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

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.

Statistical Evaluation of Transformation Methods Accuracy on Derived Pathological Vectorcardiographic Leads.

Objective: Vectorcardiography (VCG) as an alternative form of ECG provides important spatial information about the electrical activity of the heart. It achieves higher sensitivity in the detection of some pathologies such as myocardial infarction, ischemia and hypertrophy. However, vectorcardiography is not commonly measured in clinical practice, and for this reason mathematical transformations have been developed to obtain derived VCG leads, which in application in current systems and subsequent analysis can contribute to early diagnosis and obtaining other useful information about the electrical activity of the heart. Methods and procedures: The most frequently used transformation methods are compared, namely the Kors regression method, the Inverse Dower transformation, QLSV and the Quasi-orthogonal transformation. These transformation methods were used on 30 randomly selected records with a diagnosis of myocardial infarction from the Physikalisch-Technische Bundesanstalt (PTB) database and their accuracy was evaluated based on the calculation of the mean square error (MSE). MSE was subjected to statistical evaluation at a significance level of 0.05. Results: Based on statistical testing using the nonparametric multiselective Kruskall-Wallis test and subsequent post-hoc analysis using the Dunn method, the Kors regression as a whole method achieved the most accurate transformation. Conclusion: The results of statistical analysis provide an evaluation of the accuracy of several transformation methods for deriving orthogonal leads, for possible application in measuring and evaluation systems, which may contribute to the correct choice of method for subsequent analysis of electrical activity of the heart at orthogonal leads to predict various diseases.