计算机应用 ›› 2015, Vol. 35 ›› Issue (1): 262-264.DOI: 10.11772/j.issn.1001-9081.2015.01.0262

• 行业与领域应用 • 上一篇    下一篇

基于动态心电信号的实时身份识别算法

卢阳, 鲍淑娣, 周翔, 陈金恒   

  1. 宁波工程学院 电子与信息工程学院, 浙江 宁波315016
  • 收稿日期:2014-08-18 修回日期:2014-09-24 出版日期:2015-01-01 发布日期:2015-01-26
  • 通讯作者: 淑娣
  • 作者简介:卢阳(1992-),男,浙江余姚人,主要研究方向:健康物联网、网络安全;鲍淑娣(1977-),女,浙江宁波人,副教授,博士,主要研究方向:信息安全、躯感网、健康物联网;周翔(1994-),男,浙江金华人,主要研究方向:生物识别、网络信息系统;陈金恒(1993-),男,浙江丽水人,主要研究方向:物联网.
  • 基金资助:

    国家自然科学基金资助项目(61102087);浙江省自然科学基金资助项目(LY14F010005).

Real-time human identification algorithm based on dynamic electrocardiogram signals

LU Yang, BAO Shudi, ZHOU Xiang, CHEN Jinheng   

  1. School of Electronic and Information Engineering, Ningbo University of Technology, Ningbo Zhejiang 315016, China
  • Received:2014-08-18 Revised:2014-09-24 Online:2015-01-01 Published:2015-01-26

摘要:

心电图(ECG)信号因其具备易于监测、个体唯一性等特点在生物识别领域受到广泛关注.针对身份识别的准确性和实时性问题,给出一种快速鲁棒的、适用于微型化嵌入式平台的心电信号身份识别算法.首先,利用动态阈值法提取稳定波形用于快速生成心电模板样本和测试样本;然后,基于优化动态时间弯曲(DTW)法进行差异度计算得到识别结果;其次,考虑心电信号为非稳态时变信号,为保证模板数据与人体体征状况的一致性,对心电模板库进行动态更新管理以进一步提高识别准确性与鲁棒性.对MIT-BIH心律失常数据库和自建心电数据库的分析结果表明:所述算法的识别成功率最高达到98.6%;在安卓移动端,动态阈值与优化DTW法一次运算平均时间分别约为59.5 ms和26.0 ms,实时性能显著提高.

关键词: 生物识别, 心电信号, 嵌入式平台, 动态阈值, 动态时间弯曲法

Abstract:

Electrocardiogram (ECG) signal has attracted widespread interest for the potential use in biometrics due to its ease-of-monitoring and individual uniqueness. To address the accuracy and real-time performance problem of human identification, a fast and robust ECG-based identification algorithm was proposed in this study, which was particularly suitable for miniaturized embedded platforms. Firstly, a dynamic-threshold method was used to extract stable ECG waveforms as template samples and test samples; then, based on a modified Dynamic Time Warping (DTW) method, the degree of difference between matching samples was calculated to reach a result of recognition. Considering that ECG is a kind of time-varying and non-stationary signals, ECG template database should be dynamically updated to ensure the consistency of the template and body status and further improve recognition accuracy and robustness. The analysis results with MIT-BIH Arrhythmia database and own experimental data show that the proposed algorithm has an accuracy rate at 98.6%. On the other hand, the average running times of dynamic threshold setting and optimized DTW algorithms on Android mobile terminals are about 59.5 ms and 26.0 ms respectively, which demonstrates a significantly improved real-time performance.

Key words: biometric identification, Electrocardiogram (ECG) signal, embedded platform, dynamic threshold, Dynamic Time Warping (DTW) method

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