面向嵌入式穿戴医疗的快速经验模态分解方法

计算机工程与科学

面向嵌入式穿戴医疗的快速经验模态分解方法

王洁，冯玉杰，陈伟浩，侯刚，周宽久

（大连理工大学软件学院，辽宁大连 116620）

收稿日期:2016-04-20 修回日期:2016-06-13 出版日期:2016-08-25 发布日期:2016-08-25
基金资助:
国家自然科学基金（61472100,61572094，61402073）；中央高校基本科研业务费（DUT14QY32）

A fast empirical mode decomposition method for embedded wearable healthcare

WANG Jie,FENG Yu-jie,CHEN Wei-hao,HOU Gang,ZHOU Kuan-jiu

（School of Software Technology,Dalian University of Technology,Dalian 116620,China ）

Received:2016-04-20 Revised:2016-06-13 Online:2016-08-25 Published:2016-08-25

摘要/Abstract

摘要：

智能照护系统通过人体感知智慧衣实时采集心电（ECG）等生理数据，却不可避免地混入运动伪影造成信号失去形态学特征。经验模态分解算法（EMD）通过获得本征函数分量去除非静态、非线性信号，去除ECG信号中的运动伪影。但是，传统EMD算法计算量大，不适用于低功耗的嵌入式移动设备。提出一种Fast-EMD算法，通过采用不同的运动状态来控制相应迭代次数的方式取代计算复杂边界值SD。实验结果表明，该方法既简化了算法执行流程，又提高了R点捕获准确率，有效提升了嵌入式设备上滤波处理性能。

关键词: 智慧衣, ECG, 经验模态分解算法, 运动伪影

Abstract:

The intelligent care system can collect physiological data such as ECG in real-time through human body appreciable smart clothes. But it inevitably mixes ECG signals with motion artifacts, causing a loss of morphological characteristics of signals. By using the getting intrinsic mode functions, the empirical mode decomposition (EMD) algorithm can remove non-static and nonlinear signals, as well as the motion artifacts from ECG signals. However, the computation cost of the traditional EMD algorithm is great so it is not applicable to low-power embedded mobile devices. We propose a fast-EMD algorithm, which adopts different motion states to control the number of iterations rather than calculate the complex boundary value SD. Experimental results show that the proposed algorithm can simplify the algorithm implementation process and improve the accuracy of capturing R points. Meanwhile, it can effectively enhance the filtering processing performance of embedded devices.

Key words: smart clothes, ECG, empirical mode decomposition algorithm, motion artifacts

王洁，冯玉杰，陈伟浩，侯刚，周宽久. 面向嵌入式穿戴医疗的快速经验模态分解方法[J]. 计算机工程与科学.

WANG Jie,FENG Yu-jie,CHEN Wei-hao,HOU Gang,ZHOU Kuan-jiu. A fast empirical mode decomposition method for embedded wearable healthcare [J]. Computer Engineering & Science.

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