Journal
APPLIED SCIENCES-BASEL
Volume 11, Issue 13, Pages -Publisher
MDPI
DOI: 10.3390/app11135908
Keywords
atrial fibrillation; heart rate variability; shannon entropy; compression
Categories
Funding
- Higher Education Authority of Ireland Programme for Research in Third Level Institutions Cycle 5, CYTEMA-Puente Postdoctoral Fellowship
- Irish Research Council [SBPLY/17/180501/000192]
- Junta de Comunidades de Castilla-La Mancha
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This study investigates the effects of ECG signal compression on an entropy-based AF detection algorithm, demonstrating that compression ratios of up to 90 can be obtained while maintaining a detection accuracy of at least 0.9.
Although Atrial Fibrillation (AF) is the most frequent cause of cardioembolic stroke, the arrhythmia remains underdiagnosed, as it is often asymptomatic or intermittent. Automated detection of AF in ECG signals is important for patients with implantable cardiac devices, pacemakers or Holter systems. Such resource-constrained systems often operate by transmitting signals to a central server where diagnostic decisions are made. 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. At the same time, the diagnostic accuracy of AF detection must be preserved. This paper investigates the effects of ECG signal compression on an entropy-based AF detection algorithm that monitors R-R interval regularity. The compression and AF detection algorithms were applied to signals from the MIT-BIH AF database. 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. 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. This highlights the potential for significant energy savings on devices that transmit/store ECG signals for AF detection applications, while preserving the diagnostic integrity of the signals, and hence the detection performance.
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