Da Poian Giulia, Liu Chengyu, Bernardini Riccardo, Rinaldo Roberto, Clifford Gari D
Department of Biomedical Informatics, Emory University, Atlanta, GA, United States of America. Dipartimento Politecnico di Ingegneria e Architettura, University of Udine, Udine, Italy.
Physiol Meas. 2017 Jun 27;38(7):1405-1425. doi: 10.1088/1361-6579/aa7652.
Compressive sensing (CS) approaches to electrocardiogram (ECG) analysis provide efficient methods for real time encoding of cardiac activity. In doing so, it is important to assess the downstream effect of the compression on any signal processing and classification algorithms. CS is particularly suitable for low power wearable devices, thanks to its low-complex digital or hardware implementation that directly acquires a compressed version of the signal through random projections. In this work, we evaluate the impact of CS compression on atrial fibrillation (AF) detection accuracy.
We compare schemes with data reconstruction based on wavelet and Gaussian models, followed by a P&T-based identification of beat-to-beat (RR) intervals on the MIT-BIH atrial fibrillation database. A state-of-the-art AF detector is applied to the RR time series and the accuracy of the AF detector is then evaluated under different levels of compression. We also consider a new beat detection procedure which operates directly in the compressed domain, avoiding costly signal reconstruction procedures.
We demonstrate that for compression ratios up to 30[Formula: see text] the AF detector applied to RR intervals derived from the compressed signal exhibits results comparable to those achieved when employing a standard QRS detector on the raw uncompressed signals, and exhibits only a 2% accuracy drop at a compression ratio of 60%. We also show that the Gaussian-based reconstruction approach is superior in terms of AF detection accuracy, with a negligible drop in performance at a compression ratio ⩽75%, compared to a wavelet approach, which exhibited a significant drop in accuracy at a compression ratio ⩾65%.
The results suggest that CS should be considered as a plausible methodology for both efficient real time ECG compression (at moderate compression rates) and for offline analysis (at high compression rates).
压缩感知（CS）方法应用于心电图（ECG）分析，为心脏活动的实时编码提供了有效的方法。在此过程中，评估压缩对任何信号处理和分类算法的下游影响非常重要。CS特别适用于低功耗可穿戴设备，这得益于其低复杂度的数字或硬件实现方式，可通过随机投影直接获取信号的压缩版本。在这项工作中，我们评估了CS压缩对心房颤动（AF）检测准确性的影响。
我们在麻省理工学院 - 贝斯以色列女执事医疗中心（MIT - BIH）心房颤动数据库中，比较基于小波和高斯模型进行数据重建的方案，随后基于峰值检测（P&T）识别逐搏（RR）间期。将一种先进的AF检测器应用于RR时间序列，并在不同压缩级别下评估AF检测器的准确性。我们还考虑了一种直接在压缩域中运行的新的搏动检测程序，避免了代价高昂的信号重建程序。
我们证明，对于高达30[公式：见正文]的压缩率，应用于从压缩信号导出的RR间期的AF检测器所呈现的结果，与在未压缩的原始信号上使用标准QRS检测器时所获得的结果相当，并且在压缩率为60%时，准确性仅下降2%。我们还表明，与小波方法相比，基于高斯的重建方法在AF检测准确性方面更优，在压缩率⩽75%时性能下降可忽略不计，而小波方法在压缩率⩾65%时准确性显著下降。
结果表明，CS应被视为一种可行的方法，既适用于高效的实时ECG压缩（在中等压缩率下），也适用于离线分析（在高压缩率下）。
