Research of CDG to Identify Individuals via Deterministic Learning Theory

An approach of human identification based on cardiodynamicsgram (CDG) is proposed in this paper. Algorithm design for the electrocardiogram (ECG) is carried out to achieve human identification, which includes collecting ECG data from the PTB database, filtering the ECG, synthesizing the VCG by using the filtered 12-lead ECG, and intercepting the ST-T segment from the VCGs. Then the CDGs are obtained by using radial basis function (RBF) neural networks (NNs) to model the ST-T segment through deterministic learning (DL). The obtained knowledge is stored in constant RBF networks. Finally stable features-Spatial heterogeneity index and temporal heterogeneity index are extracted to characterize the uniqueness of an individual, and SVM is used to train the classification model in MATLAB. The test data is fed to the obtained model to evaluate our proposed method. The results show that the proposed method can achieve more than 85% correct classification rate.

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