Results demonstrate that compression ratios (CR) of up to 90 can be obtained while maintaining a detection accuracy, expressed in terms of the area under the receiver operating characteristic curve, of at least 0.9. The accuracy of AF detection on reconstructed signals is evaluated under varying degrees of compression using the state-of-the-art Set Partitioning In Hierarchical Trees (SPIHT) compression algorithm. The compression and AF detection algorithms were applied to signals from the MIT-BIH AF database. This paper investigates the effects of ECG signal compression on an entropy-based AF detection algorithm that monitors R-R interval regularity. At the same time, the diagnostic accuracy of AF detection must be preserved. In this context, ECG signal compression is being increasingly investigated and employed to increase battery life, and hence the storage and transmission efficiency of these devices. Such resource-constrained systems often operate by transmitting signals to a central server where diagnostic decisions are made. Automated detection of AF in ECG signals is important for patients with implantable cardiac devices, pacemakers or Holter systems. Although Atrial Fibrillation (AF) is the most frequent cause of cardioembolic stroke, the arrhythmia remains underdiagnosed, as it is often asymptomatic or intermittent.
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