Abstract
Repolarization alternans (RA) has been implicated in the pathogenesis of ventricular arrhythmias and sudden cardiac death. We developed a 12-lead, blue-tooth/Smart-Phone (Android) based electrocardiogram (ECG) acquisition and monitoring system (cvrPhone), and an application to estimate RA, in real-time. In in-vivo swine studies (N = 17), 12-lead ECG signals were recorded at baseline and following coronary artery occlusion. RA was estimated using the Fast Fourier Transform (FFT) method using a custom developed algorithm in JAVA. Underlying ischemia was detected using a custom developed ischemic index. RA from each lead showed a significant (p < 0.05) increase within 1 min of occlusion compared to baseline (n = 29). Following myocardial infarction, spontaneous ventricular tachycardia episodes (n = 4) were preceded by significant (p < 0.05) increase of RA prior to the onset of the tachy-arrhythmias. Similarly, the ischemic index exhibited a significant increase following myocardial infarction (p < 0.05) and preceding a tachy-arrhythmic event. In conclusion, RA can be effectively estimated using surface lead electrocardiograms by analyzing beat-to-beat variability in ECG morphology using a smartphone based platform. cvrPhone can be used to detect myocardial ischemia and arrhythmia susceptibility using a user-friendly, clinically acceptable, mobile platform.
Highlights
Electrocardiographic (ECG) alternans, a phenomenon of beat-to-beat oscillation in electrocardiographic waveforms during the repolarization phase of the cardiac cycle known as repolarization alternans (RA), has been demonstrated to be an important marker of cardiac electrical instability and ventricular tachy-arrhythmic events (VTE)[1,2]
We have shown that Repolarization alternans (RA) can be effectively estimated from body surface ECG signals, through Bluetooth, using a smartphone; second, the smartphone can provide a viable platform to process ECG signals in real-time and, if needed, enable generation of alerts for the patient and the treating physician of an impending arrhythmia while the patient maintains an ambulatory status; third, there is a strong connection between RA and the ischemic index, especially before a tachy-arrhythmic event, indicating the significance of RA in predicting a tachy-arrhythmic event, at least in this model
Optical mapping studies in normal hearts have shown that discordant APD alternans is linked to a state of reduced cardiac electrical stability, manifested by the observation that when alternans is followed by ventricular fibrillation (VF), it only occurs after discordant APD alternans, but never concordant APD alternans[21]
Summary
Electrocardiographic (ECG) alternans, a phenomenon of beat-to-beat oscillation in electrocardiographic waveforms during the repolarization phase of the cardiac cycle known as repolarization alternans (RA), has been demonstrated to be an important marker of cardiac electrical instability and ventricular tachy-arrhythmic events (VTE)[1,2]. Beyond a risk stratification marker for patients that are candidates to receive ICD therapy, recent clinical studies have indicated that elevated levels of RA may have important predictive significance of short-term arrhythmia susceptibility. Analysis of body-surface ECG signals from ambulatory patients (Holter monitors) with coronary artery disease has demonstrated a sharp surge in the magnitude of RA within minutes prior to spontaneous VTEs3. Analysis of intra-cardiac electrograms (EGMs) from ICDs has demonstrated a sharp elevation in RA magnitude immediately prior to spontaneous ventricular arrhythmias[4,5]. The increased availability of new technologies and an ever-improving health information technology infrastructure, with >90% of American adults owning a cell phone and 55% having a Smart-Phone[7], indicates that mobile-health technologies will soon function as monitoring www.nature.com/scientificreports/. The central goal of this study is to investigate the hypothesis that one may develop methods for estimating RA, by recording cardiac electrical activity from the body surface, measuring the beat-to-beat variability in the morphology of ECG waveforms, and using the measured beat-to-beat variability to estimate the RA using the on-board computing power of a Smart-Phone, in order to alert the patient and the treating physician of an impending arrhythmia
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