Department of Electrical Engineering, University of Texas at Dallas, 800 W Campbell Rd, Richardson, TX, 75080, USA.
Harvard Medical School, A-111, 25 Shattuck St, Boston, MA, 02115, USA.
Ann Biomed Eng. 2018 Jan;46(1):122-134. doi: 10.1007/s10439-017-1944-z. Epub 2017 Oct 13.
In this study, to advance smart health applications which have increasing security/privacy requirements, we propose a novel highly wearable ECG-based user identification system, empowered by both non-standard convenient ECG lead configurations and deep learning techniques. Specifically, to achieve a super wearability, we suggest situating all the ECG electrodes on the left upper-arm, or behind the ears, and successfully obtain weak but distinguishable ECG waveforms. Afterwards, to identify individuals from weak ECG, we further present a two-stage framework, including ECG imaging and deep feature learning/identification. In the former stage, the ECG heartbeats are projected to a 2D state space, to reveal heartbeats' trajectory behaviors and produce 2D images by a split-then-hit method. In the second stage, a convolutional neural network is introduced to automatically learn the intricate patterns directly from the ECG image representations without heavy feature engineering, and then perform user identification. Experimental results on two acquired datasets using our wearable prototype, show a promising identification rate of 98.4% (single-arm-ECG) and 91.1% (ear-ECG), respectively. To the best of our knowledge, it is the first study on the feasibility of using single-arm-ECG/ear-ECG for user identification purpose, which is expected to contribute to pervasive ECG-based user identification in smart health applications.
在这项研究中，为了推进具有日益增长的安全/隐私要求的智能健康应用，我们提出了一种新颖的高度可穿戴的基于 ECG 的用户识别系统，该系统结合了非标准便捷的 ECG 导联配置和深度学习技术。具体来说，为了实现超级可穿戴性，我们建议将所有 ECG 电极置于左上臂或耳朵后面，并成功获得微弱但可区分的 ECG 波形。之后，为了从微弱的 ECG 中识别个体，我们进一步提出了一个两阶段框架，包括 ECG 成像和深度特征学习/识别。在前一阶段，将 ECG 心跳投射到二维状态空间中，以揭示心跳的轨迹行为，并通过分割然后命中方法生成二维图像。在第二阶段，引入卷积神经网络从 ECG 图像表示中自动学习复杂模式，而无需繁重的特征工程，然后进行用户识别。使用我们的可穿戴原型在两个采集数据集上的实验结果分别显示出 98.4%（单臂-ECG）和 91.1%（耳-ECG）的有希望的识别率。据我们所知，这是第一项关于使用单臂-ECG/耳-ECG 进行用户识别目的的可行性研究，有望为智能健康应用中的基于 ECG 的用户识别做出贡献。
