基于PLR-DTW的ECG身份识别方法

Biometric Identification Method for ECG Based on the Piecewise Linear Representation(PLR)and Dynamic Time Warping(DTW)

为提高心电(ECG)身份识别方法的正确率同时降低算法复杂度,本文提出了一种新的基于分段线性表示(PLR)和动态时间规整(DTW)的ECG身份识别方法。该方法首先对ECG信号进行预处理,根据R波峰值点提取ECG周期波形;再采用PLR法对ECG平均周期信号降维,提取其有效特征;最后用改进的DTW法衡量测试数据和模板之间的相似度,得到匹配结果。在PTB和MIT-BIH ECG数据库的验证下,与基于小波变换的ECG身份识别方法相比,该方法的身份识别正确率提高了近8%,算法的运行时间降低了近30%,该方法为基于ECG身份识别的工程应用提供了良好的理论基础。

To treat the problem of identification performance and the complexity of the algorithm, we proposed a piecewise linear representation and dynamic time warping （PLR DTW） method for ECG biometric identification. Firstly we detected R peaks to get the heartbeats after denoising preprocessing. Then we used the PI.R method to keep important information of an ECG signal segment while reducing the data dimension at the same time. The im proved DTW method was used for similarity measurements between the test data and the templates. The perform- ance evaluation was carried out on the two ECG databases： PTB and MIT BIH. The analystic results showed that compared to the discrete wavelet transform method, the proposed PLR-DTW method achieved a higher accuracy rate which is nearly 8 % of rising, and saved about 30% operation time, and this demonstrated that the proposed method could provide a better performance.