Key result
An AF detector using compressively sensed ECG achieved an AUC of 0.91 at 50% compression, compared to 0.93 for uncompressed data, demonstrating feasibility of detection in the compressed domain.
Why the study?
Continuous ECG monitoring produces large amounts of data that increase storage, power, and bandwidth requirements, and reconstructing compressively sensed ECG data is computationally expensive.
Does an AF detector using compressively sensed ECG maintain high detection performance compared to uncompressed ECG data?
Does an AF detector using compressively sensed ECG maintain high detection performance compared to uncompressed ECG data?
Absolute Event Rate: 0.91% vs 0.93%
An AF detector using compressively sensed ECG data achieves high diagnostic performance comparable to uncompressed data, potentially reducing storage and transmission requirements for continuous monitoring.
May ease data burden in continuous AF monitoring; leaves open prospective clinical validation.
Atrial fibrillation (AF) is a serious cardiovascular condition that can lead to complications, including but not limited to stroke, heart attack, and death. AF can be diagnosed using an electrocardiogram (ECG); however, continuous monitoring produces a large amount of data that can increase storage, power, and transmission bandwidth requirements. Compressive sensing has been used to mitigate increased requirements of continuous monitoring. An AF detector using a deterministic compressively sensed ECG is proposed. By detecting AF in the compressed domain, the computationally expensive process of reconstructing the ECG can be avoided. The detector was based on a random forest trained on features extracted using the wavelet transform, empirical mode decomposition, discrete cosine transform, and statistical methods. ECG data from the long-term AF Database available on PhysioNet were used. The performances of the detectors trained using features from compressed and uncompressed ECG were compared. Using the trained detector, the area under the receiver operating curve (AUC) and the weighted average precision (AP) were both 0.93 for uncompressed data using record-based tenfold cross validation. The AUC and AP were 0.91 and 0.90 at 50% compression, 0.92 and 0.91 at 75% compression, and 0.82 and 0.91 at 95% compression, respectively.
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Abdelazez et al. (2020) studied Atrial fibrillation. AF detector using compressively sensed ECG vs. Uncompressed ECG data was evaluated on Area under the receiver operating curve (AUC). An AF detector using compressively sensed ECG achieved an AUC of 0.91 at 50% compression, compared to 0.93 for uncompressed data, demonstrating feasibility of detection in the compressed domain.
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