Detection of atrial arrhythmia for cardiac rhythm management by implantable devices

Implantable atrial defibrillators (IAD) should provide pacing therapy whene ver appropriate tie, typical atrial nutter) to minimize shock-related patie nt discomfort. Additionally, IADs should provide diagnostics regarding atri al arrhythmia type and frequency of occurrence to enable improved physician management of atrial arrhythmia. To achieve this, IADs should accurately c lassify atrial arrhythmia such as atrial fibrillation (AF) and atrial flutt er (AFL) This article evaluates the performance of an algorithm, atrial rhy thm classification (ARC), designed to classify AF and AFL. The ARC algorith m uses maximum rate, standard deviation, and range of the 12 most recent at rial cycle lengths to plot a point in a three-dimensional space. A decision boundary divides the space into 2 regions-faster/unstable atrial cycle len gths (AF) or slower/stable cycle lengths (AFL). Classifications are made on a sliding window of 12 consecutive cycles until the end of the episode is reached. In this way, continuous episode feedback is provided that can be u sed to help guide device therapy, measure arrhythmia type and frequency of occurrence. Bipolar (l-cm) electrogram episodes of AF (n = 16) and AFL (n = 7) were acquired from 20 patients and retrospectively analyzed using the A RC algorithm. The sensitivity and specificity in this study was 0.993 and 0 .982, respectively. The ARC algorithm would have appropriately guided atria l therapy and minimized discomfort associated with defibrillation shocks in this small patient data set warranting further studies. The ARC algorithm may also be beneficial as a diagnostic tool to assist physician management of atrial arrhythmia.