Detection of atrial arrhythmia for cardiac rhythm management by implantable devices

Abstract Implantable atrial defibrillators (IAD) should provide pacing therapy whenever appropriate (ie, typical atrial flutter) to minimize shock-related patient discomfort. Additionally, IADs should provide diagnostics regarding atrial arrhythmia type and frequency of occurrence to enable improved physician management of atrial arrhythmia. To achieve this, IADs should accurately classify atrial arrhythmia such as atrial fibrillation (AF) and atrial flutter (AFL). This article evaluates the performance of an algorithm, atrial rhythm classification (ARC), designed to classify AF and AFL. The ARC algorithm uses maximum rate, standard deviation, and range of the 12 most recent atrial cycle lengths to plot a point in a three-dimensional space. A decision boundary divides the space into 2 regions— faster/unstable atrial cycle lengths (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 used to help guide device therapy, measure arrhythmia type and frequency of occurrence. Bipolar (1-cm) electrogram episodes of AF (n=16) and AFL (n=7) were acquired from 20 patients and retrospectively analyzed using the ARC algorithm. The sensitivity and specificity in this study was 0.993 and 0.982, respectively. The ARC algorithm would have appropriately guided atrial 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.