03.005

Source DB:  PubMed          Journal:  Med Eng Phys        ISSN: 1350-4533            Impact factor:   2.242


  7 in total

1.  Reliable Detection of Myocardial Ischemia Using Machine Learning Based on Temporal-Spatial Characteristics of Electrocardiogram and Vectorcardiogram.

Authors:  Xiaoye Zhao; Jucheng Zhang; Yinglan Gong; Lihua Xu; Haipeng Liu; Shujun Wei; Yuan Wu; Ganhua Cha; Haicheng Wei; Jiandong Mao; Ling Xia
Journal:  Front Physiol       Date:  2022-05-30       Impact factor: 4.755

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.  Automatic Classification of Myocardial Infarction Using Spline Representation of Single-Lead Derived Vectorcardiography.

Authors:  Yu-Hung Chuang; Chia-Ling Huang; Wen-Whei Chang; Jen-Tzung Chien
Journal:  Sensors (Basel)       Date:  2020-12-17       Impact factor: 3.576

Review 4.  Review of Processing Pathological Vectorcardiographic Records for the Detection of Heart Disease.

Authors:  Jaroslav Vondrak; Marek Penhaker
Journal:  Front Physiol       Date:  2022-03-21       Impact factor: 4.755

5.  Discriminant analysis between myocardial infarction patients and healthy subjects using wavelet transformed signal averaged electrocardiogram and probabilistic neural network.

Authors:  Ahmad Keshtkar; Hadi Seyedarabi; Peyman Sheikhzadeh; Seyed Hossein Rasta
Journal:  J Med Signals Sens       Date:  2013-10

Review 6.  The role of ECG in the diagnosis of left ventricular hypertrophy.

Authors:  Ljuba Bacharova; Douglas Schocken; Edward H Estes; David Strauss
Journal:  Curr Cardiol Rev       Date:  2014-08

7.  ECG features and methods for automatic classification of ventricular premature and ischemic heartbeats: A comprehensive experimental study.

Authors:  Lucie Maršánová; Marina Ronzhina; Radovan Smíšek; Martin Vítek; Andrea Němcová; Lukas Smital; Marie Nováková
Journal:  Sci Rep       Date:  2017-09-11       Impact factor: 4.379

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.