Energy-Efficient ECG Signal Compression for User Data Input in Cyber-Physical Systems by Leveraging Empirical Mode Decomposition
Energy-Efficient ECG Signal Compression for User Data Input in Cyber-Physical Systems by Leveraging Empirical Mode Decomposition
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DOI：
10.1145/3341559
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发表时间：
2019-08
影响因子：
2.3
通讯作者：
Hui Huang;Shiyan Hu;Ye Sun
中科院分区：
文献类型：
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作者：
Hui Huang;Shiyan Hu;Ye Sun
人体生理数据是大量信息物理系统（CPS）的自然和客观的用户数据输入。心电图（Electrocardiogram，ECG）作为一种被广泛应用的生理黄金指标，用于人体的某些状态和疾病的诊断，常被用作医疗CPS、人机交互等各种CPS的用户数据输入。无线传输和可穿戴技术使人与CPS交互的长期连续ECG数据采集成为可能;然而，这些新兴技术带来了存储和无线传输大量ECG数据的挑战，导致可穿戴传感器的能源效率问题。ECG信号压缩技术通过减少ECG数据的大小为这些挑战提供了一个有前途的解决方案。在这项研究中，我们开发了第一个方案，利用经验模式分解（EMD）的ECG信号的稀疏特征建模和压缩，并进一步提出了一个新的ECG信号压缩框架的基础上EMD构建的特征字典。该方法的特点是使用非常有限的特征基进行ECG信号压缩，计算成本低，显著提高了压缩性能和能量效率。我们的方法进行了验证与心电数据从MIT-BIH心律失常数据库，并与现有的方法进行了比较。实验结果表明，该方法的压缩比高达164，均方根误差为3.48%，平均压缩比为88.08，均方根误差为5.66%，是现有方法平均压缩比的2倍多，而现有方法的恢复错误率在5%左右。对于诊断失真的角度，我们的方法实现了较高的QRS波检测性能的灵敏度（SE）为99.8%，特异性（SP）为99.6%，这表明我们的ECG压缩方法可以保留几乎所有的QRS波特征，并没有对诊断过程的影响。此外，在相同的恢复错误率下，该方法的能量消耗仅为其他方法的30%。
Human physiological data are naturalistic and objective user data inputs for a great number of cyber-physical systems (CPS). Electrocardiogram (ECG) as a widely used physiological golden indicator for certain human state and disease diagnosis is often used as user data input for various CPS such as medical CPS and human–machine interaction. Wireless transmission and wearable technology enable long-term continuous ECG data acquisition for human–CPS interaction; however, these emerging technologies bring challenges of storing and wireless transmitting huge amounts of ECG data, leading to energy efficiency issue of wearable sensors. ECG signal compression technique provides a promising solution for these challenges by decreasing ECG data size. In this study, we develop the first scheme of leveraging empirical mode decomposition (EMD) on ECG signals for sparse feature modeling and compression and further propose a new ECG signal compression framework based on EMD constructed feature dictionary. The proposed method features in compressing ECG signals using a very limited number of feature bases with low computation cost, which significantly improves the compression performance and energy efficiency. Our method is validated with the ECG data from MIT-BIH arrhythmia database and compared with existing methods. The results show that our method achieves the compression ratio (CR) of up to 164 with the root mean square error (RMSE) of 3.48% and the average CR of 88.08 with the RMSE of 5.66%, which is more than twice of the average CR of the state-of-the-art methods with similar recovering error rate of around 5%. For diagnostic distortion perspective, our method achieves high QRS detection performance with the sensitivity (SE) of 99.8% and the specificity (SP) of 99.6%, which shows that our ECG compression method can preserve almost all the QRS features and have no impact on the diagnosis process. In addition, the energy consumption of our method is only 30% of that of other methods when compared under the same recovering error rate.