Automated Identification of Myocardial Infarction Using a Single Vectorcardiographic Feature

Sadhukhan, Deboleena; Datta, Jayita; Pal, Saurabh; Mitra, Madhuchhanda

doi:10.1007/978-3-319-74808-5_57

Cited by 2 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kors regression transformation has advantages over Frank’s orthogonal lead system. It is much easier to identify myocardial infarction [41,42,43,44,45,46], hypertrophy [47,48,49], and ischaemic heart disease [50,51] from the VCG compared to the ECG. However it is important to properly estimate the VCG from the ECG.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Different Electrocardiography with Vectorcardiography Transformations

Jaroš

Martínek

Danys

2019

Sensors

View full text Add to dashboard Cite

This paper deals with transformations from electrocardiographic (ECG) to vectorcardiographic (VCG) leads. VCG provides better sensitivity, for example for the detection of myocardial infarction, ischemia, and hypertrophy. However, in clinical practice, measurement of VCG is not usually used because it requires additional electrodes placed on the patient’s body. Instead, mathematical transformations are used for deriving VCG from 12-leads ECG. In this work, Kors quasi-orthogonal transformation, inverse Dower transformation, Kors regression transformation, and linear regression-based transformations for deriving P wave (PLSV) and QRS complex (QLSV) are implemented and compared. These transformation methods were not yet compared before, so we have selected them for this paper. Transformation methods were compared for the data from the Physikalisch-Technische Bundesanstalt (PTB) database and their accuracy was evaluated using a mean squared error (MSE) and a correlation coefficient (R) between the derived and directly measured Frank’s leads. Based on the statistical analysis, Kors regression transformation was significantly more accurate for the derivation of the X and Y leads than the others. For the Z lead, there were no statistically significant differences in the medians between Kors regression transformation and the PLSV and QLSV methods. This paper thoroughly compared multiple VCG transformation methods to conventional VCG Frank’s orthogonal lead system, used in clinical practice.

show abstract

Section: Discussionmentioning

confidence: 99%

Comparison of Different Electrocardiography with Vectorcardiography Transformations

Jaroš

Martínek

Danys

2019

Sensors

View full text Add to dashboard Cite

show abstract

“…The creation of new features or improvements in analysis techniques can contribute to better processing of VCG data and achieve early detection of heart disease. The new VCG feature was also proposed by Sadhukhan et al (2017) , which consists of a combination of the volume ratio of the 3-D QRS and the ST-T loop. From a statistical analysis, they found that this new feature provide high sensitivity (98.8%) between the MI and HC groups.…”

Section: Heart Disease Analysis From Vectorcardiographymentioning

confidence: 99%

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

Vondrak

Penhaker

2022

Front. Physiol.

View full text Add to dashboard Cite

Vectorcardiography (VCG) is another useful method that provides us with useful spatial information about the electrical activity of the heart. The use of vectorcardiography in clinical practice is not common nowadays, mainly due to the well-established 12-lead ECG system. However, VCG leads can be derived from standard 12-lead ECG systems using mathematical transformations. These derived or directly measured VCG records have proven to be a useful tool for diagnosing various heart diseases such as myocardial infarction, ventricular hypertrophy, myocardial scars, long QT syndrome, etc., where standard ECG does not achieve reliable accuracy within automated detection. With the development of computer technology in recent years, vectorcardiography is beginning to come to the forefront again. In this review we highlight the analysis of VCG records within the extraction of functional parameters for the detection of heart disease. We focus on methods of processing VCG functionalities and their use in given pathologies. Improving or combining current or developing new advanced signal processing methods can contribute to better and earlier detection of heart disease. We also focus on the most commonly used methods to derive a VCG from 12-lead ECG.

show abstract

Automated Identification of Myocardial Infarction Using a Single Vectorcardiographic Feature

Cited by 2 publications

References 19 publications

Comparison of Different Electrocardiography with Vectorcardiography Transformations

Comparison of Different Electrocardiography with Vectorcardiography Transformations

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

Contact Info

Product

Resources

About