利用分析和外观模型的混合心电图人体识别方法

摘要

In order to improve the sensitivity of Electrocardiogram(ECG) identification to noise and changes of signal,and to prioritize heartbeat features,a hybrid ECG human identification method is proposed.Heartbeat and appearance properties are jointly analyzed in the proposed method,in which Fisher linear discriminant analysis method is used to obtain the main attributes from these properties and the heartbeat features are calculated in Fisher space.The feature in time domain is used to calculate ECG characteristics.After the heartbeat feature in time domain is deducted by a series of heartbeat,the morphological characteristics are obtained by standardizing and scaling ECG signals.By maximizing the rate between the between-class individual hash and in-class individual hash,the most discriminative features are selected.Experimental results show that the recognition accuracy of the proposed method in a large database of healthy individuals is 99.24%.When the body is in the case of heart disorders,the Equal Error Rate(EER) is only 0.76%.In a mixed state,EER is 1.31%.So the robustness is well verified.%为提高心电图(ECG)生物识别对噪声和变化信号的敏感度,优化区分心跳特征,提出一种混合的ECG人体识别方法.联合心跳分析和外观属性,使用Fisher线性判别分析方法从这些属性中获取主要属性,在Fisher空间计算心跳特征,使用时域特征计算ECG特征.在一系列心跳推导时域特征后,通过标准化和缩放ECG信号获取形态学特征.采用最大化类间个体散列与类内个体散列的比率选择最具区分性的特征.实验结果表明,该方法在健康个体组成的大型数据库上的识别准确率为99.24%.当人体处于心率失调的情况下等错误率(EER)为0.76%,处于混合状态时EER为1.31%,验证了该方法具有较好的鲁棒性.