Mai Vu, Khalil Ibrahim, Meli Christopher
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:2745-8. doi: 10.1109/IEMBS.2011.6090752.
This paper proposes a new method to identify people using Electrocardiogram (ECG), particularly the QRS complex which has been proven to be stable against heart rate variability and convenient to be used alone as a biometric feature. 324 QRS complexes are extracted from ECGs of 18 subjects in Physionet's MIT-BIH Normal Sinus Rhythm Database (NSRDB). Multilayer Perceptron (MLP) and Radial Basis Function (RBF) neural networks are used to classify those QRS complexes. If the training data are chosen carefully to cover a wide range of input values (i.e. QRS complexes), then the classification accuracy rates can reach above 98% using MLP and 97% using RBF.
本文提出了一种利用心电图（ECG）识别人员的新方法，特别是QRS复合波，它已被证明对心率变异性具有稳定性，并且便于单独用作生物特征。从Physionet的麻省理工学院-比哈尔正常窦性心律数据库（NSRDB）中18名受试者的心电图中提取了324个QRS复合波。使用多层感知器（MLP）和径向基函数（RBF）神经网络对这些QRS复合波进行分类。如果仔细选择训练数据以覆盖广泛的输入值范围（即QRS复合波），那么使用MLP的分类准确率可以达到98%以上，使用RBF的分类准确率可以达到97%。
