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Deep Arm/Ear-ECG Image Learning for Highly Wearable Biometric Human Identification

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Abstract

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.

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Acknowledgments

This research is supported by the Recruitment of Global Experts (the Thousand Talents Plan) and National Natural Science Foundation of China (61574044).

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Authors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Dallas, 800 W Campbell Rd, Richardson, TX, 75080, USA

    Qingxue Zhang & Dian Zhou

  2. Department of Microelectronics, Fudan University, 220 Handan Rd, Shanghai, 200433, China

    Dian Zhou

  3. Harvard Medical School, A-111, 25 Shattuck St, Boston, MA, 02115, USA

    Qingxue Zhang

  4. Massachusetts General Hospital, 55 Fruit St, Boston, MA, 02114, USA

    Qingxue Zhang

Authors
  1. Qingxue Zhang
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  2. Dian Zhou
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Correspondence to Qingxue Zhang.

Additional information

Associate Editor Leonidas D. Iasemidis oversaw the review of this article.

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Zhang, Q., Zhou, D. Deep Arm/Ear-ECG Image Learning for Highly Wearable Biometric Human Identification. Ann Biomed Eng 46, 122–134 (2018). https://doi.org/10.1007/s10439-017-1944-z

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  • DOI: https://doi.org/10.1007/s10439-017-1944-z

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