Heart depolarization vector locus cardiogram and its clinical diagnostic applications

• Main Authors: Nagenthiran, T., Ghista, Dhanjoo N., Prasad, V. Ramanan.
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Article
• Language: English
• Published: 2013
• Subjects: DRNTU::Engineering::Mechanical engineering::Bio-mechatronics
• Online Access: https://hdl.handle.net/10356/85396; http://hdl.handle.net/10220/11499
• Institution: Nanyang Technological University

This study demonstrates the development of the heart depolarization vector locus cardiogram (HPVLC, from limb leads and a modified Einthoven's triangle) as a diagnostic measure of the left ventricular depolarization strength. Our work involves the reconstruction of the "equivalent heart electrical-activity vector (HAV)" for the QRS complex from limb leads voltages of a sample ECG recording, and plotting the progression of the cardiac vector during the QRS complex. A realistic visualization of the progression of the equivalent-dipole HAV during the QRS complex is possible by staging the HPVLC of the QRS complex from the onset of the QRS till the end of the depolarization stage. This can enable the characterization of the HPVLC by means of an analytical function. By studying the HPVLC for various electro-cardiological disorders, it is possible to determine the ranges of the analytical function's parameters for normal and disordered electro-cardiological states, for diagnostic purpose. We have seen that the monitored ECG is theoretically derived from HAV components on the sides of the Einthoven triangle. Nevertheless, in cardiac practice, the monitored ECG is employed in diagnosis of heart diseases. From the ECG, we can obtain the heart rate, and therefrom the heart rate variability, which too has diagnostic applications. Many nonlinear methods have been proposed to analyze ECG and HRV for detection of cardiac abnormalities, using linear and nonlinear methods. Herein, we have shown how HRV signal can be analyzed in terms of four recurrence quantification analysis (RQA) features, which are then combined into an Integrated Index to enable better separation of normal and diabetic subjects.