Atrial arrhythmias are extremely common and are often associated with significant morbidity and mortality. The ability to develop and prescribe new treatments for atrial arrhythmias depends on the availability of better diagnostic tools that are simple, inexpensive, and effective enough to support their use for screening large numbers of patients. During Phase I, we developed signal processing algorithms that permit noninvasive assessment of patients suffering from atrial fibrillation. T algorithms were tested on patient data and found to assist clinicians in predicting patient response to cardioversion in an attempt to terminate the arrhythmia and restore sinus rhythm. During Phase II, we will investigate alternate algorithms for identification and classification of atrial arrhythmias. Our goal is development of a tool that will allow the clinician to rapidly identify specific arrhythmias and measure the progress of individual patients through different courses of therapy.
The technology developed under this program could be used in diagnostic devices like electrocardiogram monitors.
