High-Resolution Analysis of Ambulatory Electrocardiograms to Detect Possible Mechanisms of Premature Ventricular Beats

Analysis of unpredictable components within QRS complex using a finite-impulse-response prediction model for the diagnosis of patients with ventricular tachycardia

This study proposes a finite-impulse-response (FIR) prediction model to analyze the unpredictable intra-QRS potentials (UIQP) for identifying ventricular tachycardia patients with high-risk ventricular arrhythmias. The simulation study shows that a QRS complex including abnormal intra-QRS potentials (AIQP) has a higher UIQP and UIQP-to-QRS ratio in comparison with one without AIQP. The clinical results demonstrate that the mean UIQP-to-QRS ratios of VT patients in leads X, Y and Z were significantly larger than those of the normal subjects, and the linear and logical combination of UIQP-to-QRS ratios and ventricular late potential parameters can enhance diagnosis performance for VT patients.

LifeShirt acquisition system to monitor ECG from ambulatory swine and the implementation of an arrhythmia detection algorithm

A wearable cardiopulmonary monitoring system, a LifeShirt, was used to acquire continuous electrocardiograms (ECGs) from ambulatory swine. The animals received intracoronary injections of autologous mesenchymal stem cells, and the LifeShirt was used for long-duration ECG monitoring in pre-defined periods post cell infusion. The system used here was developed for measurements from non-human primates and canines; however, we demonstrated that it could be used to non-invasively measure ECGs from swine without creating undue stress or restricting movement. A MATLAB-based analysis algorithm was developed to automatically detect premature ventricular contractions (PVCs) that arose 8-10 hours after cell delivery with spontaneous resolution 2-3 days post-infusion. Template based cross-correlation was used to detect the PVCs and identify regions of consecutive ventricular rhythm. The final algorithm was highly specific and sensitive when tested on records from the MIT-BIH arrhythmia database. The algorithm was subsequently used to automatically identify and quantify PVCs from over 200 hours of ECG data obtained from nine ambulatory swine.