Dynamic QRS-complex and ST-segment monitoring by continuous vectorcardiography during carotid endarterectomy

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.

Novel set of vectorcardiographic parameters for the identification of ischemic patients

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.

Numerical simulation of motion-induced dynamic noise in a ubiquitous ECG application

Wearable ubiquitous biomedical applications, such as ECG monitors, can generate dynamic noise as a person moves. However, the source of this noise is not clear. We postulated that the dynamic ECG noise has two causes: the change in displacement of the heart during motion and the change in the electrical impedance of the skin-gel interface due to motion-induced deformation of the skin-gel interface. Using a three-dimensional electrophysiological heart model coupled with a torso model, dynamic noise was simulated, while the displacement of the heart was changed in the vertical and horizontal directions, independently and while the skin-gel interface was deformed during motion. To determine the deformation rate of the skin and sol-gel layers, motion-induced deformation of the two layers was simulated using a three-dimensional finite element method.

Continuous ST-segment monitoring: raising the bar

ST-segment monitoring is recommended by clinical experts to assist in the early detection of transient myocardial ischemia; however, a gap exists between the recommendations and clinical practice. This article provides a review of research to support the use of ST-segment monitoring in a variety of clinical situations. Patient selection for monitoring, techniques, and strategies to facilitate successful implementation of ST monitoring are included. Nurses are encouraged to advocate for patients by raising the bar of electrocardiogram monitoring practice to aid in optimizing patient outcomes.